An sEMG based adaptive method for human-exoskeleton collaboration in variable walking environments. (April 2022)

• Record Type: Journal Article
• Authors: He, Yong; Li, Feng; Li, Jinke; Liu, Jingshuai; Wu, Xinyu
• Is Part Of: Biomedical signal processing and control. Volume 74(2022)
• Publication Date: 2022-04
• Subjects: Surface Electromyography (sEMG) -- Motion estimation -- Human–machine collaboration -- Exoskeleton robot; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103477
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A closed-loop LSTM-ARILC control framework is proposed for human-exoskeleton collaboration control. LSTM is used to map the sEMG features to human joint motion. Human-exoskeleton coupling fuzzy dynamical model considering the passive impedance force is established. Higher accuracy in continuous motion estimation and stronger robustness to environmental change are achieved. Abstract: In this paper, a novel long short memory network – adaptive robust iterative learning control (LSTM-ARILC) framework is proposed to achieve accurate continuous motion estimation and adaptive following control for the exoskeleton robot, which is applied to offer assistance to human walking in variable environments. The LSTM network is established to estimate the human lower limb motion through the processed surface Electromyography (sEMG) signals, and the human-exoskeleton coupling fuzzy dynamic model is further developed. Then the closed-loop ARILC controller is designed to compensate the estimated errors and realize the adaptive and robust following control, and its asymptotic stability is rigorously proved via Lyapunov theory. The performance of the proposed method is evaluated with numerical experiments and compared with adaptive PID controller and adaptive fuzzy sliding mode controller (SMC). The maximum following errors of ARILC after 6 iterations can be reduced by more than 99% compared to the initial iteration, and the controller is quite less sensitive to the environment changes than the other two controllers, which proves that the proposed control framework is much more robust and effective in variable walking environments.