SIEMENS Introduction

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.............. Notes on Control Engineering ............ .:.>:.:.:.:.: ......... .............. =.=.:m ...........

Working with the SlEPlD S5 Program

SlEPlD S5 Steps for Commissioning a Controller

System [dentif ication "Identification and Controller Optimization"

and Setting of PID Controllers "Control LOOP Simulationw

Manual "Operating and Monitoring"

"Controller Table"

Program Structure

Index

Notes Remarks Forms

SIEPID S5 Contents

Contents

. . . . . . . . . . . . . . . . . Introduction

Structure of the Manual . . . . . . . . . . . . . . . . . . . . . . . . Purpose and Aims of SlEPlD S5

. . . . . . . . . . . . . . What is SlEPlD S53 How does SlEPlD S5 Function? . . . . . . . . . . Components Supplied with SlEPlD S5 . . . . . . . Device Configuration . . . . . . . . . . . . . . Hardware and Software Requirements . . . . . . . Adapting the Graphics Output . . . . . . . . . . . Changes Compared with the Previous Version (l .X) . .

. . . . . . . . . . Notes on Control Engineering

. . . . . . . . . . . . . . . What is a System?

. . . . . . . . . . How a PID Controller Functions

. . . . . The Distinction between S and K Controllers

. . . . . What does "Control Loop Simulation" Mean?

Which Models are Available for Process Identification? . Evaluation for a Step Change in the Manipulated Variable Analysis of any Changes in the Manipulated Variable . Analysis . . . . . . . . . . . . . . . . . . . Controller Design according to Absolute Value Optimization

. . . . . . . . Option of Operator-Controlled Tuning

. . . . . . . . What are the Limits of the Method?

Contents

. . . . . . . Working with the SlEPlD S5 Program

General Information . . . . . . . . . . . . . . . Creating a Working Diskette . . . . . . . . . . . Installing the Program . . . . . . . . . . . . . . Starting the Program . . . . . . . . . . . . . . Main Menu . . . . . . . . . . . . . . . . . . Documentation . . . . . . . . . . . . . . . .

Steps for Commissioning a Controller . . . . . . Stages Involved in the Procedure . . . . . . . . . How to Assign Parameters for a Continuous Controller . FB 201 : S5-100U Controller . . . . . . . . . . . FB 80: Compact Controller of the S5-115U Controller . FB 102: Controller Structure R64 . . . . . . . . . FB 62: PID Controller of the Modular PID and Fuzzy Controller . . . . . . . . . FB 176: IPD Controller of the Modular PID and Fuzzy Controller . . . . . . . . . How to Assign Parameters for a Step Controller . . . . FB 202: Step Controller of the S5-100U Controller . . . FB 102: Step Controller in R64 . . . . . . . . . . FB 176 + FB 177: IPD Controller with Pulse Output of the Modular PID and Fuzzy Controller . Connecting Controllers via the Open Interface . . . .

. . . . . . . . Ways of Acquiring Measured Values Reading in the Measured Values from the PLC . . . .

. . . . . . . . . . . . . Reading in from Diskette . . . . . . . . . Simulation of the System in the PG

"Identification and Controller Optimization" . . . . General Notes on the "Identification and

. . . . . . . . . Controller Optimization" Function

. . . . . . . . . Reading in the Measured Values

Starting Measured Value Recording . . . . . . . . . . . . . . . . . . . Saving the Measured Values

. . . . . . . . . . . . . . . . . . . Analysis Analysis for an Ideal Manipulated Variable Step . . . . Analysis for any Manipulated Variable Step . . . . . Tuning the Controller Parameters . . . . . . . . . Transferring the Controller Parameters to the PLC . . . SlEPlD S5 as Measured Value Recorder . . . . . . The Structure of the SlEPlD S5 Measured Value Files .

. . . . . . . . . . . "Control Loop Simulation"

Entering the Controller Parameters . . . . . . Performing the Control Loop Simulation . . . . Tuning the ControllerParameters . . . . . . Possible Deviations between Transient Functions Non-Linearities in the System . . . . . . . . Deviations in the Control Algorithm . . . . . . Deviations in the Excitation of the Control Loop . Deviationsin the Delay Response . . . . . . No Reproducible Measurement Results . . . . Errors in the Dimensionsof Actuators . . . . .

Contents

Contents SlEPlD S5

"Operating and Monitoring" . . . . . . . . . . . General Notes on the "Operating and Monitoring" Function

Linking the PC with the SlMATlC CPU . . . . . . . Checking the Controller Mode . . . . . . . . . . . Matching the Scale of the Controlled Variable . . . .

"Controller Table" . . . . . . . . . . . . . . . Data Management using the Controller Table . . . . .

Program Structure . . . . . . . . . . . . . . . Program Structure of SlEPlD S5 . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . Index

Introduction

This manual provides you with information about the SIEPID S5 program, by explaining general control terminology, how to install and start up the program and how the program functions.

SEPID S5 is used to install and optimize control systems. Via the open interface of SEPID $5, you can also optimize hardware controllers. SEPID S5 is a very user-friendly and practice-oriented software tool.

This manual is intended as a reference work and is structured according to functions. This structure allows you to find the infornation required both easily and quickly.

Structure of the Manual SlEPlD S5

1 .l Structure of the Manual

This section provides you with the following: an overview of the manual information about the basic requirements for using the program and information about preparations for starting up the controller according to your existing knowledge.

This section is also intended to help you to find information quickly. It informs you about the individual functions and aspects of the SIEPID S5 program in detail.

Note Starting up and installing the programmable controller and handling the PLC control system are not part of this description.

You want to get to know the components, the package ? and the hardware and software requirements for m SIEPID SS:

Chapter 1 informs you about the purpose and aims of SIEPID S5 and describes the components supplied with the program and the necessary device configuration. In addition to this, changes and improvements compared with the previous version are listed.

? You require information about general aspects of control systems:

Chapter 2 explains the basics of control systems and terminology. If you already have experience in this field and the terminology and technology are familiar, you can skip this chapter. If not, the chapter will help you to "get to grips" with the material.

SIEPID S5 Structure of the Manual

You want to know how to work with the SIEPID S5 program:

In Chapter 3, you will learn how to install the SIEPID S5 program, create back-up diskettes and start SIEPID SS. Apart fkom this, you will be informed about the main menu and the program structure (refer also to Chapter 9). A demonstration mode is provided to allow you to work with the program without a PLC, by simulating all the access to the PLC.

This chapter also explains the documentation functions provided by SIEPID S5.

You want to know the steps involved in installing and ? starting up a controller:

Chapter 4 explains the steps involved in installing and starting up a controller with SIEPID S5, which is largely oriented on the conventional procedure for installing a controller.

The chapter includes points to note about continuous controllers and step controllers of the SIMATIC S5 series. The connection of a controller via the open interface is also described.

The chapter also deals with the various ways in which measured values can be acquired.

Structure of the Manual SlEPlD S5

? You require information about the function "Identification and Controller Optimizationw:

The finction described in Chapter 5 "Identification and Controller Optimization" is used to identlfy both self-regulating and non-selsregulating systems. The identification and design of the controller are performed in several stages. After reading in the measured values, the measured values are logged, saved and then analyzed dependent on various factors. You will also learn how to '%une" the model parameters and transfer the controller parameters to the PLC.

Chapter 5 fhther informs you how to use SIEPID S5 as a recording tool and explains the structure of the corresponding measured value files.

You want to learn about the vcontrol loop ? simulationw:

Chapter 6 explains how to make the best use of the "control loop simulation" finction. Control loop simulation is an important tool for model-supported calculation and utilization of the time characteristics of the system or the control loop. Once you have entered the controller parameters, the simulation can be performed. It is then possible to tune the controller parameters. This chapter also explains certain deviations between transient fimctions and their effects.

SlEPlD S5 Structure of the Manual

m You want to work with the "operating and ? monitoringtt function:

Chapter 7 explains how to use this hction for operating and monitoring SIMATIC S5 control systems. You will first learn how to connect the PG to the PLC. Following this, there is an explanation of how to check the controller mode and how to adjust the scale of the controlled variable.

7 You want to know about the ftcontroUer tablev function:

Chapter 8 explains the "controller table" function. This fbnction is used to manage data from a maximum of 99 control loops. The chapter explains which control loop data can be stored in a standard data file. Apart from specific control loop data, link data can also be stored in the controller table and called up as needed.

? You require information about the program structure of SIEPID SS:

Chapter 9 explains the structure of the SIEPID S5 program in the form of a schematic diagram.

Purpose and Aims of SIEPID S5 SlEPlD S5

1.2 Purpose and Aims of SlEPlD S5

1.2.1 What is SlEPiD The SIEPID S5 pmgram is used for installing, starting up and S53 optimizing controllers and is a practice-oriented software tool for

calculating process-adapted settings for PID controllers in the SIMATIC S5 automation system.

SIEPID S5 is a particularly convenient program to use. This is achieved by the following:

the graphic visualization of important inter-relationships, the uniform design of the operating menus and the detailed comments available for each step.

In conjunction with an AT-compatible computer, SIEPID S5 is a high-performance controller installation tool.

1.2.2 How does SlEPlD Normally, the controller settings during the installation of a S5 Function? system are determined experimentally. This is achieved by

deliberately modifLing the control parameters and then checking the time characteristics in the real process.

The great disadvantage of this is the considerable time required for optimization and the uncertainty as to the actual quality of the empirically determined settings.

On the other hand, there is a variety of modern theoretical procedures for automatic optimization of controllers. Using these procedures is, however, usually complicated and must be left to experts in control engineering.

The information software SIEPID S5 was developed to allow a practice-oriented application of such procedures. This allows modern control engineering methods to be implemented as an easy-to-use tool.

SIEPID S5 Purpose and Aims of SlEPlD S5

Steps in the The theoretical basis of SIEPID S5 includes reliable procedures procedure for the following:

process identification,

m controller design offline simulation of the control loop and operating and monitoring control systems.

These steps, in conjunction with an easy-to-operate user interface and numerous options for documenting the results are integrated in the SIEPID S5 software.

Process identification

The process identification plays an important role in the experimental development of a mathematical model of the process to be controlled.

This identification can be performed either in the open loop or closed loop mode.

Components Supplied with SIEPID S5 SIEPID S5

1.3 Components Supplied with SlEPlD S5

When you order SIEPID SS, you receive the following components:

1. A 3.5" diskette in MS-DOS format containing the operation and analysis program with the following files: SIEPIDSS EXE SIEPIDSS OVR TOOL BOX GRPTOOLl TEX GRPTOOL2 TEX DENT1 TEX DENT3 TEX OPT1 TEX OPT3 TEX SSBEBO TEX SSDATEIN TEX SSSIMl TEX SSSIM2 TEX SSTEXI TEX S5TOOL2 TEX SSTOOL3 TEX SSTOOL31 E X SSTOOL32 TEX S5TOOLA TEX SSTOOLAl TEX S5TOOL42 TEX SSTOOL5 TEX SSVAR TEX SSZYK TEX SIEPIDSS TEX TEXTOOLl TEX TEXTOOL2 TEX TEXTOOL3 TEX

2. This description

SIEPID S5 Device Configuration

1.4 Device Configuration m 1.4.1 Hardware and The SIEPID S5 program can be run on the following devices: Software Requirements

- PG 770, PG 750, PG 730 C, PG 730 SX - AT-compatible PCs with real-time clock.

The following requirements must also be met: Graphics with Hercules or EGANGA card Serial interface COMl or COM2 Hard disk with 1 Mbyte of fiee storage space Optional printer and numerical co-processor MS-DOS operating system Version 3.3 and higher

For documentation using the hardcopy function, a parallel interface is required on the PG/PC. The program supports the following printers. - DR 2 10-N 9-wire printer DIN A4 order no. 6AP1800-OAA00 - DR 21 1 -N 9-wire printer DIN A3 order no. 6AP1800-OBA00 - DR 230-N 24-wire printer DIN A4 order no. 6AP1800-OCA00 - DR 23 1-N 24-wire printer DIN A3 order no. 6AP1800-ODAOO with the IBM standard character set and IBM proprinter emulation.

The link between the PG and the controlling PLC (programmable controller) must be established via the serial interface using a suitable cable, e.g. 6ES5 734-2BD20.

