Pulse neural network–based adaptive iterative learning control for uncertain robots

Abstract

An adaptive iterative learning control algorithm based on pulse neural network (PNN) is proposed for trajectory tracking of uncertain robot system. Sliding mode variable structure control is used to improve the robustness to disturbance and perturbation, and boundary layer is used to eliminate the chattering of sliding mode control. In the iterative domain, the unknown parameters are tuned and used for part of the controller. Running in parallel, the PNN can perform real-time state estimation for improving the system convergence. We analyze the stability and convergence of this algorithm by using the Lyapunove-like methodology. The simulation results show that the expected control purpose can be achieved using the proposed algorithm.