Iterative Learning Control Design for a Class of Discrete Systems With Actuator Nonlinearity

Abstract

This paper develops iterative learning control designs to handle actuator nonlinearities that can occur during implementation. Unlike existing designs, the new results can be adapted to several commonly encountered nonlinearities. The application of the new design is highlighted through a detailed case study based on a model for the dynamics of a robot system constructed from measured frequency response data. In particular, this case study shows that the control law developed can accelerate error convergence and compensate for the effects of nonlinear actuator dynamics.