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arXiv · 2010.10831

Experimental Automatic Calibration of a Semi-Active Suspension Controller via Bayesian Optimization

Gianluca Savaia·Youngil Sohn·Simone Formentin·Giulio Panzani·Matteo Corno·Sergio M. Savaresi
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Abstract

The End-of-Line (EoL) calibration of semi-active suspension systems for road vehicles is usually a critical and expensive task, needing a team of vehicle and control experts as well as many hours of professional driving. In this paper, we propose a purely data-based tuning method enabling the automatic calibration of the parameters of a proprietary suspension controller by relying on little experimental time and exploiting Bayesian Optimization tools. A detailed methodology on how to select the most critical degrees of freedom of the algorithm is also provided. The effectiveness of the proposed approach is assessed on a commercial multi-body simulator as well as on a real car.

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Gianluca Savaia, Youngil Sohn, Simone Formentin, Giulio Panzani, Matteo Corno, Sergio M. Savaresi. 2020-10-21. Experimental Automatic Calibration of a Semi-Active Suspension Controller via Bayesian Optimization. https://arxiv.org/abs/2010.10831

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