dSPACE Implementation of Improved Indirect Field-oriented

Control of Asynchronous Motor using Adaptive Fuzzy Proportional

Integral Controller for Electric Vehicle Applications

Chaymae Laoufi

1

, Ahmed Abbou

2

, Mohammed Akherraz

3

and Zouhair Sadoune

4

Electrical Engineering Department, Mohammed V University, the Mohammadia School of Engineers, Rabat, Morocco

4

Department of Physics, Faculty of Science, Ibn Tofail University, Kenitra, Morocco

abbou@emi.ac.ma,

akherraz@emi.ac.ma

sadoune.zouhair@gmail.com

Keywords: Indirect Field-Oriented Control; Adaptive Fuzzy Proportional Integral Controller; Adaptive Gains;

Conventional Proportional Integral Controller

.

Abstract:

This paper deals with the Indirect Rotor Field-Oriented Control of asynchronous motor whose speed

is controlled by an a

daptive fuzzy proportional integral controller. This motor drive is used to propel

an electric vehicle. The design and the experimental implementation of the

Adaptive Fuzzy

Proportional Integral Controller

are presented. This controller is proposed as a solution to compensate

for the effect of the variation of the machine parameters and the external conditions. The

characteristic of this controller is its capacity to adapt in real time its gains in order to reject the

machine parameter disturbances. A series of experimental tests were performed to test the

performance the improved drive using the proposed controller. Simulation and Experimental

results showed the high-speed tracking and the rejection disturbance capacity of the adaptive fuzzy

proportional integral controller.

This paper presents the design and the experimental implementation of the adaptive fuzzy proportional integral

controller applied to the indirect field oriented control of asynchronous motor used to

propel the electric vehicle. This intelligent controller is proposed to reduce the impact of the

variation of the machine parameters and the external conditions on the performances of the drive,

and so, to improve the performances of the electric vehicle control. The experimental

implementation was carried out using dSPACE system and the experimental results showed the

high-speed tracking and the rejection disturbance capacity of the adaptive fuzzy proportional

integral controller compared to the conventional proportional integral controller.

1 INTRODUCTION

Limitations on the emission of greenhouse gases and

the traffic restrictions in the urban areas imposed by

the environmental protection requirements have

given a strong impulse toward the development of

electrical propulsion systems for electric vehicles.

The robustness, the high power-to-weight ratio, the

low cost and the ease of maintenance make the use of

the asynchronous motor advantageous in a propulsion

chain of an electric vehicle.

High efficient drives are indispensable in automotive

applications. The indirect field-oriented control

(IFOC) is an established strategy for high dynamic

performance induction motor drives.

Laouﬁ, C., Abbou, A., Akherraz, M. and Sadoune, Z.

dSPACE Implementation of Improved Indirect Field-Oriented Control of Asynchronous Motor using Adaptive Fuzzy Proportional Integral Controller for Electric Vehicle Applications.

DOI: 10.5220/0009775601370144

In Proceedings of the 1st International Conference of Computer Science and Renewable Energies (ICCSRE 2018), pages 137-144

ISBN: 978-989-758-431-2

Copyright

c

137

Its characteristic is the decoupling of the torque and

the flux and hence the fast torque response. This

command was proposed by Blaschke and Hasse in

early 1970s. Since then, great efforts have been made

to improve the performance and the robustness of this

drive (Abbou , et al, 2009) (Bennassar , et .al 2013).

The proportional integral (PI) controller is the

conventional controller used in said command.

However, the linearity, the sensitivity to the variation

of the machine parameters and the incapacity to

control the nonlinear systems are major weaknesses

of this controller (Khuntia, et .al, 2009),( Singh, G , et

.al 2014).

The adaptive fuzzy proportional integral controller

has been developed to correct these problems. Its

ability to adjust its gains when a disturbance of the

machine parameters occurred makes it the most

recommended controller to deal with systems subject

to disturbances (M.Masiala; et.al, 2006), ( Chebre,

et.al; 2007).

In this paper, we present the model and the

experimental implementation of the indirect rotor

field-oriented control using both the adaptive fuzzy

proportional integral controller and the conventional

proportional integral (PI) controller. Experimental

results are presented to highlight the improved

performances of the drive obtained by using the

adaptive fuzzy proportional integral controller in

comparison with the conventional PI controller.

2 INDIRECT ROTOR FIELD-

ORIENTED CONTROL

The principle of the field oriented control (FOC) is

based on the separate control of the torque and the

flux in similarity to the DC machine with separate

excitation. The algorithm of the indirect rotor field-

oriented control (IRFOC) is based on the orientation

of the rotor flux Φ

r

on the direct axis of the rotating

reference frame.

