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Feedforward enhancement through iterative learning control for robotic manipulator

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conference contribution
posted on 15.11.2021, 12:21 by Chengyuan LiuChengyuan Liu, Mingfeng Wang, Xuefang Li, Svetan Ratchev
This work presents an iterative learning control (ILC) algorithm to enhance the feedforward control (FFC) for robotic manipulators. The proposed ILC algorithm enables the cooperation between the ILC, inverse dynamics, and a PD feedback control (FBC) module. The entire control scheme is elaborated to guarantee the control accuracy of the first implementation; to improve the control performance of the manipulator progressively with successive iterations; and to compensate both repetitive and non-repetitive disturbances, as well as various uncertainties. The convergence of the proposed ILC algorithm is analysed using a well established Lyapunov-like composite energy function (CEF). A trajectory tracking test is carried out by a seven-degree-of-freedom (7-DoF) robotic manipulator to demonstrate the effectiveness and efficiency of the proposed control scheme. By implementing the ILC algorithm, the maximum tracking error and its percentage respect to the motion range are improved from 5.78° to 1.09°, and 21.09% to 3.99%, respectively, within three iterations.

Funding

Innovate UK project Wing LIFT under the Grant number 113162

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

Pages

1067 - 1072

Source

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 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.

Publication date

2021-10-05

Copyright date

2021

ISBN

9781665418737

eISSN

2161-8089

Language

en

Location

Lyon, France

Event dates

23-27 August 2021

Depositor

Dr Chengyuan Liu. Deposit date: 14 November 2021

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