Device Configuration SIEPlD S5

Graphics / CO-processor

Fig. 1.1 illustrates the SIEPID S5 configuration with control loop:

AI: Analog input AQ: Analog output SEI: Serial interface

p p - p - p

Fig. 1.1 : SIEPID S5 configuration

At the start of the program, a check is made to see whether the computer has a numeric co-processor. If this exists, it is used, otherwise it is emulated. SIEPID S5 contains graphics drivers for Hercules and EGANGA.

SIEPID S5 Device Con fisuration

Controller packages SIEPID SS can be used with the following control packages for 8 -

programmable controllers in SIMATIC S5:

SS-100U control for SS-9SU, SS-100U with CPU 103

system SS-1 15U with CPU 941B, 9428,

S5-115U control for S5-115U with CPU 941B, 942l3,

system 943B, 944B

R64 controller for SS-13SU with CPU 922,928,928B

structure SS- 1SSU with CPU 922,928,928B

Modular PID and for SS- 1 1SU with CPU 945

fuzzy control SS-13SU with CPU 922*, 928,928B

SS-1SSU with CPU 922*, 928,928B,

9461947,948

There is also a fhther open interface with which you can operate control systems on the programmable controllers SS-9SU, SS-100U, SS-1 15U, SS-135U and SS-1 SSU.

The type of control system used must be specified in a menu. The number of the controller data block to be processed is also required as additional information for all software controls. With compact controllers (SS- 1 1SU control system, controller structure R64) this information is sufficient, with the other control packages, the block assignment must also be specified.

The computer tests whether a link to the controller is possible. If an error message is displayed, check the cable.

In general, a standard single loop is assumed. PI and P I ' controllers which generate a continuous manipulated variable (continuous controllers) or binary signals for controlling a valve (step controller) can be tuned. SIEPID SS can be used on self-regulating and non-self-regulating systems. The settling time of the system must be greater than 2.5 seconds.

* Exceptions: &zy blocks and simplified system racks

Device Configuration SIEPlD S5

1.4.2 Adapting the As standard, SIEPID S5 supports Hercules and EGANGA video Graphics Output adapters. To avoid display difficulties with monochrome EGA

monitors or computers with LCD or plasma displays, which emulate a color screen with various gray tones, a screen form is displayed immediately following the SIEPID S5 logo screen form in which you can select a monochrome monitor as the default. All fhther screen displays are then monochrome.

This mode is also required when you want to generate hardcopies of the screen forms using the keys <Shift + PRT $C>, in which a correct reproduction of inverse text displays is required.

1.4.3 Changes Compared with the Previous Version (l .X)

The user interface has been improved.

The link to the PLCs has been improved; no extra OB 13 with a 100 ms clock pulse must be installed. An additional FB is also no longer required on the PLC.

Bipolar control systems can also be evaluated.

The control parameters can now be modified directly fiom the controller screen form. A quality value for the link is also now displayed in the controller screen form.

When recording data, the excitation can be modified.

Non-selfiregulating systems (ITn models) can now also be identified.

An additional function "operating and monitoring" PLC control systems has been installed,

All old settings are automatically saved.

Device Conrnuration

When the link is terminated, the old and current settings of 8 the configuring switches are output again.

With the S5-115U, it is possible to switch over between an internal and external setpoint. All the link data are stored in the controller table.

Information about the controller structure is displayed.

A demonstration mode is available to test the user interface. An open interface has been implemented for K and S controllers.

Step controllers fiom the S5-100U control package, control structure R64 and modular PID and fizzy control system are supported.

Notes on Control Engineering 2 This chapter explains certain important control engineering concepts and terms. If you are already familiar with control engineering and its terminology and technology, you can skip this chapter. Otherwise, the chapter will help you to work your way into the material.

What is a System? SIEPID S5

What is a System?

Single variable systems

The term 'Lystem" is the part of a technical system which can be demarcated by its function and which is to be influenced by the application of control principles. In a more general context, the tern "process" is also used.

These are systems with one controlling input variable (manipulated variable y) and one output variable of interest (controlled variable X), i.e. single variable systems. It is assumed that these are adequately linear or can be linearized around a working point. The dynamic response of such a system can be characterized by its step response. The system is known as a self-regulating system when the step response assumes a stationary final value as illustrated in the following figure.

Fig. 2.1 Dynamic response of a system characterized by the step response

In a non-self-regulating system, the step response does not assume a stationary final value. It contains an integral action component which has the effect that the step response permanently rises or falls until it reaches the system limits.

SIEPID S5 What is a System?

Forms of The usual mathematical forms of description for the dynamic mathematical response are, for example, differential equations or a complex description transfer hct ion G(s). The latter as a fkactional rational function

represents a parametric model which is approximated to the transfer response of the real system. SIEPID S5 uses methods of experimental identification to calculate the parameters of a

Q particular model type. The result of the identification is the essential basis of an adequate controller design.

The models used in SIEPID S5 for process identification are described in more detail in Section 2.5.

How a PID Controller Functions SIEPID S5

2.2 How a PID Controller Functions

PI0 controller PID controllers remain the preferred tool for precise guidance of dynamic processes. The PID algorithm used in digital systems is a discrete-time implementation of the continuous PID controller with the following transfer function:

@ = @ ( l + L + Tvs R(s) = 4s) Tns l+T lS

where: Kp = proportional gain, Tn = integral action time, 'h = derivative action time,

T1 = dead time of the D-action.

Dead time of the The parameter T1 of the PID controller is fixed at 115 of TV when D-action designing the controller. T1 delays the action of the D-component.

At a value of T l W l O , the D-action component is almost ideal. To reduce the disturbance susceptibility of the PID controller to measured value noise, T1 must be increased.

PI controller If Tv=O, we obtain a PI controller. PI and PID controllers are, however, subject to their own rules. Due to the D-action component, a PID controller has a a larger Kp and a smaller Tn than a corresponding PI controller. If the D-action component of a PID controller is weakened by the value T1 being too high, the control quality deteriorates.

S/EPlD S5 The Distinction between S and K Controllers

2.3 The Distinction between S and K Controllers

The difference between step controllers and continuous controllers is to be found in the output actuating signal.

S controller

K controller

S controllers are step controllers and they output pulses which are integrated by the stepper motors to form the actuating signal.

K controllers are continuous controllers and output a continuous actuating signal.

What does "Control Loop Simulation" Mean? SlEPlD S5

2.4 What does "Control Loop Simulation" Mean?

In SIEPID S5, simulation is an important tool for model-supported calculation and visualization of the time response of the system or control loop. In terms of the control response of the closed loop, "simulationt1 means the calculation and graphical display of the time characteristics of the controlled variable x(t) and the manipulated variable y(t) following a step change in the setpoint W.

Control loop Control loop simulation is the key to practice-oriented controller simulation design and is therefore the basis of operator-controlled tuning of

controller parameters. This is the fundamental component of the main menu function "identification and controller optimizationt1. In addition to this, "control loop simulation'' represents a separate main menu function. Basically, it is used for the numerical simulation of model control loops and, if necessary, tuning, independent of the identification problem.

A classic single control loop is simulated, consisting of a system transfer function G(s) and a PID controller R(s).

PID controller System

c Fig. 2.2 Single standard control loop

SIEPID S5 What does "Control Loop Simulation" Mean?

Simulation procedure The loop is simulated using a problem-matched transition matrix approach. While the controller is simulated with the actual controller sampling time, the step size selected for the system is so small that a quasi continuous response is achieved. By suggesting values for variables to be selected by the operator for the simulation, this function is also no problem for less mathematically

I oriented users.

The simulation is, of course, much faster than the real-time process, i.e. the settling stages which take considerable time in reality are calculated here very quickly. This allows a fast overview of the control quality to be expected without having to risk critical situations occurring by experimenting with the real system during installation.

Which Models are Available for Process Identification? SIEPID S5

2.5 Which Models are Available for Process Identification?

h the framework of an adaptive procedure for automatic controller optimization, the experimental identification is a key function. Its task is to provide a suitable mathematical model to describe the dynamic system based on measurable process values, such as the response of input and output values between two fixed operating statuses.

2.5.1 Evaluation for a If the excitation in the open control loop involves a step hc t ion Step Change in the in the manipulated variable, three different parametric process Manipulated models are available: Variable

PTn model: X (S) - G(s) = - K Y (S) - (l+ Ts)"

X (4 - PTn-Tt model: G(s) = - K Y (S) - (l+ Ts)"

IT3 model: G(S) = - K Y (S) - (l+ bTs)(l+ 2dTs + f s2)

where: K = process gain T = time constant

Tt = dead time

n = model order

b = delay factor

d = damping

S = Laplace operator

x(s) = controlled variable

y(s) = manipulated variable G(s) = transfer function in

fkequency range

SIEPID S5 Which Models are Available for Process Identification?

The PTn model, a delay element of the nth order with n identical time constants, is generally an adequate approximation of the majority of industrial processes with aperiodic settling responses.

In case of significant dead times, the PTn-Tt model should be preferred. Step responses with overshoot can be approximated by the oscillating m3 model.

2.5.2 Analysis of any A random change in the manipulated variable can occur in a closed Changes in the loop or when using step controllers. If the manipulated variable is Manipulated changed in the open control loop during the identification, an Variable analysis of any change in the manipulated variable must also be

made. It is not possible to decide on a suitable process model type simply based on the step response, since the settling response is also decided by the dynamic characteristics of the controller. Despite an aperiodic system step response, the control loop step response can overshoot and vice-versa. For this reason, and owing to the much more involved analysis in this situation, the PTn model and the ITn model are used to describe the situation:

FTn model: - X (S) - G(s) - - - K Y (S) (l+ T s ) ~

ITn model: - X (S) G(s) - -= K Y ( s ) ~ ( l + T s ) ~

where: K= process gain

T= time constant

n= model order

The ITn model involves a delayed system with an integral action component. This is known as a non-self-regulating system.

Which Models are Available for Process Identification? SIEPID S5

2.5.3 Analysis

Procedure

With step controllers, the PTn model is always wed. The PTn model is a special version of the PTn model with dead time and the oscillating PT3 model. The quality of its identification can be recoguized by the user when analyzing the measured process response compared to the calculated model response (+ Chapter 5.5, Fig. 5.1).

The analysis is the most time-consuming stage of the process identification. The parameters of the process model used are determined based on the existing measured values. This is achieved by using methods of simulation and parameter optimization taking into account particularly simple and numerically uncritical analytical relationships.

The general procedure in the open and closed control loop is in principle the same for identification. A reliable procedure for optimum model adaptation has been implemented. The actual aim is to approximate the model response as well as possible to the measured controlled variable by selecting suitable model parameters.

The process gain K is calculated initially from the changes of the stationary values of the manipulated and controlled value. During the subsequent optimization of the remaining parameters, the models with PTn components vary the model order from n=l to n=10.

Optimization The optimization of the oscillating PT3 model is the most complicated, since here various types of solution must be distinguished within the time range depending on the values of the parameters;

PTn model with n = 2 or 3 (for d=l and b=O or b=l), IYIT model with d > 1 and b = 0,

PT3 model with d = 1 and b > 0 and b 0 1,

PT3 model with 0.1 I d < 1 and b 2 0.

Each of these models is optimized separately and the most suitable selected.

SIEPlD S5 Which Models are Available for Process Identification?

Result of the analysis The result of the analysis represents the parameters of the optimum model of the system. This is then used for the subsequent controller design. In addition to this, but without significance for the controller design, process characteristic values are calculated defined by the chronological course of a process step response and intended simply as a rough characterization of the process response for the interested user.

With the PTn model (with or without dead time) the delay time Tu and the recovery time (Ta) are determined (refer to the figure below).

Fig. 2.3 Defmition of the process characteristics

Relationships The relationship between the model parameters n and T and the between the model process characteristics Tu and Ta are as follows (rounded): parameters and the process characteristics

A dead time is added to Tu.

2-11

Which Models are Available for Process Identification? SIEPID S5

From the oscillating PT3 model, an oscillating I T model is obtained with b=O. Here, the relationship between the overshoot o and the damping d is as follows:

xmax - xend where o = xend

The higher the delay factor b, the greater the overshoot is reduced and the settling time increased.

