Ai, Q, Ke, D, Zuo, J et al. (4 more authors) (2020) High-Order Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle With Enhanced Convergence. IEEE Transactions on Industrial Electronics, 67 (11). pp. 9548-9559. ISSN 0278-0046
Abstract
Pneumatic artificial muscles (PAMs) have been widely used in actuation of medical devices due to their intrinsic compliance and high power-to-weight ratio features. However, the nonlinearity and time-varying nature of PAMs make it challenging to maintain high-performance tracking control. In this article, a high-order pseudopartial derivative-based model-free adaptive iterative learning controller (HOPPD-MFAILC) is proposed to achieve fast convergence speed. The dynamics of PAM is converted into a dynamic linearization model during iterations; meanwhile, a high-order estimation algorithm is designed to estimate the pseudopartial derivative component of the linearization model by only utilizing the input and output data in previous iterations. The stability and convergence performance of the controller are verified through theoretical analysis. Simulation and experimental results on PAM demonstrate that the proposed HOPPD-MFAILC can track the desired trajectory with improved convergence and tracking performance.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 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. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Convergence, iterative learning control(ILC), model-free adaptive control (MFAC), pneumatic artificial muscle (PAM) |
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) |
Funding Information: | Funder Grant number Royal Society IE161218 Academy of Medical Sciences GCRFNG\100213 Royal Society RGS\R2\180237 EPSRC (Engineering and Physical Sciences Research Council) GCRF_IS_2017 |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Aug 2020 14:27 |
Last Modified: | 03 Aug 2020 14:27 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/tie.2019.2952810 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163931 |