Parameter Identification Based on a Modified PSO Applied to Suspension System

Alireza Alfi, Mohammad-Mehdi Fateh

Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, Iran.
Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, Iran..

DOI: 10.4236/jsea.2010.33027                

Abstract

This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offline and online parametric identification of dynamic models. The MPSO is applied for identifying a suspension system introduced by a quarter-car model. A novel mutation mechanism is employed in MPSO to enhance global search ability and increase convergence speed of basic PSO (BPSO) algorithm. MPSO optimization is used to find the optimum values of parameters by minimizing the sum of squares error. The performance of the MPSO is compared with other optimization methods including BPSO and Genetic Algorithm (GA) in offline parameter identification. The simulating results show that this algorithm not only has advantage of convergence property over BPSO and GA, but also can avoid the premature convergence problem effectively. The MPSO algorithm is also improved to detect and determine the variation of parameters. This novel algorithm is successfully applied for online parameter identification of suspension system.

Keywords

Particle Swarm Optimization, Genetic Algorithm, Parameter Identification, Suspension System

Conflicts of Interest

The authors declare no conflicts of interest.

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