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A digital signal processorbased crosscoupled functional link (FL) radial basis function network (FLRBFN) control is proposed in this study for the synchronous control of a dual linear motors servo system that is installed in a gantry position stage. The dual linear motors servo system comprises two parallel permanent magnet linear synchronous motors (PMLSMs). First, the dynamics of the fieldoriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbance and friction force, is introduced. Then, to achieve accurate trajectory tracking performance with robustness, an intelligent control approach using FLRBFN is proposed for the fieldoriented control PMLSM servo drive system. The proposed FLRBFN is a radial basis function network embedded with a FL neural network. Moreover, to guarantee the convergence of the FLRBFN, a discretetype Lyapunov function is provided to determine the varied learning rates of the FLRBFN. In addition, since a crosscoupled technology is incorporated into the proposed intelligent control scheme for the gantry position stage, both the position tracking errors and synchronous errors of dual linear motors will converge to zero, simultaneously. Finally, some experimental results are illustrated to depict the validity of the proposed control approach.
References


1)

Kim, S., Chu, B., Hong, D., Park, H.K., Park, J.M., Cho, T.Y.: `Synchronizing dualdrive gantry of chip mounter with LQR approach', 2003 IEEE Int. Conf. on Advanced Intelligent Mechatronics, p. 838–843.

2)

O. Sawodnya ,
H. Aschemannb ,
S. Lahres
.
An automated gantry crane as a large workspace robot.
Control Eng. Pract.
,
1323 
1338

3)

A. RodriguezAngeles ,
H. Nijmeijer
.
Mutual synchronization of robots via estimated state feedback: a cooperative approach.
IEEE Trans. Control Syst. Technol
,
4 ,
542 
554

4)

M.T. Yan ,
M.H. Lee ,
P.L. Yen
.
Theory and application of a combined selftuning adaptive control and cross coupling control in a retrofit milling machine.
IEEE/ASME Trans. Mechatronics
,
2 ,
193 
211

5)

D. Sun
.
Position synchronization of multiple motion axes with adaptive coupling control.
Automatica
,
3 ,
997 
1005

6)

D. Sun ,
G. Feng ,
C.M. Lam ,
H. Dong
.
Orientation control of a differential mobile robot through wheel synchronization.
IEEE/AMSE Trans. Mechatronics
,
3 ,
345 
351

7)

D. Zhao ,
S. Li ,
F. Gao ,
Q. Zhu
.
Robust adaptive terminal sliding modebased synchronized position control for multiple motion axed systems.
IET Control Theory Appl.
,
1 ,
136 
150

8)

F.L. Lewis ,
S. Jagannathan ,
A. Yesilderek
.
(1999)
Neural network control of robot manipulators and nonlinear systems.

9)

W. Yu ,
X. Li
.
Nonlinear system identification using discretetime recurrent neural networks with stable learning algorithms.
Inf. Sci.
,
131 
147

10)

S. Jagannathan
.
(2006)
Neural network control of nonlinear discretetime systems.

11)

C.C. Chen ,
C.H. Hsu ,
Y.J. Chen ,
Y.F. Lin
.
Disturbance attenuation of nonlinear control systems using an observerbased fuzzy feedback linearization control.
Chaos Solitons Fractals
,
885 
900

12)

T.L. Chien ,
C.C. Chen ,
Y.C. Huang ,
W.J. Lin
.
Stability and almost disturbance decoupling analysis of nonlinear system subject to feedback linearization and feedforward neural network controller.
IEEE Trans. Neural Netw.
,
7 ,
1220 
1230

13)

J.S.R. Jang ,
C.T. Sun ,
E. Mizutani
.
(1997)
Neurofuzzy and soft computing: a computational approach to learning and machine intelligence.

14)

J.S.R. Jang ,
C.T. Sun
.
Functional equivalence between radial basis function networks and fuzzy inference systems.
IEEE Trans. Neural Netw.
,
1 ,
156 
159

15)

P.H. Shen ,
F.J. Lin
.
Intelligent backstepping slidingmode control using RBFN for twoaxis motion control system.
IEE Proc. Electr. Power Appl.
,
5 ,
1321 
1342

16)

S. Chen ,
X. Hong ,
C.J. Harris
.
Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and Doptimality experimental design.
IEE Proc. Control Theory Appl.
,
2 ,
136 
146

17)

X. Hong
.
Modified radial basis function neural network using output transformation.
IET Control Theory Appl.
,
1 ,
1 
8

18)

J.C. Patra ,
R.N. Pal
.
A functional link artificial neural network for adaptive channel equalization.
Signal Process.
,
181 
195

19)

J.C. Patra ,
A.C. Kot
.
Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks.
IEEE Trans. Syst. Man Cybern. B
,
4 ,
505 
511

20)

K.A. Toh ,
W.Y. Yau
.
Fingerprint and speaker verification decisions fusion using a functional link network.
IEEE Trans. Syst. Man Cybern. C
,
3 ,
357 
370

21)

Chen, C.H., Lin, C.T., Lin, C.J.: `A functionallinkbased fuzzy neural network for temperature control', 2007 Proc. IEEE Foundations of Computational Intelligence Conf., p. 53–58.

22)

I. Boldea ,
S. Nasar
.
(1997)
A. Linear electric acturators and generators.

23)

F.J. Lin ,
P.H. Shen ,
P.H. Chou ,
S.L. Yang
.
TSKtype recurrent fuzzy network for DSPbased permanent magnet linear synchronous motor servo drive.
IEE Proc. Electr. Power Appl.
,
6 ,
921 
931

24)

G. Ferretti ,
G. Magnani ,
P. Rocco
.
Single and multistate integral friction models.
IEEE Trans. Autom. Control
,
12 ,
2292 
2297

25)

C.C. Ku ,
K.Y. Lee
.
Diagonal recurrent neural networks for dynamic systems control.
IEEE Trans. Neural Netw.
,
1 ,
144 
156

26)

F.J. Lin ,
R.J. Wai ,
C.C. Lee
.
Fuzzy neural network position controller for ultrasonic motor drive using pushpull DC–DC converter.
IEE Proc. Control Theory Appl.
,
1 ,
99 
107

27)

S.J. Yoo ,
Y.H. Choi ,
J.B. Park
.
Generalized predictive control based on selfrecurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach.
IEEE Trans. Circuits Syst.
,
6 ,
1381 
1395

28)

R.J. Wai ,
C.M. Li
.
Design of dynamic Petri recurrent fuzzy neural network and its application to pathtracking control of nonholonomic mobile robot.
IEEE Trans. Ind. Electron.
,
7 ,
2667 
2683

29)

Singh, A., Vance, J.B., Kaul, B., Jagannathan, S., Drallmeier, J.: `Neural network control of spark ignition engines with high EGR levels', 2006 IEEE Int. Joint Conf. on Neural Network, p. 4978–4985.

30)

He, P., Gao, W., Jaganathan, S.: `Neuro control of nonlinear discrete time systems with deadzone and input constraints', 2006 IEEE Int. Conf. Control Application, p. 2836–2841.
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