Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors

Fu, Xuewei; Ding, Chenyang; Zanchetta, Pericle; Yang, Xiaofeng; Tang, Mi; Liu, Yang

Abstract

The cogging force arising due to the strong attraction forces between the iron core and the permanent magnets, is a common inherent property of the linear motors, which significantly affects the control performance. Therefore, significant research efforts have been devoted to the compensation of the cogging force. In this paper, an identification approach based on the radial basis function neural network (RBFNN) is proposed to obtain an accurate model of the cogging force. A self-adaptive hybrid self-learning teaching-learning-based optimization (SHSLTLBO) method is utilized to train the neural network. Finally, the experimental results confirm the effectiveness and the superiority of the proposed cogging force identification method.

• Conference Name: 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA)
• Conference Location: Wuhan, China
• Start Date: Jul 1, 2021
• End Date: Jul 3, 2021
• Acceptance Date: Mar 10, 2021
• Online Publication Date: Aug 12, 2021
• Publication Date: Jul 1, 2021
• Deposit Date: Apr 21, 2022
• Publicly Available Date: Apr 21, 2022
• Publisher: IEEE
• DOI: https://doi.org/10.1109/LDIA49489.2021.9505810
• Public URL: https://nottingham-repository.worktribe.com/output/7784848
• Publisher URL: https://ieeexplore-ieee-org.nottingham.idm.oclc.org/document/9505810
• 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.