A neural network controller for a discrete-time nonlinear non-minimum phase system

The problem of controlling a discrete-time nonlinear non-minimum phase system is considered. An output re-definition strategy is developed which is applicable to a class of open-loop stable nonlinear systems whose input-output maps contain nonlinear terms from the output and linear terms from the input. No a priori knowledge about the nonlinearities of the system is required. The output re-definition scheme is based on first identifying the nonlinearities of the system using neural networks and then modifying the system zero dynamics. A stable/anti-stable factorization is performed on the zero dynamics of the system. The new output is re-defined using, the neural identifier and the stable part of the zero dynamics. A controller is then designed based on the new output whose zero dynamics are stable and can be inverted. Simulation results are resented to show the effectiveness of the proposed control scheme as compared to both linear and nonlinear conventional controllers