A New Time-Efficient Structure for Observer-Based Adaptive Fuzzy-Neural Controllers for Nonaffine Nonlinear Systems

W.-Y. Wang; I-H. Li; M.-C. Chen; S.-F. Su; Y.-G. Leu

A New Time-Efficient Structure for Observer-Based Adaptive Fuzzy-Neural Controllers for Nonaffine Nonlinear Systems

Date

2010-03-01

Authors

W.-Y. Wang

I-H. Li

M.-C. Chen

S.-F. Su

Y.-G. Leu

Publisher

ICIC International

Abstract

This paper proposes an observer-based adaptive controller with a merged fuzzy-neural network for nonaﬃne nonlinear systems under the constraint that only the system output is available for measurement. Using a conventional fuzzy-neural network leads to rule explosion which leads to huge computation time. Our proposed merged-FNN does not have this problem, and can take the place of the conventional fuzzy-neural networks under some assumptions while maintaining the property of stability. Moreover, the adaptive scheme using the merged-FNN guarantees that all signals involved are bounded and the output of the closed-loop system asymptotically tracks the desired output trajectory. Finally, this paper gives examples of the proposed controller for nonaﬃne nonlinear systems, and is shown to provide good eﬀectiveness.