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WM-5-4Adaptive Fuzzy-Neuro Control with Application to a Water Bath

Process+Marzuki Khalid, ttSigeru Omatu, an d +Rubiyah Yusof

+ Faculty of Ele ct Eng.Universiti Teknolog i MalaysiaJalan Semarak,54100 Kuala Lumpur ,Malaysia.

ABSTRACTRecently, the emergence of artificial neural networkshas made it conducive to integrate fuzzy logic controllers andneural models fo r the development of adaptive fuzzy controlsystems. In this paper, we proposed an adapt ive fuz zy-ne udcontrol scheme by integrating two neural network m odels witha basic fuzzy logic controller. Using the backpropagationalgorithm, the first neural network is trained as a plant emulatorand the second neural network isused as a compensator for thebasic fuzzy controller to improve its performance on-line. T hefunction of the neural network plant emulator is to provide thecorrect error signal at the output of the neural fuzzycompensator without the need for any m athematical modelingof the plant. The difficulty of fine-tuning the scale factors andformulating the correct control rules in a basic fuzzy controllermay be reduced using the proposed scheme. The scheme isapplied to the temperature control of a water bath process. Theperforinance of the adaptive fuzzy-neural controller iscompared to the basic fuzzy logic controller and a conventionaldigital-PI controller under identical conditions of varyingcomplexities in the process. The experim ental results show thatthe adaptive fuzzy-neural control scheme is superior inperformance than the o ther two controllers.KEY WORDSFuzzy logic cont ro l , neural networks,adaptive, water bath, real-time, perfonnancecomparison.INTRODUCTIONThe idea of applying fuzzy logic to control systems wasfirst conceived hy Maindani and his colleagues 3-6.Based onZadehs fuzzy set theory 1.2, and the simple conventionalproportional-plus-integral-plus-derivative (PID) ontroller,Mamdani and Assilian ~4 developed what is now referred to ;ISthe basic fuzzy logic controller, which is used to regulate theoutputs of a process around 3 given set-point using a digitalcomputer. Due to the limitations of memory space and speed ofthe sequential Von N e u m n computers, fuzzy logic controllerswere not very popular in the beginning and much of theirapplications centred around slow varying processes. Some ofthe earlier applications of fuzzy logic to process con trol can befound in Mamdani and Tong x.There are a nuinher of advantages of applying fuzzylogic to the control of industrial processes over traditionalcontrollers. Perhaps one of the main advantages of applyingfuzzy logic control is that a controller can be developed alonglinguistic lines which has clos e associations with the field ofartificial intelligence (AI). One of the aims of AI is to r e p l x ehuman beings carrying out precise tasks by machines and hencethe link between AI and control theory is strong. The fuzzycontroller consists of a set of linguistic conditional Statementsor rules (referred to fuzzy association matrix rules or FAMrules) which define the individual control situations. Theselinguistic conditional statements can he easily developed fromcoininon sense or from engineering judgement of the process tohe controlled.Many industrial processes are difficult to be controlledaccurately and it has been claimed that fuzzy logic control candeal successfully with such processes which are usually multi-variable, inheren tly nonlinea r, and time-varying in nature. Inaddition, the fuzzy logic controller can a lso deal with ill-defined=

f+ De pt of Information Scienceand Intelligent SystemsFaculty of EngineeringUniversity of TokushimaTokushima 770, Japan.

