Controller tuning with Bayesian optimization and its acceleration: Concept and experimental validation

被引：3

作者：

Fujimoto, Yusuke ^{[1
]}

Sato, Hiroki ^{[1
]}

Nagahara, Masaaki ^{[1
]}

机构：

[1] Univ Kitakyushu, Fukuoka, Japan

来源：

ASIAN JOURNAL OF CONTROL | 2023年 / 25卷 / 03期

基金：

日本科学技术振兴机构; 日本学术振兴会;

关键词：

Bayesian optimization; data-driven control; FEEDBACK; DESIGN;

D O I：

10.1002/asjc.2847

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work discusses a data-driven approach to controller parameter tuning based on Bayesian optimization. In particular, we propose to design the prior mean function based on a model of the plant. By encoding the information on the model, the optimization needs a much fewer iterations than standard approaches. The effectiveness of the proposed method is demonstrated with a practical experiment.

引用

页码：2408 / 2414

页数：7

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