Robust Reinforcement Learning Control System with H∞ tracking performance compensator

Robust control theory generally guarantees robustness and stability of the closed-loop system, however it requires mathematical model of the system to design the control system. Therefore, it can´t often deal with nonlinear systems because of difficulty of modeling of the system. Other, reinforcement learning method can deal with the nonlinear system without mathematical model, however, it usually doesn´t guarantee the stability of control. In this paper, we propose a “Robust Reinforcement Learning Control System (RRLCS)” through combining reinforcement learning to treat unknown nonlinear systems and using robust control theory to guarantee the robustness and stability of the system. As a robust control method, we adopt H_∞ control which is robust to modelling error and disturbance. On the other hand, as a reinforcement learning method, we adopt an Actor-Critic method with minimal amount of computation for the continuous action and state space. Moreover, we analyze the stability of the proposed system using H_∞ tracking performance and Lyapunov function. Finally, through the computer simulation for controlling the inverted pendulum system, we show the effectiveness of the proposed method comparing with an Adaptive Fuzzy Control method with H_∞ tracking performance compensator (AFC) and an Auto-Structuring Fuzzy Neural Control System method (ASFNCS).