precise motion control of linear pulse motor based on ...yu.ac.kr/~jipark/files/iastedohp.pdf ·...

IASTED MIC 2000

1Automatic Control Lab. YNU, Korea

Precise Motion Control of Linear Pulse Motor based on Disturbance

Compensation using Neural Network

2000. 2. 14.

Young Gun Kwon, Jung-il Park

School of Electrical and Electronic Engineering,Yeungnam University, Korea

IASTED MIC 2000


Purpose

• Propose a neural network-based learning controller

– Finding the current commands to reduce the ripple of error occurred when we control a linear pulse motor

IASTED MIC 2000


Introduction

• Importance of a linear motor

• What is a linear motor?

• What is a linear pulse motor(LPM)?

– Merits of LPM

– Difficulties in controlling LPM using conventional control algorithm

IASTED MIC 2000


• Excitation methods of LPM– Micro-step control to reduce force ripple

• Force ripple – Due to nonlinear factor or unmodeled

dynamics

• Studies for reducing force ripple – Finding the accurate model via either

experiments or theoretical studies for dynamics of a LPM

IASTED MIC 2000


• Purpose of this paper :

– Getting a better performance by finding the current commands considering nonlinear factor and unmodeled dynamics

– Introducing the neural network-based current controller considering these factors at a time

• Validity of the proposed controller

– confirmed by motion control experiments

IASTED MIC 2000


Principle of Hybrid type LPM

N S

Permanent magnet

Electromagnet

N S

Permanent magnet

Electromagnet

N S

Permanent magnet

Electromagnet

N S

Permanent magnet

Electromagnet

(a) (b)

(c) (d)

IASTED MIC 2000


Neural network-based current controller

• Linear motor model

• Generating force

eL FFdtdxB

dtxdM =++2

2

(1)

( )

+

−= BABAe ix

pix

pKxiiF ππ 2cos2sin,, (2)

IASTED MIC 2000


• Current commands

– If we use the current commands in equation (3), the force ripple is generated by unmodeled dynamics.

– Propose a neural network-based current control scheme

=

−= x

pix

pi BA

ππ 2cos,2sin(3)

IASTED MIC 2000


• Structures of neural network applied in control fields

– Feedback error learning type

– Direct inverse model learning type

– Indirect model learning type

IASTED MIC 2000


Structure of proposed controller

setpoint y

OutputPlantT H K

2HRM0205-1020L

err MotorDriver

xia

ib

++ A, /A

B, /B

PIDcontroller

CurrentCommand

+

-

++

NeuralNetwork1

++

uPID

uNN1

uIN

NeuralNetwork2

ibia

IASTED MIC 2000


Structure of used neural networksetpoint(t)

output(t)

out_velocity(t)

velocity(t)

LPF

uNN1

(a) Neural network 1

IASTED MIC 2000


setpoint(t)

setpoint(t-1)

Output(t)

Output(t-1)

IA

IB

IB

IA

(b) Neural network 2

IASTED MIC 2000


Specifications of neural network

– Bipolar sigmoid activation function

– Momentum term

– Standard back-propagation algorithm

IASTED MIC 2000


• The j-th hidden layer output

( ) ∑=

===N

kkjkjjj Qjxvgh

0,,2,1;; Lψψ (4)

• i-th output of output layer

( ) ∑=

===Q

jjijiii Mihwfy

0,,2,1;; Lψψ (5)

• Cost function

( )∑=

=M

iPIDUE

1

2

21 (6)

IASTED MIC 2000


( ) ( )

( ) ( ) ( )nhnnwwEnwnw

jyiij

ijijij

ηδ

η

+=

∂∂

−=+1(7)

( ) ( )i

iPIDyi

fUwhereψψδ

∂∂

=,

( ) ( )

( ) ( ) ( )nxnnvvEnvnv

khjjk

jkjkjk

ηδ

η

+=

∂∂

−=+1(8)

