fuzzy logic speed control of dc drive

8/2/2019 Fuzzy Logic Speed Control of Dc Drive

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SESSION:

ANN, FUZZY LOGIC AND GENETIC ALGORITHMS APPLICATION

TO VARIABLE SPEED DRIVES.

FUZZY LOGIC SPEED CONTROL OF DC DRIVE

Presented By

Ch.kalyan chakrawarthi(1/4 c.s.e)

Y.pramodh kumar(1/4 E.CE)

Koneru lakshmaih engineering college, vijayawada

ABSTRACT:

DC motors are used widely in many industrial and domestic applications. For the

proper use of these motors, an efficient speed control system is required. Though there

are many conventional methods, speed control of DC motors using Fuzzy Logic is more



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advantageous and easy compared to all other systems. This paper presents the theory,

design and simulation of a fuzzy logic based controller used for speed control of

separately excited D.C. motor. The FLC algorithm has been simulated on Simulink

toolbox in Matlab. Here two mathematical models of a Dc drive (Linear and non-linear)

are investigated and two controllers (PI and Fuzzy) are used. The first model is build as

linear transfer function of converter and DC motor. The second model is build using

advanced blocks from Power System Block set (PSB) library. The advantages of using

fuzzy and PI controller are analyzed.

KEYWORDS:

Control systems, DC drive, Fuzzy control, MATLAB, Power System Block set.

INTRODUCTION:

Engineers, in general deal with problems that are more complex in nature

where the degree of automation is very important aspect. Though many technologies are

employed for development of sophisticated control systems, Fuzzy logic is one of the



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most successful technologies and at present is being extensively explored as a means to

develop controllers for plants with complex and often not precisely known models. In

many applications Fuzzy logic controllers perform better than conventional controllers.

Based on the nature of fuzzy human thinking, Professor Lotfi A Zadeh

originated the “Fuzzy logic”, in 1965, according to him: fuzzy logic is mathematical

imprecise description. Fuzzy logic is an innovative technology that enhances

conventional system design with engineering expertise. Using Fuzzy logic, we can avoid

the need for rigorous mathematical solution. Fuzzy logic enables computers to think more

like we do by endowing them with approximate reasoning capabilities. It provides

algorithmic and philosophical framework necessary to exploit the tolerance for

imprecision and arrive at answers to more complex problems.

LINEAR MODEL OF DC DRIVE:

The linear model consists of two parts: converter/rectifier and DC motor. A

linear model of DC motor as shown in fig. was build using Simulink blocks. There are

two inputs (voltage and load) and two outputs (angular motor velocity and current).

Its parameters are computed automatically from nominal catalogue data:

motor power, voltage, current, speed, etc). It is very convenient to use nominal motor

data as rotor inductance and resistance.

Converter/rectifier is described as first order inertia



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kp(s)

Gconv = --------------

Tmip·s +1

Where kp -gain of converter/rectifier, Tmip -mean dead time of converter/rectifier

The dead time Tmip may vary from zero to one-half the period of an AC

source (0.01s for 50 Hz). It is assumed that six-phase thyristor bridge with mean dead

time Tmip=1.67ms is used in the Converter.

Simple transfer function model of motor current vs. voltage was used

kia

Gmot = ------------Ta·s +1

Where: kia - gain of DC motor, Ta - armature circuit time constant

USING PSB TO MODEL THE DC DRIVE:

An advanced set of linear and nonlinear blocks can be found in Power System

Blockset . Three AC sources, three-phase six-pulse converter, pulse generator and DC

motor are taken from the library. They are used to prepare high quality model of three-

phase DC drive. The three-phase bridge converter is the most frequently used motor

control system. Two of six thyristors conduct at any time instant. Gating of each thyristor

initiates a pulse of load current; therefore this is a six pulse controlled rectifier. The three-

phase six-pulse rectifier is also capable of inverter operation in the fourth quadrant.

