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Improved data-driven model-free adaptive control method for an upper extremity power-assist exoskeleton

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Abstract

The widespread application of power-assist exoskeletons in physical labor and daily activities has increased the demand for robust control strategies to address challenges in human-exoskeleton interaction. Factors such as collisions and friction introduce uncertain disturbances, making it difficult to establish an accurate human-exoskeleton interaction model, thereby limiting the applicability of current model-based control methods. To overcome these problems, this study proposes an improved data-driven model-free adaptive control method (IMFAC) for the upper extremity power-assist exoskeleton. The stability and convergence of the closed-loop system are rigorously proven. To optimize the initial conditions of IMFAC, we propose an improved snake optimizer (ISO) algorithm incorporating opposition-based learning. The proposed ISO-IMFAC method is evaluated in two scenarios: a nonlinear Hammerstein model benchmark and a physical exoskeleton platform. Experimental results demonstrate that ISO-IMFAC outperforms other popular data-driven control methods across six metrics: integrated absolute error (4.756), mean integral of time-weighted absolute error (0.457), maximum error (1.167), minimum error (0), mean error (0.032), and error standard deviation (0.169). Additionally, the ISO-IMFAC method effectively drives the exoskeleton without relying on its dynamic model. In two load-bearing experiments conducted with five subjects wearing the exoskeleton, the proposed method reduces average muscle exertion per unit time by over 50% and extended working time by more than 180%. These findings highlight the significant potential of the proposed method to enhance user endurance and reduce physical strain, paving the way for practical applications in diverse real-world scenarios. The code is released at https://github.com/Shurun-Wang/ISO-IMFAC.

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Funding

This work was supported by Pengcheng Shangxue Education Fund, the National Key R&D Program of China under Grant 2017YFE0129700 and China Scholarship Council under Grant 202206690046.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China

    Shurun Wang, Hao Tang, Zhaowu Ping, Qi Tan & Bin Wang

  2. School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Shurun Wang

  3. Industrial Automation Engineering Technology Research Center of Anhui Province, Hefei, 230009, China

    Hao Tang

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Correspondence to Hao Tang.

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Wang, S., Tang, H., Ping, Z. et al. Improved data-driven model-free adaptive control method for an upper extremity power-assist exoskeleton. Appl Intell 55, 504 (2025). https://doi.org/10.1007/s10489-025-06415-3

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