Asymmetric Integral Barrier Lyapunov Function-Based Human–Robot Interaction Control for Human-Compliant Space-Constrained Muscle Strength Training

李雅普诺夫函数 控制理论(社会学) 机器人 控制(管理) 空格(标点符号) 计算机科学 培训(气象学) 人工智能 非线性系统 物理 量子力学 气象学 操作系统
作者
Jianfeng Li,Xin Wang,Ran Jiao,Mingjie Dong
出处
期刊:IEEE transactions on systems, man, and cybernetics [Institute of Electrical and Electronics Engineers]
卷期号:54 (7): 4305-4317 被引量:4
标识
DOI:10.1109/tsmc.2024.3378479
摘要

In this article, an asymmetric integral barrier Lyapunov function (AIBLF)-based control scheme is proposed for human–robot interaction (HRI), with which robot-aided human-compliant space-constrained muscle strength training can be achieved. First, an admittance model is exploited to generate compliant desired trajectory with the input of human–robot interaction torque. Then, on the basis of the super-twisting algorithm, a nonlinear observer is built to estimate and further compensate for the lumped disturbance applied to the robotic driving joint, including the active torque from human subject, the robotic model uncertainty, the friction, etc. Finally, an AIBLF-based controller involving nonlinear observer is proposed to solve the trajectory tracking issues in addition to the general constraint of training task space, in which the AIBLF strategy is utilized to establish an asymmetric-constrained training task space with adjustable boundary effects. This approach ensures that the training environment is tailored to accommodate individual needs and preferences, promoting a safer and more comfortable training experience. The convergence of all states and stability analysis for the closed-loop system are presented via the Lyapunov stability theory. The effectiveness of the proposed control scheme is verified by a single-joint muscle strength training robot in various experiments, and it is worth noting that this method can be easily extended to other multijoint robotic systems with the demand of human compliance and space constraint.
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