SlEPlD S5 Controller Design according to Absolute Value Optimization

2.6 Controller Design according to Absolute Value Optimization

Tunina The identification and controller design must be matched with each other. For this reason, the system models are based on PI and PID controllers using the method of absolute value optimization. This is a proven analytic method which has the aim of approximating the value of the control frequency response F of the total controlled system to the theoretically ideal situation (F1 = 1 up to the highest possible frequencies.

The rules for making settings are particularly simple with the PTn model.

For (ideal) PID controllers, the original rules for making settings are as follows:

With the real PID controllers, the delay time of the D-action is also set to the value TvI5.

For the PI controller, the following is obtained:

For such controllers, a maximum overshoot of 10%, largely independent of the order n, is characteristic.

Controller Design according to Absolute Value Optimization SlEPlD S5

Exceptions The cases n<3 with the PID controller and n=l with the PI controller are obviously not adequately covered by the absolute value optimization since Kp assumes a value that cannot be implemented. This can be explained in that the aim of the method is maximum dynamic improvement and thrit this aim in the special cases mentioned can theoretically be achieved.

To obtain a possible controller setting in keeping with the absolute value optimization, the required settling time of the control loop must be specified. What can be achieved in practical terms depends on the limit values of the manipulated variable.

Setting rules as described for the other examples can be used with the other system models.

The parameters Kp and Tn are set for the step controller damped by 25% compared with the continuous controller, i.e.:

Kps = 0.75 Kpk* T& = 1.25 Tnk

* S = step contmller k = continuous controller

SIEPID S5 Option of Operator-Controlled Tuning

2.7 Option of Operator-Controlled Tuning

Apart from the computer-controlled optimization, SIEPID S5 provides tools for operator-controlled tuning, both for the model parameters and the controller parameters. This allows efficient optimization under manual control. This function represents the response of a transient fhction at a glance. Without introducing quality values artificially, it can measure the time response achieved in total against the ultimate aims. The previously popular optimization of controller parameters via analog computer simulation serves as the model.

Model of analog The effects of a continuous parameter variation adjusted with a computer simulation potentiometer could be evaluated immediately based on the

simultaneously changing shape of the step response of the monitor. To a certain extent, this technique is followed in SIEPID S5. It is plausible to use it here primarily for controller optimization, however, in the meantime extremely specialized quantitative requirements are made on control systems which conventional setting procedures can no longer take into account. With some applications, for example, the low overshoot of the controlled variable when designing using the absolute value optimization may be unacceptable. In other cases, the response to disturbances in the control loop may be of prime importance. Such cases require a suitable correction of the controller parameters calculated with the standard methods. With this in mind, SIEPID S5 provides a digital simulation of the control loop optimized for operation and calculation time. The similarity with an analog computer is reflected as follows:

Option of Operator-Controlled Tuning SlEPlD S5

Advantages the action required by the operator to trigger a new simulation with suitably modified parameters is reduced to pressing a single key and

the numeric calculation and representation of the transition hct ion are so fist that the reaction on the screen is practically simultaneous with pressing the key.

Subsequent A similar optimization tool can, if necessary, be used to tune the improvement of the model parameters. The main aim of this operator-controlled, model parameters menu-supported improvement of the model parameters is to allow

the best possible compensation of any type of disturbance which was not adequately suppressed by the automatic model optimization. Once again, the aim is to minimize the differences between the model response and the measured response.

Model quality When improving the model parameters, the fact that models with the same overall time constant have similar step responses, can be utilized. With the PTn-Tt model, Tsum = n T + Tt and with the oscillating PT3 model Tsum = (b + 2 d) T. If the model order is increased with the PTn model, the time constant T must be correspondiigly reduced. To support the operator, the program indicates whether the new parameters improve or deteriorate the model quality or whether it remains the same. The model quality is the total deviation between the calculated and measured step response.

SlEPlD S5 Option of Operator-Controlled Tuning

Matching the With each change of the model parameters, the controller controller parameters parameters are recalculated and displayed. With this, you can

recognize the extent to which a change in the model parameters affects the controller parameters. In this way, the llrobustness" of the controller &sign can be evaluated by checking how far the model parameters can deviate from the optimum values without changing the controller parameters too much.

What are the Limits of the Method? SIEPID S5

2.8 What are the Limits of the Method?

While determining the controller parameters based on an identification in the closed control loop, diierences can occur between the transition fixnctions of the simulated and the real control loop as a result of the following:

Non-lineanties in the The process model is calculated for only one working point of the system system and one step direction. When simulating the closed control

loop, it is assumed that the system has a linear response around this working point. With non-linear systems, the real control loop changes to a different working range when the setpoint is changed. If the dynamics of the system also change, deviations in the control response are inevitable. If in doubt, the system should be identified at diierent working points and in both step directions. The worst case model (lughest gain, highest order) must then be used for the controller design and it is necessary to find out how far the proposed control parameters stray.

Differences in the control algorithm

There may be differences between the real controller and the control algorithm in the simulation. For example, in the simulation, the stroke and speed of adjustment are not limited. A limit in the speed of adjustment means that the manipulated variable cannot achieve sudden changes. This is particularly important with PID control systems and when controlling PT1 systems with a PI controller. In both cases, the initial value of the manipulated value is the maximum value. If this maximum value itself is damped by the delay in the controller, adjustment energy is lost. In the identification, this delay time is added to the system dynamics. The effects of the delay are less the slower the system response.

SIEPID S5 What are the Limits of the Method?

Stroke limits Limiting the stroke has the following effects: in the simulation, it is not the absolute values of the manipulated and controlled variables that are output, but the changes relative to the current working point. These changes must therefore be added to the stationary values of the control and manipulated variables at the working point. In the real control loop, the limit cannot be reached.

Q Further discrepancies can occur when the controlled variable in the real controller is filtered by a low pass filter or a dead time. In the simulation, the dead band can lead to a permanent control deviation.

Different excitation of In the simulation, a setpoint step is applied to the control loop. If, the control loop however, the real control loop is excited by a setpoint ramp, e.g.

with compact controllers, the transition function of the real control loop is damped more than in the simulation.

Delay response of Differences can occur particularly when the delay response of the the system system is compensated by the controller, i.e. with PT1 systems

with PI controllers or PT2 systems with PID controllers. In these situations, the controller gain can theoretically be increased without limits without the control loop becoming unstable. In real control loops, however, there are limits to the possible increase in gain.

No reproducible Problems can occur when the sensors do not return reproducible measured results measured results due perhaps to characteristic curves, deterioration

or incorrect installation. The Daction component is particularly sensitive to such disturbances. If this can occur, the more "robusttt PI controller should be preferred. The same problem can, however, occur when designing controllers for processes with greatly changing time responses. In such cases, the process must be identified at different working points. Using the calculated controller parameters, a parameter control for the controller can be designed.

What are the Limits of the Method? SIEPID S5

Errors in the Extremely small values in system gain can only be compensated dimensions of by relatively large values in the controller gain. This leads to actuators extremely high deflection of the control value when the setpoint is

changed. With a PID controller, this effect is further accentuated by the D-action component. A reduction of the controller gain means that the control loop reacts more slowly and that the compensation of disturbances deteriorates. Extremely high values in the system gain mean small values in the controller gain.

This results in "weak" control actions leading to poorly compensated disturbances.

Measurement accuracy

During the settling phase, 101 measured values are stored. The values are not read in controlled by the clock pulse, but asynchronously at the highest possible speed. Systems with a settling time of approximately 2.5 seconds and upwards can be identified. Owing to the robust evaluation, multiple values have only a negligible influence.

When identimg systems in which the range of change of the measured values is extremely small (1 ... 2%), the graphic display of the measured values on the screen is unsatisfactory since the values are related to the range 0 to 100%. In addition to this, with such small changes, the automatic final value detection does not respond. In this case, the remedy is to change the measuring range. The manipulated and controlled variables must also be increassed by the same factor. In this case, you should also check whether or not the measuring range has the wrong dimensions.

Working with the SlEPlD S5 Program

In this chapter you will learn how to install the SIEPID S5 program, create back-up diskettes and start SIEPID S5. The chapter also includes information about the main menu and the program structure (refer to Chapter 9).

To be able to work with the program without a PLC there is a DEMO mode in which all accesses to the PLC are simulated. This chapter also discusses the documentation functions available with SIEPID SS.

General Information SlEPlD S5

3.1 General Information

3.1 .l Creating a The SIEPID S5 program package is supplied on a 3.5" diskette. Working Diskette Please make a working copy and back-up copy of this original

diskette and put this away for safe keeping. Copies can be made using the MS-DOS operating system.

3.1.2 Installing the Program installation is straight-forward. If the computer you are Program using has a hard disk, all the files on the diskette should be copied

to a directory on the hard disk and the program is called up fiom this directory. File access from the hard disk is much faster while the program is running and it is therefore advisable to install SIEPID on the hard disk. SIEPID can also be called up directly from the working diskette. A working diskette must not be read-only, since SIEPID S5 generates data files. Depending on your PG, calling up the program from diskette can take up to 30 seconds.

To make practical use of SIEPID S5, a serial link between the SIMATIC CPU and PG is necessary, otherwise process variables cannot be acquired and controller data cannot be influenced. The programmable controller and the software controller should be in operation before SIEPID S5 is started.

SIEFID S5 General Information

3.1.3 Starting the Call the program as follows: Program SIEPID S5 4urI"uRN>

in the appropriate directory under MS-DOS.

The measured value files and the controller table can only be processed in the directory in which SlEPID S5 is installed. El If you cannot start SIEPID S5, check whether other programs are running in the background and taking up too much main memory.

When using SIEPID S5, make sure that there is sufficient processing time available on the CPU (comparable with using the STATUS VARIABLE function in LAD, CSF, STL). Otherwise the CPU may change to the STOP mode.

Main Menu SlEPlD S5

Main Menu

After the logo screen form and the second screen form for setting the monitor type, the main menu appears. This is the screen form fiom which all functions can be reached and to which you return on completion of a function with the exception of the "program endt8 function.

The main menu provides the following options: 1: Identification and controller optimization

2: Control loop simulation

3: Operating and monitoring

4: Controller table

5: SIEPID configuration

6: DEMO mode 7: Terminate program

The main menu items 1 to 7 can be selected with the arrow keys and are then graphically illustrated. A position can be selected as follows:

positioning the selection bar and pressing the 4WI'URN> key or directly by typing in the code number.

The same principle applies to all the following menus. As shown in the bottom line, you can always return to the previous menu with <ESC>. Menu item 1 "Identification and Controller Optimization" represents the central finction of SIEPID SS. The majority of the description deals with this installation function.

The control loop simulation fhnction is used for the numerical analysis of the time response of control loops separate from problems regarding identification.

SIEPID S5 Main Menu

Working in the DEMO mode

Using the '%ontroller table" you can store system and controller parameters for up to 99 control loops on diskette or hard disk and use them for simulation at a later time.

The schematic diagram of the program in Chapter 9 provides you with an overview of the structure and sequences of SIEPID S5.

The DEMO mode is another method of generating process variables which is particularly uselkl for training and demonstration purposes. This is the numeric simulation of the system to be identified on the PG. Operation in this mode is largely the same as when working with a direct process link. With this lknction, you can try out the process identification hnctions in a practice m. Compared with the acquisition of real process variables, the following differences in the simulation must be remembered:

no link to the PLC required fixed, unchangeable process model of the type PTn, third order identification only in the open loop (without controller) screen forms are restricted or slightly modified fixed, unchangeable excitation

Documentation

3.3 Documentation

By using all the printout and storage possibilities, you can obtain complete documentation of the controller installation after using SIEPID S5.

Outputting hardcopies The results of the identification and simulation can be output as hardcopies. The program operates in both the text and graphics mode:

In the text mode, you can make hadcopies of all the operating screen forms using the keys <SHIFT + PRT SC>. Texts displayed in inverse video are, however, only correctly printed out when you select the monochrome mode in the screen form following the logo screen form. In the graphics mode, a special hardcopy program is called. Refer to Section 1.4 for the printers supported.

