Neural dynamic programming based online controller with a novel trim approach

Abstract

Neural dynamic programming (NDP) is a generic online learning control system based on the principle of reinforcement learning. Such a controller can self tune with a wide change of operating conditions and parametric variations. Implementation details of a self-tuning NDP based speed controller of a permanent-magnet DC machine along the online training algorithm are given. A simple solution is developed for finding the trim control position for the NDP controller NDP controller that can be extended to other problems. The DC machine is chosen for the implementation because it can be easily operated in a variety of operating conditions, including parametric variations, to prove the robustness of the controller and its multiobjective capabilities. The simulation results of the NDP controller are compared with the results of a conventional PI controller to access the overall performance.