Supervised Learning Control for Compliant Pneumatic Artificial Muscle Robots With Preassigned-Time Performance

Pneumatic artificial muscle (PAM) actuators exhibit practical compliance and great payload-to-weight ratios when driving robotic exoskeletons. However, filling with highly compressed gas makes PAMs susceptible to sensor noises, which may degrade the state response and increase control efforts. In ad...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2024-09, Vol.54 (9), p.5352-5364
Main Authors: Liu, Gendi, Diao, Shuzhen, Yang, Tong, Zhang, Xinlin, Fang, Yongchun, Sun, Ning
Format: Article
Language:English
Subjects:
Atmospheric modeling
Dynamic observer
Force
Hysteresis
motion control
Noise
Observers
pneumatic artificial muscle (PAM)
preassigned time
Robots
Supervised learning
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Summary:Pneumatic artificial muscle (PAM) actuators exhibit practical compliance and great payload-to-weight ratios when driving robotic exoskeletons. However, filling with highly compressed gas makes PAMs susceptible to sensor noises, which may degrade the state response and increase control efforts. In addition, most of the existing optimal controllers require linearized operations or complex network calculations. To this end, a supervised learning control method with preassigned-time performance is studied, which achieves satisfactory motion control of the compliant PAM robots. In particular, the utilized dynamic observer with time-varying gains significantly reduces the effect of observation noises, and enhances the state convergence speed by combining with the preassigned-time constraints. Simultaneously, the improved supervised learning algorithm further optimizes input air consumption, which only involves the iterative adjustment of network weights. In contrast to the literature, this article presents a new solution to minimize energy consumption of the compliant PAM robots, while ensuring that the output states converge within the preassigned time, independent of parameter design. Rigorous stability analysis is provided and several experiments validate the tracking efficacy of the proposed method.
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2024.3405657