Fuzzy Logic Controller-based High Performance Induction Motor Drive

S. Behera B. Subudhi S.P.Das & S.R.Doradla UCE, Burla IGIT, Sarang IIT Kanpur

Abstract: A Fuzzy Logic Controller (FLC) is proposed for high performance induction motor drive. The d-q model of the complete scheme is formulated and analyzed by MATLAB simulator with FLC. Later, the scheme is implemented on a laboratory-sized experimental setup. The simulation and experiment are carried out under identical operating conditions and presented. The comparison of simulation and experimental results under identical operating conditions reveals good agreement.

Keywords:- Fuzzy Logic Controller, MATLAB simulation, and Direct torque control (DTC) Scheme

I. INTRODUCTION

Commencing from domestic appliances to

industries, the induction motors are widely used

because of their simplicity, ruggedness and

robustness. The operational schemes of variable

voltage and variable frequency are generally

used for speed and torque control of induction

motor drives. The literature related to ac drives

covering scalar control, field-oriented control,

and direct-torque control (DTC) has been well

documented [1-5]. In all these schemes, a better

torque profile and current response have been

focused. The conventional proportional-integral

controller (PI), which acts as one of the major

part of these controlling processes, plays crucial

role during mathematical modeling and

implementation. These PI controllers have got

certain limitations. It has no reach to the systems,

which are quite complex and the mathematical

models are not available. In such a situations,

Fuzzy Logic has much importance to act. Fuzzy

Logic [5,6] has emerged as one of the active

areas of research particularly in control

applications. Whenever a logic in the spirit of

human thinking can be introduced, Fuzzy Logic

controller (FLC) finds reliable applications there.

The applications of FLC to vector oriented

controlled induction motor have been

documented [8,9]. In this paper, the application

of fuzzy logic controller in field of high

performance drives has been illustrated with

some prominent simulation and experimental

results. Typical performance results under a step

change of load torque and speed reversal for both

simulation and implementation are presented and

are seen to be in good agreement.

II. FUZZY LOGIC CONTROLLER IN DRIVES

In most dc/ac drives, speed regulation is

required with certain precisions. Obviously these

types of systems operate on close-loop basis

which constitute mostly different sensors (speed,

voltage, current), control blocks (i.e.,

analog/digital) and the power-handling unit. The

performance of the system in terms of their

control system parameters (i.e., settling time,

rising time, overshoot) mostly depends upon the

electrical, mechanical time constants of the

drives and the type of control techniques

employed. When the motor in such a drive is of

high inertia and low electrical time constant, the

transient performance such as speed is not up to

expectation with conventional PI controller.

With PID (proportional-integral-derivative)

controller, though the transient performance can

be improved slightly, but it is still far beyond the

expected one. In such a case, fuzzy logic

controller can be utilized for improvements of

both transient and steady state performances. The

fuzzy logic control approach for dc/ac motor

drive systems is very useful since exact

mathematical model of it is not required. The

function of fuzzy logic controller is to transform

linguistic rules into control strategy based on

expert knowledge.

The fuzzy logic control system (Fig.1) can

be divided into four main functional blocks

namely Knowledge base, Fuzzification, Inference

mechanism and Defuzzification. The knowledge

base is composed of data-base and rule-base. The

data-base, consisting of input and output

membership functions, provides information for

appropriate fuzzification operations, the

inference mechanism and defuzzification. The

rule-base consists of a set of linguistic rules

relating the fuzzy input variables to the desired

control actions. Fuzzification converts a crisp

input signal, the error (e), and error change (∆e)

into fuzzified signals that can be identified by

level of membership in the fuzzy sets. The

inference mechanism uses the collection of

linguistic rules to convert the input conditions to

fuzzified output. Finally, the defuzzification

converts the fuzzy outputs to crisp control

signals, which in the system acts as the changes

in the control input that finally drives the motor.

This control input may be change in torque, flux,

or speed component. The typical input

membership functions for speed error and

change in speed error are shown in Fig. 2 and

Fig. 3 respectively, whereas the output

membership function for change in control input

is shown in Fig. 4. The output generated by

fuzzy controller must be crisp which is used to

control the drive unit and this is accomplished

by the defuzzification block. Knowledge base

involves defining the rules represented as a set of

if then rules, for example if speed error is

positive large ‘PL’ and error change is negative

large ‘NL’, then change in output is zero ‘Z’.

