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

TL;DR

This paper introduces a data-driven, automatic calibration method for semi-active suspension controllers using Bayesian Optimization, reducing time and expert involvement in vehicle tuning.

Contribution

It presents a novel Bayesian Optimization-based approach for automatic suspension controller calibration, applicable to real vehicles and simulators.

Findings

Effective calibration achieved with minimal experimental data

Method reduces calibration time and expert effort

Validated on both simulator and real vehicle

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.