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Soft Computing
Published in: Soft Computing 6/2010

01-04-2010 | Original Paper

Adaptive output recurrent cerebellar model articulation controller for nonlinear system control

Author: Chih-Hui Chiu

Published in: Soft Computing | Issue 6/2010

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Abstract

In this study, an adaptive output recurrent cerebellar model articulation controller (AORCMAC) is investigated for a nonlinear system. The proposed AORCMAC has superior capability to the conventional cerebellar model articulation controller in efficient learning mechanism and dynamic response. The dynamic gradient descent method is adopted to online adjust the AORCMAC parameters. Moreover, the analytical method based on a Lyapunov function is proposed to determine the learning-rates of AORCMAC so that the stability of the system can be guaranteed. Furthermore, the variable optimal learning-rates are derived to achieve the best convergence of tracking error. Finally, the effectiveness of the proposed control system is verified by the several simulation and experimental results. Those results show that the favorable performance can be obtained by using the proposed AORCMAC.
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Appendix
Available only for authorised users
Literature
Agarwal A (1997) A systematic classification of neural-network-based control. IEEE Control Syst Mag 17:75–93CrossRef
Chen JY, Tsai PS, Wong CC (2005) Adaptive design of a fuzzy cerebellar model arithmetic controller neural network. IEE Proc Control Theory Appl 152(2):133–137CrossRef
Chiang CT, Lin CS (1996) CMAC with general basis functions. Neural Netw 9(7):1199–1211CrossRef
Chow TWS, Fang Y (1998) A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics. IEEE Trans Ind Electron 45(1):151–161CrossRefMathSciNet
Gonzalez-Serrano FJ, Figueiras-Vidal AR, Artes-Rodriguez A (1998) Generalizing CMAC architecture and training. IEEE Trans Neural Netw 9(6):1509–1514CrossRef
Hwang KS, Lin CS (1998) Smooth trajectory tracking of three-link robot: a self-organizing CMAC approach. IEEE Trans Syst Man Cybern B 28(5):680–692CrossRef
Hwang CL, Jan C, Chen YH (2001) Piezomechanics using intelligent variable-structure control. IEEE Trans Ind Electron 48(1):47–59CrossRef
Jagannathan S (1999) Discrete-time CMAC NN control of feedback linearizable nonlinear systems under a persistence of excitation. IEEE Trans Neural Netw 10(1):128–137CrossRef
Jan JC, Hung SL (2001) High-order MS_CMAC neural network. IEEE Trans Neural Netw 12(3):598–603CrossRef
Kim YH, Lewis FL (2000) Optimal design of CMAC neural-network controller for robot manipulators. IEEE Trans Syst Man Cybern C 30(1):22–31CrossRef
Kuschewski JG, Hui S, Zak SH (1993) Application of feed-forward neural networks to dynamical system identification and control. IEEE Trans Control Syst Technol 1(1):37–49CrossRef
Lane SH, Handelman DA, Gelfand JJ (1992) Theory and development of higher-order CMAC neural networks. IEEE Control Syst Mag 12(2):23–30CrossRef
Li YM, Leong SH (2004) Kinematics control of redundant manipulators using a CMAC neural network combined with a genetic algorithm. Robotica 22:611–621
Lin CM, Hsu CF (2002a) Neural-network-based adaptive control for induction servomotor drive system. IEEE Trans Ind Electron 49(1):115–123CrossRef
Lin CM, Hsu CF (2002b) Recurrent neural network adaptive control of wing-rock motion. J Guidance Control Dyn 25(6):1163–1165CrossRef
Lin FJ, Lin CM, Hong CM (2000) Robust control of linear synchronous motor servodrive using disturbance observer and recurrent neural network compensator. IEE Proc Electr Power Appl 147(4):263–272CrossRef
Lo JC, Kuo YH (1998) Decoupled fuzzy sliding-mode control. IEEE Trans Fuzzy Syst 6:426–435CrossRef
Peng YF, Chiu CH (2008) The implementation of wheeled robot using adaptive output recurrent CMAC. 2008 IEEE international joint conference on neural networks, pp 2942–2947
Peng YF, Hsu CF, Lin CM and Chiu CH (2005) Intelligent adaptive control scheme for uncertain nonlinear systems using H control technique. 2005 IEEE international conference on mechatronics and automation, pp 2216–2221
Su SF, Lee ZJ, Wang YP (2006) Robust and fast learning for fuzzy cerebellar model articulation controllers. IEEE Trans Syst Man Cybern B 36(1):203–208CrossRef
Wu TF, Tsai PS, Chang FR, Wang LS (2006) Adaptive fuzzy CMAC control for a class of nonlinear systems with smooth compensation. IEE Proc Control Theory Appl 153(6):647–657CrossRef
Zheng Y, Luo S, Lv Z (2006) Control Double inverted pendulum by reinforcement learning with double CMAC network. In: Proceedings of the 18th international conference on pattern recognition, pp 639–642
Metadata
Title
Adaptive output recurrent cerebellar model articulation controller for nonlinear system control
Author
Chih-Hui Chiu
Publication date
01-04-2010
Publisher
Springer-Verlag
Published in
Soft Computing / Issue 6/2010
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-009-0431-3

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