Online Reinforcement-Learning-Based Adaptive Terminal Sliding Mode Control for Disturbed Bicycle Robots on a Curved Pavement

Abstract

The reaction wheel is able to help improve the balancing ability of a bicycle robot on curved pavement. However, preserving good control performances for such a robot that is driving on unstructured surfaces under matched and mismatched disturbances is challenging due to the underactuated characteristic and the nonlinearity of the robot. In this paper, a controller combining proximal policy optimization algorithms with terminal sliding mode controls is developed for controlling the balance of the robot. Online reinforcement-learning-based adaptive terminal sliding mode control is proposed to attenuate the influence of the matched and mismatched disturbance by adjusting parameters of the controller online. Different from several existing adaptive sliding mode approaches that only tune parameters of the reaching controller, the proposed method also considers the online adjustment of the sliding surface to provide adequate robustness against mismatched disturbances. The co-simulation experimental results in MSC Adams illustrate that the proposed controller can achieve better control performances than four existing methods for a reaction wheel bicycle robot moving on curved pavement, which verifies the robustness and applicability of the method.