Iterative learning control for flexible manipulator using fourier basis function

Abstract

Perfect tracking of the tip position of a flexible-link manipulator (FLM) is unable to be achieved by causal control because it is a typical non-minimum phase system. Combined with non-causal stable inversion, an adaptive iterative learning control scheme based on Fourier basis function is presented for the tip trajectory tracking of FLM performing repetitive tasks. In this method, an iterative identification algorithm is used to construct the Fourier basis function space model of the manipulator, and a pseudoinverse type iterative learning law is designed to approximate the stable inversion of the non-minimum phase system, which guarantees the convergence and robustness of the control system. Simulation results show the performance and effectiveness of the proposed scheme.