A Sliding-mode Iterative Learning Control for a Nonlinear Discrete-time System via a Data-driven Design method

摘要

In this work, a sliding-mode iterative learning control (SM-ILC) is proposed for a nonlinear and non-affine discrete-time system, which is transferred into a linear incremental input-and-output form equivalently. The unknown parameters in the linear incremental input-and-output model can be estimated by using an iterative project algorithm. The optimal learning control law is developed with a sliding surface defined along the iteration direction. The proposed SM-ILC is a data-driven approach and can achieve a better control performance because much more control knowledge is utilized. Both the convergence analysis and the simulation results have been provided to show the effectiveness of the proposed method.