© The Institution of Engineering and Technology
A TSKtype recurrent fuzzy network (TSKRFN) control system is proposed to control the position of the mover of a fieldoriented control permanentmagnet linear synchronous motor (PMLSM) servo drive system to track periodic reference trajectories in this study. The proposed TSKRFN combines the merits of selfconstructing fuzzy neural network (SCFNN), TSKtype fuzzy inference mechanism, and recurrent neural network (RNN). Moreover, the structure and the parameter learning phases are preformed concurrently and online in the TSKRFN. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradientdescent method using a delta adaptation law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSPbased control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviour of the proposed TSKRFN control system is robust with regard to uncertainties.
References


1)

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

2)

F.J. Lin ,
C.H. Lin ,
C.M. Hong
.
Robust control of linear synchronous motor servodrive using disturbance observer and recurrent neural network compensator.
IEE Proc., Electr. Power Appl.
,
263 
272

3)

K.K. Tan ,
T. Lee ,
H. Dou
.
Precision motion control with disturbance observer for pulsewidthmodulateddriven permanentmagnet linear motors.
IEEE Trans. Magn.
,
1813 
1818

4)

T.H. Liu ,
Y.C. Lee ,
Y.H. Chang
.
Adaptive controller design for a linear motor control system.
IEEE Trans. Aerosp. Electron. Syst.
,
2 ,
601 
616

5)

F.J. Lin ,
R.J. Wai ,
C.M. Hong
.
Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic input.
IEEE Trans. Neural Networks
,
1 ,
68 
88

6)

Sankaranarayanan, S., Khorrami, F.: `Adaptive variable structure control and application to friction compensation', IEEE CDC Conf. Rec., 1997, p. 4159–4164.

7)

Maulana, A.P., Ohmori, H., Sano, A.: `Friction compensation strategy via smooth adaptive dynamic surface control', IEEE CCA Conf. Rec., 1999, p. 1090–1095.

8)

E.C. Park ,
H. Lim ,
C.H. Choi
.
Position control of XY table at velocity reversal using presliding friction characteristics.
IEEE Trans. Control Syst. Tech.
,
1 ,
24 
31

9)

H.S. Ahn ,
Y.Q. Chen ,
H. Dou
.
Stateperiodic adaptive compensation of cogging and coulomb friction in permanentmagnet linear motors.
IEEE Trans. Magn.
,
1 ,
90 
98

10)

L.X. Wang
.
(1997)
A Course in Fuzzy systems and control, englewood cliffs.

11)

F.J. Lin ,
D.H. Wang ,
P.K. Huang
.
FPGAbased fuzzy slidingmode control for a linear induction motor drive.
IEE Proc. Electr. Power Appl.
,
5 ,
1137 
1148

12)

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

13)

S.X. Yang ,
M.Q.H. Meng
.
Realtime collisionfree motion planning of a mobile robot using a neural dynamicsbased approach.
IEEE Trans. Neural Netw.
,
6 ,
1541 
1552

14)

T. Takaki ,
M. Sugeno
.
Fuzzy identification of systems and its applications to modeling and control.
IEEE Trans. Syst. Man Cybern.
,
116 
132

15)

M. Sugeno ,
T. Yasukawa
.
A fuzzylogic based approach to qualitative modeling.
IEEE Trans. Fuzzy Syst.
,
7 
31

16)

C.C. Chuang ,
S.F. Su ,
S.S. Chen
.
Robust TSK fuzzy modeling for function approximation with outliers.
IEEE Trans. Fuzzy Syst.
,
6 ,
810 
821

17)

M. Mahfouf ,
M.F. Abbod ,
D.A. Linkens
.
Online elicitation of Mamdanitype fuzzy rules vai TSKbased generalized predictive control.
IEEE Trans. Syst. Man Cybern.
,
3 ,
465 
475

18)

C.J. Lin ,
C.C. Chin
.
Prediction and identification using waveletbased recurrent fuzzy neural networks.
IEEE Trans. Syst. Man Cybern.
,
5 ,
2144 
2154

19)

C.T. Sun
.
Rulebased structure identification in an adaptivenetworkbased fuzzy inference system.
IEEE Trans. Fuzzy Syst.
,
1 ,
64 
73

20)

C.T. Lin
.
A neural fuzzy control system with structure and parameter learning.
Fuzzy Sets Syst.
,
183 
212

21)

C.F. Juang ,
C.T. Lin
.
An online selfconstructing neural fuzzy inference network and its application.
IEEE Trans. Fuzzy Syst.
,
1 ,
12 
32

22)

F.J. Lin ,
S.L. Yang ,
P.H. Shen
.
Selfconstructing recurrent fuzzy neural network for DSPbased permanent magnet linear synchronous motor servo drive.
IEE Proc., Electr. Power Appl.
,
2 ,
236 
246

23)

F.J. Lin ,
R.J. Wai ,
K.K. Shyu ,
T.M. Liu
.
Recurrent fuzzy neural network control for piezoelectric ceramic linear ultrasonic motor drive.
IEEE Trans. Ultrason. Ferroelectr., Freq. Control
,
4 ,
900 
913

24)

P. Campolucci ,
A. Uncini ,
F. Piazza ,
B.D. Rao
.
Online learning algorithms for locally recurrent neural networks.
IEEE Trans. Neural Netw.
,
340 
355
http://iet.metastore.ingenta.com/content/journals/10.1049/ipepa_20060148
Related content
content/journals/10.1049/ipepa_20060148
pub_keyword,iet_inspecKeyword,pub_concept
6
6