ứng dụng mạng thần kinh nhân tạo trong điều khiển tay máy 4 bậc tự do hoạt động từ xa

In particular, one of feasible and stability solution is Tele-Operation Manipulator Technology By using Ihe master manipulator to record the movements and behaviour of Ihe driver in a s

APPLYING NEURAL NETWORK TO CONTROL 4DOF TELE - OPERATION

MANIPULATOR

U'NG DUNG MANG TIMN KINH NHAN TAG TRONG

DIEU KHIEN TAY MAY 4 BAC TV' DO HOAT D O N G TU' XA

Tu Diep Cong Thanh

Ho Chi Minh City University i>f Technology

Received May 16 ,2011 ABSTRACT

Almost all interactive problems between humans and the unsafe environment such as dangerous, toxic, infectious or sterile in the world can be solved by robot technology In particular, one

of feasible and stability solution is Tele-Operation Manipulator Technology By using Ihe master manipulator to record the movements and behaviour of Ihe driver in a safe environment, then transmits these parameters over a LAN to the slave manipulator which is controlled in the toxic, hazardous or sterile environments and strictly comply with people's behaviour is proposed in this paper

Keywords: Tela-operation, Control, LAN

TOM TAT

Hau het cdc v§n dS tuvng t^c giifa con ngutri va cdc moi tn/ong nguy hiem, dgc hai, lay nhiem ho$c v6 trung diu di/pc g/d/ guyM bSng ky thu$t robot Trong dd, mgt trong nhung giai phap mang tinh kha thi, on dinh va tri/c quan nhit la ky thu$t Tele-Manipulator BSng each su" dung tay may tuxmg tir de ghi nhan cac chuyen dong va hanh vi cua ngutri diSu khiSn trong moi tnjong an toan, sau do truyen cac thong so ndy qua mang LAN cho tay may chinh thuc hi$n dung theo hanh vi cua nguui didu khidn trong moi truing ddc h^i va nguy hiSm ho$c vd trung la huung nghien cuu dd xuit tmng bai bao nay

TLT khoa: Hoat dong tu- xa, Dieu khien, Mang LAN

L INTRODUCTION

Tele-Operation Manipulator (TM) system

is a remote control manipulator consists of two

arms: the master and slave Slave manipulator

is controlled to perform the same motion as

master manipulator To implement this control,

master manipulator is controlled by human The

desired motion of the master manipulator is

recognized by sensors and these values is

transmitted via LAN lo the slave manipulator

controller

In 1898, Nikola Tesia made boat control

model using radio in New York first to now, the

TM has a history of development over a century

[1] TM system as the first true master - slave is

made a pure mechanical structure is benevolent

R Goertz late in 1940 at the National

Laboratory Argone [2] In 1954, R Goertz

system development operations with the first

electro-mechanical servo controller With the

TM system appear in many areas more efficient service lo people such as explosives detection arm of national defense and arm on the spacecraft, hand-picking machine of nuclear fuel in nuclear power indushy and especially the type of ann surgery in remote health One of the outstanding research of robots for medical applications is manipulator system tor remote microsurgery institute KAIST, Korea [3] and surgical manipulator system accuracy in medicine at the University of Washington, USA [4]

TM control to execute as well as the ability to monitor and respond in real time, a number of studies related to model algorithms and system control are presented, such as adaptive control using a slide control algorithm

is presented by Plato [5], techniques to reduce transmission time over the network in conttol

TM suggested by Lee [6], Sano technical

TM [7], with Towhidkhah modeling and

predictive control [8], and robust control with

random time delay proposed by Prokopiou [9],

etc

In this study, a tow cost TM system is

presented A newly proposed neural network

controller is applied to control four degrees of

freedom (4D0F) manipulator via LAN Results

obtained will be presented through experiment

II EXPERIMENTAL SETUP

The overview of system and schematic

diagram of system are presented in figure I and

figure 2 respectively

.CA

.- 1^, ^s.,

Fig I Overview of the proposed TM system

receives the responese of 4DOF TM To control slave manipulator, PC server will compute the control signals and send these signals to low cost circuit using PIC 18F4450 through PC client via LAN Control software is coded based on C#, and the phoptograph of experinental system is shown in figure 3

