A neural networks based direct adaptive controller for multivariable systems

This paper presents a direct multivariable adaptive controller using neural network which adapts to the changing parameters of the multivaziable nonlineaz system with nonminimum phase behavior, mutual interactions and time delays. It base on the theory which a nonlinear multivaziable systerns to be controlled is divided a linear part and a nonlineaz part. The controller pazameters of the linear part are obtained by the recursive least square algorithm at the parameter estimation stage, whereas the nonlinear pazt is achieved the through the Back-propagation neural network. This controller is performed on-line. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt a nonlinear multivariable system with nomninimum phase, noises and time delays and with changed system pazameter after a constant time. The proposed method is effective compared with the conventional direct multivariable adaptive controller using neural network.