Summary of engineering doctoral thesis: Research and develop the control algorithms using artifical neural network to estimate motor parameters and control ac motors

The objectives of the thesis: Propose algorithms for controlling speed and flux of AC motors; propose rotor speed and flux estimation algorithms for speed sensorless controlller of AC motors.

VIETNAM ACADEMY OF SCIENCE AND TECHNOLOGY

GRADUATE UNIVERSITY SCIENCE AND TECHNOLOGY

This thesis is accomplished at: Graduate University of Science and Technology, Vietnam Academy of Science and Technology

Supervisors 1: Assoc Prof DSc Pham Thuong Cat

Supervisors 2: Dr Pham Minh Tuan

Examiner 1:

Examiner 2:

Examiner 3:

The thesis is to be presented to the Defense Committee of the

Graduate University of Science and Technology - Vietnam

Academy of Science and Technology

At Date Month Year 2016

The complete thesis is availabe at the library:

- Graduate University of Science and Technology

- Vietnam National Library

INTRODUCTION

1 A thesis statement necessary

Nowadays, AC motor is widely used both in industrial applications and in domestics ones because of perfective technique specifications such as impact, high power, economic, convinient design, control and maintenance AC motor is used in pumps, compressors, oil and gas industry, industrial or domestic fan, elevator, crane in construction industry, robotic etc… Therefore, the three last decades, AC motor is used instead of DC motor because of eleminating the disadvantages of dc motor such as high maintenance cost for brush – commutator system, vibration environments, iginite flammable environments Consequently

AC motor is widely applied However, there are still some control problems of AC motor when it can be more applied Many researches want to improve the effective operation, reduce the production price but the results are still drawbacks For example, the effect of control methods using Kalman filter, nonlinear filters or observers using sliding mode control to estimate rotor speed and flux depends on control algorithm, estimation of some parameters and the accuracy of the motor model The mathmetic model of motor is quite difficult to obtain as desired because of uncertain parameters similaryly friction coeffection, inertia, resistance The uncertain parameters change when the system is operating In addition, the speed and flux estimation insteading of sensor with the high requirement of accuracy is quite difficult and it is necessary to research Recently the development of artifical neural network is very helpful to solve the control problem, specially controlling nonlinear subjects with uncertain parameters Artifical neural network can solve the nonlinearity effectively with self-tuning parameters when the system operates

In this thesis, we concentrate on research and develop some control and estimation algorithm for ac motor with uncertain parameters

2 The objectives of the thesis

- Propose algorithms for controlling speed and flux of AC motors

- Propose rotor speed and flux estimation algorithms for speed sensorless controlller of

AC motors

3 The main contents of the thesis

Two control algorithms and two estimation algorithms of motor parameters are proposed a) The speed control algorithm for AC motor with uncertain parameters and changing

loads on rotating coordinate (d,q) using artifical neural network

b) The speed and flux control algorithm for AC motor with uncertain parameters and

changing loads on stationay coordinate (α,β) using the decoupling method

c) The speed estiamtion algorithm for AC motor using artifical neural network and adaptation

self-d) The speed estiamtion algorithm for AC motor using self-adaptation

Lyapunov stability theory and Barbalats’s lemma are used to prove the system asympotic stability of the algorithms Simulations will be implemented on Matlab

Outline:

Chapter 1, Presenting some problems of motor control

Chapter 2, Developing control algorithm of asynchrounous motors

Chapter 3, Developing estimation algorithms of speed and flux of asynchronous

motors

Conclusion

CHAPTER 1 OVERVIEW 1.1 Problem statement

1 - Obtaining accurately economically rotor flux and speed estimator algorithm,

2 - Developing AC motor control algorithm with uncertain parameters

3 - Designing intelligent motor controller based on the advanced production technology

of electronics

1.2 AC control method

AC motor control methods are classified as following diagram

Figure 1.1 Classification of IM variable frequency control

Nowadays motion control in industrial aplications is required accurately Motor control methods are used as scalar control voltage/frequency (V/F), direct torque control and filed oriented control In this thesis, field oreinted control method is ued to research and apply for three-phase AC motor with speed and moment control high performance requirement

Recent researches are focus on identifying the effection of rotor resistance without considering uncertain parameters such as friction coefficient, inertia or changing load Therefore, this thesis proposes control algorithm and speed estimation of AC motor with uncertain parameters

1.3 Research problems

- Developing rotor speed and flux estimation of AC motor

- Developing AC motor control algorithm with uncertain parameters

- Using Lyapunov stability theory and Barbalat’s lemma to prove global asympotic stability of system and then using Matlab to simulate and check the validity of proposed control algorithm and estimator

Direct torque control DTC

Circular flux trajectory

Hexagonal flux trajectory

Rotor flux Oriented

Stator flux oriented

IRFO

Natural Field Orientation NFO

AC motor control

CHAPTER 2 DEVELOPING FLUX AND SPEED CONTROL ALGORITHM OF AC MOTOR

WITH UNCERTAIN PARAMETERS

This chapter will present two flux and speed control algorithm

- Speed and flux control algorithm of AC motor uses artifical neural network with online

learning rules to compensate uncertain on rotating coordiante (d,q)