( )j

jM

iijyihj

gwwhere

ϕϕ

δδ∂

∂=∑

=1,

IASTED MIC 2000


Experimental environments• Methods of experiments

– Using DSP board to implement control algorithm• TMS320C31

– Motion speed • High speed : 4.5mm/sec

• Low speed : 0.2mm/sec

IASTED MIC 2000


• Specifications of experimental system

– LPM(2HRM0205-1020L) • Pitch length : 0.8mm

• Stroke : 1m

– PWM current driver

– Linear scale resolution : 20㎛

– DSP board : TMS320C31 • Sampling time : 2.5msec

IASTED MIC 2000


Structure of experimental system

PCDSP

DAC

DAC

Counter

Driver LPM scale A B/A /B

IASTED MIC 2000


Block diagram of current driver

LPMPWM

L298NStep

motor driver

LA25-NPCurrentdetector

inputIa, Ib

+

-

PI

IASTED MIC 2000


Experiment results• Initial conditions of NN1 :

– learning rate =0.3

– momentum coefficients =0.9

• Initial conditions of NN2

– learning rate =0.1

– momentum coefficients =0.9

• Initial weights :– chosen as values from –0.1 to +0.1

IASTED MIC 2000


Desired velocity profile

0 1 2 3 4 5 6 7 8 9 100

1

2

3

4

5

Velo

city

[mm

/sec

]Time [sec]

0 4 8 12 16 20 24 28 32 36 400.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 x10-3

Velo

city

[m/s

]

Time [sec]

Low speed High speed

IASTED MIC 2000


Results at low speed

0 4 8 12 16 20 24 28 32 36 40-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10 x10-3

Erro

r [m

]

Time [sec]0 4 8 12 16 20 24 28 32 36 40

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10 x10-3

Erro

r [m

]

Time [sec]

Open loop controller only FF+PI controller

IASTED MIC 2000



0 4 8 12 16 20 24 28 32 36 40-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10 x10-3

Erro

r [m

]

Time [sec]0 4 8 12 16 20 24 28 32 36 40

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10 x10-3

Erro

r [m

]

Time [sec]

NN2+PI+FF controllerNN1+PI+FF controller

IASTED MIC 2000



0 5 10 15 200.00

0.02

0.04

0.06

0.08

0.10

0.12x10-3

MSE

Learning iteration

0.0050.050PI+FF+NN2

0.0100.075PI+FF+NN1

0.0200.075PI + FF

0.0400.095Open loop

Offset error

(mm)

Peak to peak

error(mm)Controller

Mean squared error, PI+FF+NN2 controller

Performance comparison of each controller at low speed

IASTED MIC 2000


Results at high speed

0 1 2 3 4 5 6 7 8 9 10

-0.1

0.0

0.1

0.2

0.3 x10-3

Erro

r [m

]

Time [sec]0 1 2 3 4 5 6 7 8 9 10

-0.1

0.0

0.1

0.2

0.3 x10-3

Erro

r [m

]

Time [sec]

Open loop controller only PI+FF controller

IASTED MIC 2000


Results at high speed

0 1 2 3 4 5 6 7 8 9 10

-0.1

0.0

0.1

0.2

0.3 x10-3

Erro

r [m

]

Time [sec]

PI+FF+NN2 controller

IASTED MIC 2000


Performance comparison of each controller at high velocity

Controller

Peak to

peak

error(mm)

Offset

error(mm)

Open loop 0.17 0.100

PI+FF 0.13 0.030

PI+FF+NN2 0.10 0.025

IASTED MIC 2000


Conclusions• Introduced neural network of feedback

error learning type changes a current command to improve position accuracy.

• Neural network works efficiently for reducing the position ripple error.

• We must develop better neural network to operate for periodic error.

precise motion control of linear pulse motor based on ...yu.ac.kr/~jipark/files/iastedohp.pdf ·...

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