A fuzzy-neural adaptive control for MIMO nonlinear system with application

The fuzzy identification proposed by Takaki and Sugeno (1985) is extended to a MIMO adaptive controller based on a hybrid neural network structure. The network is roughly divided into the premise and consequence corresponding to the T-S model. Each parameter of the consequence function can be adjusted by the extended Bp algorithm so that automatic rule modification can be realized. The membership function of each fuzzy subset can be modified by a genetic algorithm. In this way, more pre-knowledge for the plant need not be required. Finally, the MIMO fuzzy-neural control is used to simulate a real example.