Fig 3 Photograph of the experimental apparatus

HI CONTROL SYSTEM The overall of control system is shown in figure 4 To improve control performance, neural network controller is proposed The structure of the newly proposed control algorithm using neural network is shown in figure 5

The input layer has three neurons There are six neurons in hidden layer All layers are connected in only the forward direction Well-known steepest descent learning method is applied for online adaptive control The input to each neuron is given as the weighted sum of outputs fi-om the previous layer The output of each neuron is generated by linear function in the input layer In hidden and output layers, the sigmoid function is used And the structure of neural network is shown in figure 6

Fig 2 Schematic diagram of system

The system includes master manipulator

is controlled by human and enforce slave

manipulator motion the same with the master

manipulator motion Parameters of motion of

the master manipulator are recognized by the

encoders (USDigital S5 Optical 1024R/P) and

sent to PC server (computer 2.4 Ghz Pentum

IV) through PCI 1874 circuit PC server

transmits these informations to the PC client via

LAN (computer Pentum IV 2.4 Ghz) as well as

To construct learning rule, the following symbols are defined:

/ ' : Input to the jth neuron in the input layer o' :Output from the jth neuron in the input layer /" : Input to the kth neuron in the hidden layer

of :Output of the kth neuron in the hidden layer

" : Output from the output layer

cOji, : Weight from the jlh neuron in the input

layer to the kth neuron in the hidden layer

Q)"": Weight fi-om the kih neuron in Ihe hidden

layer to the output layer

naizii

PIC Ma (Mr wwuet)

iv« nr4i3

2~:

Fig 4 The overall of control system

' j Neuron Network • -

'•'-The operation of each neuron is described as:

" ; = ' • ; ( 2 )

"/'=./.„ ,0i'] / i ' = K o j (3)

/

o" = A,, ,{i"l / ' • ' = X ' " " V <4)

The leaning process is based on the back propagation algorithm, which minimizes E given by:

E = -{Setpo\n\-Output)' =-e- (5)

The weights are updated by the following increments to minimize E:

BE

A < BE

where rj>0\s learning rate lo determine the speed of leaning — ~ in Eq (6) can be

calculated by:

dE ^ dE di^

dro^" ~ di''da?:'"

^r^^lKVho."

— = -«" ^^

di° dcol'"

-S''>,o»

Fig 5 Structure of tfte proposed controller ^ 'S a generalized error calculated by:

*f / _ Q _ dE dy do°

- , ' ~ dy do" dl"

'B'-f}

do" a/.,i ,.('°) a," di" /sigmoun' )

(7)

(8)

(9)

(10)

(II)

(12)

Fig 6 The structure of neural network

As done by Yamada and Yabuta [10], for convenience, — ^ is assumed to be

constant-dy

The increment of weight can be written

as:

A a , r = - n - ^ = n-S''>^oi' (14)

dCOi

Consequently, the weight is updated by:

< ' ' = < ° + , , x „ C x 4 , „ „ „ ( / ' ' ) x o , " (15)

The update equation, the weight 0)''^' can

be derived in the same manner

<=<+7x5/'>=°; (16)

S" dE cy CO tl co^

cy co Cl r o , ct^ (17)

e X C X f:,„.,„ (i° ) X <0r X 4 W ('." )

And the control signal is given by:

u^=K„o'' (18)

K.„: Proportional gain of the output of neural

<

<

c

<

80

40

-

' 120 -,

100

-eo

40

20

0

100

80

20

100

60

-40

0

-:f^

1 ^

^ r

— Response Reference

1 ,

s ,

1 ^

U

J l._

r j "

\ pJ J '- ;

network controller The effectiveness of the proposed controller will be demonstrated through experiment

IV EXPERIMENTAL RESULTS

At first, proposed controller is applied for control the motion of slave manipulator The control parametters of proposed controller are chosen through trial and error The learning parameter of neural network controller is given through trial and error and equal 0.001 The initial starts of the weights of neural network are random within ranger -0.1 to 0.1 And the proportional gain of the output of neural network controller is 150 The experimental result of step response of TM are shown in figure 7 From figure 7, it shows that responses

of system is stable In addition, with fast changes of joint angles, and performances with good tracking are also obtained

