Chattering Reduction of Sliding Mode Control for Quadrotor UAVs Based on Reinforcement Learning

Abstract

Sliding mode control, an algorithm known for its stability and robustness, has been widely used in designing robot controllers. Such controllers inevitably exhibit chattering; numerous methods have been proposed to deal with this problem in the past decade. However, in most scenarios, ensuring that the specified form and the parameters selected are optimal for the system is challenging. In this work, the reinforcement-learning method is adopted to explore the optimal nonlinear function to reduce chattering. Based on a conventional reference model for sliding mode control, the network output directly participates in the controller calculation without any restrictions. Additionally, a two-step verification method is proposed, including simulation under input delay and external disturbance and actual experiments using a quadrotor. Two types of classic chattering reduction methods are implemented on the same basic controller for comparison. The experiment results indicate that the proposed method could effectively reduce chattering and exhibit better tracking performance.