The control rules are evaluated by inference

mechanism. Various fuzzy control rules are

shown in [6,7]. Many defuzzification strategies

are available [6], such as, the weight-average

criterion, the mean-max membership, and center-

of-area (centroid) method. The defuzzification

technique using centroid method is summarized

as follows and shown in Fig. 5. Suppose the

speed error is –20 and change in speed error is -

17, then e will be NS and the change in error will

be PS or PL. So the rules relating the error and

error rate are :-

If ‘e’ is ‘NS’ and ‘de’ is ‘PS’ then change in

control input is ‘Z’ (Fig. 5(a)).

If ‘e’ is ‘NS’ and ‘de’ is ‘PL’ then change in

control input is ‘PS’ (Fig. 5(b))

By using the most commonly used

defuzzification method, i.e. the Center-of-area

method, the defuzzified values of increment in

control inputs are obtained as

C* = (C1 * A1 + C2 * A2)/ (A1+A2)

where C1, A1: - Centroid and area derived

(i.e., from Fig. 5(a)) from membership function

for change in control input. Similarly, C2 and A2

represent centroid and area shown in Fig. 5(b).

III. HARDWARE IMPLEMENTATION.

The hardware implementation of the proposed

scheme has been shown in Fig. 6. The hardware

section comprises IGBT drivers for inverter,

current sensors, voltage sensor, and a tacho-

generator coupled with induction motor-dc

generator set. The data-acquisition card used is

ACL-8112PG and it has been attached to a PC

having Pentium- IV CPU. PI-type FLC replaces

the conventional PI controller. The control

strategies of conventional DTC scheme are given

in [1-3].

IV. SIMULATION AND EXPERIMENTAL RESULTS

The FLC-based DTC scheme incorporating

the inverter is modeled and analyzed in

MATLAB simulator. The input dc voltage is 120

V. The simulation is carried out to observe

various responses for step changes in speed, load

torque. Typical simulation results are given in

Figs. 7-10.

Figure 7 shows the response of speed with

conventional PI and PI-type FLC under both

forward and reverse directions. It is observed that

performance with PI-type FLC is better than

conventional controller. In Figs.8-10, both

simulations and experimental results of FLC-

based DTC scheme were presented for different

conditions. Fig.8 and Fig.9 show the responses of

stator flux and speed respectively from rest with

and without load torque. It may be noted that

both stator flux and rotor speed are unaffected

when a load torque of 1 N-m is subjected for a

small interval. The responses of speed and flux

from both simulation and experiment during four-

quadrant operation for a reference speed of 500

rpm are shown in Fig. 10. It may be noted that

response of flux is undisturbed during four-

quadrant operation.

V. CONCLUSION

The PI-type FLC has been successfully

implemented in a direct torque controlled

induction machine. The complete setup has been

analyzed with MATLAB simulator. The transient

and steady state responses from FLC-based DTC

scheme have shown good agreement between

simulation and experimental results. The

performance of the given drive indicates that such

controller will find wide range of application in

ac drives [8, 9] as far as automation is concerned

due to better performance, high reliability,

robustness and cheapness.

REFERENCES

[1] I. Takashashi and T. Nouguchi, “A new quick-response and high-efficiency control strategy of an induction motor,” IEEE Trans on Industry Applications, vol. IA.22, no. 5, pp. 820-827, Sep/Oct - 1986.

[2] M. Depenbrock, “Direct self-control (DSC) of inverter-fed induction machine,” IEEE Trans on Power Electronics, vol. 3, no. 4, pp. 420-429, Oct- 1988.

[3] J.N. Nash, “Direct torque control, induction motor vector control without encoder,” IEEE Trans on Ind. Appl., vol. 33, no. 2, pp. 333-341, Mar/Apr- 1997.

[4] B.K. Bose, “High performance control of induction motor drives,” IEEE Industrial Electronics Society Newsletter, pp. 7-11, Sep-1998.

