Kanarachos, Stratis, Dizqah, Arash Moradinegade, Chrysakis, Georgios and Fitzpatrick, Michael E (2018) Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based fruit fly optimisation. Applied Soft Computing, 62. pp. 463-477. ISSN 1568‐4946

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Abstract

In the UK, in 2014 almost fifty thousand motorists made claims about vehicle damages caused by potholes. Pothole damage mitigation has become so important that a number of car manufacturers have officially designated it as one of their priorities. The objective is to improve suspension shock performance without degrading road holding and ride comfort. In this study, it is shown that significant improvement in performance is achieved if a clipped quadratic parameter varying suspension is employed. Optimal design of the proposed system is challenging because of the multiple local minima causing global optimisation algorithms to get trapped at local minima, located far from the optimum solution. To this end an enhanced Fruit Fly Optimisation Algorithm − based on a recent study on how well a fruit fly’s tiny brain finds food − was developed. The new algorithm is first evaluated using standard and nonstandard benchmark tests and then applied to the computationally expensive suspension design problem. The proposed algorithm is simple to use, robust and well suited for the solution of highly nonlinear problems. For the suspension design problem new insight is gained, leading to optimum damping profiles as a function of excitation level and rattle space velocity.

Item Type:	Article
Schools and Departments:	School of Engineering and Informatics > Engineering and Design
Research Centres and Groups:	Dynamics, Control and Vehicle Research Group
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA0168 Systems engineering T Technology > TJ Mechanical engineering and machinery > TJ0170 Mechanics applied to machinery. Dynamics
Depositing User:	Arash Moradinegade Dizqah
Date Deposited:	21 Mar 2019 10:24
Last Modified:	16 Feb 2021 16:02
URI:	http://sro.sussex.ac.uk/id/eprint/82685

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