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

Title:
An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to a Linear Motor
Authors:
Xuewei Fu, Xiaofeng Yang, Pericle Zanchetta, Mi Tang, Yang Liu, Zhenyu Chen
Contributors:
Fu, Xuewei, Yang, Xiaofeng, Zanchetta, Pericle, Tang, Mi, Liu, Yang, Chen, Zhenyu, Fu, X., Yang, X., Zanchetta, P., Tang, M., Liu, Y., Chen, Z.
Source:
IEEE Transactions on Industrial Informatics. 19:6160-6169
Publisher Information:
Institute of Electrical and Electronics Engineers (IEEE), 2023.
Publication Year:
2023
Subject Terms:
data-based control, Feedforward control, Adaptive control system, Informatic, Instrument, 02 engineering and technology, Tuning, Iterative method, Linear motor, Data based control, Data driven, Feed forward, Fast recursive algorithm, Feedforward system, Job analysi, data-driven, 0202 electrical engineering, electronic engineering, information engineering, Task analysi, Iterative feedforward tuning, Tuning method, Estimation
Document Type:
Fachzeitschrift Article
File Description:
ELETTRONICO
ISSN:
1941-0050
1551-3203
DOI:
10.1109/tii.2022.3202818
Rights:
IEEE Copyright
Accession Number:
edsair.doi.dedup.....04161de04f289150dbea980388c299da
Database:
OpenAIRE

Weitere Informationen

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. IEEE