This implies (Abbou, et.al, 2009):

Φ



 Φ



and Φ



0 (1)

By applying this principle, the expressions of the

rotor flux and the torque are given by the following

equations:

If Φ



is constant [3]:

Φ



Mi



(2)

C



p







Φ



i



(3)

These equations show that decoupling between the

flux and the torque is ensured. In fact, the magnitude

of the rotor flux Φ

r

is determined only by the direct

component of the stator current i

sd

while the

electromagnetic torque C

em

is determined by the

quadrature component of the stator current i

sq

.

The block diagram of speed regulation by the indirect

rotor field oriented control of an induction motor

intended for a propulsion chain of an electric vehicle

is presented by the Figure1.

Figure 1: Structure of IRFOC used in a propulsion chain of

an electric vehicle.

3 ADAPTIVE FUZZY

PROPORTIONAL INTEGRAL

CONTROLLER

The adaptive fuzzy proportional integral controller is

a hybrid controller including a conventional

proportional integral (PI) controller and a fuzzy logic

regulator. This controller is developed with the aim of

ensuring a robustness with respect to the variation of

the machine parameters and the experimental

conditions by tuning in real time the gains of the

conventional PI controller via a fuzzy logic regulator.

As shown by its architecture (Figure 2), the fuzzy

logic controller compares the measured rotor speed

with the desired speed and generates the adaptive

factors ∆K



and ∆K



. These are used to calculate the

new gains of the conventional PI controller according

to the following algorithm:

Kp



i  1  Kp



i  ΔKpi (4)

Ki



i  1  Ki



i  ΔKii (5)

ICCSRE 2018 - International Conference of Computer Science and Renewable Energies

138

Figure 2: Design of adaptive fuzzy proportional integral

controller.

The structure of the fuzzy logic regulator is

determined as follows:

The input variables; the speed error (e

ω

) and its

derivative (







); are described by the following

linguistic variables:

 HN : High Negative

 AN : Average Negative

 LN : Low Negative

 Z : Zero

 LP : Low Positive

 AP : Average Positive

 HP : High Positive

The output variables; ∆K



and ∆K



; are described

by:

 H : High

 L : Low

The membership functions for input and output

variables are defined in the interval [-0.1 0.1] as

follows: (Laoufi , et.al, 2013), (Laoufi , et.al, 2014):

Figure 3. Membership functions for input variables

Figure 4. Membership functions for output variables

The adaptive factors; ∆K



and ∆K



; are calculated by

the bases rules described in Tables 1 and 2.

Table 1: Matrix inference used to control the output

variable ΔK

p

HN AN LN Z LP AP HP

HN H H H H H H H

AN L H H H H H H

LN L L H H H L L

Z L L L H L L L

LP L L H H H L L

AP L H H H H H L

HP H H H H H H H

dSPACE Implementation of Improved Indirect Field-Oriented Control of Asynchronous Motor using Adaptive Fuzzy Proportional Integral

Controller for Electric Vehicle Applications

139

Table 2: Matrix inference used to control the output

variable ΔKi

HN AN LN Z LP AP HP

HN H H H H H H H

AN H H L L L H H

LN H H H L H H H

Z H H H L H H H

LP H H H L H H H

AP H H L L L H H

HP H H H H H H L

4 EXPERIMENTAL RESULTS

AND ANALYSIS

The experimental setup used to implement in real

time the proposed adaptive fuzzy proportional

integral controller applied to the indirect rotor field-

oriented control is shown in Figure 5:

Figure 5. The used test bench

The main components of the used test bench are:

 The squirrel asynchronous motor of 3 KW

power, characterized by the nominal values of

the current, voltage and speed: 7.2A/12.5A,

220V/380V and 1400rpm;

 The two-level voltage inverter type

SEMIKRON;

 The dSPACE acquisition card (DS1104)

comprising a Real-Time Interface (RTI),

which is the link between the

dSPACE hardware and the development

software MATLAB/Simulink/Stateflow from

MathWorks.

 The adaptation card developed to ensure the

compatibility of the dSPACE I/O board with the

inverter and the induction machine.

 The DC motor used to apply a resistive torque.

In order to examine the performance of the adaptive

fuzzy proportional integral controller, a series of

measurement has been accomplished. In the first test,

a step change of 100 rad/s has been applied to the

speed reference. The second test consist to test the

performance of the proposed control in the nominal

reference speed (146 rad/s:1400 rpm). The third and

fourth tests aim to investigate the efficiency of the

proposed controller to reject the perturbation. So, a

resistive torque of 10 N.m has been applied as a

disturbance. In the fifth and sixth tests; and in order

to evaluate the robustness of the control to the change

of direction of rotation of the machine; the speed has

been changed between 100 rad/s and -100 rad/s and

between 10 rad/s and -10 rad/s.

