design and implementation of the universal servo control

Xiong, Y. et al.

Paper:

Design and Implementation of the Universal Servo ControlAlgorithm Verification System Based on High-Speed

Communication FieldbusYonghua Xiong∗,∗∗, Ke Li∗,∗∗, Zhen-Tao Liu∗,∗∗,†, and Jinhua She∗,∗∗,∗∗∗

∗School of Automation, China University of GeosciencesNo.388 Lumo Road, Wuhan, Hubei 430074, China

E-mail: [email protected]∗∗Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

No.388 Lumo Road, Wuhan, Hubei 430074, China∗∗∗School of Engineering, Tokyo University of Technology

Katakura, Hachioji, Tokyo 192-0982, Japan†Corresponding author

[Received November 28, 2020; accepted January 14, 2021]

In recent years, there have been several breakthroughsin the theoretical research of servo control algorithms.However most of these control algorithms remain inthe simulation stage. They are difficult to be applieddirectly to practical platforms or complex industrialsites because of the lack of an experimental systemsuitable for the verification of their effectiveness. Toaddress this problem, we designed a multi-functionservo control algorithm verification experiment sys-tem (MVES) within the MATLAB/Simulink theoreti-cal simulation model directly to communicate with theTwinCAT 3 PLC master program to perform differ-ent servo control experiments. The MVES supportsvarious Simulink models. However, its and the oper-ation is simple and convenient, which greatly reducesthe workload of the algorithm test and has importantpractical value. Two sets of comparative experimentswere used to verify the versatility and superiority ofMVES.

Keywords: servo experiment system, versatility, multi-function experiment device, TwinCAT 3

1. Introduction

The alternating-current servo system is one of the corecomponents of industrial robots and an important toolin the field of automation and industrial production. Ithas been widely used in machinery manufacturing, met-allurgy, transportation, and other industries.

In recent years, simulation research on servo con-trol theory based on MATLAB/Simulink has becomea research hotspot for colleges and universities [1–3].Serveral significant breakthroughs have been made in thetheoretical research of servo control algorithms. However,there are problems in the following three areas in the pro-cess of applying theoretical models to practical industrial

servo systems.(1) The theoretical model cannot be applied directly to

an industrial servo system. It needs to be converted into aPLC industrial programming language or other assemblylanguages for system control [4]. The conversion work-load is large, and more equipment I/O interfaces need tobe provided. Moreover, the operation is inconvenient forresearchers.

(2) Theoretical models cannot simulate complex in-dustrial environments, and effective algorithm verificationcannot be performed until the theoretical model is appliedto actual industrial systems [5].

(3) Most servo algorithm verification devices are notversatile [6]. A set of devices often only supports a fewspecific servo experiments, and it is impossible to performvarious types of servo algorithm verification experiments.

In general, researchers apply servo control algorithmsto practical industrial systems in two ways: the first wayis to convert Simulink servo algorithms with good simula-tion effects into a PLC industrial programming languageor other assembly languages [7]. Then, the control effectis tested through the test system. The disadvantages ofthis method are evident: the conversion workload is large,and a specific test system is required; furthermore, thereare several restrictions, which are often only applicableto the verification of specific servo algorithms [8]. Thesecond method is to use the simulation model to performthe experiment, and then use the control parameters witha good simulation effect to test the specific test system ofthe same type. It can be applied to practical industrial sys-tems if it achieves good results. This method is limited toa specific test system. Moreover, its test system and actualsystem are often of the same type, which does not satisfiythe test requirements of different industrial systems. Usu-ally, the control parameters obtained via simulation can-not guide a complex test system significantly [9]. There-fore, the testing process must be readjusted. Fig. 1 showsthe two industrial application methods of the servo algo-rithms.

248 Journal of Advanced Computational Intelligence Vol.25 No.2, 2021and Intelligent Informatics

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© Fuji Technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nd/4.0/).

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Universal Servo Control Algorithm Verification System

Fig. 1. Industrial application flow of the control algorithm.

