Basis function based adaptive iterative learning control for flexible manipulator

Perfect tracking of the flexible-link manipulator (FLM) tip position has not been achieved by causal control because the FLM is a typical non-minimum phase system. Combined with non-causal stable inversion, an adaptive iterative learning control (ILC) scheme based on Fourier basis functions is presented for the tip trajectory tracking of the repeat running FLM. In this method, an iterative identification algorithm is used to calculate the basis function space model of the manipulator, and a pseudo-inverse type ILC law is designed to approximate the stable inversion of the system, which guarantees the convergence and robustness of the control system. Simulation results show the performance and effectiveness of the proposed scheme.