- Speed and flux control algorithm of AC motor does not decouple and then using

artifical neural network to compensate uncertain on static coordiante (α,β)

The mathmethic model of AC motor on rotating coordinate (d,q) when flux rqon axis q

is eliminated From the equation (2.15) results

2.2 Build speed control algorithm for three-phase asynchronous as motor with

uncertain parameters on rotating coordinate (d,q)

2.2.1 Build a controller model

From the equation (2.16), results in

k, k

J B are known;   Jk, Bk are unknown

( )

In summary, the motor control problem becomes determining the control signal u(t) that

regulates motor speed  reaching reference speed ref when there some uncertain parameters

Figure 2.2 Motor control model

Speed controller ref

2.2.2 Build a speed control algorithm of motor

where u0 is feedback signal written in PD form and u 1 a signal compemsating unkown

parameters f And then:

J

  ,

k

f f J

   , '

k

D D

K K J

Finally, the motor control problem becomes determining the control signal u to '

guarantee the system (2.31) asympotic stability when f is unknown ' f is aproximated by '

a neural network with output ˆf

Theorem 1 [1][2]: Speed of induction motor ω (2.16), (2.22) aproaches the disired speed

ω ref while friction coefficicent B, inertia moment J and load moment m L are unkonwn if control rule u(t) and study rule w  of neural network are defined as below

Rotor speed regulator as shown on Figure 2.3

R

L L

where G is positive diagonal matrix and ξ i sdq isdq is error vector between the disired

cunrrenr and regulated current

Hence the error vector ξ  0 meaning isdq isdq

Building the current regulator as shown on Figure 2.4:

Figure 2.3 Rotor speed regulator of the motor

2.2.4 Simulation results

Motor control system model with uncertain parameters and speed feedback signal as shown on Figure 2.2 Simulation was conducted using a four-pole squirrel-cage induction motor from LEROY SOMER with the parameters shown in Table 1 The reference angular velocity varies in a trapezoid shape as seen in Figure 2.5 with the maximum ref  100Rad/s (956 prm) and reference flux r*ref=1.5 (Wb) Motor is mounted on the driller system

Table 1 Motor parameters

Rated Power 1.5 KW Stator inductance (L s) 0.253 H

Rated stator voltage 220/380 V Rotor inductance (L r) 0.253 H

Rated stator current 6.1/3.4 A Mutual inducatnce (L m) 0.213 H

Stator resistance(R s) 4.58 Ω Motor inertia (J) 0.023 Nms2/rad

Rotor resistance (R r) 4.468 Ω Viscous coefficient

friction (B)

0.0026 Nms/rad

Figure 2.5 is rotor desired speed and is started in time t=0,1(s)

Figure 2.5 Desired speedrefThe motor speed control system was simulated with these assumed uncertain parameters:

where : m L1 is steady load of system, 3 (Nm),

m L2 is unknown load while drill on the material as shown on Figure 2.6a

L m

 is unknown load depended on the structure of material as shown on Figure 2.6b

20 40 60 80 100

sd

i

Figure 2.6a m L2 unknown load while drill on the material

Figure 2.6b Δm L unknown load depended on the structure of material

Figure 2.6c m L load of the system

Figure 2.8 Error between desired rotor speed and real rotor speed using neural network

0 0.5

Time (s)

-4 -3 -2 -1 0 1

Time (s)

Figure 2.9 Setting time of speed with the load m L

- When the system starts, the error of speed is about 3,5% When the load is changed suddenly, the error of speed is about 1,5%

- The rotor speed is reached the steady state after the short time about 1s by using the neural network, the speed is approached the desired speed

2.3 Build speed and flux control algorithm for three-phase asynchronous as motor with uncertain parameters on stationary coordinate (,)

M3~ m L

Hình 2.12 Motor control model

r m r

   are unknown parts

From the known parameters, rvà r can be found

with f = ΔMx + ΔNx D 1Dv D  1DQ  Q are unknown parts that determine after

In summary, the motor control problem becomes determining the control signal v

that regulates motor speed and flux reaching desired values    ref,

2.3.2 Speed and flux control method

ref r ref

x x

Therefore, when s0 , then e0

Figure 2.13 The neural network structure

The form of the neural network:



 

  

  output function vector of input

neuron i; τ bounded approximation error: η 0 Therefore, to make s0 and error

ref

e  x - x  0 we need to choose v and the learning rule for the weighted W to make the

system (2.56) asymptotically stable

Theorem 2 [4][6]: Speed andflux of the AC motor in equation (2.14) approach the

desired values    ref, 2  2 2  2

ref

r r r r

     while , J B,R r and changeable load TL

are unknown if the control signal v and weighted W are defined as below:

