Disturbance observer-based terminal sliding mode control for the training safety improvement in robot-assisted rehabilitation

Existing control methods for exoskeletons often face challenges in adapting to individual differences, ensuring robustness in dynamic environments, and achieving real-time performance. For instance, certain approaches fail to balance rehabilitation efficacy with wearer comfort, while others suffer from issues such as chattering and limited disturbance rejection capabilities. This paper proposes a control framework for exoskeleton rehabilitation robots, emphasizing the strict implementation of safety protocols to guarantee that patients in the early stages of rehabilitation can accurately follow normal human activity postures. Firstly, an interpolating polynomial is optimized to generate the desired trajectory, with consideration of the minimum jerk principle. Secondly, a motion-dependent function is proposed for smooth switching between two modes of normal training and safe stopping. Thirdly, a non-singular fast terminal sliding mode method based on a nonlinear disturbance observer is proposed to accurately track the desired joint angles, with the objective of achieving a tracking error that tends to zero in a finite time. Furthermore, the stability of the closed-loop system is demonstrated through the application of the Lyapunov method. Ultimately, the simulation results demonstrate the efficacy and resilience of the proposed control framework.

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Publication Details

Journal
Intelligence & Robotics
Published
2025-04-11
DOI
https://doi.org/10.20517/ir.2025.17
Citations
3
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
Field-Weighted Citation Impact
3.08
Controls
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article

Disturbance observer-based terminal sliding mode control for the training safety improvement in robot-assisted rehabilitation

Renjie Ma, Yaqi Zhang, Weiyi Xie
3 citations
Intelligence & Robotics
Stroke Rehabilitation and Recovery
3.08
article

Disturbance observer-based terminal sliding mode control for the training safety improvement in robot-assisted rehabilitation

Renjie Ma, Yaqi Zhang, Weiyi Xie
article en
3 citations

Abstract

Existing control methods for exoskeletons often face challenges in adapting to individual differences, ensuring robustness in dynamic environments, and achieving real-time performance. For instance, certain approaches fail to balance rehabilitation efficacy with wearer comfort, while others suffer from issues such as chattering and limited disturbance rejection capabilities. This paper proposes a control framework for exoskeleton rehabilitation robots, emphasizing the strict implementation of safety protocols to guarantee that patients in the early stages of rehabilitation can accurately follow normal human activity postures. Firstly, an interpolating polynomial is optimized to generate the desired trajectory, with consideration of the minimum jerk principle. Secondly, a motion-dependent function is proposed for smooth switching between two modes of normal training and safe stopping. Thirdly, a non-singular fast terminal sliding mode method based on a nonlinear disturbance observer is proposed to accurately track the desired joint angles, with the objective of achieving a tracking error that tends to zero in a finite time. Furthermore, the stability of the closed-loop system is demonstrated through the application of the Lyapunov method. Ultimately, the simulation results demonstrate the efficacy and resilience of the proposed control framework.

Intelligence & RoboticsVol. 5(2)
Shantou University (CN)
Openalex Percentile: Top 9%
Stroke Rehabilitation and Recovery
3.08
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Disturbance observer-based terminal sliding mode control for the training safety improvement in robot-assisted rehabilitation — Renjie Ma, Yaqi Zhang, et al. · Intelligence & Robotics (2025) | TGRS Research Map | TGRS