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.
Cite this paper
Yin, M. and Zhang, Y. (2026). Development of Artificial Muscle Control Algorithm for Pneumatic Soft Robot. Open Access Library Journal, 13, e15080. doi: http://dx.doi.org/10.4236/oalib.1115080.
Cao, Y., Song, A. and Wang, C. (2018) Adaptive Fuzzy PID Control for Pneumatic Muscle Actuator with Model Unexpected. <i>Journal of Intelligent & Robotic Systems</i>, 92, 661-673.
Zhang, Q., <i>et al</i>. (2022) LSTM-Based Hysteresis Modeling and Adaptive Control of a Pneumatic Artificial Muscle-Driven System. <i>IEEE</i>/<i>ASME Transactions on Mechatronics</i>, 27, 2178-2188.
Katzschmann, R.K., DelPreto, J., MacCurdy, R. and Rus, D. (2018) Exploration of Underwater Life with an Acoustically Controlled Soft Robotic Fish. <i>Science</i> <i>Robotics</i>, 3, eaar3449. <br>https://doi.org/10.1126/scirobotics.aar3449
Runge, G., Wiese, M., Gunther, L. and Raatz, A. (2017) A Framework for the Kinematic Modeling of Soft Material Robots Combining Finite Element Analysis and Piecewise Constant Curvature Kinematics. 2017 3<i>rd International Conference on</i><i> Control</i>,<i> Automation and Robotics</i> (<i>ICCAR</i>), Nagoya, 22-24 April 2017, 7-14. <br>https://doi.org/10.1109/iccar.2017.7942652
Schulte, H.F. (1961) The Characteristics of the McKibben Artificial Muscle. In: <i>The </i><i>Application of External Power in Prosthetics and Orthotics</i> (<i>National Research Council Publication No</i>. 874), National Academy of Sciences-National Research Council, 94-115.
Xiong, D., Zhang, D., Zhao, X. and Zhao, Y. (2021) Deep Learning for EMG-Based Human-Machine Interaction: A Review. <i>IEEE</i>/<i>CAA</i> <i>Journal</i> <i>of</i> <i>Automatica</i> <i>Sinica</i>, 8, 512-533. <br>https://doi.org/10.1109/jas.2021.1003865
Feng, J.J. and Meng, X.F. (2025) Dynamic Modeling and Intelligent Control of Bio-Inspired Soft Robots Based on Pneumatic Artificial Muscles. <i>Journal of Tianjin University of Technology</i>, 41, 112-125.