systems of unknown dynamics as they do not require a priorimathematical model of the plant for implementation, asrequired by many traditional adaptive controllers. Anotheradvantage is that fuzzy logic controllers can now be feasiblyimplemented in digital 9 or analog lo VLSI circuitry wheresampled information can be encoded in a paralleldistributedframework.In contrast to the above advantages of fuzzy logiccontrollers, there are a number of problems in theirdevelopment. The fuzzy logic controller consists of three scalefactors which have to be selected or tuned in prior for itssatisfactory operation. The selection of these scale factors isakin to the tuning of a PID-controller parmeters. The widthand overlap of the contiguous membership functions of thefuzzy variables also has some effect on the performance of thefuzzy controller.Although the fuzzy conditional statements or rules canhe formulated based on em pirical knowledge of the process, itis not always easy to select the best set of rules that would makethe controller operate satisfactorily for all kinds of conditions.For exam ple, it would he iinpossihle to form ulate a set of rulesto accomm odate unknown load disturhances that would occurduring operation of the process. In addition it is also difficult toconfigure the correct consequents of each of the fuzzy rulesforinulated for satisfactory operation, especially those aroundthe steady-state rule.In order to overcome such prohlems, there has beenconsiderable research efforts in developing what is known asadaptive fuzzy logic controllers. One of the first such effortswas the developm ent of the self-organizing fuz zy logic contro lsystem of Procyk and Mamdani I I . In this system, the FAMrules can he composed and decomposed based on amathematical model of the plant. Some other developments ofadaptive fuzzy control system s in the past decade can k oundRecently, the emergence of artificial neural networkshas made it conducive to integrate fuzzy logic controllers andneural models for the development of adaptive fuzzy controlsystems. N eural networks are trainahle dynamical systems thatestimate input-output functions. Their key advantage is theirahility to learn and generalize. The integration of fuzzy andneural models is currently one of the most concentratedresearch effforts in the development of intelligent controlsystems. Some of the adaptive fuzzy-neural networks controlsystems that have been proposed recently with varyingcomplexity can he found inIn this paper we present another approach of integratingneural networks into a fuzzy logic control system. Thedifficulty of fine-tuning the scale factors and formulating thecorrect control rules in a hasic fuzzy controller may he reducedusing the proposed schem e. In this approach the learning andgeneralization capability of the neural networks i s used tocompensate on-line for any unsatisfactory performance of thebasic fuzz y controller using the back-error propagationalgorithm Is. The schem e is applied to the temperature controlof a water bath process. We show through simulations how theneural networks improve the perfonnance of a poorly tunedhasic fuzzy controller. Experiments are then conducted on thereal-time water bath process. The perfonnance of the adaptivefuzzy-neural controller is compared to a conventiocal digital-PI

in 12-14.

0-7803-1872-2/94/$4.00 1994 IEEE 17 3

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controller and the hasic fuzzy controller under identicalcondition s of varying complexities in the process.ADAPTIVE FUZZY-NEURALCONTROL SCHEMEThe adaptive fuzzy-neural control scheme consists oftwo multilayered neural network models configured in thearchitecture as shown in Fig. 1. Th e first neural network is aplant emulator and the second neural network is used as acompensator to improve the performance of the basic fuzzylogic controller. The developmenr of this system consists ofthree phases. The first phase is developing a basic fuzzy logiccontroller for the

The second phase invgilves training a neural networkmodel the forward dynamics of the plant to be controlled. T ~ Ktraining of this neural plant emulator can be done off-line aswell as on-line depending on the type of plant For fast-actingplants. such as robotic-manipulators o r servo-inotors. it ispossihle to train the neural nerwork to emulate the plant in anon-line way. However. if the plant is a slowly varying process,the neural plant einulator needs to he trained off-line 3sconvergence Is rather slow. The function of the neural networkplant emulator is to provide the correct erro r signal at the outputof the neural fuzzy compensator without the need for anymathematical inodeling of the plant.The Lhird phase involves on-line learning of the neuralfuzzy compensator. The perfonnance error which is the errorbetween the desired output an d the actual plant output ishackpropagated through the neural plant elnulator to adapt theweights of the neural fuzzy comp ensa tor on-line. Theperformance of the neural plant emulator can be furtherimproved on-line by backpropagation of the error between theneural plant emulator and the actual plant output.Learning of the Neural Fumy CompensatorThe function of the neural fuzzy compensator is tocoinpen sate for any unsatisfactorq perform ance of the basicfuzzy controller. The model structure of the neural fuzzycompensator is similar to the neural plant einulator which hasone layer of hidden neurons The hidden layer neurons havesigmoid functions and the rest of the neurons in the input andoutput layers have linear functions . The neural fuzzycompensator is trained in an on-line way by backpropagation c?fthe performance error through the neural plant emulator. Theh m z d propagation of the neural fuzzy compensator is similarto the forward propagation of a single neural network and thiscan he referred to in iy . In this section, the learning algorithm ofthe neural fuzzy compensator is derived in the following way.The weights of the neural fuzzy coinpensator betweenthe hidden and output layers are adapted using the perfonnanceerror Er as follows

aEP aEPA W ~ ,- -r)-%J awiJ

where c denotes the neural fuzzy compens ator and p denotes theneural plant emulator, and yr and yo are the desired and actualplant outputs, respectively. Using chain ru le, the error signal ofthe neural fuzzy compensator between the hidden and outputlayers may be-derived as follows= &

a%, (3where

where S i is the input to the output layer neuron of the neuralfuzzy compensator. As the input and output neurons of theneural fuzzy Compensator and neural plant emulator are linearfunctions, thus

where 0 and S denote the outputs and inputs of a neuron,respectively. and i denotes the input layer. Using chain rule,aEp 3s;asp ' aof6 ; = .- - ( 5 )

andwhere 8 is the error signal between the input and hidden layersof the neural plant emulator19. Hence, the error signal of theneural fuzzy compensator hetween the hidden and output layersis