The first method transforms the Simulink control algo-rithm into an industrial programming language. This pro-cess is labor-intensive and is not conducive to the experi-ments conducted by theoretical research scholars. Oncethe model changes, the industrial programming shouldbe modified accordingly. This is not conducive to modelmodification or debugging. In the second method, the the-oretical simulation model cannot simulate a complex in-dustrial environment, and the control parameters obtainedcannot often guide the test in complex industrial environ-ments. This method can only be applied to simple indus-trial sites and is limited by the type of test system, whichcannot satisfy the requirements of various types of servoverification experiments.

Considering that most servo experimental devices arenot universal and that the experimental conversion stepsare cumbersome, we designed a general-purpose servocontrol algorithm experimental system, including an ex-perimental platform and control software. The multi-function servo control algorithm verification experimentsystem(MVES) designed in this study is specially used forthe validity verification of various types of servo controlalgorithms, without complicated conversion or configu-ration work. MATLAB/Simulink models could directlyinteract with the PLC master program in TwinCAT 3 [10]with our experimental system. Furthermore, we used theEtherCAT communication bus to allow the PLC to controlthe servo system to perform experiments, thus eliminat-ing the cumbersome steps of model conversion and mak-ing the model debugging convenient and feasible. Onlythe Simulink simulation model needed to be adjusted dur-ing the debugging process to perform the next experimentor collect the next set of data, which made the algorithmtesting process easy to operate. Different Simulink mod-els can be used to perform different types of servo al-gorithm verification experiments. The system has strongversatility and is suitable for the Simulink models of var-ious servo control algorithms. Different simulation mod-els were tested by changing the simulation model and ad-justing the relevant parameters. Finally, we used two setsof comparative experiments to verify the effectiveness ofMVES.

Our study is one of the first attempts to perform servoexperiments using MATLAB/Simulink and TwinCAT 3PLC master program communication. The main contri-butions of this study are summarized as follows:

1) We proposed a system framework in which the MAT-

Fig. 2. Overall structure of the experimental system.

LAB/Simulink theoretical simulation model wasused to communicate with the TwinCAT 3 PLC mas-ter program to perform various types of servo con-trol experiments. This effectively solved the problemof algorithm validation before the theoretical simula-tion model was applied to a practical industry.

2) Based on the above framework, we designed and im-plemented an MVES, including hardware platformdesign and software control platform design.

3) Two representative servo control experiments wereconducted using the MVES, which was designed andbuilt independently. It was finally proven that ourexperimental system could directly perform differ-ent types of control experiments using a simulationmodel to verify the validity of the model. It was con-venient to modify and debug the model in the pro-cess, which greatly reduced the workload of the al-gorithm test and has important practical value.

The remainder of this paper is organized as follows. InSection 2, the overall design of the MVES is discussed.Section 3 describes the physical system and testing. Fi-nally, Section 4 presents the conclusions.

2. Overall Design of the Experimental System

This section provides an overview of the entire system,introduces the composition and structure of the MVES,and describes its overall working principle. Then we in-troduce the MVES from three aspects: servo motor ex-perimental device design, electrical connection mode, andsystem software development framework design.

The experimental system designed in this study hasfour main components, as shown in Fig. 2, servo motorexperimental device, industrial personal computer (IPC),driver, and encoder.

There are two sets of servo motor experimental devices:one set of 750 W low-power towing devices and one setof 7500 W high-power devices for performing differenttypes of servo control experiments. The driver receives anindustrial computer control command to control the servomotor. The encoder obtains the motor speed and convertsit into an electrical signal for transmission to the IPC. TheIPC runs the control algorithm in real time, sends the cor-responding control command, and receives the real-time

Vol.25 No.2, 2021 Journal of Advanced Computational Intelligence 249and Intelligent Informatics

Xiong, Y. et al.

Fig. 3. Low-power experimental device.

parameters returned by the encoder. The control algo-rithm of the MATLAB/Simulink model runs in real timeand transmits the control parameters to the TwinCAT 3PLC master program in the IPC. Then, the PLC sends thecontrol command to the servo driver through the Ether-CAT bus, and the servo driver controls the motor to per-form relevant experiments. During the operation of theexperimental system, we implemented the direct controlof the servo motor using the MATLAB/Simulink con-trol algorithm. Our system does not need to convert thecontrol algorithm into other industrial programming lan-guages to control the motor, which greatly reduces theworkload of the theoretical simulation model in the actualindustrial application. The theoretical simulation modelruns in real time in the MATLAB environment, which isindependent of the main PLC control program. There-fore, this experimental system can support various typesof servo control algorithms and has strong versatility.

