Ppt 2

A Mini Project Report On

“FUZZY LOGIC SPEED CONTROL OF DC MOTOR”

Submitted to Jawaharlal Nehru Technological University in partial fulfillment of the requirements For the award of the degree of

BACHELOR OF TECHNOLOGY

IN

ELECTRICAL AND ELECTRONICS ENGINEERING Submitted By

T.UPENDER 08301A0241 D.RAHUL KRISHNA 08301A0209 SK.SHAHBAZ HUSSAIN 08301A0212

Department of Electrical and Electronics Engineering

ANWARUL -ULOOM COLLEGE OF ENGINEERING & TECH(Affiliated to Jawaharlal Nehru Technological University)

YENEPALLY (V), VIKARABAD (M), R.R- DIST.

2011-2012

INTRODUCTION This paper presents a simulation of the speed control of a DC motor using Fuzzy Logic Control (FLC) at MATLAB environment.

The Fuzzy Logic Controller designed in this study applies the required control voltage based on motor speed error (e) and its change (ce).

The performance of the driver system was evaluated through digital simulations using Simulink toolbox of Matlab".

The simulation results show that the control with FLC outperforms PI control in terms of overshoot and steady state error.

The speed of DC motors can be adjusted within wide boundaries so that this provides easy controllability and high performance. DC motors used in many applications such as still rolling mills, electric trains, electric vehicles, electric cranes and robotic manipulators require speed controllers to perform their tasks.

Then semiconductor components such as MOSFET, IGBT and GTO have been used as electric switching devices In general, the control of systems is difficult and mathematically tedious due to their high nonlinearity properties.

To overcome this difficulty, FLC can be developed. The best applications of FLC are the timevariant systems that are nonlinear and ill-defined. One of the most important FLC applications in real life is the metro system in the city Sendia of Japan in 1987. Nowadays ,

FLC applications are successfully used in many fields including automatic focus cameras, household materials such as dishwashers, automobile industry etc. In this study, the speed response of a DC motor exposed to fixed armature voltage was investigated for both under loaded and unloaded operating conditions

The FLC system designed for operating at fixed speedunder different load conditions are simulated at MATLAB/Sumlink environment. In this study, we used a chopper circuit as a motor driver.

OBJECTIVES

To implement speed control of a separately excited dc motor using fuzzy logic controller based on MATLAB and compare the response with P, PI and PID Controller

CONTENTS

Mathematical model of DC motorSimu link Model of DC MotorTypes of controllersResponses of P, PI, PID ControllersFLC description and designDefining of membership functions and rulesApplicationsAdvantagesdisadvantagesFuture scope of flcconclusionreference

MATHEMATICAL MODELS OF DC MOTORS

MATHE MATICAL MODELS OF DC MOTOR

SIMULINK MODEL OF DC MOTOR

SPECIFICATIONS OF DC MOTOR

Nameof Parameter Value

Motor Rating 5 HPRated Armature Voltage 500VRated Speed 1500rpmArmatureresistance (Ra) 11.2 ohmsArmatureInductance (La) 0.1215H Moment of Inertia of rotor ( j) 0.02215Kg-m2 Viscous frictional Coefficient (B) 0.02953Nm-sBack EMFConstant (K) 1.28N-m/ACoulomb Friction Coefficient 0.5161Nm-A

TYPES OF CONTROLLERS USED

Proportional ControllerProportional and integral ControllerProportional Integral and Derivative ControllerFuzzy Logic Controller

P CONTROLLER

RESPONSE WITH P CONTROLLER

PI CONTROLLER

RESPONSE WITH PI CONTROLLER

PID CONTROLLER

RESPONCE WITH PID CONTROLLERS

CONTROLLER SPESIFICATIONS

Proportional Gain Kp= 5Integral gain ki= 0.5Derivative Gain Kd= 0.8

OVERVIEW OF FLC

FLC DESIGNFLC design in MATLAB is based on mandani fuzzy type. The details of the designed controller are,Two Inputs: Error and Change of ErrorOne Output: Change of Alfa (Duty cycle)And Method: minimumOr Method: maximumImplication Method: minimumAggregation Method: maximumDefuzzification Method: Center of GravityThe triangular and trapezoidal membership functions are used to subdivide the input and output universes and to define the degree of membership.

