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Explicit model predictive control of an active suspension system

Theunissen, Johan, Sorniotti, Aldo, Gruber, Patrick, Fallah, Saber, Dhaens, M, Reybrouck, K, Lauwerys, C, Vandersmissen, B, Al Sakka, M and Motte, K (2018) Explicit model predictive control of an active suspension system In: plus 2018 – 9th International Munich Chassis Symposium, 12 - 13 June 2018, Munich, Germany.

180502_Chassis_Tech_18_paper_US_TEN_final.pdf - Accepted version Manuscript

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Model predictive control (MPC) is increasingly finding its way into industrial applications, due to its superior tracking performance and ability to formally handle system constraints. However, the real-time capability problems related to the conventional implicit model predictive control (i-MPC) framework are well known, especially when targeting low-cost electronic control units (ECUs) for high bandwidth systems, such as automotive active suspensions, which are the topic of this paper. In this context, to overcome the real-time implementation issues of i-MPC, this study proposes explicit model predictive control (e-MPC), which solves the optimization problem off-line, via multi-parametric quadratic programming (mp-QP). e-MPC reduces the on-line algorithm to a function evaluation, which replaces the computationally demanding on-line solution of the quadratic programming (QP) problem. An e-MPC based suspension controller is designed and experimentally validated for a case study Sport Utility Vehicle (SUV), equipped with the active ACOCAR suspension system from the Tenneco Monroe product family. The target is to improve ride comfort in the frequency range of primary ride (< 4 Hz), without affecting the performance at higher frequencies. The proposed e-MPC implementations reduce the root mean square (RMS) value of the sprung mass acceleration by > 40% compared to the passive vehicle set-up for frequencies < 4 Hz, and by up to 19% compared to the same vehicle with a skyhook controller on the 0-100 Hz frequency range.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Authors :
Dhaens, M
Reybrouck, K
Lauwerys, C
Vandersmissen, B
Al Sakka, M
Motte, K
Date : 26 September 2018
DOI : 10.1007/978-3-658-22050-1_17
Copyright Disclaimer : Copyright 2018 Springer Fachmedien Wiesbaden GmbH
Uncontrolled Keywords : Model predictive control, explicit solution, multi-parametric programming, active suspension, ride comfort
Depositing User : Melanie Hughes
Date Deposited : 22 May 2018 11:18
Last Modified : 06 Nov 2018 14:55

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