10.57647/mjee.2026.2001.05

Adaptive Control of Freedom Upper-Limb Exoskeleton Robots Using Extreme Learning Machine and Terminal Sliding Mode

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  • Morteza Mirzaee*1,
  • Reza Kazemi1,
  1. Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran

Received: 2024-11-06

Revised: 2025-02-04

Accepted: 2025-03-01

Published in Issue 2026-03-31

Copyright (c) 2026 Mohammed Mahfoudi, Ismail Angri, Abdellah Najid, Mohammed Fattah, Moulhime El Bekkali (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mirzaee, M., & Kazemi, R. (2026). Adaptive Control of Freedom Upper-Limb Exoskeleton Robots Using Extreme Learning Machine and Terminal Sliding Mode. Majlesi Journal of Electrical Engineering, 20(1 (March 2026). https://doi.org/10.57647/mjee.2026.2001.05
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Abstract

This paper presents a novel Adaptive Dynamic Terminal Sliding Mode Controller (ADTSMC) with an exponential reaching law, specifically designed for precise position control of 5-Degree of Freedom (DOF) upper-limb exoskeleton robots. The proposed control strategy addresses critical challenges in precision, stability, and adaptability, introducing several key innovations. Firstly, the incorporation of a Terminal Sliding Mode Surface (TSMS) ensures finite-time convergence of system states, significantly enhancing tracking performance. Secondly, the integration of Dynamic Second-Order Sliding Mode Control (DSMC) with an exponential reaching law mitigates chattering, providing smoother and more reliable control inputs. Thirdly, an Extreme Learning Machine (ELM) is utilized to optimize controller parameters in real-time, offering robustness against uncertainties and variations in system dynamics while managing the complex nonlinearities of upper-limb exoskeletons. The ELM helps maintain stable learning and avoids issues such as gradient vanishing. The stability of the closed-loop system is rigorously validated using a Lyapunov candidate function, ensuring reliable operation under varying conditions. Simulation results show that the proposed adaptive DSMC outperforms existing methods in terms of convergence speed, tracking accuracy, and chattering suppression, demonstrating a significant improvement in performance, with reductions of approximately 74.3% in performance indices.

Keywords

  • Exoskeleton robot,
  • Sliding Mode Control,
  • Neural network,
  • Adaptive control,
  • Rehabilitation robotics
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