2.1. Servo Motor Experimental Device DesignThe servo motor experimental device is mainly com-

posed of two sets of 750 W and one set of 7500 W servomotors of the IS620N series of the Huichuan Company.The two sets of 750 W motors were coaxially mountedto form the supporting experimental device, as shown inFig. 3. The main body of the device consists of two 750 Wmotors, a magnetic powder brake, and a magnetic powderclutch. The right-side motor is connected to the magneticpowder clutch through an asynchronous transmission beltand is directly connected to the magnetic powder brake.A magnetic slot on the right side of the magnetic clutchcan be used to install the flywheel. Another motor is di-rectly connected to the flywheel, and the two motors aremounted coaxially. On the one hand, experiments suchas inertia identification, and parameter self-tuning, can beperformed independently. On the other hand, a pair of ex-perimental devices can be formed, and a disturbance sup-pression experiment can be performed to verify the syn-chronization accuracy of the motion control of the servosystem. The left-side motor is used for the inertia identi-fication experiment by adding flywheels of different qual-ities. The right-side motor can dynamically adjust themoment of inertia using the magnetic powder clutch toperform the inertia identification experiment. Simultane-

Fig. 4. High-power experimental device.

ously, it can directly connect with the magnetic powderbrake with continuously adjustable load torque and canperform the disturbance suppression experiment for dif-ferent forms of loading. The entire device is fixed ona steel base plate with rubber pad isolation. The mainbody of the 7500 W experimental device is composed ofa 7500 W motor, magnetic powder brake, and flywheel.The structural diagram is shown in Fig. 4. The experi-mental principle of the same pair of towing devices is thesame. The power is compared with that of the towing de-vice by changing the number of flywheels to create thevariable-load condition.

2.2. Electrical Connection DesignThe motor used in this experimental set is from the

IS620N series of the Huichuan Company. We combinethe features of this series with experimental functions (aset of high-power and a set of low-power). The electricalconnection diagram shown in Fig. 5 is designed consider-ing the electrical safety and stability of the device.

The main circuit power supply of the 750 W servo mo-tor is 220 V three-phase AC power, and that of the 7500 Wservo motor is 380 V three-phase AC power. The iso-lation transformer is used for isolation protection. Thecircuit breaker protects the power line and cuts off thepower when an overcurrent condition occurs [11–13]. Theservo system can be turned on or off using an electromag-netic contactor [14], and the bleeder resistor (which canalso support an external bleeder resistor) is used to pro-vide a current loop when the busbar capacitance is insuf-ficient [15, 16].

The IPC communicates with the servo driver throughthe EtherCAT Fieldbus. Digital servo bus technology isthe communication basis of the digital communicationcontroller and servo driver. In recent years, this tech-nology has been developed considerably, particularly interms of transmission speed and transmission distanceperformance. EtherCAT real-time Ethernet bus technol-ogy performed well in terms of transmission rate and cy-cle time, and the communication time required by the ex-perimental device in this study was in the range of mi-croseconds; hence, communication with the EtherCATbus was the best choice [17].



Fig. 5. Electrical connection diagram of the experimentalsystem.

2.3. System Software Development Framework De-sign

As the control algorithm and servo motor device com-municate through the main control program, under thispremise, the communication feedback speed is guaranteedto be fast, which places high requirements on the operat-ing environment of the main control program. After re-search and comparison, we chose Beckhoff’s TwinCAT 3platform, which is a control software based on a PC plat-form and the Windows operating system. The logic of PCstandard hardware is used to implement logic operationand motion control in TwinCAT 3, which supports multi-core CPU and integrates C++ programming and MAT-LAB/Simulink modeling [18]. The real-time kernel envi-ronment is capable of executing real-time modules writtenin C++, and various convenient debugging options maketroubleshooting and debugging easier. The open interfaceof TwinCAT 3 is compatible with existing tools and pro-vides new features, with an extended real-time functionalcycle of 50 microseconds.

