Wu, W, Li, D, Meng, W orcid.org/0000-0003-0209-8753 et al. (3 more authors) (2019) Iterative Feedback Tuning-based Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle. In: Proceedings of the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 08-12 Jul 2019, Hong Kong, China, China. IEEE , pp. 954-959. ISBN 978-1-7281-2494-0
Abstract
Iterative feedback tuning (IFT) method is a data-driven control method, which can tune the parameters of the system controller without knowing the system model. Pneumatic artificial muscles (PAMs) are flexible actuators that are widely used in the field of rehabilitation robots because of their flexibility and light weight. However, its nonlinearity, difficult modeling and time-varying parameters make it difficult to control. In this paper, a model-free adaptive iterative learning control (MFAILC) method based on IFT is proposed for a strong nonlinear system such as PAM. The method obtains the dynamic linearization model of PAM behavior according to the dynamic linearization theorem, then designs the controller structure, and finally uses the IFT method to optimize the controller parameters. The method proposed in this paper was compared with the MFAILC method. The simulation results show that the proposed method has a faster convergence speed and smaller tracking errors in the desired trajectory tracking control, and its effectiveness is also verified.
Metadata
Item Type: | Proceedings Paper |
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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. |
Keywords: | Adaptation models; Tuning; Iterative learning control; Nonlinear systems; Roads; Trajectory; Adaptive systems |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Feb 2020 16:44 |
Last Modified: | 05 Feb 2020 20:21 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/aim.2019.8868584 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156511 |