FUZZY LOGIC:

FUZZY SETS:

The fuzzy logic problem can be defined as an input/output, static, non-

linear mapping problem through a “black box “, as shown in the following fig.



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All the input information is defined n the input space, it is processed in the

black box, and the solution appears in the output space. In general, mapping can be static

and dynamic, and the mapping characteristics are determined by the black box‟s

characteristics. The black box cannot only be a fuzzy system, but also neural network,

general mathematical system such as differential equations, algebraic equations. If X is

the universe of discourse and its elements are denoted by x, then a fuzzy set A in X is

defined as a set of ordered pairs.

A = {( x, A( x)), | x

Where A( x) is called the membership function (or MF) of x in A. The

membership function maps each element of X to a membership value between 0 and 1.

3.4 IF-THEN RULES:

Fuzzy sets and fuzzy operators are the subjects and verbs of fuzzy logic.

These if-then rule statements are used to formulate the conditional statements that

comprise fuzzy logic. A single fuzzy if-then rule assumes the form

if x is A then y is B

Where A

and B

are linguistic values defined by fuzzy sets on the ranges X and Y. The if-

part of the rule “ x is A” is called the antecedent or premise, while the then-part of the rule

“ y is B” is called the consequent or conclusion.

MEMBERSHIP FUNCTIONS:



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A membership function (MF) is a curve that defines how each point in the

input space is mapped to a membership value (or degree of membership) between 0 and

1. A membership function can different shapes such as Triangular, Trapezoidal,

Gaussian, Two-sided Gaussian etc. The simple and most commonly used MF is the

Triangular type.

FUZZY INFERENCE SYSTEMS:

Fuzzy inference is the process of formulating the mapping from a given

input to an output using fuzzy logic. The mapping then provides a basis from which

decisions can be made, or patterns discerned. The process of FIS involves membership

functions, fuzzy logic operators, and if-then rules. There are two types of fuzzy inference

systems that can be implemented in the Fuzzy Logic

Mamdani-type

Sugeno-type

These two types of inference systems vary somewhat in the way outputs are

determined. Fuzzy inference systems have been successfully applied in fields such as

automatic control, data classification, decision analysis, expert systems, and computer

vision. Because of its multidisciplinary nature, fuzzy inference systems are associated

with a number of names, such as fuzzy-rule-based systems, fuzzy expert systems, fuzzy

modeling, fuzzy associative memory, fuzzy logic controllers, and simply fuzzy systems.

Mamdani‟s method was among the first control systems built using fuzzy set theory. It

was proposed in 1975 by Ebrahim Mamdani. Mamdani‟s effort was based on Lotfi

Zadeh‟s 1973 paper on fuzzy algorithms for complex sys tems and decision processes.

Mamdani-type inference expects the output membership functions to be fuzzy sets. After



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the aggregation process, there is a fuzzy set for each output variable that needs

defuzzification

In the Fuzzy Logic Toolbox, there are five parts of the fuzzy inference process:

Fuzzification of the input variables

Application of the fuzzy operator in the antecedent

Implication from the antecedent to the consequent

Aggregation of the consequents across the rules

Defuzzification.

Depending on these guidelines, both the inputs are fuzzified. Seven fuzzy variables

(Linguistic variables) are selected for the speed control of a separately excited DC Motor.

These variables are

Negative Big - NB

Negative Medium - NM

Negative Small - NS

Zero - ZE

Positive Small - PS

Positive Medium - PM

Positive Big - PB

The word „Negative‟ indicates that actual speed is greater than the reference

speed and so the error is negative. Similarly, the word „Positive‟ indicates that actual

speed is less than the reference speed and so the error is positive. The terms Big, Medium

and Small indicate the degree or range by which the Positive or Negative error varies



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from the actual value.

Depending on these IF-THEN rules, the fuzzy logic controller performs the

specified operation on the system giving required outputs. But writing of these rules

generally depends on the experience and commonsense of the person who is writing

those rules.

