Adaptation of a non-linear controller based on Reinforcement Learning

Abstract

Closed-loop control systems, which utilize output signals for feedback to generate control inputs, can achieve high performance. However, robustness of feedback control loops can be lost if system changes and uncertainties are too large. Adaptive control combines the traditional feedback structure with providing adaptation mechanisms that adjust a controller for a system with parameter uncertainties by using performance error information on line. Reinforcement learning (RL) is one of the many methods that can be used for adaptive control. The aim of this thesis is to adapt a non-linear Anti-lock Braking System (ABS) controller of a passenger car obtained as a simplified symbolic approximation of the solution to the Bellman equation to model-plant mismatches and process variations. Results for adaptation to dry and wet asphalt have been obtained successfully and have been compared with hand tuned and adaptive proportional-integral (P-I) controllers.