The figures 6 and 7 show the precise speed tracking

and the better stator current signal when using the

adaptive fuzzy proportional integral controller with

less ripples. As shown in figure 7, by the use of the

adaptive fuzzy proportional integral controller, the

current remains a periodic sinusoidal signal. In fact,

from the frequency spectrum of the stator current

(Figures 11), the adaptive fuzzy proportional integral

controller gives a reduced THD (27.35%) compared

to the conventional PI controller (THD=29.98%).

The feature of the adaptive fuzzy proportional

integral controller is its capacity to reject the

disturbances. In fact, unlike the conventional PI

controller, the effect of the perturbation (resistive

torque) is not observed on the speed response of the

indirect rotor field oriented control using the adaptive

fuzzy proportional integral controller (Figure 12 to

15).

Also, the adaptive fuzzy proportional integral

controller gives a fast speed response to the change of

the rotational direction and a good dynamic behavior

even at low speed (Figure 16 and 17). This high

performance of this controller is due to its adaptive

gains which adapt in real time to compensate the

parameter variation as shown in Figure 18.

ICCSRE 2018 - International Conference of Computer Science and Renewable Energies

140

Figure 6. (a) Speed response of IRFOC using the

adaptive fuzzy proportional integral controller;

(b) the conventional PI controller.

Figure 7. (a) Measured stator current of asynchronous

motor controller by IRFOC using the adaptive fuzzy

proportional integral controller; (b) the conventional

PI controller

Figure 8. (a) Speed response of IRFOC using the

adaptive fuzzy proportional integral controller;

(b) the conventional PI controller (case of the

nominal reference speed).

Figure 9. (a) Measured stator current of asynchronous

motor controller by IRFOC using the adaptive fuzzy

proportional integral controller; (b) the conventional

PI controller (case of the nominal reference speed).

dSPACE Implementation of Improved Indirect Field-Oriented Control of Asynchronous Motor using Adaptive Fuzzy Proportional Integral

Controller for Electric Vehicle Applications

141

Figure 10. (a) Measured phase voltage of

asynchronous motor controller by IRFOC using the

adaptive fuzzy proportional integral controller; (b)

the conventional PI controller (case of the nominal

reference speed).

Figure 11. (a) Frequency spectrum of the measured

stator current in the case of using the adaptive fuzzy

proportional integral controller; (b) the conventional

PI controller

5 CONCLUSION

Figure 12. (a) Speed response of IRFOC using the

adaptive fuzzy proportional integral controller;

(b) the conventional PI controller; in the case of

applying a resistive torque.

ICCSRE 2018 - International Conference of Computer Science and Renewable Energies

142

Figure 13. (a) Measured stator current of asynchronous

motor controlled by IRFOC using the adaptive fuzzy

proportional integral controller; (b) the conventional PI

controller; in the case of applying a resistive torque.

Figure 14. (a) Speed response of IRFOC using the adaptive

fuzzy proportional integral controller ; (b) the

conventional PI controller; in the case of applying a

resistive torque in a given time interval.

Figure 15. (a) Measured stator current of asynchronous

motor controller by IRFOC using the adaptive fuzzy

proportional integral controller; (b) the conventional PI

controller; in the case of applying a resistive torque in a

given time interval.

Figure 16. (a) Speed response of IRFOC using the adaptive

fuzzy proportional integral controller; (b) the conventional

PI controller; in the case of changing the direction of

rotation of the machine (high speeds).

dSPACE Implementation of Improved Indirect Field-Oriented Control of Asynchronous Motor using Adaptive Fuzzy Proportional Integral

Controller for Electric Vehicle Applications

143

Figure 17. (a) Speed response of IRFOC using the adaptive

fuzzy proportional integral controller; (b) the conventional

PI controller; in the case of changing the direction of

rotation of the machine (low speeds).

Figure 18. Adaptive gains of the adaptive fuzzy

proportional integral controller K

P

(a), K

I

(b)

5 CONCLUSION

In this paper, the authors propose an intelligent

controller, the adaptive fuzzy proportional integral

controller, to improve the performance of an indirect

rotor field oriented control for induction motor used

in a propulsion chain of an electric vehicle. This drive

has been implemented in real time using dSPACE

Package and the experimental results were

satisfactory. The proposed controller presents a high

performance of speed tracking even in low speeds and

a high capacity to reject the disturbance of the

induction machine parameters.

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