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The Scientific World Journal
Volume 2014 (2014), Article ID 174102, 12 pages
Research Article

Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

1Department of Mechanical Engineering, University of Engineering and Technology, Peshawar, KPK, 25000, Pakistan
2Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4

Received 25 August 2013; Accepted 30 October 2013; Published 16 January 2014

Academic Editors: M. Gobbi, A. Kosar, and K. I. Ramachandran

Copyright © 2014 Abroon Jamal Qazi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control.