Robust neuro-fuzzy control of multivariable systems by tuning consequent membership functions
A robust neuro-fuzzy controller with tuning mechanism of membership functions and neural weights to achieve the tracking control of composite multivariable systems is proposed. The control strategy is developed to facilitate robust property by self-tuning the consequent membership functions of the fuzzy controllers. By an on-line tuning mechanism, the fuzzy system can effectively deal with the equivalent uncertainties that may appear in the subsystems due to plant uncertainty, function approximation error, or external disturbance. By using Lyapunov stability theory, the overall system with the proposed controller has been proved to be uniform ultimate bounded. Simulation results of a two-link robot control demonstrate the effectiveness and robustness of the design. (C) 2001 Elsevier Science B,V. All rights reserved.
neuro-fuzzy control multivariable systems consequence membership function