Performance of Fuzzy-Logic Based Indirect

Vector Control for Induction Motor Drive Rabinarayana Parida, M.I.E.E.E, BPUT,Orissa,rabiparida@rediffmail.com

Prof. K.B. Mohanty, M.I.E.E.E,NIT,Rourkela,barada5@rediffmail.com

Abstract-This paper presents a novel speed control scheme of an

induction motor (IM) using fuzzy-logic control. The fuzzy-logic

controller (FLC) is based on the indirect vector control. The

fuzzy-logic speed controller is employed in the outer loop. The

complete vector control scheme of the IM drive incorporating

the FLC is experimentally implemented using a digital signal

processor board DS-1104 for the laboratory 1-hp squirrel-cage

IM. The performances of the proposed FLC-based IM drive are

investigated and compared to those obtained from the

conventional proportional-integral (PI) controller-based drive

both theoretically and experimentally at different dynamic

operating conditions such as sudden change in command speed,

step change in load etc. The comparative experimental results

show that the FLC is more robust and, hence, found to be a

suitable replacement of the conventional PI controller for the

high-performance industrial drive applications.

Index Terms - Digital signal processor, fuzzy-logic

controller, induction motor, PI controller, real-time

implementation, speed control.

1. INTRODUCTION

AC MOTOR drives are used in a multitude of industrial and

process applications requiring high performances. In high-

performance drive systems, the motor speed should closely

follow a specified reference trajectory regardless of any load

disturbances, parameter variations, and model uncertainties.

In order to achieve high performance, field-oriented control

of induction motor (IM) drive is employed [1]. However, the

controller design of such a system plays a crucial role in

system performance. The decoupling characteristics of

vector-controlled IM are adversely affected by the parameter

changes in the motor. The motor-control issues are

traditionally handled by fixed-gain proportional-integral (PI)

and proportional-integral-derivative (PID) controllers.

However, the fixed-gain controllers are very sensitive to

parameter variations, load disturbances, etc. Thus, the

controller parameters have to be continually adapted. The

problem can be solved by several adaptive control techniques

such as model reference adaptive control (MRAC) [2],

sliding-mode control (SMC) [3], variable structure control

(VSC) [4], and self-tuning PI controllers [5], etc. The design

of all of the above controllers depends on the exact system

mathematical model. However, it is often difficult to develop

an accurate system mathematical model due to unknown load

variation, unknown and unavoidable parameter variations

due to saturation, temperature variations, and system

disturbances. In order to overcome to above problems,

recently, the fuzzy-logic controller (FLC) is being used for

motor control purpose [7]–[12]. The mathematical tool for

the FLC is the fuzzy set theory introduced by Zadeb [6]. As

compared to the conventional PI, PID, and their adaptive

versions, the FLC has some advantages such as: 1) it does not need any exact system mathematical model; 2) it can

handle nonlinearity of arbitrary complexity; and 3) it is based

on the linguistic rules with an IF-THEN general structure,

which is the basis of human logic. However, the application

of FLC has faced some disadvantages during hardware and

software implementation due to its high computational

burden [7]. The earlier reported works for fuzzy-logic

applications in motor drives [8]–[11] are mainly theoretical

and based on either simulation or experimental results at low-

speed operating conditions. This paper investigates the

successful application of the FLC for normal speed control of

IM drives. The complete vector control scheme of IM

incorporating the FLC has been successfully implemented in

real time using digital-signal-processor (DSP) controller-

board DS1104. The performances of the proposed drive have

also been compared with those obtained from the

conventional PI controller both theoretically and

experimentally. It is found that the proposed FLC is

insensitive to temperature changes, inertia variations, and

load torque disturbances. This novel FLC could be a suitable

replacement for the conventional PI controller for high-

performance drive systems.

II. DESIGN OF FLC FOR IM

For the proposed FLC, the speed error and rate of change of

the speed error are considered as the input linguistic variables

and the torque-producing current component is considered as

the output linguistic variable.

Thus, the functional relation of the FLC can be expressed as

[14].