In order to improvement control performance of system, sinusoidal form and triangle form are tested, and the experimental result is shown in figure 8 and figure 9 respectively From experimental results, it is shown that the response of system with respect

to proposed controller is stable and good

\jwmm

^\NWmN\

^^^,jwmiw^A

jjmym\

20 30 40 so 60

Fig 7 Step response of proposed controller Fig 8 Sine response of proposed controller

- Response

Reference

\ • ' '

^ V

•

'

: A '

I f

5 ft

^1

H J!

^ 1

^ <

80

40

20 •

0 •

120

100 •

eo

-eo

40

20

eo

20

eo

-eo

4 0

20

0

-/'

"

J\

— Response Reference

A'-

,

.1

fi

J"

n

r '''

-Fig 9 Triangle response of proposed controller

Finally, doing practice with movement of

master manipulator and checking performance

of salve manipulator Fxr"-''":r', l I'C^MU •>

shown in figure 10 And it is no doubt that the

TM system works well and the proposed

algorithms are fine The responses of slave

manipulator are almost tracking with the

reference input which is given from the motion

of master manipulator

V CONCLUSIONS

In this paper, a low cost tele-operation

manipulator system as well as an intelligent

control using neural network are proposed It is

Fig 10 Real response of TM system

shown that the proposed control methods had a good performance for tele-operation rmnimilator It can be leen from exnerimental results that these controllers had an adaptive control capability and strong robustness The controllers designed by this method do not need any training procedure in advance, but they use only the inputs and outputs of the plant for the adaptation of coiiir^'I parameters and can tune the parameters iieratively

Here, it can be seen that the neural network controller is the most suitable -controlling strategy to develop TM

REFERENCES

1 N Tesla "Method of and Apparatus for Controlling Mechanism of Moving Vessels or Vehicles" (1898)

2 Raymond Goertz and R Thompson "Electronically Controlled Manipulator" Nucleonics, (1954)

3 Dong-Soo Kwon Ki Young Woo Se Kyong Song Wan Soo Kim Hyung Suck Cho "Microsugical Telerobot system" In Intelligent Robots and Systems Int Conf, Oct., (1998)

4 Blake H., Diana F., Hawkeye K., Mitch L., Jacob R., Ganesh S., "Evaluation of RAVEN Surgical Telerobot during the NASA Extreme Environment Mission Operations 12 Mission", Report of University of Washington, (2009)

Robust Neuropredictive Teleoperation", Jour, of Intelligent and Robotic Systems, Vol.25, No.4, pp.311-340,(1999)

6 Lee, S and Lee, H S.: Modeling, design and evaluation of advanced teleoperator control systems with short time delay, IEEE Trans Robotics Automat Vol.9, pp 607-623, (1993)

7 Sano, A., Fujimoto, H., and Tanaka, M.: "Gain-scheduled compensation for time delay of bilateral teleoperation", IEEE Internal Conf on Robot, Automat, pp 1916-1923, (1998)

8 Towhidkhah, F., Gander, R E and Wood, H C "Model predictive control: A model for joint movement", J Motor Behavior, Vol 29, No 3 pp., 209-222, (1997)

9 Prokopiou, P A., Harwin W S., and Tzafestas, S G.: "Variable-time-delays-robust telemanipulation through master state prediction," lEEE/ASME Internal Conf on Advan Intel Mechatronics, pp.19-22, (1999)

10.Yamada T, Yabuta T., "Neural network controller using auto-tuning method for nonlinear ftinctions," IEEE Trans Neural Networks; 595-601,(1992)

Author's address: Tu Diep Cong Thanh - Tel: 84-838655348 - Email: tdcthanh^hcmul.cdu.vn

Faculty of Mechanical Engineering

Ho Chi Minh City University of Technology

268 Ly Thuong Kiel Sir., Disl 10, Ho Chi Minh City, VietNam