Steps for Commissioning a Controller 4 This chapter describes the steps involved in commissioning a controller, based largely on the conventional methods. You will learn the differences between the SIMATIC S5 continuous controllers and step controllers. The linking of a controller via the open interface is also described.

You will also find information in this chapter about the various methods of measured value acquisition.

Stages Involved in the Procedure SIEPID S5

4.1 Stages Involved in the Procedure

Conventional The procedure is oriented largely on the conventional procedure procedure for commissioning a controller:

establishment of suitable conditions for commissioning measurement of the process response evaluation of the acquired process model controller design tuning adoption of the new controller parameters documentation of the results

SIEPID S5 provides special methods for adaptive control. These methods were developed specially for PC-supported controllers and tested thoroughly on real processes.

Adaptive methods In keeping with the character of adaptive methods, the tasks "process identification1' and "controller optimization" are of major importance. The steps involved reflect the procedure of a methodical control system engineer. The following figure illustrates this schematically.

4- Controller System A I

l- Y X

Control loop

Fig. 4.1 : Steps involved in adaptation

SlEPlD S5 Stages Involved in the Procedure

Basic principle The basic principle of control adaptation is the analysis of a step response in the system or the control loop with the aim of obtaining a suitable parametric model of the system. This is then used as the basis for an analytical controller design based on setting instructions.

Atter codhation by the operator, the finely tuned controller parameters are transferred to the PLC by the SIEPID S5 program. The practice-oriented overall concept drastically reduces the work involved in setting suitable parameters.

How to Assign Parameters for a Continuous Controller SIEPID S5

4.2 How to Assign Parameters for a Continuous Controller

Definition The term continuous controller means a PID controller supplying a continuous actuator signal as its output variable.

The following SIMATIC S5 continuous controllers are supported: m FB 20 1: S5-100U controller m FB 80: compact controller of the SS-l 15U controller m FB 102: controller structure R64 m FB 62: PID controller of the modular PID and fizzy controller m FB 176: IPD controller of the modular PID and fizzy controller

4.2.1 FB 201 : S5-100U Controller

D6 / DW numbers For the S5-100U controller, the DB number and DW numbers of the individual parameters must be specified. The blocks of the S5-100U controller have a standard sampling time of 1 second. If other values are selected, the "integral action time TI" and "derivative action time TV'' parameters must be recalculated. Once you have entered the sampling time, SIEPID S5 is capable of performing the recalculation automatically.

Parameter SIEPID S5 performs the following parameter recalculation: recalculation

m Kp (SIEPID $5) = 0.001 * K(PLC) Tn (SIEPID S5) = TA * 1000 1 TI(PLC)

m TV (SIEPID S5) = TV(PLC) * TA

Where: TV (SIEPID S5) is the value in SIEPID S5, TV (PLC) is the value in the PLC controller.

SIEPID S5 How to Assign Parameters for a Continuous Controller

Range limits

Control word

Note The value selected for the sampling time TA must not be too small in relation to the integral action time TN. Suggestion: TN/lo < TA I % time constant of the closed control loop. Otherwise when converted to the integration time TI of OB 25 1, TI=O may result due to futed point division. This would be the same as deactivating the I action. Conversion: TI (OB 25 1) = TA * 1000lTN (SIEPID).

The range limits for setpoint, actual value and manipulated variable have the following defaults:

Start of range = - 2047 and

End ofrange = 2047

The following bits in the control word STEU can be modified automatically by SIEPID S5:

BIT 0 = O/1 manuaYautomatic mode

4.2.2 FB 80: Compact Controller of the S5-115U Controller

DB / D W numbers With the compact controller of the S5-115U controller, only the controller DB number must be entered.

Range limits The range limits for setpoint, actual value and manipulated variable are read out of the controller DB: Start of range, address = DW 12 End of range, address = DW 13

The defaults for the limits of the manipulated variable are: Start of range = 0 and End of range = 1000

How to Assign Parameters for a Continuous Controller SIEPID S5

Control word The following bits in the control words DW 3 and DW 46 can be modified automatically by SIEPID S5: m D3.1,D3.3,D3.4,D3.5,D3.7 operating bits

D46.1 Manual / automatic mode switchover

Note The value selected for the sampling time TA must not be too small in relation to the integral action time TN. Suggestion: TN/10 TA I l/z time constant of the closed control loop. Otherwise when converted to the integration time TI of OB 25 1, TI=O may result due to futed point division. This would be the same as deactivating the I action. Conversion: TI (OB 251) = TA * 1000KN (SIEPID).

4.2.3 FB 102: Controller Structure R64

D8 / D W numbers With the controller structure R64, only the controller DB number must be entered.

Range limits The range limits for setpoint, actual value and manipulated variable have the following defaults:

Start of range = -10000 and

End of range = 10000

Control word The following bits in the control word DW 180 can be changed automatically by SIEPID SS:

D 180.1 1 Manual 1 automatic mode switchover (0= automatic mode, l= manual mode).

SlEPlD S5 How to Assign Parameters for a Continuous Controller

4.2.4 FB 62: PID Controller of the Modular PID and Fuzzy Controller

/ D W numbers With the modular PID and fizzy controller, the DB number and the DW numbers of the individual parameters must first be input. The controller cannot operate in the manual mode. The identification must therefore be made in the closed loop and the setting "analysis for any change in the manipulated variable" must be selected.

Range limits

Control word

The range limits for setpoint, actual value and manipulated variable have the following defaults:

Start of range = -10000 and

End of range = 10000

The following bits in the control word STEW can be changed automatically by SIEPID S5: • SPEIA 1 = O P action activated • SIEIA 2 = 0 I action activated

SDEIA 3 = 011 D-action activated or deactivated depending on controller structure

• SBED 7= 1 operating bit set

When the derivative action time is entered via "change controller parameters" in the controller operation screen form, the damping time constant T1 is set to TVl5. If you require a different value, this must be assigned using "change data word".

How to Assign Parameters for a Continuous Controller SlEPlD S5

4.2.5 FB 176: IPD Controller of the Modular PID and Fuzzy Controller

DB / DW numbers

Range limits

Control word

With the modular PID and fizzy controller the DB number and the DW numbers of the individual parameters must first be input.

The range limits for setpoint, actual value and manipulated variable have the following defaults:

Start of range = -10000 and

End of range = 10000

The following bits in the control word STEW can be changed automatically by SIEPID S5:

SPEIA 3 = 0 P action activated SWA 4 = 0 I action activated SDEIA 5 = 011 D-action is activated or deactivated

depending on the controller structure

The following bits in the control word RSP can be changed automatically by SIEPID S5:

RBED 7= 1 operating bit is used RAIH 8=0/1 automatic1manua1 switchover RISPO 10 = 0 I action released RISNE 11 = O I action released RTAIE 12 = 1 dead zone activated RTPD 1 4 = 1 P and Dactions run through the dead zone

When the derivative action time is entered via "change controller parameters" in the controller operation screen form, the damping time constant T1 is set to TVl5. If you require a different value, this must be assigned using !'change data word1'.

SlEPlD S5 How to Assign Parameters for a Step Controller

4.3 How to Assign Parameters for a Step Controller

Definition The tern three step controller in this context means a controller which has two binary signals (HIGHILOW) as output value and therefore controls an integral actuator connected in series.

The following step controllers are supported: FB 202: step controller of the S5- l OOU controller FB 102: step controller in R64 FB 176 t 177: IPD controller with pulse output of the

modular PID and controller

The following are not supported: The S controller in the S5-115U controller. Only continuous PID controllers are supported in the S5-115U. S controllers can be connected via the open interface. This case is, however, dealt with in a separate section dealing with the open interface (refer to Section 4.4 "Linking Controllers via the Open Interface'').

Caution SIEPID requires between 0.6 and 1.4 S to read values in from the CPU. For this reason the actuating speed of the valve may not be too high, e.g. with a actuating speed of 5% 1 S an error of up to 7% occurs due to the sampling delay.

The following situations must also be distinguished: l. Drives with electronic position feedback (EPF) 2. Drives with limit switches and 3. Drives without feedback

Drives with EPF If it exists, the measured position feedback (EPF) of the drive should be used as the value of the manipulated variable.

Drives without EPF If no EPF exists, as in cases 2 and 3, it is also possible to total the actuating pulses of the controller via an integrator and to use this calculated value as the value of the manipulated variable. Setting up this block and the required wiring is however left up to the user.

How to Assign Parameters for a Step Controller SlEPlD S5

Format of the position feedback

Range of values

DB numbers

Calculating the motor actuating time

Minimum pulse duration

On/off threshold

When selecting a step controller, the address and format of the position feedback must also be specified.

Along with the format of the setpoint, actual value, value of the manipulated variable or position feedback, the range start and range end must also be specified. Within this defined range, the value is then normalized to 0 to 100%.

For the DB numbers of the positional feedback and pulse shaper you can spec* any values that you require. They have the default DB number of the controller.

In the controller operating screen form it is possible to determine the motor actuating time by means of a menu item. The controller must be set to "manual mode". For drives with EPF, it is adequate to move the drive by at least 10%. The adjustment duration is then calculated for an adjustment value of 100%. For drives with limit switches, the drive is first moved to the limit position "CLOSED" and it is then driven to the other limit position and back to "CLOSED".

The minimum pulse duration is calculated according to the following rule of thumb: minimum pulse duration [S] 2 0.005 * motor actuating time [S]

The values for the on and off thresholds are calculated according to the following equations:

on threshold 2 100 * absolute value (K) * minimum pulse duratiodmotor actuating time off threshold = 0.5 * on threshold

The factor K is the processgain. If K is not yet known, the default K = 1 is set.

SlEPlD S5 How to Assign Parameters for a Step Controller

4.3.1 FB 202: Step Controller of the S5-100U Controller

DB / D W numbers With the step controller of the S5-100U controller the DB number and DW numbers of the individual parameters must fist be input.

The blocks of the S5-100U controller have a default sampling time of 1 second. For other values, the parameters "integral action time TI", "derivative action time TV", "minimum pulse duration TMIN" and "actuating time TS" must be recalculated. Once you have entered the sampling time, SIEPID S5 is capable of performing the recalculation automatically.

Parameter recalculation

Range limits

Control word

SIEPID S5 performs the following parameter recalculation:

Kp (SIEPID SS) = 0.001 * K(PLC) Tn (SIEPID SS) = TA * 1000 1 TI(PLC) TV (SIEPID S5) = TV(PLC) * TA TMIN (SIEPID S5) = TMIN(PLC) * TA TS (SIEPID S5) = TS(PLC) * TA

Where: TS (SIEPID S5) is the value in SIEPID S5, TS (PLC) is the value in the PLC controller.

The range limits for setpoint, actual value and position feedback have the following defaults:

Start of range = - 2047 and

End of range = 2047

The following bits in the control word STEU can be changed automatically by SIEPID S5:

BIT 0 = 011 manuallautomatic mode BIT 1 = 011 output Y+, for manual mode BIT 2 = 011 output Y-, for manual mode

The limit switch signals are connected to BIT 3 and BIT 4.

How to Assign Parameters for a Step Controller SIEPID S5

Position feedback To simulate the position feedback, the setpoint generator FB 204 can be used. The user must program the link. To synchronize the simulated and real position feedback, both must be driven to the end position.

4.3.2 FB 102: Step Controller in R64 DB, D W numbers With the step controller in R64, only the controller DB number

must be entered.

Range limits The range limits for setpoint, actual value and position feedback have the following defaults:

Start of range = -10000 and

End of range = 10000

Control word The following bits in the control word DW 180 can be changed automatically by SIEPID SS: m D 180.1 1 Manual 1 automatic mode switchover

(0= automatic mode, l= manual mode). • D 180.8 "Manual value active" is set to 0 (= not active) m DB 180.12 Set negative pulse in manual mode

DB 180.13 Set positive pulse in manual mode The step controller is switched on with the configuring switch S1 @23.0=1).

So long as the end position signals exist, these are switched to bits D180.4 "OPEN end position reached" and D180.3 "CLOSE end position reached" of the R64 data block.

SIEPID S5 How to Assign Parameters for a Step Controller

Position feedback If a measured variable for the position feedback exists, it must be simulated. For the simulation, only an R64 controller can be used. In the ramp generator, the actuator time is input by means of the parameter ramp-up time, rampdown time and increment.

