power system engineering

International Journal of Research in Computer andCommunication Technology, Vol 3, Issue 3, March- 2014

ISSN (Online) 2278- 5841ISSN (Print) 2320- 5156

www.ijrcct.org Page 369

Design and implementation of the fuzzy PID controller usingMATLAB/SIMULINK model

Mr. Tushar Upalanchiwar , Prof.A.V.SakhareComputer Science & Engineering Department, G.H Raisoni College Of Engineering.

Email Id :- [email protected]

ABSTRACT

In many industries, various types of motioncontrol system used to control various applications.These motion control systems are nothing but the DCMotors. DC motors have high efficiency, high torqueand low volume. This paper proposed PID controllerwith fuzzy technology. i.e. fuzzy PID controller, herewe are analyze the performance of the conventionalPID controller using the MATLAB/SIMULINKmodel.

Keywords: PID, Fuzzy PID controller

1. INTRODUCTION

Now a day’s DC motors are widely used inindustrial applications, the speed of DC motors canbe controlled by various driver circuits. In which thedrives application mainly involves complex processsuch as modeling, control, simulation and parameterstuning etc.

An expert knowledge is required for tuningthe controller’s parameter to get the optimalperformances. However conventional PID controlleralgorithm is simple stable easy adjustment and highreliability but most of the industrial process withdifferent degrees of non linearity and parametersvariability and uncertainty of the mathematical modelof the system.

Tuning the parameters of the conventionalPID controller is very difficult, poor robustness, sotherefore it’s difficult to achieve optimal state underfield condition in the actual production.

For all the problems with the conventionalPID controller, fuzzy is the better way to controlsystems. Fuzzy PID controller method is bettermethod of controlling to the complex and unclearmodel systems, it can give simple and effectivecontrol, play fuzzy control robustness, good dynamicresponse, rising time, overstrike characteristics.

FLC (fuzzy logic control ) control hasproven effective for complex non linear andimprecisely defined process for which standardmodel based control techniques are impractical.Fuzzy logic deals with the problems that havevagueness uncertainty and membership functionbetween 0 to 1. i.e. if the reliable expert knowledge isnot available or if control system is too complex toderive the required decision rules, then some effortshave been made to solve these problems andsimplicity the task of tuning parameters anddeveloping rules for the controller.

2. PROBLEM DEFINATION

Unfortunately, most existing conventional PIDcontroller fails where industrial process havingdegrees of non linearity and parameters variabilityand uncertainty of the mathematical model of thesystem, however conventional PID controlleralgorithm is simple stable easy adjustment and highreliability. Tuning parameters of such systems alsodifficult.

3. OBJECTIVES

Fuzzy PID controller method is better method ofcontrolling to the complex and unclear modelsystems. Fuzzy rules can be evaluated from thehuman experience and knowledge about the system.




The objective is to set fuzzy rules which makes PIDcontroller reliable for the industrial process havingdifferent degrees of non linearity’s & variation inparameters.

4. SYSTEM ARCHITECTURE

The typical FIS (fuzzy inference system) inputsare the signals of error (e(k)) and change of error(e(k)-e(k-1)). The FIS output is the control actioninferred from the fuzzy rules. Fuzzy LogicToolbox™ provides commands and GUI tools todesign a FIS for a desired control surface.

In design of a nonlinear fuzzy PID controller fora DC motor in Simulink, The plant is a single-inputsingle-output system in discrete time and our designgoal is simply to achieve good reference trackingperformance. The fuzzy controller in this example isin the feedback loop and computes PID-like actionsthrough fuzzy inference. The fuzzy PID controlleruses a parallel structure. It is a combination of fuzzyPI control and fuzzy PD control.

Fig- implementation of discrete PID controller.

Fig. implementation of fuzzy PID using matlabsimulink model.

We use the change of measurement -(y(k)-y(k-1)), instead of change of error e(k)-e(k-1), as thesecond input signal to FIS to prevent the step changein reference signal from directly triggering thederivative action. Two gain blocks, GCE and GCU inthe feed forward path from r to u, are used to ensurethat the error signal e is used in proportional actionwhen the fuzzy PID controller is linear.

5. METHODOLOGY

Designing a fuzzy PID controller involvesconfiguring the fuzzy inference system and settingthe four scaling factors: GE, GCE, GCU and GU.