FLC DESIGN

FUZZY INFERENCE SYSTEM

CONTROL SIMULATION

DEFINING RULES FOR THE FLC

APPLICATIONS OF FUZZY LOGIC SPEED CONTROL OF DC MOTOR

• Fuzzy logic is conceptually easy to understand. The mathematical concepts behind fuzzy reasoning are very simple. Fuzzy logic is a more intuitive approach without the far-reaching complexity.• Fuzzy logic is flexible. With any given system, it is easy to layer on more functionality withoutstarting again from scratch. • Fuzzy logic is tolerant of imprecise data.Everything is imprecise if you look closely enough, but more than that, most things are imprecise even on careful inspection. Fuzzy reasoning builds this understanding into the process rather than tacking it onto the end.• Fuzzy logic can model nonlinear functions of arbitrary complexity. You can create a fuzzy system to match any set of input-output data. This process is made particularly easy by adaptive techniques like Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which are available in FuzzyLogic Toolbox software.• Fuzzy logic can be built on top of the experience of experts. In direct contrast to neural networks, which take training data and generate opaque, impenetrable models, fuzzy logic lets you rely on the experience of people who already understand your system.• Fuzzy logic can be blended with conventional control techniques. Fuzzy systems don’t necessarily replace conventional control methods. In many cases fuzzy systems augment them and simplify their implementation.• Fuzzy logic is based on natural language. The basis for fuzzy logic is the basis for human communication. This observation underpins many of the other statements about fuzzy logic.

ADVANTAGES

Quicker responseReliableMore accurate than P. PI, PID ControllersDesigned through knowledge of experienceAble to simplify complex systems Fuzzy logic can model nonlinear functions of arbitrary complexity Fuzzy logic is based on natural language

DISADVANTAGES

Fuzzy logic is not a cure-all. When should you not use fuzzy logic?

The safest statement is the first one made in this introduction: fuzzy logic is a convenient way to map an input space to an output space. If you find it’s not convenient, try something else. If a simpler solution already exists, use it.

Fuzzy logic is the codification of common sense — use common sense when you implement it and you will probably make the right decision.

Many controllers, for example, do a fine job without using fuzzy logic. However, if you take the time to become familiar with fuzzy logic, you’ll see it can be a very powerful tool for dealing quickly and efficiently with imprecision and nonlinearity

FUTURE SCOPE OF FUZZY LOGIC SEED CONTROL OF DC MOTOR

Applications of fuzzy logic have rapidly increased in control engineering for production systems, public transportations and consumer products in Japan. Furthermore, expectations for new advance of fuzzy logical thinking and its applications in various fields have arisen. This report focuses not only on these current activities, but also on future scope of such activities

CONCLUSION:

In this paper we have given the introduction of the fuzzy logic and also the Advantages compared with the conventional control methods. Mainly we have explained how the fuzzy control is different from the Boolean logic. we have also given the applications of the fuzzy logic. In the applications we have explained the speed control of DC motor by using fuzzy logic.

REFERENCE

[I] Chan, C. C., Low Cost Electronic Controlled Variable Speed Reluctance Motors, IEEE Transactions on Industrial Electronics, Vol. IE-34, No. I. 95-100. February 1987. Khoei, A.. Hadidi, Kh., Microprocessor Based Closed-Loop Speed Control Systeni For DC Motor Using Power Mosfet. [2]Electronics Circuits and Systems IEEE lnternational Conference ICECS’96, Vol. 2, 1247-1250, 1996.[3] 0. Kaynak. G.Armagan, Otomasyon Magazine. “A new approach for process control: Fuzzy Logic”, July-August I592.[4] C. Elmas, “Fuzzy Logic Controllers”, Seqkin Publishing, April-2003.[5] J.Klir. George, Yuan, Bo. :“Furry Sets and Fuzzy Logic- Theory and Applications” [6] L. A. Zadeh, ‘‘ Fuzzy Sets“ Informal Control, ~01.8p, p 338- 353, 1965.[7] L. A. Zadeh, ‘. Outline of a new approach to the analysiscomplex systems and decision processes” IEEE Trans. Syst. Man Cybem, vol. SMC-3, pp. 2844, I973[8] Y. Tipsuwan, Y. Chow, “Fuzzy Logic Micmcontroller Implementation for DC Motor Speed Control”. IEEE. 1999.[9] F. Rahman, .‘Lectures 18 Control of E€-DC Conveners”, Power Electronics. ELEC424019240.