Qian, K orcid.org/0000-0002-8719-1537, Li, Z, Zhang, Z orcid.org/0000-0003-0204-3867 et al. (1 more author) (2023) Data-driven Adaptive Iterative Learning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training. IEEE Robotics and Automation Letters, 8 (2). pp. 656-663. ISSN 2377-3766
Abstract
This paper investigates the repetitive range of motion (ROM) training control for a compliant ankle rehabilitation robot (CARR). The CARR utilizes four pneumatic muscle (PM) actuators to manipulate the ankle with three rational degree-of-freedoms (DoFs) and soft human-robot interaction, but the strong-nonlinearity of the PM actuator makes precise tracking difficult. To improve the training effectiveness, a data-driven adaptive iterative learning controller (DDAILC) is proposed based on compact form dynamic linearization (CFDL) with estimated pseudo-partial derivative (PPD). Instead of using a PM dynamic model, the estimated PPD is updated merely by online input-output (I/O) measures. Sufficient conditions are established to guarantee the convergence of tracking errors and the boundedness of control input. Experimental studies are conducted on ten human participants with two therapist-resembled trajectories. Compared with other data-driven methods, the proposed DDAILC demonstrates significant improvement on tracking performance.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biomedical Sciences (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/S019219/1 EPSRC (Engineering and Physical Sciences Research Council) EP/V057782/1 Royal Society IEC\NSFC\191095 |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Dec 2022 10:57 |
Last Modified: | 10 Jan 2023 20:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/LRA.2022.3229570 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194377 |