Game theoretic Lyapunov fuzzy control for Inverted Pendulum

In this paper we propose a game theoretic Lyapunov fuzzy controller which is both safe and stable. We attempt to optimize a reinforcement learning based controller using Markov games, simultaneously hybridizing it with a Lyapunov theory based control, to impart stability. Our proposed technique results in an RL based game theoretic, adaptive, self learning, optimal fuzzy controller, which is robust and has guaranteed stability for non linear systems. Proposed controller is an “annealed” hybrid of Fuzzy Markov games and Lyapunov theory based control approaches. We have used fuzzy systems as function approximator for a continuous state action space implementation. We test the controller on the standard Inverted Pendulum Control (IPC) problem. Simulation results bring out supremacy of the designed control strategy over baseline Fuzzy Markov game based controller.