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Learning Feedforward Control for Industrial Manipulators

Liu, Chengyuan; Popov, Atanas; Turner, Alison; Shires, Emma; Ratchev, Svetan

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Authors

Chengyuan Liu

ATANAS POPOV ATANAS.POPOV@NOTTINGHAM.AC.UK
Professor of Engineering Dynamics

Alison Turner

Emma Shires

Professor SVETAN RATCHEV svetan.ratchev@nottingham.ac.uk
Cripps Professor of Production Engineering & Head of Research Division



Abstract

In this work, an iterative learning control (ILC) algorithm is proposed for industrial manipulators. The proposed ILC algorithm works coordinately with the inverse dynamics of the manipulator and a feedback controller. The entire control scheme has the ability of compensating both repetitive and non-repetitive disturbances; guaranteeing the control accuracy of the first implementation; and improving the control accuracy of the manipulator progressively with successive iterations. In order to build the the convergence of the proposed ILC algorithm, a composite energy function is developed. A case study on a four degree of freedom industrial manipulator is demonstrated to illustrate the effectiveness of the proposed control scheme. By implementing the ILC algorithm, the maximum root mean square error of the control accuracy is improved from 0.0262 rad to 0.0016 rad within ten iterations.

Citation

Liu, C., Popov, A., Turner, A., Shires, E., & Ratchev, S. (2021). Learning Feedforward Control for Industrial Manipulators. In 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) (523-528). https://doi.org/10.1109/DDCLS52934.2021.9455672

Conference Name IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS 2021)
Conference Location Suzhou, China
Start Date May 14, 2021
End Date May 16, 2021
Acceptance Date Apr 2, 2021
Online Publication Date Jun 25, 2021
Publication Date Jun 25, 2021
Deposit Date Jul 1, 2021
Publicly Available Date Jul 5, 2021
Publisher IEEE
Pages 523-528
Series Title IEEE Data Driven Control and Learning Systems Conference (DDCLS)
Series ISSN 2767-9853
Book Title 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)
ISBN 9781665424240
DOI https://doi.org/10.1109/DDCLS52934.2021.9455672
Keywords Iterative Learning Control (ILC); Industrial Manipulator; Control Accuracy; Convergence Analysis
Public URL https://nottingham-repository.worktribe.com/output/5557913
Publisher URL https://ieeexplore.ieee.org/document/9455672
Additional Information © 2021 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.

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