Enhancing skyhook for semi-active suspension control via machine learning. (September 2021)

• Record Type: Journal Article
• Authors: Savaia, Gianluca; Formentin, Simone; Panzani, Giulio; Corno, Matteo; Savaresi, Sergio M.
• Is Part Of: IFAC journal of systems and control. Volume 17(2021)
• Publication Date: 2021-09
• Subjects: Semi-active -- Suspensions -- Sequential learning -- Experiments -- Skyhook; Automatic control -- Periodicals; Relay control systems -- Periodicals; Embedded computer systems -- Periodicals; Feedback control systems -- Periodicals; Artificial intelligence -- Periodicals; Artificial intelligence; Automatic control; Embedded computer systems; Feedback control systems; Relay control systems; Electronic journals; Periodicals; 629.89
• DOI: 10.1016/j.ifacsc.2021.100161
• Languages: English
• ISSNs: 2468-6018

Abstract

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.