NDLI: Adaptive recurrent cerebellar model articulation controller for unknown dynamic systems with optimal learning-rates

Content Provider	IEEE Xplore Digital Library
Author	Ya-Fu Peng Chih-Min Lin Wei-Liang Chin
Copyright Year	2004
Description	Author affiliation: Dept. of Electr. Eng., Ching-Yun Univ., Chung Li, Taiwan (Ya-Fu Peng)
Abstract	In this study, an adaptive recurrent cerebellar model articulation controller (ARCMAC) is designed for feedback control system with unknown dynamics. The proposed ARCMAC has superior capability to the conventional cerebellar model articulation controller (CMAC) in efficient learning mechanism, guaranteed system stability and dynamic response. Temporal relations are embedded in ARCMAC by adding feedback connections in the association memory space so that the ARCMAC captures the dynamic response. The dynamic gradient descent method is adopted to adjust ARCMAC parameters on-line. Moreover, the variable optimal learning-rates are derived to achieve most rapid convergence of tracking error. Finally, the effectiveness of the proposed control system is verified by experimental results of linear piezoelectric ceramic motor (LPCM) position control system. Experimental results show that accurate tracking response and superior dynamic performance can be obtained because of the powerful on-line learning capability of the proposed ARCMAC.
Starting Page	885
Ending Page	890
File Size	434462
Page Count	6
File Format	PDF
ISBN	0780383591
ISSN	10987576
DOI	10.1109/IJCNN.2004.1380047
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-25
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Programmable control Adaptive control Optimal control Feedback control Learning systems Stability Convergence Control systems Ceramics Position control
Content Type	Text
Resource Type	Article

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