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

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.