Increment / (ramp-up time or ramp-down time) = 100% / actuating time Higher bit D 180.2 and lower bit D 180.1 must be connected to the output signals of the step controller. The actual controller section is assigned as a proportional controller (PID standard version) with gain 1. The simulated actuating signal is obtained at the output. To synchronize the simulated and real position feedback, both must be moved to the end position.

4.3.3 F6 176 + F6 177: IPD Controller with Pulse Output of In the modular PID and b z y controller, a step controller can be the Modular PID implemented with blocks FB 176 (IPD controller) and FB 177 and Fuzzy (pulse output). Controller

D6 / D W numbers The DB number and the DW numbers of the individual parameters of the IPD controller must first be input. For pulse output, a further DB number can be entered. The default is the controller DB number. In addition to this, the DD number of TMIN must be specified.

Range limits The range limits for setpoint, actual value and position feedback have the following defaults:

Start of range = -10000 and

End of range = 10000

How to Assign Parameters for a Step Controller SIEPID S5

Control word The following bits in the control word STEW can be changed automatically by SIEPID SS:

SPEIA 3 = O P action activated SIEIA 4 = O I action activated SDEIA 5 = 1 D action deactivated SHAND15=1 MANUAL value output as

manipulated variable

The following bits in the control word RSP can be changed automatically by SIEPID SS:

RBED 7 = 1 operating bit is set RA/“ 8 = 011 automatidmanual switchover RISPO 10 = 0 I action released

• RISNEll=O I action released RTAE 12 = l dead zone is activated RTPD 14= 1 P and D components run through the

dead zone

FB 176 works with the velocity algorithm and FB 177 is set to three-point signal for integral actuator @PSI).

If they exist, the end position signals of the actuator are connected to the bits SRMAU and SRMZU in the control word STWO of the pulse output.

Position feedback To simulate the position feedback, FB 96 (setpoint generator) can be used. This must be switched to the manual mode. The bits SHAUF and SHZU in the control word STEW are connected to the binary output signals of the step controller. To synchronize simulated and real position feedback, both must be moved to the end position.

Manual mode The pulse output does not have highllow bits for the manual mode. To allow SIEPID S5 to operate the controller in the manual mode, the following procedure is required:

If the controller is switched to manual, not only the manual-automatic bit is set, but also bit 15 SHAND in the control word STEW of the IPD controller. If the manipulated variable is adjusted upldown, the manual value input MANUAL is set to 100001-10000. In all other cases, the manual value remains zero.

SIEPlD S5 How to Assign Parameters for a Step Controller

The sampling time of the IPD controller (FB 176) also affects the setting of the manipulated variable as well as the SIEPID read-in time (see page 4-9 l'Cautionl'). This means the setting of the manipulated variable is

l increasingly delayed. The delay becomes greater as the sampling times increase.

Connecting Controllers via the Open Interface SlEPlD S5

4.4 Connecting Controllers via the Open Interface

Via the open interface, any other PID controller structures (continuous controllers and step controllers) on the S5-95, S5-100, S5-115, S5-135 and S5-155 programmable controllers can be connected.

Procedure The interface data must first be input. This involves the addresses of the process variables and control parameters. In addition to this, the normalization values for the individual variables must also be input.

Continuous controller In the input screen form "setting the open interface" the controller data block must first be input. This DB contains the most important parameters and process variables for a PID controller as follows:

A control word, the controller gain Kp, the integral action (reset) time, the derivative action time TV, the setpoint, the actual value and the manual manipulated variable.

A separate data block can be specified for the effective value of the manipulated variable (refer to step controllers). The variables for the PID algorithm are described in Section 5.1. A delay time of the Daction component T1 is not taken into account in the input screen form. If this exists in the controller, it must be assigned parameters with the finction !'change data word" in the menu of the controller operation screen form. In general, the value is set to 115 of the derivative action time TV.

With each parameter or process variable, the data format @W = data word or DD = double data word) must be specified. Double data words are read in the KG format.

Normalization The controller parameters Kp, Tn and TV can be normalized when they are read and written as follows:

The coefficients are preset: offset = 0 and

factor = 1.

If necessary, these can be modified for each specific parameter. For the process variables, the following must be input:

SIEPID S5 Connecting Controllers via the Open Interface

RS = Start of range

RE = End of range

Within these range limits, the process variables are normalized fiom 0 to 100%. RS has the default 0 and RE the default 100. Once again, specific values can be modified.

Step controller

The last entry to be made is the bit for the mode (manuaYautomatic) in the control word of the controller.

With step controllers, the value of the manipulated variable must be available in the form of a position feedback as a measurable signal. If it cannot be measured, it must at least be simulated.

The information about the position feedback must be entered under the value of manipulated variable parameter. The DB number does not need to correspond to the controller data block (refer to continuous controller).

The following specifications can be made for step controllers: a second control word bits for manual value of manipulated variable via OPENICLOSE keys bits for final position OPENICLOSED reached

In the manual mode, SIEPID S5 can change the value of the manipulated variable in two ways. If entries have been made under "manual value of manipulated variable via OPENICLOSE keys", SIEPID S5 controls the corresponding key (OPEN or CLOSE) until the position feedback has reached the required value of the manipulated value. If notlung is entered here, SIEPID S5 simply transfers the required value of the manipulated variable to the address of the parameter set manual (see continuous controller).

If information about the limit switches exists, a measurement of the actuating time can also be made for the limit switches.

The values calculated or proposed by SIEPID S5 for actuating time TM odoff threshold ON, OFF minimum pulse duration TMIN

must be transferred using the firnction Itchange data word" in the menu of the controller operating screen.

Ways of Acquiring Measured Values SlEPlD S5

4.5 Ways of Acquiring Measured Values

The acquisition of the relevant process variables, i.e. the manipulated variable y(t), the controlled variable x(t) and if necessary the setpoint w(t) is necessary for each experimental identification. SIEPID S5 accommodates various methods of measured value acquisition.

4.5.1 Reading in the This is normally the main application of SIEPID S5. A serial Measured Values connection is established between the PG and the programmable from the PLC controller. The process variables are then read in to the PG after

applying the excitation.

The SIEPID S5 program then reads (fetches) the values. The data are stored on the PG dynamically until a new stationary status is reached.

Storing the measured The measured values can be stored on diskette as ASCII files values <Name.SMD> and are therefore available for later analysis or

documentation. The acquired model and controller parameters are also saved by SIEPID S5 in the "controller table" as file REGTAB .DAT.

4.5.2 Reading in from Instead of a direct link to the process, the process values can also Diskette be supplied to the program by reading in a SIEPID S5 measured

value file fiom diskette or hard disk and then directly analyzing the data. Such a measured value file is normally the product of an earlier SIEPID S5 application on a real controller. Since these are ASCII files, they can also be generated externally, e.g. using a text editor, providing the files keep to the structure of a SIEPID S5 measured value file (refer to Section 5.9).

SlEPlD S5 Ways of Acquiring Measured Values

4.5.3 Simulation of the There is a third way of generating process variables, namely the System in the PG numeric simulation of the system to be identified on the PG. This

is particularly interesting for training and demonstration purposes. The parameters of a mathematical model of the system and its excitation must be specified. The steps involved correspond largely to those required for a direct process link. In this way, you can learn how Le process identificstion functions without needing a real process.

Compared with the acquisition of real process variables, the follo&ng differences should be noted when using the simulation:

no link to the PLC necessary the system is specified in the form of a transfer function identification only in the open loop (without controller) operating screen forms are restricted or modified.

"Identification and Controller Optimization"

The function described in this chapter, "Identification and Controller Optimization" is used to identlfy self-regulating and non-self-regulating systems.

The identification and controller design are performed in several steps. After reading in the measured values they are logged and saved and then analyzed depending on various factors. This chapter also explains how to tune the model parameters and transfer the controller parameters. You will also learn how to use SIEPID S5 as a recorder for measured values and get to know the structure of the corresponding measured value files.

General Notes on the "Identification and Controller Optimization" Function SIEPID S5

5.1 General Notes on the "Identification and Controller Optimization" Function

Options In the main menu you can select the following functions:

1: Identijication and controller optimization 2: Control loop simulation 3: Operating and monitoring 4: Controller table 5: SIEPID confguration 6: DEMO mode 7: Terminate program

The diagram in Chapter 9 provides you with an overview of the program structure of SIEPID S5.

Selecting menu items You can move the selection bar in the main menu using the arrow keys. A function is selected in one of two ways:

by positioning the selection bar and pressing <RETURN> or

by typing in the code number.

This applies to all subsequent menus. As displayed in the bottom line of the screen, the <ESC> key returns you to the previous menu.

Three stages of the The identification and controller design is performed in three function stages:

l. Reading in the measured values. 2. A process model is developedfiom the measured values. At the

same time, the controller parameters are calculated based on the model parameters.

3. As an option, the model parameters can be further tuned by the user.

Otherwise, the procedure for identification in an open or closed control loop is the same.

SlEPlD S5 General Notes on the "Identification and Controller Optimization" Function

Identification and The "identification and controller optimization" fullction represents controller optimization the main function of SIEPID SS. It is used to identifj

self-regulating and non-self-regulating systems. The system is approximated based on measured time responses of y(t) and x(t) by means of a parametric process model. The step response of a system can be described by the following models:

PTn model:

PT3 model: G(S) = * = K Y (S) ( 1 +b~s)( i +Z~TS+T?S~)

With the parameters K = process gain

T = time constant Tt = dead time

n = model order

b = delay factor d = damping S = Laplace operator

x(s) = controlled variable

y(s) = manipulated variable

G(s) = transfer function in the

frequency range

With the PTn and the PTn-Tt model, aperiodic settling phases with and without dead time can be described. Step responses with an overshoot are approximated by the oscillating PT3 model.

General Notes on the "Identification and Controller Optimization" Function SIEPID S5

No step response of If the closed loop is excited by a setpoint step, the manipulated the manipulated variable no longer has a step response. This is also the case when variable the manipulated variable can only be adjusted step by step using

buttons or when the manipulated variable is generated via an actuator. In these cases, the system can either be approximated by the PTn model or an ITn model.

ITn model: -a = K G(S) - ~ ( s ) s(l+Ts)"

The value K is the reciprocal of the rise time with the dimension llsec. The rise time for an integrator is the time in which the integrator rises fkom 0 to 100% driven at 100%.

Following this, and based on the identified system model, the parameters of a continuous PI and PID controller are calculated.

PID controller: R(s) = 1 =Kp ( I +-+Tvs) Tns

With the parameters: Kp = controller gain Tn = integral action time

TV = derivative action time

S = Laplace operator

xd(s) = control deviation

y(s) = manipulated variable

With the PI controller: TV = 0.

SlEPID S5 Reading in the Measured Values

5.2 Reading in the Measured Values

If you select the fbnction "identification and controller optimization" a firther screen form is displayed in which you must decide how the measured data will be acquired. There are two possibilities here:

1. Reading in the measured values from the S IUTIC CPU via the serial intevface (COMl or COM2).

2. Reading in a file with stored measured values. Following an identification, the stored measured values are saved as an ASCIIfile on the hard disk or diskette. These files can be read in again and analyzed. In this case, you spec13 the file name directly or press function key <FI> to display the files

B in the current directory. You can then position the cursor on the requiredJle and select it with <RETURN>.

A third menu item is displayed with which you can display a measured value file in the text mode. This is intended for a situation when you want to analyze a file again at a later time and do not have documentation of the file. The time and date when the file was saved and the comment line provide further information about the file.

Starting Measured Value Recording SIEPID S5

5.3 Starting Measured Value Recording

Defined output status To identifL the system, you must first bring the control loop to a of the control loop defined initial status. The following steps are necessary:

l. Establishment ofthe PG-PLClink 2. Check the controller settings 3. Switch on automatic final value detection 4. Adjust the scale of the setpoint and controlled variable 5. Wait for a stationary status 6. Apply the excitation

Establishing the The commissioning device is connected via the serial interface of PG-PLC link the PG to the PG-PLC interface of the SIMATIC CPU.

NOTE The serial interface of the PG must be a 'ITY interface. This is the case with the SIMATIC programmers. With other AT-compatible PCs, it may be necessary to adapt the usual V.24 interface using an interface converter 01.24 - TIY).