))n(),n(e(f)n(i)n(i rqdiscreteq ω∆∆=∆∫= (9)

where )1n()n()n(e rr −ω∆−ω∆=∆ is the change of

speed error, is the present sample of speed error,

)n()n(w)n( r

*

rr ω−=ω∆ is the sample of speed error,

)1n(r −ω∆ past sample of speed error, is the present sample

of actual, speed, )n(*

rω is the present sample of command

speed, and f denotes the nonlinear function. The main goal of

the control system is to track the command speed by

providing the appropriate torque-producing current

component iq depending upon the operating conditions. In

real time, the motor position information and output of the

FLC, which is considered as the command q-axis current, as

well as the command d-axis current, are used to get the

command phase current and using (5). The electrical position

of the motor can be expressed as

slre θ+θ=θ

where eθ is the rotating field position, rθ is the rotor

position due to slip speed, and slθ is the slip position due to

slip speed. In the next step, the scaling factors Kω, Ke and Ki

are chosen for fuzzification, as well as for obtaining the

actual output of the command current. These scaling factors

play a vital role for the FLC. The factors Kw and Ke are

chosen to normalize the speed error wn∆ , and the change of

speed error en∆ , respectively, so that these remain within the

limit of 1± . Factor Ki is so chosen that one can get the rated

current for rated conditions. Here, the constants are taken as

Kw = *rw (command speed), Ke =10, and Ki = 10 in order

to get the optimum drive performances. After selecting the

scaling factors, the next step is to choose the membership

function of and,nrn ew ∆∆

*

qni , which perform the

important task of the FLC. The membership functions used

for the input and output fuzzy sets are shown in Fig.2.

The trapezoidal functions are used as membership functions

for all the fuzzy sets except the fuzzy set ZE (zero) of the

input vectors. The triangular membership functions are used

for the fuzzy set ZE of the input vectors and all the fuzzy sets

of the output vector. The trapezoidal and triangular functions

are used to reduce the computation for online

implementation. Mathematically, the trapezoidal membership

function can be defined as

Trapezoidal: f (x; a, b, c, d) =

The triangular membership function can be obtained from the

trapezoidal function by setting b = c. The rules used for the

proposed IM specific FLC algorithm are shown in Table I.

Based on the above rules, the fuzzy-rule-based matrix is

shown in Table II. For this study, Mamdani-type fuzzy

inference is used [14]. The values of the constants,

membership functions, fuzzy sets for the input/output

variables, and the rules used in this study are selected by trial

and error to obtain the optimum drive performance. In this

study, the center of gravity defuzzification is used [15]. The

output function is given as

output i =

∑

∑

=

=

µ

µ

N

1k

)k(c

N

1k

)k(c

)i(

)i(i

where N is the total number of rules and )i()k(cµ denotes the

output membership grade for the kth rule with the output

subset C.

III. EXPERIMENTAL IMPLEMENTATION

The proposed FLC-based vector control of IM is

experimentally implemented using DSP-board DS1104

through both hardware and software [16]. The DSP board is

installed in a personal computer (PC) with uninterrupted

communication capabilities through dual-port memory. The

hardware schematic for real-time implementation of the

proposed FLC-based IM drive is shown in Fig.3. The

DS1104 board is based on a Texas Instrument (TI)

Incorporated TMS320C31 32-bit floating-point DSP. The

DSP has been supplemented by a set of on-board peripherals

used in digital control systems, such as A/D, D/A converters,

and incremental encoder interfaces. The DS 1104 is also

equipped with a TI TMS320P14 16-bit micro controller DSP

that acts as a slave processor and provides the necessary

digital input/output (I/O) ports and powerful timer functions

such as input capture, output capture, and pulse width

modulation (PWM) waveform generation. In this study, the

slave processor is used for digital I/O configuration. The

actual motor currents are measured by the Hall-effect

sensors, which have good frequency response and are fed to

the DSP board through the A/D converter. As the motor

neutral is isolated, only two-phase currents are fed back and

the third phase current is calculated from them. The rotor

position is measured by an optical incremental encoder,

which is mounted at the rotor-shaft end. It is then fed to the

DSP board through an encoder interface. The encoder

generates 4096 pulses per revolution. By using a fourfold

pulse multiplication, the number of pulses is increased to 4 ×

4096 in order to get better resolution. A 24-bit position

counter is used to count the encoder pulses and is read by a

calling function in the software.

The motor speed is calculated from the rotor position

by backward difference interpolation. A digital moving

average filter is used to remove the noise from the speed

signal.

The input vectors of the FLC are generated from the present

and the delayed samples of the speed error. The command

currents are generated from the FLC. The hysteresis current

controller compares the command currents with the

corresponding actual motor currents and generates the logic

signals, which act as firing pulses for the inverter switches.

