Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33765
Title: Stable fuzzy neural tracking control of a class of unknown nonlinear systems based on fuzzy hierarchy error approach
Authors: Wu, A
Tam, PKS
Keywords: Adaptive fuzzy neural control
Fuzzy hierarchy approach
Fuzzy neural tracking control
Unknown nonlinear systems
Issue Date: 2002
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on fuzzy systems, 2002, v. 10, no. 6, p. 779-789 How to cite?
Journal: IEEE transactions on fuzzy systems 
Abstract: In this paper, a stable fuzzy neural tracking control of a class of unknown nonlinear systems based on the fuzzy hierarchy approach is proposed. The adaptive fuzzy neural controller is constructed from the fuzzy neural network with a set of fuzzy rules. The corresponding network parameters are adjusted online according to the control law and update law for the purpose of controlling the plant to track a given trajectory. A stability analysis of the unknown nonlinear system is discussed based on the Lyapunov principle. In order to improve the convergence of the nonlinear dynamical systems, a fuzzy hierarchy error approach (FHEA) algorithm is incorporated into the adaptive update and control scheme. The simulation results for an unstable nonlinear plant demonstrate, the control effectiveness of the proposed adaptive fuzzy neural controller and are consistent with the theoretical analysis.
URI: http://hdl.handle.net/10397/33765
ISSN: 1063-6706
EISSN: 1941-0034
DOI: 10.1109/TFUZZ.2002.805885
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