Iterative learning-based minimum tracking error entropy controller for robotic manipulators with random communication time delays

A novel feedback control method for robotic manipulators with random communication delays by combining the optimal P-type iterative learning control (ILC) idea with a minimum tracking error entropy control strategy is presented. The control design is formulated as an optimisation problem with a proper performance index and a constraint. In specific, the performance index implies the idea of the minimum entropy control of the closed-loop tracking error. The convergence in the mean-square sense has been analysed for all the signals in the closed-loop system. The convergence condition of such a tracking error under ILC framework is treated as the constraint condition which is satisfied in the optimisation process. It has been shown that the numerical optimal solution per iteration can be obtained by using the well-known particle swarm optimisation techniques. Simulation results are provided to show the effectiveness of the proposed approach.