Semi-Active Suspension Control Design via Bayesian Optimization

被引：2
|
作者：
Savaia, Gianluca [1 ]
Formentin, Simone [1 ]
Savaresi, Sergio M. [1 ]
机构：
[1] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, MI, Italy
来源：
IFAC PAPERSONLINE | 2020年 / 53卷 / 02期
关键词：
Semi-active suspension; Vehicle dynamics; Bayesian optimization; Calibration;
D O I：
10.1016/j.ifacol.2020.12.1374
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The fine tuning of semi-active suspension control systems for road vehicles is usually a costly and burdensome task, needing control expertise and many hours of professional driving. In this paper, we propose a data-driven tuning method enabling the automatic calibration of the parameters of the suspension controller using a small number of experiments and exploiting Bayesian Optimization tools. The effectiveness of the proposed approach is validated on a commercial multi-body simulator. As a side contribution, the approach is shown to be robust with respect to variations of the testing conditions. Copyright (C) 2020 The Authors.
引用
收藏
页码：14312 / 14317
页数：6
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