Conference paper
Direct Stable Adaptive Fuzzy Neural Model Reference Control of a Class of Nonlinear Systems
In this study, using a model reference adaptation law, a stable fuzzy neural control system is developed. Despite the advantages of Model reference control design technique, which is mainly its power to exactly set trajectories of the system under control, this method is designed for linear system. In this study using fuzzy neural systems, a stable model reference controller for nonlinear systems is developed.
Lyapunov method is used to guarantee the stability of fuzzy neural training algorithm and model following of the system under control. Keywords: Fuzzy Neural Control, Model Reference Control, Stable Controller
Language: | English |
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Year: | 2008 |
Pages: | 512-512 |
Proceedings: | 2008 3rd International Conference on Innovative Computing Information and Control (ICICIC) |
ISBN: | 076953161X , 076953161x and 9780769531618 |
Types: | Conference paper |
DOI: | 10.1109/ICICIC.2008.231 |
Adaptation model Adaptive systems Computational modeling Control systems Force Lyapunov method Lyapunov methods Springs Stability analysis control system synthesis direct stable adaptive fuzzy neural model reference control design fuzzy control learning (artificial intelligence) model reference adaptive control systems neural training neurocontrollers nonlinear control systems nonlinear system stability