Fuzzy controller, designed by reinforcement learning, for vehicle traction system application

Abstract

In this article, a fuzzy controller tuned by reinforcement learning is proposed. The developed algorithm utilizes a fuzzy logic theory and a reinforcement learning for fine-tuning parameters of the membership function for the fuzzy controller. Apart from the fuzzy controller developed, a fuzzy corrector of reference input (set-point) signal to the controller is applied. The fuzzy corrector changes the input (reference) signal of the system and takes into account an original reference input and type of external disturbances. Thus, the designed fuzzy control that is tuned by reinforcement learning is capable to ensure the stable, optimal, and safe performance of the system and takes into account external disturbances. To verify the performance of the proposed controller, the adaptive fuzzy controller tuned by reinforcement learning is applied to the mathematical model of a wheel locomotion module of an electric vehicle to advance a traction control system. Therefore, the effectiveness of the proposed adaptive fuzzy controller is proven through the simulation results.