Practical stability issues in CMAC neural network control systems

Fu-Chuang Chen*, Chih Horng Chang

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


The 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 is provided to support the findings in simulations.

Original languageEnglish
Article number532355
Pages (from-to)2777-2781
Number of pages5
JournalProceedings of the American Control Conference
StatePublished - 21 Jun 1995
EventProceedings of the 1995 American Control Conference. Part 1 (of 6) - Seattle, WA, USA
Duration: 21 Jun 199523 Jun 1995


Dive into the research topics of 'Practical stability issues in CMAC neural network control systems'. Together they form a unique fingerprint.

Cite this