Robust Velocity Control of Rehabilitation Robots Using Adaptive Sliding Mode and Admittance Strategies

I. V. Merkuryev; T. B. Duishenaliev; Guijun Wu; Zh. Zh. Dotalieva; A. A. Orozbaev

doi:10.18196/jrc.v6i3.26493

Authors

I. V. Merkuryev National Research University Moscow Power Engineering Institute
T. B. Duishenaliev National Research University Moscow Power Engineering Institute
Guijun Wu Kyrgyz State Technical University named after I. Razzakov
Zh. Zh. Dotalieva Kyrgyz State Technical University named after I. Razzakov
A. A. Orozbaev Kyrgyz State Technical University named after I. Razzakov

DOI:

https://doi.org/10.18196/jrc.v6i3.26493

Keywords:

Rehabilitation Robot, Admittance Control, Adaptive Sliding Mode Control, Disturbance Observer, Velocity Tracking, Human–Robot Interaction, Lyapunov Stability, Multi-DOF Systems

Abstract

This paper investigates a velocity tracking control strategy for a planar rehabilitation training robot equipped with two independent linear actuators along the X and Y axes. A dual-loop control framework is proposed by combining admittance control and adaptive sliding mode robust control to facilitate compliant and accurate human–robot interaction during active rehabilitation. In the outer loop, admittance control converts the interaction force applied by the patient into a reference velocity, enabling compliant force-to-motion mapping. In the inner loop, an adaptive sliding mode controller augmented with a disturbance observer is designed to ensure robust tracking performance under model uncertainties and external disturbances. Lyapunov theory is employed to prove the closed-loop stability, ensuring that tracking errors asymptotically converge to zero. Compared to conventional PID control, the proposed method reduces the root mean square tracking error (RMSE) from 0.2113 m/s to 0.0747 m/s(a 64.6% reduction), decreases the maximum velocity error from 0.4553 m/s to 0.2057 m/s(a 54.8% reduction), and shortens the recovery time after disturbances from 1.26 s to 0.81 s, as validated through MATLAB simulations. Preliminary experimental results on a planar upper-limb rehabilitation robot demonstrate the controller’s real-time applicability and confirm its effectiveness in improving interaction responsiveness and motion stability. Nevertheless, the implementation introduces increased computational complexity and may require real-time optimization for deployment on embedded systems. Furthermore, while this study focuses on planar motion, the control framework can be extended to multi-DOF systems and integrated with physiological signal-based intention recognition to enable more personalized rehabilitation. These results indicate that the proposed strategy offers a promising solution for enhancing the performance, robustness, and adaptability of rehabilitation robots in clinical and home-care applications, though clinical trials have not yet been conducted.

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