One of the biggest advantages of TwinCAT 3 is its ex-cellent scalability, supporting the control layer of the Mat-lab/Simulink module, which can significantly reduce theworkload of developers. It is not necessary to know theprinciple of the control algorithm in detail when develop-ing the system. We achieved data communication usingthe ADS protocol. Thus, we only need to know the corre-sponding input and output of the system development andcontrol algorithm. The overall control framework of the

Fig. 6. Framework of the control structure.

experimental system is illustrated in Fig. 6. We imple-mented the real-time control effect of data transmissionbetween the control platform and the servo driver layerthrough the EtherCAT bus. The control platform is basedon a PC, and the entire development is undertaken in aWindows environment. TwinCAT 3 is integrated into theVisual Studio software, which is divided into the develop-ment and operation layers. In the development layer, thehuman software interaction tool human-machine interface(HMI) in TwinCAT 3 is used to create the control softwareinterfaces, which implement the main functions, such asmodule control, parameter configuration, and state modeselection. In the running layer, the TwinCAT 3 runningcore is the highest-priority service under Windows andruns as a kernel on the controller, ensuring real-time con-trol [19]. Data transmission between the development andruntime layers is performed through the ADS protocol.With the TwinCAT 3 I/O port mapping, cyclic data can beacquired during process mapping via different fieldbuses.The cyclic task drives the corresponding fieldbus. Dif-ferent fieldbuses can be run on different cycles with thesame CPU. The configuration of the fieldbus and processmapping are performed using in Visual Studio.

The biggest innovation of the MVES is that the the-oretical simulation model does not need to be convertedinto a programming language in the TwinCAT 3 environ-ment. The EtherCAT protocol can be directly used forinformation communication with the experimental deviceto conduct related experiments. Theoretical researchersdo not need to know the operation mode of the hardware-and the hardware staff only needs to map the input andoutput variables controlled by the theoretical model to theTwinCAT 3 working environment to control the motor andmove it simultaneously. Meanwhile, relevant parametersare fed back to the Simulink model, where the shortestcommunication cycle can reach 50 ms.

The experimental data flow of the MVES is shown in


Xiong, Y. et al.

Fig. 7. System control data flow.

Fig. 7. The entire communication framework consistsof three parts: the Simulink theoretical control algorithmmodel, TwinCAT 3 engineering environment, and servosystem. The Simulink theoretical control algorithm modelwas developed using in MATLAB. We used the Beck-hoff TE1410 plug-in to map the Simulink variables tothe PLC variables in the TwinCAT 3 engineering environ-ment. Before the experiment, we transplanted the sim-ulation model to the experimental system and replacedthe controlled object (simulation motor) in the simulationmodel with the TC ADS symbol interface correspondingto TwinCAT 3. This module serves as the communica-tion bridge between Simulink and the actual motor. Theprocess variables under Simulink can be mapped to thePLC variables in TwinCAT 3. The PLC project under theTwinCAT environment performs data transmission withthe driver through the EtherCAT bus. The servo driversends the control commands to the motor. The motor thenfeeds back the real-time speed and other parameters to thedriver. We implement two-way communication to per-form the experimental purpose of the verification of thecontrol algorithm thus. During the experiment, the com-munication project in Visual Studio was started first, andthen the Simulink operation module was started. Whenadjusting the experimental parameters, the parameters inthe Simulink model were directly modified. If the controlSimulink model is replaced, similarly, the controlled ob-ject in the simulation model needs to be replaced with theTC ADS symbol interface, and then the experiment canbe conducted out according to the above steps.

The framework of the control software function isshown in Fig. 8. The entire software is divided intothree modules: initialization, control, and function mod-ules. The initialization module is used to search for hard-ware devices and parameter configurations for the pre-experiment preparation of different devices to ensure theversatility of the software. The control module has threecontrol functions: inertia identification, control parame-ter auto-tuning and torque ripple and external disturbancesuppression. For each experimental module that can beadded to the software corresponding modules, the soft-ware is highly scalable, and the current control software

Fig. 8. Framework of the control function.

Fig. 9. Inertia recognition software interface.

has the above three experimental functions of the controlfunction.

The function module is an additional function of thethree control modules. When the experiment is per-formed, system state monitoring can be used to view thecurve of a specific variable and save relevant experimentaldata. The virtual oscilloscope can also reflect changes ineach variable. After the experiment is completed, relevantexperimental data can be saved, and a simple experimen-tal report can be generated.

