Iterative learning control with initial rectifying for nonlinear robotic system

A novel PD-type iterative learning algorithm is proposed for a class of nonlinear robotic systems with a fixed initial shift. Only the current tracking error signal is utilized in process of designing learning control law, which could avoid the non-causal problem. Moreover, iterative learning control law with initial rectifying strategy is proposed for the purpose of eliminating the effect of the fixed initial shift. The convergence analysis indicates that the system output achieves accurate tracking over a pre-specified time interval, as the iteration approaches infinity. Finally, experiments of robotic manipulator verified the effectiveness of the proposed control algorithm.