Entropy based indirect iterative learning control for robot manipulator with random disturbances

A novel entropy based indirect iterative learning control(ILC) methodology for robotic manipulator with random disturbance is developed by combining the minimum error entropy and the optimal strategy, which is used to update local controller parameters between any two adjacent batches. Moreover, Entropy is a unified probabilistic measure of uncertainty quantification whether the random disturbances are Gaussian or not. An innovative performance index that formulates the relation between the entropy of error and controller gains is proposed. Then the nonlinear optimal method to obtain new gains is presented, and stability is analysized. Finally, the effectiveness and feasibility of the proposed control schemes is verified by some simulation results.