Additionally, we developed a control software usingthe HMI in TwinCAT 3. The principle is the same asthat we discussed in the previous section paragraph. Thekey control command in the HMI corresponds to a vari-able in the PLC. The variable in the PLC is fed back toSimulink through TE1410. The software can be used tocontrol the model in the MATLAB/Simulink through atthe HMI control interface, and the experimental processcan be controlled in the HMI control interface. During theexperiment, the required variable changes could be ob-served, and the historical data could be retained throughthe virtual oscilloscope of the control software. The in-ertia recognition software interface is shown in Fig. 9.



The middle part of the interface is used for data visualiza-tion, which can monitor the changes in various variables.The lower part is used to enter the relevant configurationparameters of each servo experiment. At the bottom ofthe interface, we can select different experimental modesaccording to the different experiments. The lower rightshows the start and stop controls of the experiment but-ton.

3. Physical System Implementation and Test-ing

This section describes physical effects of the MVES de-signed in this study, including the overall hardware struc-ture model and the actual experimental results of eachmodule.

3.1. Experimental Device

The physical MVES is shown in Fig. 10. The entiresystem consists of an IPC, servo driver, and low-power ex-perimental servo motor. The IPC and driver communicatewith each other through the EtherCAT bus for motor con-trol. The IPC is equipped with software, such as VisualStudio and MATLAB and integrates the TwinCAT 3 de-velopment environment. The structure of the low-powerexperimental device is consistent with the description inSection 2.2, consisting of two 750 W motors, a mag-netic powder brake, a magnetic powder clutch, and fly-wheels, as shown in Fig. 11. The servo driver is integratedinto the control cabinet. The control cabinet can dynam-ically adjust the current flowing through the magneticpowder clutch to increase the inertia to simulate a con-tinuous variable-load environment. In this environment,a dynamic inertia identification experiment can be easilyachieved. We performed out two sets of servo control ex-periments to verify the practicability of the designed ex-perimental system. Our MATLAB version is 2016a.

3.2. Experimental Effect

To test the practicability of the experimental device,two representative experiments in the servo control pro-cess were selected for verification in this study: an iner-tia online identification experiment and an external distur-bance suppression experiment. In each set of experiments,the simulation and the effect diagram of the experimentswere compared using this set of experimental devices. Allthe experimental control algorithms can independentlycomplete the simulation experiment in the Simulink en-vironment, and can also perform a physical experimentusing the experimental device and data transmission tech-nology designed in this study. The physical experimentprocess can be controlled using the industrial robot high-performance servo system position-setting software thatwe designed independently.

Fig. 10. Physical map of the entire system.

Fig. 11. Physical map of the servo motor experimental device.

3.2.1. Inertia Online Identification Experiment

The control performance of an industrial servo systemis closely related to the accurate acquisition of motor pa-rameters. Research on the high-precision online identifi-cation of inertia has always been the most representativein the field of servo control. Therefore, we selected aninertia online identification experiment to verify the ef-fectiveness of the system.

The recursive least squares method has been widelyused in the field of servo control inertia identification [20].However, owing to the increase in processing data, thismethod will appear as a data saturation phenomenon,which will affect the accuracy and effect of the identifi-cation. Therefore, to avoid this phenomenon, the recur-sive least square method with a forgetting factor is gen-erally used to make certain compensation corrections tothe identified parameters. The iterative formula for theparameter estimation of the least-squares method with theforgetting factor is as follows:

A(k) =1

ξ (k)[I −D(k)φ T(k)

]A(k−1), . . . (1)

e(k) = y(k)− θ T(k−1)φ(k), . . . . . . . (2)


Xiong, Y. et al.

Fig. 12. Identification of inertia (simulation).

θ (k) = θ(k−1)+D(k)e(k), . . . . . . . (3)

D(k) = A(k−1)φ(k)[ξ (k)

+φ T(k)A(k−1)φ(k)]−1, (4)

where D(k) is the gain matrix of the Kalman filter, A(k)is the covariance of the identification error, θ (k) is theparameter to be identified, ξ (k) is the variable forgettingfactor,and e (k) is a priori error. However, due to the dif-ferent settings of the forgetting factor, the algorithm hasan imbalance in the identification accuracy and conver-gence speed. Therefore, the forgetting factor is dynami-cally adjusted according to the actual operating conditionsof the system.

