Learning Feedforward Control for Industrial Manipulators

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

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, May). Learning Feedforward Control for Industrial Manipulators. Presented at IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS 2021), Suzhou, China

• Presentation Conference Type: Edited Proceedings
• Conference Name: IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS 2021)
• 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: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 523-528
• 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.