Nonlinear identification and control using a generalized fuzzy neural network

This paper presents a robust adaptive fuzzy neural controller (RAFNC) suitable for identification and control of uncertain 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 adaptive learning ability of uncertain nonlinear systems; (3) Fast adaptation and learning speed; (4) Ease of incorporating expert knowledge; (5) Adaptive control, where structure and parameters of the RAFNC 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 RAFNC is superior over many existing schemes.