Open Access

ARTICLE

Radial Basis Function Neural Network Adaptive Controller for Wearable Upper-Limb Exoskeleton with Disturbance Observer

Mohammad Soleimani Amiri¹, Sahbi Boubaker^2,3,*, Rizauddin Ramli^4,*, Souad Kamel²

1 Department of Artificial Intelligence, Faculty of Artificial Intelligence and Cyber Security, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka, 76100, Malaysia
2 Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia
3 King Salman Center for Disability Research, Riyadh, 11614, Saudi Arabia
4 Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia

* Corresponding Authors: Sahbi Boubaker. Email: ; Rizauddin Ramli. Email:

Computer Modeling in Engineering & Sciences 2025, 144(3), 3113-3133. https://doi.org/10.32604/cmes.2025.069167

Received 16 June 2025; Accepted 20 August 2025; Issue published 30 September 2025

Abstract

Disability is defined as a condition that makes it difficult for a person to perform certain vital activities. In recent years, the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent. However, controlling an exoskeleton for rehabilitation presents challenges due to their non-linear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton. To remedy these problems, this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons, addressing the challenges of nonlinear dynamics and external disturbances. The proposed controller integrated a Radial Basis Function Neural Network (RBFNN) with a disturbance observer and employed a high-dimensional integral Lyapunov function to guarantee system stability and trajectory tracking performance. In the control system, the role of the RBFNN was to estimate uncertain signals in the dynamic model, while the disturbance observer tackled external disturbances during trajectory tracking. Artificially created scenarios for Human-Robot interactive experiments and periodically repeated reference trajectory experiments validated the controller’s performance, demonstrating efficient tracking. The proposed controller is found to achieve superior tracking accuracy with Root-Mean-Squared (RMS) errors of 0.022–0.026 rad for all joints, outperforming conventional Proportional-Integral-Derivative (PID) by 73% and Neural-Fuzzy Adaptive Control (NFAC) by 389.47% lower error. These results suggested that the RBFNN adaptive controller, coupled with disturbance compensation, could serve as an effective rehabilitation tool for upper-limb exoskeletons. These results demonstrate the superiority of the proposed method in enhancing rehabilitation accuracy and robustness, offering a promising solution for the control of upper-limb assistive devices. Based on the obtained results and due to their high robustness, the proposed control schemes can be extended to other motor disabilities, including lower limb exoskeletons.

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Keywords

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