Adaptive iterative learning control for robot manipulators

In this paper, a new self adaptive iterative learning PD control(ILC-PD) scheme is proposed for trajectory tracking of robot manipulators with unknown parameters and performing repetitive tasks. This proposed control scheme is based upon a proportional-derivative(PD) feedback structure, for which an iterative term is added to cope with the unknown model parameters and disturbances. In contrast to classical iterative learning schemes, ILC-PD method is very simple in the sense that the only requirement on the PD and learning gains are just two iterative variables and the bounds of the robot parameters are not required, which is an interesting fact from a practical point of view. Furthermore, the ILC-PD method possesses both adaptive and learning capabilities with a simple control structure, and the asymptotical convergence is guaranteed based on the Lyapunov theorem. Finally simulations are presented for a planner manipulator with two revolute degrees of freedom. The results are provided to illustrate the effectiveness of the proposed controllers.