Title: Load-dependent LPV/H2 output-feedback control of semi-active suspension systems equipped with MR damper

Authors: Jaffar Seyyed Esmaeili; Ahmad Akbari; Hamid Reza Karimi

Addresses: Department of Electrical Engineering, Sahand University of Technology, P.O. Box: 51335/1996, Tabriz, Iran ' Department of Electrical Engineering, Sahand University of Technology, P.O. Box: 51335/1996, Tabriz, Iran ' Faculty of Engineering and Science, Department of Engineering, University of Agder, N-4898 Grimstad, Norway

Abstract: This paper is concerned with the problem of load-dependent H2 control for vehicle semi-active suspension. A quarter-car model equipped with a magnetorheological (MR)-damper, which captures essential features of a real car suspension, is considered in this study. H2-norm measures the root-mean-square (RMS) value of output to white noise input. Considering the fact that road roughness is often modelled as white noise, H2-norm is used to quantify control objectives of ride comfort and safety as well as suspension deflection and control effort. To guarantee system performance against parameter variations, a linear matrix inequality (LMI)-based design framework has been utilised and a linear parameter-varying (LPV) controller is synthesised. The design procedure of the semi-active suspension requires the inverse dynamics of MR damper, which is obtained through a locally linear neuro-fuzzy (LLNF) network. To illustrate the effectiveness of the proposed approach, the system outputs for both impulse and real road inputs are compared with H controller in terms of performances.

Keywords: semi-active suspension; H2 control; H∞ control; MR dampers; load-dependent control; feedback control; semi-active suspension systems; vehicle suspension; magnetorheological damping; root mean square; RMS; road roughness; modelling; white noise; ride comfort; vehicle safety; suspension deflection; linear matrix inequalities; LMI; linear parameter varying control; LPV control; controller design; inverse dynamics; neural networks; fuzzy logic; locally linear neuro-fuzzy networks; LLNF networks.

DOI: 10.1504/IJVD.2015.071077

International Journal of Vehicle Design, 2015 Vol.68 No.1/2/3, pp.119 - 140

Received: 09 Jun 2014
Accepted: 01 Dec 2014

Published online: 11 Aug 2015 *

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