Presented by:

Ananto Mukti Wibowo2208 201 009 / 091 d 9859

DEPARTMENT OF COMPUTER SCIENCE AND ELECTRICAL ENGINEERING

GRADUATE SCHOOL OF SCIENCE AND TECHNOLOGY

KUMAMOTO UNIVERSITY

KUMAMOTO – JAPAN

1

INTRODUCTION

LITERATURE REVIEW

METHODOLOGIES

SIMULATION RESULTS

CONCLUSIONS

2

Background

Problem Definition

Research Objectives

3

The influences of internal combustion systems such as cars with gasoline engines become a serious social problem because of the environment pollution.

To alleviate the problems, automobile manufacturers forced to shift their part of productions from pure internal combustion systems to hybrid systems or electric systems.

Electric car uses battery that have dc voltage, because of this, dc motor is usually implemented.

4[4] WADA Masayoshi, “Research and development of electric vehicles for

clean transportation”, Journal of Environmental Sciences 21(2009) 745–749

Disadvatage of dc motor:◦ Often needs regular maintenance

◦ Series motors cannot be used where a relatively constant speed is required under conditions of varying load (not suitable for the hilly environment)

Solution:◦ Induction Motor

5

Induction motor advantages:◦ simple construction

◦ robust

◦ cheaper

◦ easier to maintain

◦ high torque characteristics

6

Induction Motor Speed Dynamics in Electric Car Drive◦ Starting◦ Accelerating◦ Running◦ Decelerating◦ Breaking

Induction motor needs a controller so the dynamic speed conditions can be achieved.

Proposed method: Direct Torque Control using ANN Sliding Mode Control

7

Sp

eed

Time (s)

Starting &

Accelerating Running

Decelerating &

Breaking

Develop an optimized speed controller for a three phase induction motor as an electric car drive based on Direct Torque Control using Artificial Neural Networks Sliding Mode Control.

8

Direct Torque Control (DTC)

Sliding Mode Control (SMC)

Artificial Neural Network (ANN)

9

DTC was presented by I. Takahashi in the middle of 1980’s.

DTC is a control method where the torque and speed are controlled directly based on the electromagnetic state of the motor.

The controlling variables are motor magnetizing flux and motor torque.

With DTC there is no need for modulator which slows down communication between the incoming voltage and frequency signals and the need for the motor to respond to this changing signal.

10

DTC block diagram

11

12

Sliding Mode Control (SMC) is a procedure to design robust controllers for nonlinear processes.

The SMC “reachability” condition is based on the Russian mathematician, Lyapunov, and his theory of stability of nonlinear systems to guarantee the stability of the closed loop system.

The main advantage of SMC is the robustness under uncertainties caused by load torque [3].

13[3] T.B. Reddy, J. Amarnath, D. Subba Rayuddu, "Direct Torque Control of Induction Motor Based on Hybrid PWM Method for

Reduced Ripple : A Sliding Mode Control Approach", ACSE Journal, Volume (6) Issue (4) 2006

Sign

1

Te*

1

s

Integrator

a

h

1/bBetah-a

du/dt d

2

w

1

wr*

SMC block diagram in Simulink

a, b, d are fixed parameters

introduced by friction (B) and

inertia constant (J).

Tuned Parameters:

◦ h determines the sliding surface gain

◦ β guards the trajectory in the sliding

surface

The output of SMC are the

torque reference for the DTC.

14

ANN is an information-processing system that has certain performance characteristics in common with biological neural networks.

15

X1

X3

X2 Y

w1

w2

w3Z2

Z1

v1

v2

Input Units Hidden Units Output Units

Simulation Model

Generate Data for ANN Learning

ANN Architecture Design

Learning Results

16

17

W_ref

speed ref

fl_s_0

flux ref

Torque2

Torque1

Torque

plot

Tem

Torque

Flux

Sector

Q

Switching Table

Speed plot

f l_s_abSector

Sector Selection

wr*

wTe*

SMC

Load Torque

Vdc

gate

v a

v b

v c

Inverter

TL

v a

v b

v c

Tem

Wmech

i_abc

v _abc

Induction Motor

I_s

Flux plot

v _abc

i_abc

f l_s_ab

f l_s_est

Tem_est

Flux and Torque Estimator

Flux

Vd

DC Source

Simulations are conducted using different speed references and observed from the motor start from time 0 to 0.02 seconds.