[5] S. Behera, S. P. Das and S. R. Doradla, “A novel quasi-resonant inverter for high performance induction motor

drives” IEEE APEC Conference Rec.-2003, vol-2, pp 819- 825. (i.e., Miami, USA)

[6] T.J.Ross, “Fuzzy Logic with Engineering Applications”, Mc Graw-Hill Publications, International Edition, 1997

[7] I.J.Nagrath and M. Gopal, “A Text Book of Control System Engineering”, New Age International Publication, New Edition, 2003

[8] G. C. D. Sousa, B. K. Bose and J. G. Cleland, “Fuzzy logic based on line efficiency optimization control of an indirect vector controlled induction mpotor drive”, IEEE Trans. Ind. Electronics., vol. 42, No .2, pp. 192-198, Apr 1995.

[9] E. Cerruto, A.Consoli, A. Raciti, and A. Testa, “Fuzzy adaptive vector control of induction motor drives,” IEEE Trans. Power Electronics, vol. 12, No. 6, pp. 1028-1039, Nov 1997.

Knowledge Base

Data base Rule basespeed error

e

input ∆u

error change ∆e

Fuzzification Defuzzification Inference Mechanisim

Fig. 1. Functional block diagram of fuzzy logic control

NL PLPSNS 1Z

-40 -30 -20 0 20 30 40

NL PLPSNS 1Z

0 15 50 90-15-50-90∆u ------

Fig.4. Memberhip function for change in control input

e ----- Fig.2. Memberhip function for speed error

NS PS Z

-20 17

C1, A1

NS PL PS

-20 17

C2, A2

NS PS PL1

ZNL

-20 -15 -5 0 5 15 20

(a)

∆e ----- Fig.3. Memberhip function for change in

speed error (b)

Fig. 5. Defuzzificztion technique using centeroid method

FLC

Opt

imum

Switc

hing

Tabl

e

Filter &Amplifier

ψs

ψs*

Te*Nref

N

Te

Sa

ia

Vdc

D-Q-Model

3-Ph

ase

Inve

rter

IM

Sc

Sc

Vdc

ibic

Sb

Sa Sb

Personal Computer (PC)

LIMITER

Two Level HystresisController

Three Level HystresisController

DCG

TG

Sectorstatus

Fig. 6: Hardware Implementation of FLC-based DTC Scheme

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4-60 -50 -40 -30 -20 -10

0 10 20 30 40 50 60

time(sec)

(a) (b) Fig.7. Speed response during when forward and reversal direction

(a) PI controller (b) PI-type FLC Scale: 1:10

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3

60

50

40

30

20

1.4-60

-50

-40

-30

-20

-10

0

10

Speed

time(sec)

0 0.5 1 1.5 2 2.5 3 3.50

1

2

3

4

0 0.5 1 1.5 2 2.5 3 3.50

1

2

3

4

0 0.5 1 1.5 2 2.5 3 3.50

1

2

3

4

0 0.5 1 1.5 2 2.5 3 3.50

200

400

600

800

1000

1200

Rotor speed (rpm)

Load Torque (N-m)

(a)

Ψs, (Wb)

Load Torque (N-m)

(a)

Nr, (rpm)

(Scale: 1000 rpm / 6 V)

Load

Torque (N-m))

(b) Fig. 9. Speed (Nr) responses during step change in reference speed from 0 to 500 rpm and a load torque of 1 N-m is applied by closing load switch for 1 sec. (GLoad – gate pulse of load switch)

(a) Simulation (b) Experimental scale: 1:1 scale: 1:1

Ψs, (Wb)

Load Torque (N-m)

(b) Fig. 8. Flux (Ψs) responses during step change in reference speed from 0 to 500 rpm and a load torque of 1 N-m is applied by closing load switch for 1 sec.

(GLoad – gate pulse of load switch) (a) Simulation (b) Experimental scale: 1:1 scale: 1Wb/5V, 1 N-m/5V

Nr,(rpm)

Ψs,(Wb)

0 0.5 1 1.5 2 2.5 3 3.5-1000

-500

0

500

1000

0 0.5 1 1.5 2 2.5 3 3.50

1

2

3

(a) (b) Fig. 10. Responses of rotor speed (Nr) and stator flux (Ψs) during speed reversal command in reference speed (a) Simulation Result (b) Experimental Scale: 1:1 (Scale: 1000 rpm/ 6V for Nr and 1 Wb/ 5V for Ψs)