From equation (2.66), it is clearly that V 0 and V 0 with s 0; V 0 when s0

and from equation (2.58), it is obviously that η η, are always finite Because of V 0





negative definite, the system is not guaranteed to be asympotic stability Therefore, we need use Barbalat’s lemma to stabilize the non-autonoumous system asympotical stability From the equation (2.65), we obtain:

where s s and , η η, are always finite, then V is finite, V is continuous by time

Applying Barbalat’s lemma when V is uniform continuous then V  0 s s,0

From (2.57), error e0 Therefore, xxrefin other words, rotor speed and flux converge to their respective desired values with error e = 0

Rotor speed and flux controller of the AC motor as seen as Figure 2.14

2.3.3 Simulation results

Assuming that three-phase ac motor as in 2.2.4 and the desired flux r2ref=2.25 (Wb2) Rotor resistance ˆ

R R  R , where ΔR r is changed when the motor operates, the

changing shape of ΔR r as seen in the Figure 2.15

Figure 2.15 ΔR r changes by time

Figure 2.17 Error between desired rotor speed and real rotor speed

0 0.2

Figure 2.18 Setting time the load m L

Figure 2.19 Error between desired flux r2ref and real flux r2

Figure 2.20 Setting time of real flux r2 and desired flux r2ref with the load m L

Rotor speed and flux of induction motor are reached the desired speed and flux

- When the motor starts, rotor speed and flux have the setting period with an error of

about 0,08% to speed and 70% to rotor flux

- When the load changed suddenly while the motor was operating normally, speed and rotor flux had a transient period with an error of about 0,2% to rotor angular velocity and 0.001% to rotor flux

- The setting time of rotor speed and flux is very small

-1 -0.5

0 0.5

Time (s)

2.4 Conclusion of chapter 2

In this chapter, the two algorithm control of speed and flux with uncertain parameters

(friction coefficient B, inertia moment J, rotor resistance R r , changing load) for the model

on rotating coordinate (d,q) and on stationary coordinate (α,β) are represented

The algorithm control of ac motor using the artifical neural network with online study to

compensate the uncertain parameters on rotating coordinate (d,q) The stability theory

Lyapunov and Barbalat’s lemma are used to prove the asympotic global stability of the system The simulation results in 2.2.4 show the efficient of the proposed contorl algorithm The two algorithm control of speed and flux of ac motor without decoupling and self-adaptive using the artifical neural network with online study to approximate uncertain

parameters on stationary coordinate (α,β) The simulation results in 2.3.3 show the efficient

of the proposed contorl algorithm

Based on the simulation results in 2.2.4 and 2.3.3, the control algorithm of rotor speed and flux in 2.3.2 is better than in 2.2.2 and current control in 2.2.3

- When the motor starts, rotor speed and flux have the setting period with the error of

about 0,08% in 2.3.2 while it is about 3,5% in 2.2.2 and 2.2.3

- When the load changed suddenly while the motor was operating normally, the error of

the control algorithm on stationary coordinate (α,β) in 2.3.2 is 0,2% while the error of control algorithm on rotating coordinate (d,q) in 2.2.2 and current control 2.2.3 is about

1,5%

The above results are published in [1][2][4] and [6] of the publication list

CHAPTER 3 DEVELOPPING THE SPEED AND FLUX ESTIMATION ALGORITHM OF THE

AC MOTOR WITH UNCERTAIN PARAMETERS 3.1 Speed and flux estimation Problem of AC motor

In this chapter, we propose the speed and flux estimation algorithm on the reference model:

- Neural network and self-adaptive speed estimation algorithm of asynchronous three phase ac motor with uncertain parameters

- Self-adaptive speed and flux estimation algorithm of asynchronous three phase AC motor with uncertain parameters

We also combine two control algorithms proposed in the chapter 2 with two estimation algorithms in chapter 3 in the sensorless motor control model

3.2 Developping speed and flux estimation algorithm of asynchronous three phase ac motor with uncertain parameters

3.2.1 Build a self-adaptive neural network controller of motor speed

The speed estimator of three phase AC motor as seen in Figure 3.3, input signals consist

of stator current vector i ; statorvoltage vector s u and output signals consist of estimated s

speed of motor ˆ, rotor time constant ˆand angular of rotor flux ˆ

The procedure for estimating rotor speed and flux includes the following steps:

Step 1: Separate parts of  and from stator current and voltage measurement Build the

neural network to approximate l (contains two parameters ω, η as in the equation 3.5 by

theorem 3)

Step 2: Base on the value t (from theorem 3),we find the approximation current ˆ

s

i , while the error vector of stator current (ˆ )

ς i - i 0 then results t=-l

Step 3: Build the self-tuning rule  ˆ ˆ, by theorem 4

Figure 3.3 Speed estimator, the inverse value of rotor time constant and rotor flux

Caculate

ˆis based on t

Estimation Algorithm

s

u