CHANGE IN ERROR

ERROR PB PM PS ZE NS NM NB

NB NB NB NB NM NS NS ZE

NM NB NM NM NM NS ZE PS

NS NB NM NS NS ZE PS PM

ZE NB NM NS ZE PS PM PMPS NM NS ZE PS PS PM PB

PM NS ZE PS PM PM PM PB

PB ZE PS PS PB PM PB PB

RULE BASE

DEFUZZIFICATION:

The conversion of fuzzy output to crisp output is defined as Defuzzification. There are

four methods of Defuzzification. They are

Center of area (COA) method

Height Method

Mean of maxima (MOM) method

Sugeno Method.

MODELLING AND SIMULATION RESULTS:

A classic DC drive with two PI controllers is presented on the following figure.



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Fig. DC drive with PI controllers in current and velocity loop. Linear transfer function

models are used

The simulation results (DC motor current and angular velocity vs. time) are

presented on the following figure. This is raw simulation as linear model has very low

granularity: AC component of current and switching of currents in Thyristor Bridge are

neglected. Only envelope of transients can be seen on simulation output.

Fig. Current and angular velocity of DC motor. Simulink and linear model

were used for simulation.

The following figure shows DC drive with PI controllers in current and velocity

loops. The Synchronized 6-Pulse Generator block can be used to fire the six thyristors of

a six-pulse converter. The output of the block is a vector of six pulses individually

synchronized on a three-phase commutation voltage. The pulses are generated alpha

degrees after the increasing zero crossings of the commutation voltages.



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Fig. DC drive with PI controllers in current and velocity loops. Power System Blockset isused to build advanced drive model.

The simulation results (current and angular velocity vs. time) are presented on figure.

Fig. Simulated current signal and angular velocity using Simulink and PSB

Linguistic variables and rules:

There are two fuzzy variables (error and INTEG error) and seven linguistic

variables (from big negative to big positive). The fuzzy controller attributes are:

Type: 'Mamdani'

And Method: 'prod'

Or Method: 'max'

DefuzzMethod: 'centroid'

Imp Method: 'prod'

AggMethod: 'max'

Input: [1x2 struct]



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Output: [1x1 struct]

Rule: [1x25 struct]

The membership functions (pimf and gausmf are used) and rules are design tools that

give opportunity to model a control surface and controller properties. It is obvious that

using this attributes one can more precisely fulfill a quality criterion in full operational

range. The control surface is defined with 25rules. The following figure shows DC drive

with Fuzzy controllers in current and velocity loops. The simulation results of the motor

current with PI controller and Fuzzy controller shown in the following fig.

Fig. DC drive with fuzzy controller in current loop. Power System Block set is used to

build advanced drive model



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CONCLUSION:

Both simple transfer function model and the advanced set of linear and

nonlinear blocks from Power System Blockset are useful for tuning the DC drive control

system. Advanced models build with Power System Blockset blocks are suitable for

preliminary verification of control system, as AC component of current and switching

phenomena of thyristor bridge are not neglected. The fuzzy controller is more difficult to

design comparing with PI controller but has more design parameters and is more suitable

to fulfill nonlinear quality criterion in all operational range. For real time operation a

discrete fuzzy algorithm can be implemented on microcomputer, or DSP chip, which is

more suitable for industrial application.

REFERENCES:

Fundamentals of Electrical Drives -Gopal K. Dubey

Modeling and Fuzzy Control of DC Drive

-Ghent, pp 186-190 ,Bogukila Mrozek, Zbigniew Mrozek,

Modern Power Electronics and AC Drives - Bimal K.Bose

Design and testing control system for DC-drive using Simulink and

Power System Blockset (in Polish), B. Mrozek, 2-nd National Conference

Methods and Computer System, pp 185-190, Krakow, Oct. 1999, Poland.

Fuzzy Logic – An introduction -Steven D.Kaehler

Fuzzy Logic Toolbox User‟s Guide-The Mathworks ,Inc. 1995-1998



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fuzzy logic speed control of dc drive

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