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

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2 Author(s)
Yang Gao ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Meng Joo Er

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.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:11 ,  Issue: 4 )