The rise time and steady state speed error is analyzed

The control performance is evaluated by the performance index (J)

18

Optimal gain value for ANN learning

Speed ref 10 20 30 40 50 60 70 80 90 100 110 120 130 140

h 870 590 430 239 143 105 83 69 60 52 47 42 37 34

Β 0 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.9 1 1.1

19

20

ωr*

w1,1

wj,1

X0

X0=1

Z1

Z20

Zj

Z0

Z0=1

1

2

h

β

w1,1

w20,1

w1,j

w2,20

21

The learning will stop under two conditions:◦ Reach criteria function (SEE=0)

◦ Reach maximum epoch (1000)

SSE=0.029

The motor speed reference are changed in the process to match the dynamics of movement in the electric car.

The speed steady state time of the system to reach the reference speed will be observed

Verification with data training◦ Compare system using ANN with system not using

ANN

Verification with other data

22

23

Simulation data

Time (s) 0 0,15 0,35

Speed ref (rad) 40 100 60

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50

20

40

60

80

100

120

Time (s)

Speed (rad/s)

reference

without nn

with nn

Gain value for system without ANNh=239, β=0

Without

ANN

With

ANN

Without

ANN

With

ANN

Without

ANN

With

ANN

speed ref (rad) 40 100 60

time to reach steady state

at reference speed (s)0,02 0,02 0,05 0,1 0,08 0,05

Simulation data

Time (s) 0 0,15 0,35

Speed ref (rad) 45 125 85

24

speed ref (rad) 45 125 85

time to reach steady state

at reference speed (s)0,03 0,14 0,08

Induction motor speed drive using sliding mode control can be improved with the optimization of gain value h and β.

At accelerating condition, by using ANN to tune the SMC gain is 0.05 s slower than without ANN but does not have oscillations in the response which is good in electric car dynamics.

At decelerating condition, by using ANN can improve the performance by 0.03 s without any oscillations in the speed response

25

1. Soebagio, “Teori Umum Mesin Listrik,”Srikandi, Surabaya, 2008.2. Gigih Prabowo, Mauridhi Heri Purnomo, Soebagio, “Metoda Direct Torque

Control pada Pengaturan Motor Induksi tanpa Sensor Menggunakan Sliding Mode Control”, SITIA (2008)

3. T. Brahmananda Reddy, J. Amarnath and D. Subba Rayudu, "Direct Torque Control of Induction Motor Based on Hybrid PWM Method for Reduced Ripple: A Sliding Mode Control Approach", ACSE Journal, Volume (6), Issue (4), Dec., 2006.

4. L. Fausett, (1993),"Fundamentals of Neural Networks: Architectures, Algorithm, and Applications", Prentice Hall

5. Perruquetti, W., Barbot, Jean Pierre, “Sliding Mode Control In Engineering”, Copyright 2002 by Marcel Dekker

6. Ion Boldea, S. A. Naser, “Electric Drives 2nd Edition”, CRC Press Taylor & Francis Group, 2006

7. Ned Mohan, “Advanced Electric Drives Analysis, Control and Modeling using Simulink®”, MNPERE, 2001

8. S.M. Gadoue, D. Giaouris, J.W. Finch, “Artificial intell-based speed control of DTC induction motor drives – A comparative study”, Electric Power System Research 79 (2009) 210-219.

9. Wada Masayoshi, “Research And Development Of Electric Vehicles For Clean Transportation”, Journal of Environmental Sciences 21 (2009) 745–749.

26

THANK YOU

27