H_∞追従性能補償器を備えたリアルタイム強化学習制御システム

抄録

Robust control theory generally guarantees robustness and stability of the closed-loop system. It however requires a mathematical model of the system to design the control system. It therefore can't often deal with nonlinear systems due to difficulty of modeling of the system. On the other hand, reinforcement learning methods can deal with nonlinear systems without any mathematical model. It however usually doesn't guarantee the stability of the system control. In this paper, we propose a “Real-time Reinforcement Learning Control System (RRLCS)” through combining reinforcement learning to treat unknown nonlinear systems and robust control theory to guarantee the robustness and stability of the system. Moreover, we analyze the stability of the proposed system using H_∞ tracking performance and Lyapunov function. Finally, through the computer simulation for controlling an inverted pendulum system, we show the effectiveness of the proposed method.