Result: Multi-objective robust design of the suspension system of road vehicles

Title:
Multi-objective robust design of the suspension system of road vehicles
Source:
Proceedings of the 18th IAVSD Symposium held in Kanagawa, Japan, August 24-30, 2003Vehicle System Dynamics. 41:537-546
Publisher Information:
Colchester: Taylor & Francis, 2004.
Publication Year:
2004
Physical Description:
print, 16 ref SUP
Original Material:
INIST-CNRS
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Mechanical Engineering, Politecnico di Milano (Technical University). Via La Masa, 34, 20158 Milan, Italy
ISSN:
0042-3114
Rights:
Copyright 2004 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mechanical engineering. Mechanical construction. Handling
Accession Number:
edscal.16244198
Database:
PASCAL Archive

Further Information

A new approach for the design of automotive suspension systems is addressed in the paper. The new approach is based not only on the theory of multi-objective optimisation but also on robust design. A simple two degree of freedom linear model has been used to derive a number of analytical formulae describing the dynamic behaviour of vehicles running on randomly profiled roads. Discomfort, road holding and working space are the performance indices to which reference will be made in the paper. The design variables are the suspension stiffness and damping (passively suspended system) considered as stochastic variables and the controller gains (actively suspended system). The vehicle body mass and the tyre radial stiffness have been considered as stochastic parameters. The optimal trade-off solutions (Pareto-optimal solutions) in a stochastic framework have been derived.