Control of Robotic Joint Actuated by Antagonistic Pneumatic Artificial Muscles based on Model-free Reinforcement Learning

Abstract

With its unique intrinsic flexibility and muscle-like output force characteristics, Pneumatic Artificial Muscle (PAM) has gained important applications in the driving of Coexisting-Cooperative-Cognitive Robots (Tri-Co Robots). However, due to the difficulty of accurately modeling caused by the severe non-linear characteristic of PAM, model-based control methods are not easy to achieve ideal control results. Therefore, this paper proposes a Model-free Reinforcement Learning (RL) method to control a bionic single joint actuated by antagonistic PAMs. We designed the angle tracking, angle positioning and variable stiffness experiments of the robot joint, and achieved satisfactory human-like motion effects. The research in this paper can effectively improve the control performance of robotic joint actuated by antagonistic PAMs, thereby improving the accuracy, compliance and safety of the motion control ofTri-Co Robots.