Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems

This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., f...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2003-08, Vol.11 (4), p.462-477
Main Authors: Gao, Yang, Er, Meng Joo
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive control systems
Automatic control
Control systems
Dynamical systems
Fuzzy
Fuzzy control
Fuzzy logic
Fuzzy set theory
Fuzzy systems
MIMO
Nonlinear control systems
Nonlinear dynamics
Nonlinear systems
Programmable control
Robots
Robust control
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Summary:This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fast learning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; 6) robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed controller is superior.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2003.814833