Fuzzy Neural Network Control and Identification for Uncertain Nonlinear Systems

In this paper, the problem of identification and control of uncertain nonlinear systems is investigated based on fuzzy neural network. The considered systems are unknown and with external disturbances, so fuzzy neural networks are employed to approximate the unknown system functions. By doing this, an identification model of the controlled system can be obtained. Based on this model, a controller with adaptive mechanism can be designed for the system. The controller can attenuate the external disturbance to a given level, and guarantee the stability of the closed-loop system. Satisfactory identification and control of the system can be realized at the same time. Simulation example is given to demonstrate the effectiveness of the proposed controller.