Feedforward Enhancement through Iterative Learning Control for Robotic Manipulator

Liu, Chengyuan; Wang, Mingfeng; Li, Xuefang; Ratchev, Svetan

Abstract

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.

Citation

Liu, C., Wang, M., Li, X., & Ratchev, S. (2021). Feedforward Enhancement through Iterative Learning Control for Robotic Manipulator. . https://doi.org/10.1109/CASE49439.2021.9551523

• Conference Name: IEEE 17th International Conference on Automation Science and Engineering
• Conference Location: Lyon, France
• Start Date: Aug 23, 2021
• End Date: Aug 27, 2021
• Acceptance Date: Jun 2, 2021
• Online Publication Date: Oct 5, 2021
• Publication Date: Aug 27, 2021
• Deposit Date: Jul 1, 2021
• Publicly Available Date: Aug 27, 2021
• Publisher: IEEE
• Pages: 1067-1072
• ISBN: 9781665418737
• DOI: https://doi.org/10.1109/CASE49439.2021.9551523
• Public URL: https://nottingham-repository.worktribe.com/output/5750816
• Publisher URL: https://ieeexplore.ieee.org/document/9551523
• Related Public URLs: https://case2021.sciencesconf.org/
• 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.