Reinforcement Learning-Based Model-Free Controller for Feedback Stabilization of Robotic Systems

Abstract

This article presents a reinforcement learning (RL) algorithm for achieving model-free control of robotic applications. The RL functions are adapted with the least-square temporal difference (LSTD) learning algorithms to develop a model-free state feedback controller by establishing linear quadratic regulator (LQR) as a baseline controller. The classical least-square policy iteration technique is adapted to establish the boundary conditions for complexities incurred by the learning algorithm. Furthermore, the use of exact and approximate policy iterations estimates the parameters of the learning functions for a feedback policy. To assess the operation of the proposed controller, the trajectory tracking and balancing control problems of unmanned helicopters and balancer robotic applications are solved for real-time experiment. The results showed the robustness of the proposed approach in achieving trajectory tracking and balancing control.