SIEPID supports all the sofhware controllers of SIMATIC S5:

S5-100U controller for SS-95U, S5-100U with CPU 103

S5-115U with CPU 941B, 942B,

943B, 944B

S5-115U controller for SS-l 15U with CPU 941B, 942B,

943B, 944B

Controller structure for S5-135U with CPU 922, 928,928B

R64 S5-155U with CPU 922,928,928B

Modular PID and for SS-l 15U with CPU 945

fizzy controller S5-135U with CPU 922*, 928,928B

S5-155U with CPU 922*, 928,928B,

9461947,948

* except forfizzy blocks and simpl@ed system rack

5 - 6

SlEPlD S5 Starting Measured Value Recording

There is also an open interface, with which controllers of the SS-9SU, SS-1 OOU, SS-1 1 SU, SS-135U and SS-1 SSU PLCs can be assigned parameters. The type of controller used must be specified in a menu. For all software controllers, the number of the controller data block to be processed must also be specified. With the compact controllers (SS-1 1SU controller, controller structure R64) this information is sufficient, with the other controller packages, the block assignment must also be specified.

The computer tests whether a link is possible to the controller. If an error message is displayed, please check the cable.

Checking the The controller structure is determined by bits being set in one or controller settings more controller words. After the link has been established, the

appropriate control word is displayed in the form of a table. Here, you can check the structure of the controller. If necessary, the structure can be changed in the data word. After establishing the link, the link data can be stored in the controller table and if necessary loaded again at a later time.

Normalizing SIEPID S5 can only analyze measured values in the range fiom 0 measured values to 100%. Within the SIMATIC range, the controllers work with

different ranges. To allow normalization, you must enter the following information:

start of range and end of range for setpoint, actual value

start of range and end of range for manipulated variable or position feedback

The start of the range corresponds to 0% and the end of the range 100%. The conversion formula is as follows:

value (in %) = open + value (in PLC) * 100 1 (end-open)

With the numerical representation in the controller screen form, you can select between display as a percentage and as an absolute value. Once you have checked the controller structure in the previous screen forms, the current controller settings are displayed in the next screen form. Here, the current process variables, the modes

Starting Measured Value Recording SIEPID S5

and the controller parameters are displayed. By means of a menu, individual parameters can be selected and modified.

Transferring process To transfer process variables fiom the commissioning device to the variables controller the following settings are necessary:

a for identification in the open loop: the controller must be set to MANUAL,

for identification in the closed loop: the controller must be set to AUTOMATIC.

The next screen form once again displays the controller mode and the type of excitation (setpoint or manipulated variable step). You must now confirm that the correct mode is set on the controller. If necessary, return to the controller screen form where you can change the controller mode.

Switching automatic detection

on An important part of the preparation is speclfLing how the storage final value of process variables should be terminated. Since the controlled

variable response to be determined is a transient fhction, the natural end is defined when a new stationary final value is reached. With the graphic representation, it is no problem for the operator to recognize the final value. Automatic final value recognition however demands extremely reliable criteria, especially when responses are subject to disturbances. Nevertheless, both options are implemented in SIEPID S5. In a separate screen form, the automatic recognition can be selected without losing the option of manual intervention. The final value test slows down the acquisition of measured values slightly. The automatic technique ceases to fhnction correctly when the change in the controlled variable is less then 3%. If the final value recognition is activated, the measured value recording is terminated automatically. It can, however, be stopped at any time with the <A> key. The memory must, however, have been written completely full at least once after applying the excitation. If the measured value recording is terminated before the data are compressed the first time, the identification is aborted. The end phase of the controlled variable change is filtered particularly sensitively to determine the final value more reliably.

Completing measured value recording

SlEPlD S5 Starting Measured Value Recording

This means that the curve at the end of the identification can deviate shghtly fiom the real curve.

Terminating the During the identification of non-self-regulating systems, you measured value should not wait until the system has settled at the new working acquisition with point after applying the excitation. It is advisable to terminate the non-self-regulating measured value acquisition prematurely with <A>. systems

For an identification in the open loop, the manipulated variable must be previously set to the initial value. With identification in the closed loop, the measured value acquisition should be terminated the first time the manipulated variable reaches the initial value again.

Saving the Measured Values SIEPID S5

5.4 Saving the Measured Values

On completion of the measured value acquisition, the stored data can be saved as a file on the hard disk or diskette. The data can, however, only be stored in a directory in which SIEPID S5 is installed and from which it was started. This has the advantage that the measured values are available for later analysis or documentation.

The file has the name "Name.SMD" where "Name" consists of a maximum of 8 characters (letters and numbers). The name extension ".SMD" is appended automatically. You can also input a comment line with a maximum of 70 characters to provide firther information about the file. The date and time of the measured value recording are automatically stored along with the file.

You can skip inputting the name with 4UTURN>. In this case the measured values are automatically saved as the file "MESSW'IT.SMD". You will find the measured values here under this name if you accidentally omit to enter a file name.

Note If you attempt to save a measured value file or controller table on a full hard disk, there is no error message displayed. You must therefore make sure that there is sufficient space to store the file.

SIEPID S5 Saving the Measured Values

Structure of the The measured value files are ASCII files whose detailed structure measured value files is described in Section 5.9. At the beginning of the analysis, the

responses of the recorded manipulated and controlled variables are normalized and displayed again graphically. This display can be documented using the hardcopy function. A zoom function allows you to select a higher resolution of the time responses. When planning an identification in the closed loop, the setpoint of the controller is also recorded. The stored responses of the controlled variable and the setpoint are also saved under the file name "h4ESSDAT.SM.D". This means that it is possible to determine the transient finction of the closed loop. The setpoint in this case corresponds to the excitation. Tbis file is, however, not evaluated for controller settings. For this reason it is overwritten each time measured data are recorded.

Analysis SIEPID S5

5.5 Analysis

The analysis of the measured data is the main problem of an experimental identification. The technique implemented in SIEPID S5 is intended to derive a simple but adequately accurate parametric model of the system from the acquired process information to be able to design a controller to meet the requirements.

AAer selecting the fhction "analyze" you must first decide how the analysis is performed.

There are two methods available for identification in the open loop:

I . Evaluation assuming a relatively ideal manipulated variable step

2. Evaluation for any manipulated variable step

With identification in the closed loop, the program itself selects the method for any manipulated variable response, since this is the only suitable method.

h this menu, you can also select screen display of the measured value file to check the data contents (measured values, comments) again.

5.5.1 Analysis for an This situation corresponds to the classical identification in the open Ideal Manipulated loop based on the step response of the system. You must first Variable Step select one of three possible models to approximate the step

response (taking into account the measurement results):

l . aperiodic (=PTn model) 2. aperiodic with dead time (=PTn-Tt model) 3. oscillating (=PT3 model)

The analysis can be repeated as often as required so that you can use all model types one after the other. The exact mathematical description of these models can be found in Chapter 2.

Analysis

Optimum model The model parameters are varied using a special optimization response algorithm. The calculated model step response is then compared

with the measured step response. Systematically, the program searches for the model parameters which produce the least deviation between the measured and calculated step response. This procedure is known as optimum model adaptation or model alignment. The following figure illustrates the procedure graphically.

model ruponu ....... ...... ......

X

measured process response

t t i t t t minimal ...... ..... ...... error ama

optimum

t

Fig. 5.1 Comparison between the process and model responses

The process gain is obtained from the change in the stationary values. With the PTn model, the model order is varied from n = 1 to 10. The corresponding time constant can be calculated semi-analytically. A check is also made to determine whether the step response includes a dead time. In this case, a corresponding message is displayed. It is advisable to repeat the analysis with the dead time model. A message is also displayed if the step response is subject to too strong a disturbance.

Analysis SIEPlD S5

PTn-Tt model With the PTn-Tt model, the model order is also varied fbm n = 1 to 10. With each order, an attempt is made to separate a dead time.

PT3 model The optimization of the oscillating PT3 model is the most complicated, since here, depending on the value of the model parameters, various types of solution must be distinguished in the time range. The optimization is performed separately for each type and the most suitable model found. In the analysis, the measured values are related to the initial values, i.e. no measured value changes are evaluated. This leads to a robust analysis compared with offset emrs in the measured data recording.

Terminating the On completion of the analysis, the responses of the determined analysis optimum model transition fbnction and the measured transition

function are displayed graphically together. You can then check the quality of the identification. These responses can be documented using the hardcopy fhction.

In the screen form following this, the result of the identification is displayed in the form of process characteristics (delay time, recovery time or overshoot) and the process model parameters.

Analysis

5.5.2 Analysis for any When reading in the measured values, no difference is made Manipulated whether the identification is in the open or closed loop. The reason Variable Step for this is that the method according to which the model

parameters are found can be used both in the closed and open loop. This is, for example, necessary when the excitation in the open loop was in no way an ideal step. This can occur when the manipulated variable is disturbed during the measured value recording or when the system is excited with a ramp. The decision as to which method should be used to analyze the measured values can only be made when the response of the manipulated variable is known. With an identification in the closed loop, analysis for any manipulated variable response is the only usefil method. I In this case, there are two models available for the identification

PTn model: self-regulating systems

ITn model non-self-regulating systems

PTn model

ITn model

Process gain is calculated fist. The remaining model parameters are varied using an optimization algorithm until the deviation between the calculated and measured controlled variable is minimal. During the optimization, model orders from n = 1 to 10 are checked. Since the calculation involved is considerable and the total analysis takes far longer, interim results are displayed graphically during the evaluation and you are also informed whether the quality of the current model was better or worse than the previous model. If the optimum is clearly exceeded, the analysis can be aborted.

After the measured value recording, the curve of the excitation (value of the manipulated variable) and the integral of the excitation are displayed. From this, the integration time constant is calculated. This also includes the gain of the PTn system component. The order and the time constant are calculated as for the IYTn model.

Analysis SlEPlD S5

Terminating the On completion of the analysis, the program is continued as for the analysis identification in the open loop. The result of the model

optimization can be documented with the hardcopy hction and is then displayed in the form of the process characteristics and the model parameters.

The evaluation in a closed loop is better the "smoother" the manipulated variable response. With a PID controller, a peak occurs in the manipulated variable, for example with a setpoint step. Such a peak value may be lost with a long settling phase owing to the data reduction when saving the data and may falsifL the results.

SIEPID S5 Tuning the Controller Parameters

5.6 Tuning the Controller Parameters

The controller parameters are calculated fiom the parameters of the system model using the method of absolute value optimization. This method generally provides reliable and robust controller designs representing a compromise between good control and good disturbance response.

Tuning the controller If, however, a special transient response is required of the control parameters loop, the fbnction "tuning the controller parameters" must be

selected. The menu is available after a preparatory simulation of the model control loop. The basic idea and details of its implementation are described in Section 2.6, "Controller Design with Absolute Value Optimization".

With this function, SIEPID S5 provides a digital simulation optimized in terms of operation and calculation time for tuning and checking the robustness of the controller.

Changing the The controller parameters can be increased or reduced using the controller parameters function keys in order to change the transient response of the

control loop. A submenu allows you to enter the controller parameters and the controller sampling time again, so that you can check, for example, the control quality for different sampling times.

The controller structure and the algorithm can also be changed. You can select between controller types PI and PID.

Changing the excitation

The excitation of the control loop can also be changed. You can select between a setpoint step or a disturbance variable step at the input of the system. The latter allows you to check the effectiveness of the controller when the system is subjected to disturbances and to optimize the controller so that disturbances can be compensated as well as possible. Disturbance variable steps are for example the failure of a burner in an industrial h a c e or switching over between two supply tanks in a distillation column.

Tuning the Controller Parameters SlEPlD S5

Including a dead With many controllers, it is often usefbl to filter the control zone in the control deviation by means of a dead zone to prevent the control loop loop becoming too unstable with measured value noise. An indication

for using a dead zone is the disturbance amplitude during the identification.

Differences in tuning During the controller parameter tuning, there is one difference compared with the checlung of the model parameters. When checlung the model parameters, the parameters to be varied must first be selected and then the step size selected with which this parameter will be changed. With "execute" this change then becomes effective.