Thus, these six PWM logic signals are the output of the DSP

board and fed to the base drive circuit of the inverter power

module. The D/A channels are used to capture the necessary

output signals in a digital storage oscilloscope. The complete

IM drive is implemented through software by developing a

program in high-level American National Standards Institute

(ANSI) “C” programming language. The program is

compiled by the TI “C” code generator. Finally, the program

is downloaded to the DSP controller board using loader

program LD31 [16]. The sampling frequency for

experimental implementation of the proposed FLC-based IM

motor drive system is 5 kHz.

IV. RESULTS AND DISCUSSIONS

Several tests were performed to evaluate the performance of

the proposed FLC-based vector control of the IM drive

system both theoretically and experimentally. The speed-

control loop of the drive was also designed, simulated, and

experimentally implemented with the PI controller in order to

compare the performances to those obtained from the

respective FLC-based drive system. The speed responses are

observed under different operating conditions such as a

sudden change in command speed, step change in load, etc.

Some sample results are presented in the following section.

The PI controller is tuned at rated conditions in order

to make a fair comparison. Figs. 4 and 5 show the simulated

starting performance of the drive with PI-and FLC-based

drive systems, respectively. Although the PI controller is

tuned to give an optimum response at this rated condition, the

fuzzy controller yielded better performances in terms of

faster response time and lower starting current. Fig.6 (a) and

(b) shows the speed responses of the drive system using the

PI and FLC, respectively, with a step change in the reference

speed. It is evident from Fig.6 (a) and (b) that the proposed

FLC-based IM drive system can follow the command speed

without any overshoot and steady-state error. Thus, the FLC-

based drive system is not affected by the sudden change of

the command speed. Thus, a good tracking has been achieved

for the FLC, whereas the PI-controller-based drive system is

affected with the sudden change in command speed. Fig.7(a)

and (b) shows the speed responses for step change in the load

torque using the PI and fuzzy controller, respectively. It is to

be noted that, in Fig.7 (a), the vertical scale for current iq is

to be divided by five and, in Fig. 7(b), it is to be divided by

four. The motor starts from standstill without load and, at t

=0.8s, a sudden full load is applied. The motor speed follows

its reference with zero steady-state error and a fast response

using a fuzzy controller. On the other hand, the PI controller

shows steady-state error with a high starting iq current. It is

to be noted that the speed response is affected by the load

conditions. This is the drawback of a PI controller with

varying operating conditions.

The simulated results are verified by the experimental results.

Fig.8 (a) and (b) shows the experimental speed responses of

the drive system using the PI and FLC, respectively. It is to

be noted that the fuzzy controller gives better responses in

terms of overshoot, steady-state error, and fast response. The

experimental speed responses with step increase in command

speed are shown in Fig. 9(a) and (b) for the conventional PI

and the proposed FLC-based IM drive system, respectively.

These figures also show that the FLC-based drive system can

handle the sudden increase in command speed quickly

without overshoot, undershoot, and steady-state error,

whereas the PI-controller-based drive system has steady-state

error and the response is not as fast as compared to the FLC.

Thus, the proposed FLC-based drive has been found superior

to the conventional PI-controller-based system.

VI. CONCLUSION

A novel FLC- based indirect vector control of an IM has

been presented in this paper. The FLC has been designed for

a speed-control loop. The simulation has been carried out

using the Simulink Fuzzy Logic Toolbox Manual Guide [17].

The complete IM drive incorporating the FLC has been

successfully implemented in real time using a DSP controller

board DS 1104 for the prototype 1-hp motor. In order to

minimize the real-time computational burden, simple

membership functions and rules have been used. Since exact

system parameters are not required in the implementation of

the proposed controller, the performance of the drive system

is robust, stable, and insensitive to parameters and operating

condition variations. In order to prove the superiority of the

FLC, a conventional PI-controller-based IM drive system has

also been simulated and experimentally implemented.

The performance has been investigated at different dynamic

operating conditions both theoretically and experimentally. It

is concluded that the proposed FLC has shown superior

performances over the PI controller.

APPENDIX

MOTOR PARAMETERS

1hp, 3φ, 208 V, 50 Hz, 3.4 A, P =4, Rs= 4.0Ω, R

r =

1.143Ω, Ls = 0.3676 H, L

r = 0.3676 H, L

m = 0.3489, J

m =

0.03Kg.m2, Bm = 0.00098 (N.m)/rad/s.

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