Semi-active control is the most employed technology for electronic suspension systems. The damping can be regulated to trade-off comfort and handling. Due to its success in industrial applications, semi-active control design has been extensively investigated in literature mainly from a model-based perspective. In this contribution, the authors propose a novel control strategy derived via a sequential learning framework, which selects the most significant feedback measurements for semi-active control and learns the optimal policy from data. As opposed to most of the contributions based on deep-learning approaches, the output of the proposed methodology is a control algorithm consisting of few parameters, which can be easily ported and calibrated on a real vehicle. Experimental validation on a sports-car shows that the proposed algorithm is superior in damping the body resonance with respect to the state-of-the-art skyhook algorithm. Indeed, the learned control policy consists of an augmentation of skyhook.

Enhancing skyhook for semi-active suspension control via machine learning

Savaia G.;Formentin S.;Panzani G.;Corno M.;Savaresi S. M.
2021-01-01

Abstract

Semi-active control is the most employed technology for electronic suspension systems. The damping can be regulated to trade-off comfort and handling. Due to its success in industrial applications, semi-active control design has been extensively investigated in literature mainly from a model-based perspective. In this contribution, the authors propose a novel control strategy derived via a sequential learning framework, which selects the most significant feedback measurements for semi-active control and learns the optimal policy from data. As opposed to most of the contributions based on deep-learning approaches, the output of the proposed methodology is a control algorithm consisting of few parameters, which can be easily ported and calibrated on a real vehicle. Experimental validation on a sports-car shows that the proposed algorithm is superior in damping the body resonance with respect to the state-of-the-art skyhook algorithm. Indeed, the learned control policy consists of an augmentation of skyhook.
2021
Experiments
Semi-active
Sequential learning
Skyhook
Suspensions
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1207484
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