In contrast, when tuning the control parameters, when you select a parameter the step size is automatically set to 1/10 of the current parameter value. The first modification is therefore automatically in the direction of increased damping of the control loop. Following this, you can also change the size of the change. Parameters tuned in this way can then be stored in the controller table.

SlEPlD S5 Transferring the Controller Parameters to the PLC

5.7 Transferring the Controller Parameters to the PLC

Once the controller parameters selected for the identified process are available, SEPID S5 has perfomed its main task.

Transferring the The remaining task is to transfer the values to the controller. The control parameters existing hardware link between the PG on which the design was

made and the controlling PLC allows a direct transfer of the controller settings and their activation in the control algorithm. A menu provides you with the choice of four different parameter records:

l: the original controller 2: the designed PI controller 3: the designed PID controller 4: the tuned controller

After transferring the parameters, thefamiliar controller screen form is activated to provide an impression of the achieved control response.

If necessary, you can change the mode and working point in this screen form. Before completing the identification, the current and original values of the control words should be compared again (refer also to Section 5.3). By means of a special screen form, differences can be recognized quickly and individual bits changed after consulting the manual. Exiting this screen form returns you to the main menu of the SEPID S5 program.

SIEPID S5 as Measured Value Recorder SlEPlD S5

5.8 SlEPlD S5 as Measured Value Recorder

The "identification and controller optimization" fhnction can also be used as a measured value recorder. This allows you to document transient responses of the system or control loop and, if appropriate, to compare them with the corresponding transient responses fiom the simulation.

Advantages

Error detection

The advantage compared with conventional recorders is that you do not need to set the time scale and measured value resolution.

If, for example, during commissioning the various automation components, actuators and process do not work together correctly, and a modification of the controller parameters does not achieve the require improvement, this futlction can be used to help you examine and document the dynamic response of system parts.

This function also helps you to locate errors in the dimensions or in the function or to find a more suitable assignment of manipulated and controlled variables. The function also permits the system response to be documented at greater intervals.

Please note that when the measured values are saved, the sampling time is not fixed. The measured values are not therefore equidistant.

SlEPlD S5 The Structure of the SIEPID S5 Measured Value Files

5.9 The Structure of the SlEPlD S5 Measured Value Files

Structure of the The measured value files used by SIEPID S5 have the following measured value files structure:

{ { SlEPlD S5 Measured value file: EXAMPLESMD { i { power stn., superheater 3.2, closed loop vers. 2, heat gen. pressure { { I

File structure The file consists of a file header and a maximum of 101 lines with measured values. Each line contains the measured value of the manipulated and controlled variable and the corresponding time value. The first value is the stationary status before applying the excitation.

~ - - ~

Time : 14.32

No. I Time t (sec)

Date : 07.12.1990

Contr. variable X (%) Iblanip. variable y (K: F I I

The Structure of the SIEPID S5 Measured Value Files SIEPID S5

Saving as text files

The file is implemented as an ASCII file, i.e. the measured values are also stored as text strings. The time values are in seconds, the control and manipulated variables are specified as percentages.

The file header contains the name of the file, time and date and a comment line which can contain fhther information about the file. The file header and comment lines are preceded by the " (" character.

Saving the data as text files has the advantage that you can print out the files for documentation. It is also possible to mod@ these files using a word processing program. This allows you to modify or remove measured values subsequently. Even complete measured value files can be created in this way or analyzed later with SIEPID SS. The time values do not need to be equidistant. A file must, however, contain a minimum of 20 measured values. The line numbers preceded by a blank are required for analysis.

I Note If you attempt to save a measured value file or controller table on a full hard disk, there is no error message displayed. You must therefore make sure that there is sufficient space to store the file.

"Control Loop Simulation"

In this chapter you will learn how to use the "control loop simulation" function to best effect. The control loop simulation is an important tool for model-supported determination and visualization of the time response of the system or control loop. Once the controller parameters have been input, the simulation can be performed. Following this, it is possible to tune the control parameters in the same way as the model parameters were tuned in the previous chapter.

This chapter also contains information about possible deviations between transient Mctions and their effects on the controller response.

Entering the Controller Parameters SlEPlD S5

6.1 Entering the Controller Parameters

After selecting the "control loop simulation" function, you must first enter the required controller parameters. Here, you have three options:

l . Entering parameters determined by a previous identification 2. Enteringparameters stored in the controller table 3. Direct input of model and controllerparameters via the

keyboard

After entering the parameters, you must specify the following information:

Do you want to simulate an open or closed loop? If you simulate an open loop, the step response of the model is calculated. The calculation of the step response is used to compare the transient response of the control loop for a setpoint step, whereby the quality of the controller design can be evaluated.

Input of the duration of simulation and controller sampling time The program proposes values for these times. The duration of the simulation is calculated fiom the settling time of the process model.

SlEPlD S5 Performing the Control Loop Simulation

6.2 Performing the Control Loop Simulation

The control loop simulation function is used to analyze the response of control loops numerically separately from commissioning problems.

Advantages of control loop simulation

This function has the advantage that you can easily obtain an overview of the expected control quality without having to risk critical statuses arising on the real plant during commissioning.

The simulation is an important tool in SIEPID S5 for model-supported determination and visualization of the time response of the system or control loop.

In the case of the control response of the closed control loop 'Limulationtt means the calculation and graphic display of the time responses of the controlled variable x(t) and the manipulated variable y(t) following a step change in the setpoint W.

Simulation as a In this form, simulation is a means of achieving a practice-oriented method of controller design and with it the basis for operator-controlled practice-oriented controller parameter tuning, i.e. a component of the main menu controller design finction "identification and control optimization" (refer to

Chapter 5). In addition to this, "control loop simulation'' has its own main menu function.

This serves to simulate model control loops numerically and to tune them separately from the identification problem.

A classic single standard control loop is simulated consisting of a system transfer function G(s) and a PID controller R(s).

Performing the Control Loop Simulation SlEPlD S5

Controlling the The simulation is controlled by pressing the keys displayed in the simulation menu line at the bottom edge of the screen.

The currently valid simulation parameters are displayed to the right of the simulated process variable.

The job of the operator is to m o d e the controller parameters singly so that the required transient response is achieved.

SlEPID S5 Tuning the Controller Parameters

6.3 Tuning the Controller Parameters

The "tuning the controller parameters" hction can be called up both in the main menu function "identification and controller optimization" and under "control loop simulation". The hction is described in detail in Section 5.6.

Possible Deviations between Transient Functions SIEPID S5

6.4 Possible Deviations between Transient Functions

Limits of the method When determining the controller parameters based on an identification in a closed loop, differences can occur between the transient fixnctions of the simulated and the real control loop with the following causes:

6.4.1 Non-Linearities in The process model is calculated for only one working point of the the System system and one step direction. When simulating the closed control

loop, it is assumed that the system has a linear response around this working point. With non-linear systems, the real control loop changes to a different working range when the setpoint is changed.

If the dynamics of the system also change, deviations in the control response are inevitable. If in doubt, the system should be identified at different working points and in both step directions. The worst case model (highest gain, highest order) must then be used for the controller design and it is necessary to find out how far the proposed control parameters stray.

6.4.2 Deviations in the There may be differences between the real controller and the Control Algorithm control algorithm in the simulation. For example, in the simulation,

the stroke and speed of adjustment are not limited. A limit in the speed of adjustment means that the manipulated variable cannot achieve sudden changes. This is particularly important with PID control systems and when controlling PT1 systems with a PI controller.

In both cases, the initial value of the manipulated value is the maximum value. If this maximum value itself is damped by the delay in the controller, adjustment energy is lost. In the identification, this delay time is added to the system dynamics. The effects of the delay are less the slower the system response.

SIEPID S5 Possible Deviations between Transient Functions

Stroke limits Limiting the stroke has the following effects: in the simulation, it is not the absolute values of the manipulated and controlled variables that are output, but the changes relative to the current working point. These changes must therefore be added to the stationary values of the control and manipulated variables at the working point. In the real control loop, the limit cannot be reached. Further discrepancies can occur when the controlled variable in the real controller is filtered by a low pass filter or a dead time. In the simulation, the dead band a n lead to a permanent control deviation.

6.4.3 Deviations in the Excitation of the Control Loop

In the simulation, a setpoint step is applied to the control loop. If, however, the real control loop is excited by a setpoint ramp, e.g. with compact controllers, the transition function of the real control loop is damped more than in the simulation.

6.4.4 Deviations in the Differences can occur particularly when the delay response of the Delay Response system is compensated by the controller, i.e. with PT1 systems

with PI controllers or PT2 systems with PID controllers. In these situations, the controller gain can theoretically be increased without limits without the control loop becoming unstable. In real control loops, however, there are limits to the possible increase in gain.

Possible Deviations between Transient Functions SlEPlD S5

6.4.5 No Reproducible Problems can occur when the sensors do not return reproducible Measurement measured results due perhaps to characteristic curves, deterioration Results or inconect installation. The Daction component is particularly

sensitive to such disturbances. If this can occur, the more "robust" PI controller should be preferred. The same problem can, however, occur when desiguing controllers for processes with greatly changing time responses. In such cases, the process must be identified at different working points. Using the calculated controller parameters, a parameter control for the controller can be designed.

6.4.6 Errors in the Dimensions of Actuators

Extremely small values in system gain can only be compensated by relatively large values in the controller gain. This leads to extremely high deflection of the control value when the setpoint is changed. With a PID controller, this effect is firther accentuated by the D-action component. A reduction of the controller gain means that the control loop reacts more slowly and that the compensation of disturbances deteriorates, Extremely high values in the system gain mean small values in the controller gain.

"Operating and Monitoring" 7 This chapter explains how the operating and monitoring function can be used with SIMATIC S5 controllers. You will first learn how to connect the PC with the SIMATIC CPU. Following this, the chapter describes how to check the controller mode and how to adjust the scale of the controlled variable.

General Notes on the "Operating and Monitoring" Function SIEPID S5

General Notes on the "Operating and Monitoring" Function

The 'loperating and monitoring1' fbnction is used to operate and monitor SIMATIC S5 controllers. After you have selected this hction fiom the menu, you first obtain an overview of the steps to be performed:

l . Link the PC with the SIMATIC CPU 2. Check the controller mode 3. Match the scale of the setpoint and controlled variable

These steps are now described in this order.

SIEPID S5 Linking the PC with the SIMATIC CPU

7.2 Linking the PC with the SlMATlC CPU

The commissioning device is connected to the PG-PLC interface of the SIMATIC CPU via the serial interface of the PC (COM1 or COW).

Open interface

Note The serial interface of the PG must be a TIY interface. This is the case with the SIMATIC programmers PG 730,750 and 770. With other AT-compatible PCs, it may be necessary to use an interface convertor (V.24 to TIY) to adapt the common V.24 interface.

SIEPID supports all the somare controllers of SIMATIC S5:

S5- 100U controller for S5-95U S5-10OU with CPU 103 SS-l 15U with CPU 941B, 942B,

943B, 944B

S5- 1 15U controller for S5-115U with CPU 941B, 942B,

943B, 944B Controller structure for S5-135U with CPU 922,928,928B

R64 S5-155U with CPU 922,928,928B

Modular PID and for S5-115U with CPU 945

kzzy controller S5-135U with CPU 922*, 928,9288

S5-155U with CPU 922*, 928,928B,

9461947,948

There is also an open interface, with which controllers of the S5-95U, S5-100U, SS-l 15U, S5- 135U and S5- 155U PLCs can be assigned parameters. (refer to Section 2.4).

* except forjimy blocks and simpl@ied system rack

Linking the PC with the SlMATlC CPU SIEPID S5

Controller type The type of controller used must be specified in a menu. For all software controllers, the number of the controller data block to be processed must also be specified.

Compact controller With the compact controllers (S5-115U controller, controller structure R64) this information is sufficient, with the other controller packages, the block assignment must also be specified.

Computer test The computer tests whether a link is possible to the controller. If an error message is displayed, please check the cable.

Data in the controller The set control parameters, the current measured values and the screen form controller mode are also read in. These data are then displayed in

the controller screen form.

SIEPlD S5 Checking the Contro//er Mode

7.3 Checking the Controller Mode

To check the controller mode, start fiom the controller screen form. Using this screen form you can check the following:

the controller mode the control parameters and the process variables.

