A Takagi-Sugeno fuzzy controller with reinforcement learning part

Abstract

A Takagi-Sugeno fuzzy controller with reinforcement learning part is proposed in this paper, which is used to control a real inverted cart-pendulum system. The fuzzy controller part is a zero-order Takagi-Sugeno system with four inputs and one output. The learning part is based on the gradient-descent algorithm, which modifies the consequent parameters of the fuzzy rules. Because the expected output values are unknown, a reinforcement signal instead of the output error is used in learning process. The reinforcement signal is decided by the judgment of whether the action should be punished or rewarded and the degree of punishments or rewards. The performance of controlling a real inverted cart-pendulum system proves the validity and the superiority of the proposed fuzzy controller with reinforcement learning part.