An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to A Linear Motor

Fu, Xuewei; Yang, Xiaofeng; Zanchetta, Pericle; Tang, Mi; Liu, Yang; Chen, Zhenyu

Abstract

The feedforward control can effectively improve the servo performance in applications with high requirements of velocity and acceleration. The iterative feedforward tuning method (IFFT) enables the possibility of both removing the need for prior knowledge of the system plant in model-based feedforward and improving the extrapolation capability for varying tasks of iterative learning control. However, most of IFFT methods require to set the number of basis functions in advance, which is inconvenient to the system design. To tackle this problem, an adaptive data-driven IFFT based on fast recursive algorithm (IFFT-FRA) is developed in this paper. Explicitly, based on FRA the proposed approach can adaptively tune the feedforward structure, which significantly increases the intelligence of the approach. Additionally, a data-based iterative tuning procedure is introduced to achieve the unbiased estimation of parameters optimization in presence of noise. Comparative experiments on a linear motor confirms the effectiveness of the proposed approach.

Citation

Fu, X., Yang, X., Zanchetta, P., Tang, M., Liu, Y., & Chen, Z. (2022). An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to A Linear Motor. IEEE Transactions on Industrial Informatics, 1-10. https://doi.org/10.1109/tii.2022.3202818

• Journal Article Type: Article
• Acceptance Date: Aug 21, 2022
• Publication Date: 2022
• Deposit Date: Jan 6, 2023
• Publicly Available Date: Jan 6, 2023
• Journal: IEEE Transactions on Industrial Informatics
• Print ISSN: 1551-3203
• Electronic ISSN: 1941-0050
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 1-10
• DOI: https://doi.org/10.1109/tii.2022.3202818
• Keywords: Electrical and Electronic Engineering; Computer Science Applications; Information Systems; Control and Systems Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/10638862
• Publisher URL: https://ieeexplore.ieee.org/document/9870555