TITLE:
Development of Artificial Muscle Control Algorithm for Pneumatic Soft Robot
AUTHORS:
Meiqi Yin, Yingxin Zhang
KEYWORDS:
Pneumatic Artificial Muscle, Nonlinear Control, Hysteresis Compensation, Deep Reinforcement Learning, Digital Twins, EMG Signal, Soft Robot
JOURNAL NAME:
Open Access Library Journal,
Vol.13 No.7,
July
21,
2026
ABSTRACT: Pneumatic artificial muscle (PAM), as a key driving component of soft robot, faces the difficulty of high precision control because of its strong nonlinear and hysteresis characteristics. This paper systematically reviews the evolution of PAM modeling and control strategies, from physical mechanism modeling to data-driven methods, from classical control to intelligent algorithms, and discusses the cutting-edge progress of multimodal perception and human-machine collaboration. On this basis, an integrated research framework of “multi-physical field modeling-reinforcement learning optimization-digital twin verification-myoelectric feature fusion” is proposed, focusing on dynamic modeling of filament winding angle, LSTM hysteresis compensation, DDPG adaptive control, etc. The core content is launched, and the system performance is verified through rehabilitation and space scenarios, aiming to provide systematic solutions and implementation paths for PAM intelligent control.