The forgetting factor ξ (k) is set to be related to theasymptotic memory length N(k):

ξ (k) = 1− 1N(k)

, . . . . . . . . . . . . (5)

where N(k) indicates that the measured data are discardedafter N(k) sampling steps [21]. It was adjusted based onthe weighted sum of the least squares error. When e (k)is very small, the identification process tends to be stable.At this time, a relatively large N(k) is selected to collectas much data as possible to improve the identification ac-curacy; in contrast, when e (k) is large, a small N(k) isselected to ensure a high the identification update speed.

When we used Simulink only for the simulation exper-iment, the effect was as shown in Fig. 12. The inertiawas increased at 0.5 s, and reduced at 1 s. The iden-tification accuracy and speed were excellent. We usedthis set of experimental systems to perform experiments.Here, input variables in Simulink are directly connectedto the HMI control interface keys through variable map-ping, and the algorithm is started using the start runningbutton. The inertia of the magnetic powder clutch cur-rent is adjusted through the control cabinet to simulatethe variable-load condition. The experimental results arepresented in Fig. 13. In the experimental process shownin Fig. 13, after the motor starts for a period of time, thespeed is stable, and the moment of inertia is stable at ap-

0.0165

0.0273

Fig. 13. Inertia online identification (physical experiment).

proximately 0.0165 kg·m2. Then, we increase the currentflowing through the magnetic powder clutch to increasethe inertia, which increases to 0.0273 kg·m2. The currentis canceled at 44 s, and the inertia returns to the originalstate. We also add a slight disturbance at 53 s in the thisexperimental process.

From the above experimental results, the experimentalsystem designed in this study satisfies the high-precisionrequirements of inertia online identification. It responsespeed is fast and the identification accuracy is high. More-over it has high practical value in complex industrial sites.

3.2.2. External Disturbance Suppression Experiment

In the industrial production process, the servo system isinevitably affected by disturbances from the external en-vironment. External disturbances seriously affect the con-trol accuracy of the motor. Therefore, research on the sup-pression of external disturbances has attracted widespreadattention. The external disturbance suppression experi-ment is one of several basic types of experiments in thefield of servo control. Therefore, we selected the exter-nal disturbance suppression as a verification experimentfor the effectiveness and practicability of the system de-signed in this study. In this study, Simulink simulationand physical experiments were ferformed using an the ex-ternal disturbance suppression algorithm. For this pur-pose, we used the ordinary PI controller and the slidingmode + equivalent input disturbance (EID) controller de-signed by Jiang [22].

The mechanical motion equation for the table-attachedpermanent magnet synchronous motors is as shown inEq. (6):

dωdt

=1J

(32

npψ f iq −Bω −Tl

), . . . . . (6)

where ω is the mechanical angle speed, J is the rotationalinertia, np is the number of pole pairs, ψ f is the magneticchain, B is the viscous damping coefficient, and Tl is theload torque.



Fig. 14. Simulation results of the two controllers.

The state variable is defined as

x1 = ω∗ −ω . . . . . . . . . . . . . . (7)u = iq . . . . . . . . . . . . . . . . (8)

where ω∗ is the target input of ω .The design of the sliding mode controller combined

with EID is as follows:

u =2

3npψ f

[Jc

σεsat( s

σ

)

+Jc

qs−Bx1 +Bω∗ −Tl

]. . . (9)

In formula (9), s is defined as follows:

s =cJ

[(−Bx1)− 3

2npψ f u+Bω∗ −Tl

]. . . . (10)

The simulation results are shown in Fig. 14. In onegroup, we used only the PI controller, where in the othergroup, we used the sliding mode + EID controller. Whenonly the PI controller was used, the range of speed fluctua-tion was slightly larger than that in the sliding mode + EIDcontrol. When the sliding mode + EID control was used,the disturbance suppression effect was more evident, andthe rotation speed was stable at approximately 500 r/min.

