Data-driven Rational Feedforward Tuning Algorithm Based on Self-Adaptive Hybrid Self-learning TLBO

Siwen Chen; Yang Liu; Fazhi Song

doi:10.1109/DDCLS58216.2023.10166702

Data-driven Rational Feedforward Tuning Algorithm Based on Self-Adaptive Hybrid Self-learning TLBO

Siwen Chen
, Yang Liu
, Fazhi Song

School of Instrumentation Science and Engineering

Harbin Institute of Technology
Huazhong University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Data-driven feedforward tuning algorithm, extensively used in ultra-precision motion stage, can significantly compensate tracking errors and optimize task adaptability. However, the rational basis based feedforward optimization usually faces non-convex optimization, numerical stability and initial value problems. The purpose of this paper is to propose a feedforward tuning algorithm based on the heuristic method self-Adaptive hybrid self-learning TLBO. The algorithm can converge to the ideal feedforward parameter, so that the system can achieve high tracking accuracy, and has good numerical properties and low initial value requirements. The effectiveness and superiority of the proposed method are verified by simulation.

Original language	English
Title of host publication	Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1516-1521
Number of pages	6
ISBN (Electronic)	9798350321050
DOIs	https://doi.org/10.1109/DDCLS58216.2023.10166702
State	Published - 2023
Event	12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023 - Xiangtan, China Duration: 12 May 2023 → 14 May 2023

Publication series

Name	Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023

Conference

Conference	12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023
Country/Territory	China
City	Xiangtan
Period	12/05/23 → 14/05/23

Keywords

Feedforward Control
Rational Basis Function
Self-Adaptive Hybrid Self-learning TLBO

Access to Document

10.1109/DDCLS58216.2023.10166702

Cite this

Chen, S., Liu, Y., & Song, F. (2023). Data-driven Rational Feedforward Tuning Algorithm Based on Self-Adaptive Hybrid Self-learning TLBO. In Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023 (pp. 1516-1521). (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS58216.2023.10166702

Chen, Siwen ; Liu, Yang ; Song, Fazhi. / Data-driven Rational Feedforward Tuning Algorithm Based on Self-Adaptive Hybrid Self-learning TLBO. Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1516-1521 (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023).

@inproceedings{502354912ffc4db59f90546786e6d36b,

title = "Data-driven Rational Feedforward Tuning Algorithm Based on Self-Adaptive Hybrid Self-learning TLBO",

abstract = "Data-driven feedforward tuning algorithm, extensively used in ultra-precision motion stage, can significantly compensate tracking errors and optimize task adaptability. However, the rational basis based feedforward optimization usually faces non-convex optimization, numerical stability and initial value problems. The purpose of this paper is to propose a feedforward tuning algorithm based on the heuristic method self-Adaptive hybrid self-learning TLBO. The algorithm can converge to the ideal feedforward parameter, so that the system can achieve high tracking accuracy, and has good numerical properties and low initial value requirements. The effectiveness and superiority of the proposed method are verified by simulation.",

keywords = "Feedforward Control, Rational Basis Function, Self-Adaptive Hybrid Self-learning TLBO",

author = "Siwen Chen and Yang Liu and Fazhi Song",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023 ; Conference date: 12-05-2023 Through 14-05-2023",

year = "2023",

doi = "10.1109/DDCLS58216.2023.10166702",

language = "英语",

series = "Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1516--1521",

booktitle = "Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023",

address = "美国",

}

Chen, S, Liu, Y & Song, F 2023, Data-driven Rational Feedforward Tuning Algorithm Based on Self-Adaptive Hybrid Self-learning TLBO. in Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1516-1521, 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023, Xiangtan, China, 12/05/23. https://doi.org/10.1109/DDCLS58216.2023.10166702

Data-driven Rational Feedforward Tuning Algorithm Based on Self-Adaptive Hybrid Self-learning TLBO. / Chen, Siwen; Liu, Yang ; Song, Fazhi.
Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1516-1521 (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data-driven Rational Feedforward Tuning Algorithm Based on Self-Adaptive Hybrid Self-learning TLBO

AU - Chen, Siwen

AU - Liu, Yang

AU - Song, Fazhi

PY - 2023

Y1 - 2023

N2 - Data-driven feedforward tuning algorithm, extensively used in ultra-precision motion stage, can significantly compensate tracking errors and optimize task adaptability. However, the rational basis based feedforward optimization usually faces non-convex optimization, numerical stability and initial value problems. The purpose of this paper is to propose a feedforward tuning algorithm based on the heuristic method self-Adaptive hybrid self-learning TLBO. The algorithm can converge to the ideal feedforward parameter, so that the system can achieve high tracking accuracy, and has good numerical properties and low initial value requirements. The effectiveness and superiority of the proposed method are verified by simulation.

AB - Data-driven feedforward tuning algorithm, extensively used in ultra-precision motion stage, can significantly compensate tracking errors and optimize task adaptability. However, the rational basis based feedforward optimization usually faces non-convex optimization, numerical stability and initial value problems. The purpose of this paper is to propose a feedforward tuning algorithm based on the heuristic method self-Adaptive hybrid self-learning TLBO. The algorithm can converge to the ideal feedforward parameter, so that the system can achieve high tracking accuracy, and has good numerical properties and low initial value requirements. The effectiveness and superiority of the proposed method are verified by simulation.

KW - Feedforward Control

KW - Rational Basis Function

KW - Self-Adaptive Hybrid Self-learning TLBO

UR - https://www.scopus.com/pages/publications/85165949233

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DO - 10.1109/DDCLS58216.2023.10166702

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BT - Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 12 May 2023 through 14 May 2023

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Chen S, Liu Y , Song F. Data-driven Rational Feedforward Tuning Algorithm Based on Self-Adaptive Hybrid Self-learning TLBO. In Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1516-1521. (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023). doi: 10.1109/DDCLS58216.2023.10166702

Data-driven Rational Feedforward Tuning Algorithm Based on Self-Adaptive Hybrid Self-learning TLBO

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