Application of ANN for Control of PWM Inverter-Fed Induction Motor Drives

207 Pages·2006·4.89 MB·English

POLITECHNIKA
WARSZAWSKA
WARSAW UNIVERSITY OF TECHNOLOGY
Faculty of Electrical Engineering

ROZPRAWA DOKTORSKA
Ph.D. Thesis

Dariusz L. Sobczuk, M. Sc.

Application of ANN for Control of PWM
Inverter Fed Induction Motor Drives

WARSZAWA
1999
Warsaw University of Technology

Faculty of Electrical Engineering
Institute of Control and Industrial Electronics

Ph.D. Thesis

M.Sc. Dariusz L. Sobczuk

Application of ANN for Control of PWM
Inverter Fed Induction Motor Drives

Thesis supervisor
Prof. dr hab. Marian P. Kaźmierkowski

Warsaw – Poland, 1999
Contents
1. Introduction 4
2. Mathematical description of induction motor 7
2.1. Introduction 7
2.2. Basics of induction motor model 7
2.2.1. Space vectors 7
2.2.2. Vector Equilibrium equations 9
2.2.3. Vector Equilibrium equations in per unit system 11
2.2.4. Orientation of the model 11
3. Basics of Artificial Neural Networks (ANN) 14
3.1. Introduction 14
3.2. Artificial neuron model 15
3.2.1. Activation function 16
3.3. ANN topologies 18
3.3.1. The layer of neurons 18
3.3.2. Linear filter 19
3.3.3. Multilayer Neural Networks (MNN) 20
3.4. Learning and training of ANN 21
3.4.1. Introduction 21
3.4.2. Learning algorithms for feedforward neural networks 22
3.5. Structures of Neuromorphic controllers 32
3.5.1. Introduction 32
3.5.2. Inverse control 34
3.5.3. Adaptive neurocontrol 38
4. ANN based current controllers (CC) 40
4.1. Introduction 40
4.2. Basics of CC in three-phase PWM converters 41
4.2.1. General requirements and performance criteria 41
4.2.2. Nonlinear current controllers 43
4.2.3. Linear current controllers 49
4.3. Off line trained neural comparator 54
4.3.1. Introduction 54
4.3.2. NN controller in three-phase ABC coordinates 54
4.3.3. NN controller in α-β coordinates 63
4.3.4. Results 64
4.3.5. Current controller based on single layer perceptron model 79
4.4. Optimal mode ANN CC 85
4.4.1. Optimal mode CC 85
4.4.2. Three layers NN controller trained by optimal PWM pattern 89
Contents

5. Speed estimation of induction motor 97
5.1. Introduction 97
5.2. Basic techniques of speed estimation 97
5.2.1. Open loop estimators 99
5.2.2. Closed loop estimators – observers 102
5.2.3. Model Reference Adaptive Systems 107
5.2.4. Estimator based on Artificial Intelligence 109
5.3. ANN based speed estimators 113
5.3.1. Speed estimation with on-line ANN 113
6. ANN sensorless field oriented control of IM 120
6.1. Principle of field oriented control (FOC) 120
6.2. Block scheme description 125
6.2.1. General description 125
6.2.2. Description of block scheme implemented in practice 127
6.3. Design of current controller 130
6.3.1. Introduction 130
6.3.2. Decoupled current regulators 130
6.3.3. Controllers design for field oriented IM 131
6.3.4. Internal model control 133
6.4. Design of speed controller 136
6.4.1. Introduction 136
6.4.2. Design methods 137
6.5. Rotor and stator resistance adaptation 139
6.5.1. Introduction 139
6.5.2. Stator resistance adaptation 140
6.5.3. Rotor resistance adaptation 142
7. Simulation and experimental results 145
7.1. Introduction 145
7.2. The choice of the learning rate 145
7.3. Steady state behaviour 147
7.4. Dynamic behaviour 158
8. Conclusions 168
References 155
Appendices 170
A1. Description of the simulation program 170
A2. Laboratory setup 194
A3. Motor parameters 202
A4. Notation 203
1. Introduction
1. INTRODUCTION