The external disturbance suppression experiment wasperformed using this set of experimental devices, and alow-power device was used to form the disturbance inthe drag mode. The left-side motor was the observationmotor, the right-side motor formed a disturbance, and theobservation control algorithm suppressed the disturbance.The experimental results obtained using only the PI con-troller are shown in Fig. 15. In the experiment where onlyused a PI controller was used, we added the disturbanceat 7 s, and the rotation began to fluctuate. At 12 s, wedynamically adjusted the current of the magnetic clutchto increase the inertia, and the speed suddenly jumpedand then immediately returned to the original target speed.Fig. 15 shows the effect of speed tracking. We can achievethe high-precision tracking of speed in an environmentwith disturbances using our platform. When we the slid-

Fig. 15. Disturbance suppression experiment (PI).

Fig. 16. Disturbance suppression experiment (sliding mode+ EID).

ing mode + EID controller was used, the disturbance sup-pression effect was significantly better than when only thePI controller was used, and the range tracking of speedfluctuation range was small. However, the speed of theinertia is continued to increase instantaneously. However,it returned to normal, and the overall disturbance suppres-sion effect was evident. Fig. 16 shows the experimentalresults of the speed tracking.

The above comparison experiments showed that thisexperimental system could verify the effectiveness of theservo system disturbance suppression algorithm, whichcan support the verification of the disturbance suppressionalgorithm using different controllers. The experimentalresults are also consistent with the simulation results. Theexperimental system designed in this study has good ver-satility, and can support the validity verification of variousservo control algorithms. It also enables various Simulinktheoretical models to perform motor control experimentsdirectly.

3.3. SummaryIn this section, two sets of servo comparison experi-

ments were used to verify the effectiveness and versatil-


Xiong, Y. et al.

ity of the MVES system. The MVES can directly usethe servo simulation model to verify the effectiveness ofthe algorithm. This system can support various servocontrol models, and its operation process is simple andeasy. Complex industrial programming conversion is notrequired, which can reduce the workload of theoreticalresearchers, enabling them to focus on adjusting the the-oretical control algorithm. This can bring great conve-nience to their related research. The developed experi-mental system possesses the following features that makeit unique and suitable for testing the servo system controlalgorithm:

(1) Excellent versatility: We considered the principle ofversatility when designing the structure of the motorto support various servo control experiments. Eachmotor can be tested separately or combined to forman experiment on a towing device. The MVES issuitable for the Simulink models of various servocontrol algorithms. Different simulation models canbe developed by changing the simulation model andadjusting the relevant parameters.

(2) Outstanding performance: The experimental resultsshowed that the overall performance of the MVESwas excellent, the control performance and sensitiv-ity were high, and the communication speed was suf-ficiently fast to meet the requirements of real-timemotor control.

(3) Brilliant operability: The MVES designed in thestudy did not require cumbersome operation proce-dures while supporting multiple servo control algo-rithm verification. We ran the Simulink model inMATLAB and replaced the motor module to startthe experiment directly; the operation was consistentwith the simulation experiment. This system couldprovide a convenient and effective experimental plat-form for researchers who perform theoretical simu-lations, and it has strong practicability.

4. Conclusion

In this paper, we proposed and implemented an MVESthat uses the MATLAB/Simulink theoretical simulationmodel to communicate with the TwinCAT 3 PLC masterprogram to perform different types of servo control exper-iments, which effectively solved the problem of algorithmvalidation before the theoretical simulation model was ap-plied to a practical industry. The theoretical simulationmodel of MATLAB/Simulink could be directly applied tothe system to verify the validity of the algorithm validitywithout complicated conversion and configuration work.The MVES supports various Simulink simulation models.Moreover, its operation is simple and convenient, whichgreatly reduces the workload of the test algorithms of the-oretical researchers.

Two sets of experiments were performed to verify theeffectiveness and versatility of the system: online in-

ertia identification and external disturbance suppression.Each of there experiments included a comparative anal-ysis. The comparative analysis results showed that theMVES supports multiple theoretical simulation modelsand has strong practicability and versatility. The MVESprovides theoretical researchers with a convenient algo-rithm verification platform, which reduces their experi-mental workload and has good practical value.

In the future, we will consider how to apply this systemto an actual industrial site so that it has better use valueand a wider range of applications.

AcknowledgementsThis work was supported in part by the National Key R&DProgram of China under Grant 2017YFB1300900, in part bythe National Natural Science Foundation of China under GrantsNos.61873249 and 61976197, in part by the Hubei Provincial Nat-ural Science Foundation of China under Grant No.2015CFA010,and in part by the 111 project under Grant B17040.

