Performance enhancement of DC motor drive using Fuzzy Logic Controller

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 08, 2015 | ISSN (online): 2321-0613

Performance Enhancement of DC Motor Drive using Fuzzy Logic Controller Shrabani Pal1 Jayanta Bhusan Basu2 1,2 Assistant Professor 1,2 Department of Electrical Engineering 1,2 Siliguri Institute of Technology, Siliguri, Darjeeling Abstract—This paper demonstrates how the performance of a DC motor drive can be enhanced using a fuzzy logic controller. The performance of the DC motor drive is judged without controller, with a most commonly used controller (Ziegler-Nichols Tuned PID) and fuzzy logic controller (FLC). For the considered system 7 fuzzy rules are framed for FLC. Comparison between the outputs in different scenario was done on the basis of the simulation result obtained by MATLAB. Finally, the result shows that the fuzzy logic approaches using the designed set of fuzzy rules enhance the performance of the DC drive system.

Key words: DC Drive, PID Controller, Ziegler-Nichols, FLC (Fuzzy Logic Controller) I. INTRODUCTION DC motor is a machine which converts electrical energy to mechanical energy. DC motors are widely used in industry, robot manipulators and home appliances where speed and position control of motor is required because of their high performance reliabilities, flexibilities, and low costs. So, the performance analysis of speed control of DC motor is very much necessary. The speed of DC motor can be adjusted to a great extent so as to provide easy control and performance.

There are many conventional and numeric methods for controlling of the speed of DC motor. The tunings methods are very much important of controller. The most commonly used controller is PID controller which can be tuned by Ziegler-Nichols method. The modern control system used intelligent control like knowledge based expert system, fuzzy logic and neural networks. All three approaches are interesting and very promising areas of research and development [1]. Fuzzy logic controller can replace the PID controller very successfully because they can cover a much wider range of operating conditions than classical controller.

The aim of this paper is characteristic analysis of the speed of the DC motor and performance enhancement of DC motor using Fuzzy Logic Controller. In this paper one knowledge based approach is proposed using ‘if-then’ rules which control action is implemented through fuzzy-logic controller.

II. MODELLING OF DC MOTOR The DC or direct current motor works on the principal, when a current carrying conductor is placed in a magnetic field; it experiences a torque and has a tendency to move.

The basic principle behind DC motor speed control is that the output speed of DC motor can be varied by controlling armature voltage for below and up to rated speed keeping field constant. To analyze the performance of the DC motor using simulation an appropriate model needs to be established.

Fig. 1: Model of D.C. Motor In general the torque of the DC motor is proportional to the current (Ia) (1.1) Tm  K t I a The back emf is proportional to the angular velocity of the shaft of the motor.

Eb=Kbw (1.2) From the above figure the electrical and mechanical equations are Va  Ra .I a  La .

Tm  J m .

dI a  Eb dt

dw  Bm .w dt

(1.3) (1.4)

Where : Va= armature voltage (V) Ra= armature resistance (ohm) La=armature inductance (Henry) Ia = armature current (A) Eb= Back emf (V)  m  angular position of rotor shaft (rad) w = angular speed of the motor (rad/sec) Tm= motor torque (N/m) Jm= rotor inertia (kgm2) Bm= viscous friction coefficient (Nms/rad) Kt = torque constant (Nm/A)\ Kb= back emf constant (Vs/rad) Applying the Laplace Transform, the above modeling equation can be expressed as follows: Va (s)  R a .I a (s)  L a .sIa (s)  K b W(s) (1.5) (1.6) K t I a (s)  J m .s.W(s)  Bm .W(s) From the above equation the transfer function of the DC motor is Kt W(s)  Va (s) L a J m s 2  (R a J m  L a B m )s  (R a B m K b K t )

The speed of the motor: W(s)= s. m (s) So, the equation no. 1.7 can be written as

(1.7)

Kt  m (s)  Va (s) L a J m s 3  (R a J m  L a B m )s 2  (R a B m K b K t )s

Using the above equation the SIMULINK model of the DC motor is developed as shown in Fig. 2.

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Performance Enhancement of DC Motor Drive using Fuzzy Logic Controller (IJSRD/Vol. 3/Issue 08/2015/114)

fuzzy implication and the rules of inference in fuzzy logic.

Defuzzification is a scale mapping, which converts the range of values of output variables into corresponding universe of discourse and also yields a non-fuzzy control action from an inferred fuzzy control action. The transformation of input and output variables are done by another variable that is membership function. The range of this variable is (0.0 to 1.0) and shape can be defined by the user.

Fig. 2: Simulink model of DC motor III. TUNING OF PID CONTROLLER USING ZIEGLER - NICHOLS METHOD

The PID controllers are widely used as industrial applications because of its simple structure. The tuning of the parameter of P, I and D is very much important. One of the best methods of the tuning of the parameter of PID controller is Ziegler-Nichols method. The mathematical expression of Proportional-Integral-Derivative (PID) controller is de(t ) c(t )  K p e(t )  K i e(t )dt  K d dt Where Kp is the proportional gain, Ki is the integral gain and Kd is the derivative gain.

In this paper using the following simulink model the speed of the DC motor have analyzed



Fig. 4 Components of Fuzzy-Logic Controller In this paper a fuzzy logic controller has been designed to enhance the performance of the DC motor.