The induction motor thanks to its well known advantages as simply construction,
reliability, raggedness and low cost has found very wide industrial applications.
Furthermore, in contrast to the commutator dc motor, it can also be used in aggressive
or volatile environments since there is no problems with spark and corrosion. These
advantages, however, are occupied by control problems when using induction motor
in speed regulated industrial drives. This is due primarily three reasons:
(a) - the induction motor is high order nonlinear dynamic system with internal
coupling,
(b) - some state variables, rotor currents and fluxes, are directly not measurable,
(c) - rotor resistance (due to heating) and magnetising inductance (due to saturation)
varies considerably with a significant impact on the system dynamics.
Therefore in the early 70s besides the simple scalar u/f=const control method, the
more complicated vector approaches have been developed. The field oriented control
(FOC) has been proposed almost in the same time by Hasse and Blaschke. In this
method the motor equations are transformed in the field coordinates system (which
rotates with the rotor flux vector). This transformation corresponds to the decoupled
torque production in separately excited DC motor. The disadvantage of this method is
its complicity. It is necessary to design the flux, torque, and two current controllers,
and of course make signals transformation to the field-oriented coordinate system.
Current Control (CC) technique of the Voltage Source (VS) Pulse Width Modulated
(PWM) inverters is the key problem in the FOC systems. There are many possibilities
to design CC, but only few are based on artificial neural network (ANN) approach.
Among the main advantages of the ANN controllers are fast signal processing,
learning and generalisation abilities, robustness and insensitivity to the load parameter
changes.
Currently, thanks to digital processor based control implementation induction motor
drives have reached the status of modern technology in a wide range applications
from low-cost to high performance systems. Recently, a very intensive research is
concentrated on the elimination of the mechanical speed sensor without distortion the
4
1. Introduction
dynamic performance of the speed control loop. The advantages of such sensorless
speed control drive can be summarized as follows:
- lower cost,
- reduced size of the drive motor,
- elimination of the sensor cable,
- higher reliability.
In recent years a lot of methods have been developed which allows to eliminate speed
sensors, because in many industrial applications it is neither possible nor desirable to
use a mechanical sensors. These methods can be generally divided in two groups:
closed loop sensorless speed control where the motor speed is estimated from other
measurable quantities as stator voltage and currents and open loop speed control with
slip compensation where the motor synchronous speed is controlled and the effect of
load torque changes on motor shaft speed is only compensated. For speed estimation
very often advanced observer technique or neural networks are applied. Slip
compensation methods, in contrast, are usually much simpler but cannot guarantee
good dynamic performances.
Among published papers only one is based on Neural Networks (NN) approach.
Therefore, the author has formulated the following tasks of the thesis:
• Basing on the Artificial Neural Network approach, development of the current
controllers in field oriented controlled PWM voltage source inverter fed
induction motor,
• Basing on the Artificial Neural Network theory, estimate the mechanical speed of
the induction motor, with the measuring of the stator currents and voltages.
In the both groups of problems the new original solutions have been proposed (the
author is the co-author of the patent no. P310512). To carry out this tasks at first the
simulation investigation have been performed, and after that the experimental set-up
has been design and the laboratory experiments have been done. Note that the
sensorless FOC of induction motor, described in this thesis, could be used in the high
performance motor drives of the electrical vehicles (for example hybrid cars) and in
the factories, in which the measurement of the mechanical speed is expensive or even
impossible.