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Name:Yonghua Xiong

Affiliation:School of Automation, China University of Geo-sciencesHubei Key Laboratory of Advanced Control andIntelligent Automation for Complex Systems

Address:No.388 Lumo Road, Wuhan, Hubei 430074, ChinaBrief Biographical History:2004-2009 Ph.D. Student in engineering, Central South University2006-2008 Visiting Scholar, City University of Hong Kong2007-2010 Lecturer, Central South University2010-2014 Associate Professor, Central South University2014- Professor, China University of GeosciencesMain Works:• Y. Xiong, S. Huang, M. Wu, J. She, and K. Jiang, “AJohnson’s-Rule-Based Genetic Algorithm for Two-Stage-Task SchedulingProblem in Data-Centers of Cloud Computing,” IEEE Trans. on CloudComputing, Vol.7, No.3, pp. 597-610, 2019.• L. Wu, Y. Xiong, M. Wu, Y. He, and J. She, “A Task Assignment Methodfor Sweep Coverage Optimization Based on Crowdsensing,” IEEE Internetof Things J., Vol.6, No.6, doi: 10.1109/JIOT.2019.2940717, 2019.• Y. Xiong, J. She, and K. Jiang, “A Lightweight Approach to Access toWireless Network Without Operating System Support,” IntelligentAutomation and Soft Computing, Vol.24, No.2, pp. 275-283, 2018.

Name:Ke Li


Address:No.388 Lumo Road, Wuhan, Hubei 430074, ChinaBrief Biographical History:2014-2018 B.S. Student in engineering, China University of Geosciences2018- M.S. Student in engineering, China University of Geosciences

Name:Zhen-Tao Liu


Address:No.388 Lumo Road, Wuhan, Hubei 430074, ChinaBrief Biographical History:2008 Received M.E. from Central South University2013 Received Dr.Eng. from Tokyo Institute of Technology2013-2014 Lecturer, Central South University2014-2018 Lecturer, China University of Geosciences2018- Associate Professor, China University of GeosciencesMain Works:• Z.-T. Liu, A. Rehman, M. Wu, W.-H. Cao, and M. Hao, “SpeechPersonality Recognition Based on Annotation Classification UsingLog-likelihood Distance and Extraction of Essential Audio Features,”IEEE Trans. on Multimedia, 2020.• Z.-T. Liu, Q. Xie, M. Wu, W.-H. Cao, D.-Y. Li, and S.-H. Li,“Electroencephalogram Emotion Recognition Based on Empirical ModeDecomposition and Optimal Feature Selection,” IEEE Trans. on Cognitiveand Developmental Systems, Vol.11, No.4, pp. 517-526, 2019.Membership in Academic Societies:• The Institute of Electrical and Electronics Engineers (IEEE)• The Chinese Association for Artificial Intelligence (CAAI)

Name:Jinhua She

Affiliation:School of Automation, China University of Geo-sciencesHubei Key Laboratory of Advanced Control andIntelligent Automation for Complex SystemsSchool of Engineering, Tokyo University ofTechnology

Address:No.388 Lumo Road, Wuhan, Hubei 430074, ChinaKatakura, Hachioji, Tokyo 192-0982, JapanBrief Biographical History:1993 Ph.D. in engineering from Tokyo Institute of Technology1993-2001 Lecturer, Tokyo University of Technology2001-2010 Associate Professor, Tokyo University of Technology2010- Professor, Tokyo University of TechnologyMain Works:• X. Yin, J. She, M. Wu, D. Sato, and K. Hirota, “Disturbance rejectionand performance analysis for nonlinear systems based on nonlinearequivalent-input-disturbance approach,” Nonlinear Dynamics, Vol.100,No.4, pp. 3497-3511, 2020.• M. Wu, W. Cao, X. Chen, and J. She, “Intelligent Optimization andControl of Complex Metallurgical Processes,” Springer, 2019.Membership in Academic Societies:• The Society of Instrument and Control Engineers (SICE)• The Institute of Electrical Engineers of Japan (IEEJ)• The Japan Society of Mechanical Engineers (JSME)• The Japan e-Learning Association (JeLA)• Asian Control Association (ACA)• The Institute of Electrical and Electronics Engineers (IEEE), Fellow


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