You are once again reminded of the correct setting of the controller.

Correct controller The controller should be set to AUTOMATIC mode with the setting internal setpoint and the actuator output must be FREE

(=controller on).

Confirmation If you have selected the setting above, you must co- that the controller is correctly set. You can then return to the controller screen form by pressing the ESC key and switch over the mode.

Matching the Scale of the Controlled Variable SlEPlD S5

7.4 Matching the Scale of the Controlled Variable

During the measured value recording, the time responses of the setpoint, manipulated variable and controlled variable are displayed graphically on the screen.

Measuring range from 0 to 100 %

Expanding the measuring range

Normally, the measured variables are displayed over the full measuring range of 0 to 100%. If the controlled variable changes are only slight, this means that the graphic representation of the time responses is inexact.

For this reason it is possible to expand the measwing range for setpoint and manipulated variable.

To do this you must enter the minimum value and maximum value of the required range. The range must, however, exceed 10%. This normalization, however, involves only the setpoint and controlled variable and is used to set a better graphic output. The manipulated variable remains unchanged in the range fiom 0 to 100%.

"Controller Table"

This chapter describes the hction of the 'Icontroller table1'.

Using this hction, a maximum of 99 control loops can be managed. In this chapter you will see which control loop data can be stored in a standard data file.

Apart from the specific control loop data, you can also store link data in the controller table which can be called up if necessary.

Data Management using the Controller Table SlEPlD S5

8.1 Data Management using the Controller Table

The controller table is stored in the file REGTAB.DAT. This must be located in the directory in which SIEPID S5 is installed and fiom which it is started.

The "controller table" function is used to manage the data fiom a maximum of 99 control loops. The control and system parameters can be displayed for individual control loops. Related parameter records can be addressed using the controller number.

The control loop data described below can be stored in the standard data file REGTAB.DAT.

I Note If you attempt to save a measured value file or controller table on a full hard disk, no error message is displayed. Make sure therefore that there is sufficient space on the disk to store the file.

Control loop data 1. Controller number (1 -99) 2. Controller name

(technological designation consisting of a maximum of 50 characters)

3. Controller type (P, PI, PD, PID controller) 4. Controller parameters (Kp, Tn, TV, Ty, Tmin, On, Off

5. System parameters

(n, K, T of a PTn model or ITn model)

n, K, T, Tt of a PTn-Tt model

or K, T, b, d of an oscillating PT3 model)

6. Link data

SIEPID S5 Data Management using the Controller Table

Data input Data is always input using the function "store data in the controller table". After establishing a link to the CPU or after completing controller optimization or simulation, the specific data for control or the link can be saved.

Example of a controller table The numbers and names of the controllers can be listed as shown in the following example:

Example of a controller table

Controller table

Assigning the You can assign controller numbers in any order. They are controller numbers displayed in the table in ascending order.

No.

2 8

22 70

Name

Temperature controller for cooling house

Flow controller

rpm controller

Pressure controller for tank 1

Data Management using the Controller Table SIEPID S5

Program Structure

This chapter illustrates the structure of the SIEPID S5 program.

Program Structure of SIEPID S5 SIEPID S5

9.1 Program Structure of SlEPlD S5

I Inpulbg U* Ink ddl: ddmrs D8 no., DW m.

. WYI, W2 raw mWng ol U*

p m nri~Ua o ~ d n ~ n l u , i n ~ , d#L adquriq nil& ~ b n Irlc in ailhllcr hllc

IknMimIim: W. nlu wdin

ruicblc dpqt

- wk mldlu

. twc lid nlw Yubm n p ~ d salt

2: mtrruqtntwu srlhtr

3: Chnp nl@l - I:ehnp~jul*d

wi1Uc 5: O J d l e lmbr

lthalinl Lin 6: Chnp calrdlw

gmlnr l: h p &l u 4 d 8: Conbdl* nlliqs

3 111 mllrolw (Fe W: K wnlrdlw) - 4: Mwnlrdlw (F8 102. K nd S ronlrdlu]

I: Yodubl conboL1 (FE I. AD wnhllu m nmml nodc]

6: Yoddlr &LI (fB 176 IPD mbdk]

I: Opcn inlubm

R a d in &b km ht mbdk hblc

1 w l d in Yw 'calrdh bY'

10 tnhd ril at tqtald

ddrm D0 no, OW m. WYI, W2 raw nnldr'rq 01 Ihc

pmsr nrittln - ouw 01 nmii rtha in I dml mlfqurirq sdl&

hItdlu #W lum

I: AVTOUATIC m&,

2: Iffl&ltwqooEPM m

8 Chqt nfpinl 4: Chqc mnipbbd

ruiblc I: Otlc~nl mbr

tdu~liq Lin 6: Chnp Fonlrdw

)tmlcn 1: Chqc ddt r w d 8. hbdk #ILp

SlEPlD S5 Program sl~chrn

Index

Index SIEPID S5

lndex A absolute value

. optimization . . . . . 2-13 2.14, 5.17 actuating time . . . . . . . . . . . . . 4-1 1 analysis . . . . . . . . . . 2. 10, 5-1 2. 5- 16 ASCII file . . . . . . . . . 4. 18. 5- 1 1. 5.22

co-processor . . . . . . . . . . . . . . 1 . 10 commissioning a controller . . . . 3.6. 4-2 connection . . . . . . . . . . . . . . - 4 - 1 8 continuous controller . . . . . . . . . . 4-4 control action . . . . . . . . . . . . . . 2.6 control algorithm . . . . . . . . 2.18, 6.6 control loop . . . l- 10,2.6,2.19,6.3. 6.7 control loop simulation . . . . . . 2.6, 6.3 control quality . . . . . . . . 2.4. 5-1 7, 6.3 control response . . . . . . . . . . . . . 6.3 controlled system . . . . . . 2.6,4.19. 6.3 controlled variable . . 2.2.2.6,4.18, 6.3 controller . . . . . . . . . . . . 2.4, 5.19 controller design . . . . . . . 2.6.2.13, 6.3 controller optimization . . . . . 2.15, 5-2 controller parameters . . . . . . . . . 2- 15

. . . . . . . controller table 4-18. 5.2, 8-2

D . . . . . . . . . . . . . . . . damping 2-12 . . . . . . . . . dead time 2.8,2.11. 5.14

. . . . . . . . dead time of the D-action 2.4 . . . . . . . . . dead zone 2- 19,5.18, 6.7

. . . . . . . . . . . . . . delay element 2.9 . . . . . . . . delay time .2.11,2.13, 5.14

. . . . . . . . . . derivative action time 2.4 . . . . . . . . . . . . . . disturbance 5-1 7

disturbance variable step . . . . . . . 5.17 documentation . . . . . . . . . . 3.6, 5. 10

E EPF . . . . . . . . . . . . . . . . . . . 4-9 excitation . . . . . . . . . . . . . . . . 2. 19

F FB 102 . . . . . . . . . . . . . . 4.6, 4.12 FB 176 . . . . . . . . . . . . . . . . . 4-8 FB201 . . . . . . . . . . . . . . . . . 4-4

. . . . . . . . . . . . . . . . . FB 202 4.11 FB 62 . . . . . . . . . . . . . . . . . . 4-7 FB 80 . . . . . . . . . . . . . . . . . . 4-5

. . . . . . . . . . . . . . . . . . . file 4.18 final value . . . . . . . . . . . . . . . 2-2

. . . . . . . final value detection 2.20, 5-8

G graphics driver . . . . . . . . . . . . . 1 . 10

. . . . . . . . . . . . . graphics mode 3-6

H . . . . . . . hardcopy 1.9. 1.12,3.6, 5.14

I identification . . .2.3.2.8.2.20. 3.4 . 3.6,

. . . . . . . . . 4-18 . 4-19,5.3. 5.12 . . . . . . . . . . . . . . . installation 3-2

. . . . . . . . . . integral action time 2-4 . . . . . . . . . . . . . interface. open 4.16

. . . . . . . . . . . . . . . ITn model 2-9

. . . . . . . . . . . . . . K controller 2-5

SIEPID S5 Index

L . . . . . . . . . . . . . . . . limiting 2.19

. . . . . . . . . . . . limiting the stroke 6.7 . . . . . . . limits of the method 2- 18 2-20

link . . . . . . . . . . . . . . . . . . . 5.19 M manipulated variable . . . .2.2,2.6, 2. 14,

. . . . . . . . . . .2.18,4-18,6.3, 6.6 measured value . . . . . .2.20, 5- 1 0, 5.22

. . . . . . measured value acquisition 4-18 measured value file 4- 18, 5.11 . 5.12, 5.2 1 measured value noise . . . . . . . . . 5- 18 measurement accuracy . . . . . . . . 2-20 measwing range . . . . . . . . . . . . 2-20 minimum pulse duration . . . . . . . 4-1 1 model . . . . . . . . . . . 2.8,2.11, 5.12 model adaptation . . . . . . . .2.10, 5.13 model order . . . . . . . . . . . . . . . 2.8 model parameters . . . . . . . 2- 15 . 2- 16 model quality . . . . . . . . . . . . . 2- 16 model type . . . . . . . . . . . . . . . 5-12 modular control . . . . . . . . . . . . 1 . 1 1 modular controller . . . . . . . . . 5.6, 7-3

N non-linearity . . . . . . . . . . . . . . 2- 18 non-self-regulating system . . . . . . . 2.2 numeric simulation . . . . . . . . . . 4. 19

0 optimization . . . . 2- 10, 2. 15, 5- 13, 5- 15 order . . . . . . . . . . . . . . . . . . 5- 13 overall time constant . . . . . . . . . 2-1 6 overshoot . . 2.9, 2.12 . 2.13,2.15, 5.14

parameter control . . . . . . . . 2- 19. 6.8 PI controller . . . . . . . . . . . 2.4, 2.14 PID algorithm . . . . . . . . . . . . . 2-4 PID controller . . . . . . . . . . . . . 2. 13 position feedback . . . . . . . . . . . 4-9 process . . . . . . . . . . . . . . . . . 2-2 process characteristics . . . . . . 2. 1 1, 5- 14 process gain . . . . . . . . 2.8, 2. 10, 5- 13 process model . . . . . . . .2.8. 2. 18. 6.6 process model parameters . . . . . . . 5. 14 process variable . . . . . . . . . 2.8, 4-18 program abort . . . . . . . . . . . . . 3-3 program structure . . . . . . . . . . . 9-2 proportional gain . . . . . . . . . . . . 2-4 PT3 model . . . . . . . . . . . . 2-8 . 2-9 PTn model . . . . . . 2.8 . 2.9, 2. 13, 5.13 Wn-Tt model . . . . . . . . . . . . . 2-8

reading in . . . . . . . . . . . . . . . . 4-18 recovery time . . . . . . . . . . . . . . 5. 14

S controller . . . . . . . . . . . . . . . 2-5 sampling time . . . . . . . . . . . . . 2-7 self-regulating system . . . . . . . . . 2-2 setpoint 4-18 setpoint step 5. 17

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

setting rules . . . . . . . . . . . . . . 2. 13 settling phase . . . . . . . . . . . . . . 2.20 settling time . . . . . . . 2- 14,2.20, 5-17 simulation . . . 2.6,2.16,2.19. 3-5 . 3.6,

4-19, 5.2,6.3, 6.7 . . . . . . . . . . . single variable system . . . . . . . . . 2-2 standard control loop . . . . 1 . 1 1,2.6, 6.3 step response . . . . . . . . . . . 2.2. 5-12 storing . . . . . . . . . . . . . . 4.18. 5.8

Index

system . . . . . . . . .2.2, 2. 18.5.12. 6.6 system model . . . . . . . . . . . . . 4- 19

T text mode . . . . . . . . . . . . . . . . 3.6 three-step controller . . . . . . . . . . . 4.9 time constant . . . . . . . . . . 2.9, 5.13

SIEPID S5

transfer Wction . . . . . . . . . 2.4, 4. 19 transient function . . 2.18. 5.8.5.11. 6.6 tuning . . . . . . . . . . . 2.15 . 2.17, 5.17 tuning controller parameters . . . . . . 5.18

W working point . . . . . . . .2.2, 2. 18, 6.6