The error signal between the hidden and output layers of theneural fuzzy emulator is derived as follows

wherehence

f (SIC) = o;[l - 0 ; )2 = $ U 041 - 0;) (9 )

In this scheme. the weights of the neural plant emulatorare not kept constant but are further iinproved on-line. This isdone by backpropagation of the following error equation atevery sample where yo and 7 are the outputs of the actual plantand emulator, respectively

DESCRIPTION OF THE WATER BATHTEMPERATURE CONTROL SYSTEMThe proposed scheme is applied to a water hathtempe rature control system which is an example of an importantcomponent in batch reactor processes. A schem atic diagram ofthe the experimental setup is shown in Fig. 2. A YamatoScience Inc. laboratory water bath (BT-15 model) is the maincomponent of this temperature control system.The system can be divided into five main components: (i)the water bath, (ii) the sensor m odule, (iii) the programm ableinput-output (PIO) interface board, (iv) the microcomputer, and(v) the actuator. A brief description of the five componentsfollows. The capacity of the water bath is 8 litres withdimension 250 x29 0~1 00 mm3 ). The water bath is heated by a600W base heater which is connected to a thyristor (SI6G12S-12 ) circuit. To ensure even tem perature distribution, a stirrer isused which can rotate at 120 rpm. The sensor module has heendeveloped using diodes (lS1588) nd high gain amplifiers(A741). It consists of 3 wo step amplification circuit and cantransform the measured temperature over the range of 0 "C to100 O C nto the corresponding voltage range of 0 volt to 10 voltwith a resolution of 0.24 OC.Th e P I 0 interface circuit bo vd consists of an analog-to-digital ( A D ) onverter, a digital-to-analog ( DIA) converte r anda programmable peripheral interface device (PD8255A). Anexternal clock is designed to operate the AID and D/Aconver ters. The clock circuit is designed using crystal oscillatorand JK flip flops. The microcom puter used in this experiiuent isthe NEC PC 9801F having an Intel 8086 16-bit CPU with a 10

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MHz clock speed. A simple control routine is written usingMicrosoft-C to provide the control input to the actuator throughthe D/A and also to m easure the output temperature.A thyristor (S16G12S-12) is used xi an actuator for theheater and is switched on and off according to the followingconstraints where u(kT) is the output of the neural controller, Tis the sampling interval, k is the sampling number (kd .1.2. 3 ...1an d Vi is the input voltage to the actuator:

if u(kT) s 0.0, then Vi-0.0 volt. hea ter offif u(kT k5.0 . then Vi-5.0 volts. heater onan d if O.O

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It is a fact that there a nuinkr ot parameters that have tohe tuned and selected correctly before the basic fuzzy logiccontroller can perform satisfactorily which are difficult andlaborious. The performance of the system can be improved eve nwhen the width and overlap of the inemhership functions areincorrectly configured, the scale factors poorly tuned, and someof the FAM rules consequents are wrongly chosen (see 1 9 ) .In this paper, we have also applied the proposed schemefo r the teinpenture regulation of a real-time water bath process.The adaptive fuzzy-neural controller w s omp and to the basicfuzzy logic controller and the conventional PI-controller underidt+mtical conditions of varying complexities in the process. Theexperimental results show thar the adaptive fuzzy-neural waterbath system performed better than the other two systems. Theadaptive learning and generalization capability of the neuralnetworks help to compensate and iinprove the performance ofthe basic fuzzy logic controller on-line.REFERENCESI2

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MARLUKI KHAUD c t d : FUZZY CONTROL OFA W A E R BAT14 PROCLSS

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h g . . A FA M hank matrix for thc tcmpcraturc control idwalcr h l hsystem. Thc anteccdcntsare the error and thc riltc ofchangcof error.and the consequent of each rule is given in the box .

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