Genetic Algorithms for Optimising Active Controls in Railway Vehicles

T. X. Mei, T. H. E. Foo, R. M. Goodall

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)


This paper presents optimisations of active control designs for railway vehicle suspensions using Genetic Algorithms. Genetic Algorithm (GA in short) is a stochastic process aimed at providing global optimisation solutions for a wide range of applications. In this paper, two active suspension controls are designed for the railway vehicles with the aid of GA. The paper first studies the controls for actively steering wheelsets of railway vehicles. The basic aim of a controller is to stabilise the potentially unstable vehicles and to improve the ride quality without interfering with the natural curving action of the solid axle wheelsets. The Genetic Algorithm is used to assist the design of an optimal controller by choosing the weighting factors in order to achieve the best performance with the minimum interference to curving. In the second study, 'classical controllers' controlling the front and rear ideal actuators of a flexible vehicle body are investigated. The aim is to minimise the flexible effect of the railway vehicle thereby improving the ride quality. GA is used to fine-tune the gains of the controllers in order to obtain the best overall ride quality of the entire vehicle body.

Original languageEnglish
Title of host publicationIEE Colloquium on Optimisation in Control
Subtitle of host publicationMethods and Applications
Number of pages8
Publication statusPublished - 1998
Externally publishedYes
EventIEEE Colloquium Optimisation in Control: Methods and Applications - Savoy Place, London, United Kingdom
Duration: 10 Nov 199810 Nov 1998

Publication series

NameIEE Colloquium (Digest)
PublisherInstitution of Electrical Engineers
ISSN (Print)0963-3308


ConferenceIEEE Colloquium Optimisation in Control
Country/TerritoryUnited Kingdom
Internet address


Dive into the research topics of 'Genetic Algorithms for Optimising Active Controls in Railway Vehicles'. Together they form a unique fingerprint.

Cite this