Practical stability issues in CMAC neural network control systems

Fu-Chuang Chen; Chih Horng Chang

doi:10.1109/87.481771

Practical stability issues in CMAC neural network control systems

Fu-Chuang Chen^*, Chih Horng Chang

^*Corresponding author for this work

Department of Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

72 Scopus citations

Abstract

The cerebellar model articulation controller (CMAC) neural network is a practical tool for improving existing nonlinear control systems. A typical simulation study is used to clearly demonstrate that the CMAC can effectively reduce tracking error, but can also destabilize a control system which is otherwise stable. Then quantitative studies are presented to search for the cause of instability in the CMAC control system. Based on these studies, methods are discussed to improve system stability. Experimental results on controlling a real world system are provided to support the findings in simulations.

Original language	English
Article number	481771
Pages (from-to)	86-91
Number of pages	6
Journal	IEEE Transactions on Control Systems Technology
Volume	4
Issue number	1
DOIs	https://doi.org/10.1109/87.481771
State	Published - Jan 1996

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Practical stability issues in CMAC neural network control systems

Abstract

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