In this example we followed the following steps,

1. Design a conventional linear PIDcontroller

The conventional PID controller is a discretetime PID controller with Backward Euler numericalintegration method used in both the integral andderivative actions. The controller gains are Kp, Kiand Kd. The controller is implemented in Simulink asbelow,

PID controller gains can be tuned eithermanually or using tuning formulas. In this example,




we use the pidtune command from Control SystemToolbox to obtain an initial PID design

Ts*z z-1C= Kp + Ki * ------ + Kd * ------

z-1 Ts*z

with Kp = 30, Ki = 28.6, Kd = 6.9, Ts = 0.1

2. Design an equivalent linear fuzzy PIDcontroller

By configuring the FIS and selecting fourscaling factors, we obtain a linear fuzzy PID controlthat reproduces the exact control performance as theconventional PID controller does.

First, configure the fuzzy inference systemso that it produces a linear control surface frominputs E and CE to output u.

The FIS settings summarized below arebased on design choices describedin: Use Mamdani style fuzzy inference system. Use algebraic product for AND connective. The ranges of both inputs are normalized to

[-10 10]. The input sets are triangular and cross

neighbor sets at membership value of 0.5. The output range is [-20 20]. Use singletons as output, determined by the

sum of the peak positions of the input sets. Use the center of gravity method (COG) for

defuzzification.

Next, we determine scaling factors GE, GCE,GCU and GU from the Kp, Ki, Kd gains used by theconventional PID controller. By comparing theexpressions of the traditional PID and the linearfuzzy PID, the variables are related as: Kp = GCU * GCE + GU * GE Ki = GCU * GE Kd = GU * GCE

Assume the maximum reference step is 1,whereby the maximum error e is 1. Since theinput range of E is [-10 10], we first fix GE at10. GCE, GCU and GCU are then solved fromthe above equations. GE = 10; GCE = GE*(Kp-sqrt(Kp^2-4*Ki*Kd))/2/Ki; GCU = Ki/GE; GU = Kd/GCE;

By using this equations, we can implement fuzzyPID controller by comparison to the conventionalPID controller.

6. RESULTS AND DISCUSSION

Fig- simulation results of conventional PID controllerfor speed controlling of dc motor

Fig- simulation results of fuzzy PID controllerfor speed controlling of DC motor

The comparative assessment based on the speedcontrolling of the DC motor between conventionalPID controller and fuzzy PID controller shows that,




the performance of fuzzy PID controller is better thanconventional PID controller in controlling speed, andtime response of specifications.

7. CONCLUSION

In spite of the fact that PID controllers designedby the conventional method give a goodperformance, they create poor robustness and highexceeding. It is obvious that in case of few parameterchanges of the plant led to decline of the performanceof the conventional PID controller drastically.Thus, it is not enough to control process dynamicsswimmingly although it is a good start to tune PIDparameters. Therefore, inthis paper to overcome problems of conventionaltuning methods, fuzzy base PID approach weresearched.

8. REFERENCES

[1] Afshan Ilyas, Shagufta Jahan, MohammadAyyub,” Tuning Of Conventional PID And FuzzyLogic Controller Using Different DefuzzificationTechniques” International Journal of Scientific &Technology Research Volume 2, Issue 1, January2013ISSN 2277-8616 138 IJSTR

[2] Changhua Lu and Jing Zhang, “Design andSimulation of a Fuzzy-PID Composite Parameters'Controller with MATLAB” 2010 InternationalConference on Computer Design And Applications(ICCDA 2010).

[3] Afshan llyas,Shagufta Jahan, Mohammad Ayyub,”Tuning Of Conventional Pid And Fuzzy LogicController using Different DefuzzificationTechniques” international journal of scientific &technology research volume 2,issue 1,january2013,issn 2277-8616

[4] Hua Xiongli “A Comparative Design and tuningfor Conventional fuzzy control”IEEE Transaction onsystem,man and cybernetics –part Bcybernetics,Vol.24 No.5,October 1995

[5] Venugopal P, “design of tuning methods of pidcontroller using fuzzy logic” International Journal ofEmerging trends in Engineering and DevelopmentIssue 3, Vol.5 (September 2013)

[6] C.C.Lee, “Fuzzy logic in Control Systems: FuzzyLogic controller –Part I”,IEEE Transactions onsystems,Man,and Cybernetics ,Vol.20,No.2,pp.404-418,1990

[7]weinjing huang, yuan zhang, xiowei gou and leisun “ simulation and realization of servo rocketsystem based on interval fuzzy PID control.” 2012international conference on electrical and computerengineering advances in biomedical engineering ,vol.11

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