An Improved Reinforcement Learning Based Heuristic Dynamic Programming Algorithm for Model-Free Optimal Control

Abstract

For complicated processing industrial area, model-free adaptive control in data-driven schema is a classic problem. This paper proposes an improved reinforcement learning (RL) based heuristic dynamic programming algorithm for optimal tracking control in industrial system. The proposed method designs a double neural networks framework and employs a gradient-based optimization schema to present the optimal control law. Inspired by the experience replay buffer in deep RL learning, historical system trajectories in short-term are also considered in the training phase which achieves the stabilization of network learning. An experimental study based on an simulated industrial device shows that the proposed method is superior to other algorithms in terms of time consumption and control accuracy.