The input and output variables for fuzzy controller has been set. The inputs are chosen as the speed of error, and change of error and the output is control. The universe of discourse for both input and output variables has been divided and expressed as per the following figures. Here the error is e(t)=r(t)-y(t) and change in error is e(t)-e(t-1).

The input variables of the fuzzy logic controllers are as follows:

Fig. 5: Input variable “ERROR”

Fig. 3: Simulink model of DC motor with PID controller IV. FUZZY LOGIC CONTROLLER In modern control system fuzzy logic has become a most successful tool for developing sophisticated control system.

Today the fuzzy logic control technology has been widely used in industry. Fuzzy logic control is a control algorithm based on a linguistic control strategy, which is derived from expert knowledge into an automatic control strategy. The operation of a fuzzy logic controller is based on qualitative knowledge about the system being controlled. Fuzzy logic controller contains four main parts which are fuzzifier, rule base, inference engine and defuzzifier. In fuzzifier, the values of input avariables are converted into linguistic values. Rule base is consisting of a database and provides necessary definitions which are used to define the rules of linguistic values. Inference engine is used for decision making logic which is the main part of the fuzzy logic. It simulates human decision making capability based on fuzzy concepts and of inferring fuzzy control actions employing

Fig. 6: Input variable “CHANGE IN ERROR The output variable of the controller is

Fig. 7: Output variable “CONTROL” In fuzzy inference engine the truth value for the premise of each rule is computed which results in one fuzzy subset to be assigned to each output variable for each rule.

Mostly MIN and PRODUCT is used as inference rules. The basic function of rule base is to represent in a structured the

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Performance Enhancement of DC Motor Drive using Fuzzy Logic Controller (IJSRD/Vol. 3/Issue 08/2015/114)

control policy of an experienced process using and/or operator.

The rules implemented in fuzzy inference engine is shown in Fig. 8

Fig. 11 Simulation result of Without controller Then using tuned value of the PID controller the simulation has been done. Fig.12 shows the response of the system when a PID controller is used.

Fig. 8: Rules of fuzzy logic controller The surface viewer of the fuzzy logic controller is shown in Fig. 9.

Fig. 12 Simulation result using PID controller.

Lastly one fuzzy logic controller has been designed and checks the performance of the DC drive.

Fig. 13 Inputs to the FLC

Fig. 9: Surface viewer of the fuzzy logic controller

Fig. 14 Output from FLC

Fig. 10: Simulink model of DC motor using fuzzy logic controller V. SIMULATION In this paper using the MATLAB Simulation performance of the DC drive has been studied. At first without controller the performance has judged. Fig. 11 shows the response of the system without controller.

Fig. 15 Simulation response using FLC

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Performance Enhancement of DC Motor Drive using Fuzzy Logic Controller (IJSRD/Vol. 3/Issue 08/2015/114)

Scientific & Technology Research Volume 1, Issue 2, ISSN 2277-8616 pp. 52 – 54 ,March 2012

Fig. 16 Motor speed characteristic using FLC VI. CONCLUSION In this paper the step response of DC motor drive is judged using a PID controller tuned by Ziegler-Nichols tuning and Fuzzy logic controller. Comparing the simulation results it is found that the Fuzzy Logic Controller is better than other method. The Fuzzy Logic Controller has the following effects on the step response specifications:  Reduced Overshoot  Improved Rise Time  Zero Steady state error The fuzzy controller also enhances the dc motor starting torque and better speed regulation during transient period.

Hence it is concluded that the proposed Fuzzy Logic Controller provides better dynamic performance characteristics of DC motor drive.

REFERENCES [1] Nader Jamali Sufi Amlashi “Design and implementation of Fuzzy Position Control System for Tracking Applications and Performance Comparison with Conventional PID” IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 1, No. 1, pp. 31 - 44 ISSN: 2252-8938, March 2012.

[2] Assilian, S. and Mamdani, E.H., An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller.

International Journal of Man-Machine Studies, 7(1), 113, 1974.

[3] Manafeddin N. and Onur B. “DC motor position control using fuzzy proportional-derivative controllers with different defuzzification methods.” TJFS: Turkish Journal of Fuzzy Systems (eISSN: 1309–1190) An fficial Journal of Turkish Fuzzy Systems Associatio, 1(1), 36-54, 2010.

[4] C. C. Lee, ”Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I”,IEEE Trans. Syst. Man, Cybern., vol. 20, no. 2, pp.404 - 418, 1990.

[5] C. C. Lee, ”Fuzzy Logic in Control Systems: Fuzzy LogicController-Part II”,IEEE Trans. Syst. Man, Cybern., vol. 20, no. 2, pp. 419 - 435, 1990.

[6] Vikas S. Wadnerkar, Mithun M. Bhaskar, Tulasi Ram Das and A.D. Raj Kumar, “A New Fuzzy Logic based Modelling and Simulation of a Switched Reluctance Motor”, Journal of Electrical Engineering & Technology Vol. 5, No. 2, pp. 276 - 281, 2010.

[7] Atul Kumar Dewangan, Sashai Shukla, Vinod Yadu “Speed Control of a Separately Excited DC Motor Using Fuzzy Logic Control Based on Matlab Simulation Program” International Journal of

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