Presented by: Ananto Mukti Wibowo · Induction motor speed drive using sliding mode control can be...
Transcript of Presented by: Ananto Mukti Wibowo · Induction motor speed drive using sliding mode control can be...
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