5
1. Introduction
In the author’s opinion the following results of the thesis are his original achievements:
• Development in C language of simulation packet DSIM for the investigation
of PWM voltage source inverter fed induction motors control,
• Development of the two types of the Artificial Neural Network Current
Controllers. The simulation study and experimental verification of these
approaches,
• Development of the new Artificial Neural Network based induction motor
speed estimator. The simulation study and experimental verification of this
approach
• Implementation of the closed loop field oriented control (FOC) with the
neural network based speed estimator (instead of the tachogenerator,
encoder or resolver) on experimental set up with floating point DSP –
TMS320C31.
The thesis consists of eight chapters. Chapter 1 is an introduction. Chapter two is
devoted to the mathematical model of the induction motor. Chapter 3 is the simple
introduction to the Artificial Neural Network theory, which is used, in the further part
of this thesis. Chapter 4 describes the Current Controllers design methods. In the same
chapter two ANN based current controller design approaches are presented and the
simulation and experimental results are shown. In Chapter 5 the speed estimation
methods are described and discussed. The new ANN based speed estimator is shown
and compared with other methods. In Chapter 6 in turn the Field Oriented Control
(FOC) and its implementation is described and discussed in details. Finally in
Chapter 7 the simulation and experimental results in closed loop FOC with the neural
network based speed estimator are presented and studied. Chapter 8 includes
conclusions. Description of the simulation program and the laboratory set-up is given
in Appendix.

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2. Mathematical description of induction motor
2. MATHEMATICAL DESCRIPTION OF INDUCTION MOTOR

2.1. Introduction

In this chapter the continuous model of induction motor will be presented and, in the
further part of this thesis, used as the controlled plant. This mathematical description
is based on the complex space vector in per unit (p.u.) notation [12]. The
mathematical model in the coordinates, which rotate with angular speed (ω), is
K
presented. Finally some special cases, i.e. α-β and x-y coordinates are considered.

2.2. Basics of induction motor model
2.2.1. Space vectors

The induction motor model will be presented in space vector notation. Therefore, in
this subsection we introduce the short description of this approach.
A three-phase symmetric system can be replaced by one resultant space vector
represented as the complex number.
Let us:
k = [k , k , k ]T (2.1)
A B C

and the elements of this vector satisfy the condition:

k +k + k =0 (2.2)
A B C

then a space vector is defined as

2[ ]
2
k = 1k +ak +a k (2.3)
A B C
3

where a and a2 are calculated using following formula:
7
2. Mathematical description of induction motor

1 3
j2π/3
a =e =− + j
2 2
(2.4)
1 3
2 j4π/3
a =e =− − j
2 2

It is obviously that space vector k is divided into real and imaginary parts:

k =k + jk (2.5)
α β

Using eq. 1.2, eq.1.3 we can obtain

k =k
α A
k −k (2.6)
B C
k =
β
3

and

k =k
A α
1 3
k =− k + k (2.7)
B α β
2 2
1 3
k =− k − k
C α β
2 2

These equations describe transformations from ABC to αβ and from αβ to ABC
respectively.

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2. Mathematical description of induction motor
2.2.2. Vector equilibrium equations

The following simplifying assumptions are made when deriving the set of equation
describing induction motor [12]:

1. the motor under consideration is a symmetrical three-phase one;
2. only the basic harmonic is considered, while the higher harmonics of
the spatial field distribution and of the magnetomotive force (MMF)
in the air gap are disregarded;
3. the distributed stator and rotor windings are replaced by a specially
formed so-called concentrated coil;
4. the effects of anisotropy, magnetic saturation, iron losses and eddy
currents are neglected;
5. the coil resistances and reactances are taken to be constant.

Using above assumptions one can obtain the following equation of the motor [12]:

dΨ
sK
U = R I + + jΩ Ψ
sK s sK K sK
dt
dΨ′
U′ = R I′ + rK + j(Ω − p Ω )Ψ′
rK r rK K b m rK
dt
Ψ = L I +L I′ (2.8)
sK s sK M rK
Ψ′ = L′ I′ +L I
rK r rK M sK
dΩm = 1 − p ms L Im(I* I′ )−M 
 b M sK rK L
dt J  2 

where:
• Ω denotes the rotor angular speed,
m
• Ω denotes the angular speed of reference frame K,
K
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