A Neural Network Based Method for Solving Discrete-Time Nonlinear Output Regulation Problem in Sampled-Data Systems

Abstract

Many of nonlinear control systems are sampled-data system, i.e. the continuous-time nonlinear plants are controlled by digital controllers. So it is important to investigate that if the solution of the discrete-time output regulation problem is effective to sampled-data nonlinear control systems. Recently a feedforward neural network based approach to solving the discrete regulator equations has been presented. This approach leads to an effective way to practically solve the discrete nonlinear output regulation problem. In this paper the approach is used to sampled-data nonlinear control system. The simulation of the sampled-data system shows that the control law designed by the proposed approach performs much better than the linear control law does.

The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administration Region (Project No. CUHK4209/00E).