Iterative learning-based path control for robot-assisted upper-limb rehabilitation

Maqsood, Kamran; Luo, Jing; Yang, Chenguang; Ren, Qing-Yuan; Li, Yanan

doi:10.1007/s00521-021-06037-z

Cited by 7 publications

(6 citation statements)

References 37 publications

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“…Maqsood et al proposed a 2 DOF robotic planner platform using an iterative trajectory learning algorithm and an impedance matching method to overcome the unknown user behavior and prevent patient deviation from the trajectory [50]. No knowledge of the patient's dynamics is required for the proposed approach to achieve algorithm convergence.…”

Section: Robotics Therapy For the Upper Limbmentioning

confidence: 99%

Overview of Robotic Based System for Rehabilitation and Healthcare

Chellal

Lima

Fernandes

et al. 2021

Communications in Computer and Information Science

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material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Robotics Therapy For the Upper Limbmentioning

confidence: 99%

Overview of Robotic Based System for Rehabilitation and Healthcare

Chellal

Lima

Fernandes

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…Stability issues can be a major issue affecting user experience, motivation, engagement, and product adoption rate [14], [15]. Pulley-drive robots attempted to employ a differential drive with pulleys and belts, to address the stability issues and improve dynamics calculation [5], [13], [16], [17]. However, the pulley-drive robots require high maintenance due to the frequent wear and tear on the belt drives.…”

Section: A End-effector Upper Limb Rehabilitation Devicesmentioning

confidence: 99%

A new structure of end-effector traction upper limb rehabilitation robot

Han

et al. 2021

2021 IEEE International Conference on Real-Time Computing and Robotics (RCAR)

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This paper presents the development of an upper limb end-effector based rehabilitation device for stroke patients, offering assistance or resistance along any 2-dimensional trajectory during physical therapy. It employs a non-backdrivable ball-screw-driven mechanism for enhanced control accuracy. The control system features three novel algorithms: First, the Implicit Euler velocity control algorithm (IEVC) highlighted for its stateof-the-art accuracy, stability, efficiency and generalizability in motion restriction control. Second, an Admittance Virtual Dynamics simulation algorithm that achieves a smooth and natural human interaction with the non-backdrivable end-effector. Third, a generalized impedance force calculation algorithm allowing efficient impedance control on any trajectory or area boundary. Experimental validation demonstrated the system's effectiveness in accurate end-effector position control across various trajectories and configurations. The proposed upper limb endeffector-based rehabilitation device, with its high performance and adaptability, holds significant promise for extensive clinical application, potentially improving rehabilitation outcomes for stroke patients.

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“…No offline methods have reported results for systems with varying control directions. Hybrid methods have the popular iterative learning control (ILC) [13,14] that is commonly used for systems with repetitive tasks. This method has the advantage of decreasing error in each cycle but requires a bit of prior knowledge of the system.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Adaptive Controller Based on Hyperbolic Cost Function for Non-Affine Discrete-Time Systems with Variant Control Direction

Flores-Padilla,

Treesatayapun

2024

ASI

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As technology evolves, more complex non-affine systems are created. These complex systems are hard to model, whereas most controllers require information on systems to be designed. This information is hard to obtain for systems with varying control directions. Therefore, this study introduces a novel data-driven estimator and controller tailored for single-input single-output non-affine discrete-time systems. This approach focuses on cases when the control direction varies over time and the mathematical model of the system is completely unknown. The estimator and controller are constructed using a Multiple-input Fuzzy Rules Emulated Network framework. The weight vectors are updated through the gradient descent optimization method, which employs a unique cost function that multiplies the error by a hyperbolic tangent. The stability analyses demonstrate that both the estimator and controller converge to uniformly ultimately bounded (UUB) functions of Lyapunov. To validate the results, we show experimental tests of force control that were executed on the z-axis of a drive-controlled 3D scanning robot. This system has a varying control direction, and we also provide comparison results with a state-of-the-art controller. The results show a mean absolute percentage tracking error smaller than one percent on the steady state and the expected variation in the system’s control direction.

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Iterative learning-based path control for robot-assisted upper-limb rehabilitation

Cited by 7 publications

References 37 publications

Overview of Robotic Based System for Rehabilitation and Healthcare

Overview of Robotic Based System for Rehabilitation and Healthcare

A new structure of end-effector traction upper limb rehabilitation robot

Data-Driven Adaptive Controller Based on Hyperbolic Cost Function for Non-Affine Discrete-Time Systems with Variant Control Direction

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