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

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 feedforwa...

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Vydané v:IEEE transactions on industrial informatics Ročník 19; číslo 4; s. 6160 - 6169
Hlavní autori: Fu, Xuewei, Yang, Xiaofeng, Zanchetta, Pericle, Tang, Mi, Liu, Yang, Chen, Zhenyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Acceleration
Algorithms
Basis functions
data driven
Data-based control
Electric motors
Estimation
fast recursive algorithm
Feedforward control
Feedforward systems
Informatics
Instruments
iterative feedforward tuning (IFFT)
Iterative methods
linear motor
Optimization
Systems design
Task analysis
Tuning
ISSN:1551-3203, 1941-0050
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Shrnutí: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 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 a fast recursive algorithm (IFFT-FRA) is developed in this article. 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 the presence of noise. Comparative experiments on a linear motor confirm the effectiveness of the proposed approach.
Bibliografia:ObjectType-Article-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3202818