A Swarm Intelligence Based Scheme for Complete and Fault-tolerant Identification of a Dynamical Fractional Order Process

Simple item page
dc.creatorMaiti, Deepyaman
dc.creatorAcharya, Ayan
dc.creatorKonar, Amit
dc.date2008-11-01
dc.date.accessioned2026-07-07T10:14:44Z
dc.date.available2026-07-07T10:14:44Z
dc.descriptionSystem identification refers to estimation of process parameters and is a necessity in control theory. Physical systems usually have varying parameters. For such processes, accurate identification is particularly important. Online identification schemes are also needed for designing adaptive controllers. Real processes are usually of fractional order as opposed to the ideal integral order models. In this paper, we propose a simple and elegant scheme of estimating the parameters for such a fractional order process. A population of process models is generated and updated by particle swarm optimization (PSO) technique, the fitness function being the sum of squared deviations from the actual set of observations. Results show that the proposed scheme offers a high degree of accuracy even when the observations are corrupted to a significant degree. Additional schemes to improve the accuracy still further are also proposed and analyzed.
dc.description2008 IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA. Paper Identification Number 239
dc.identifierhttps://arxiv.org/abs/0811.0078
dc.identifierhttp://arxiv.org/abs/0811.0078
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/172983
dc.subjectOther Computer Science
dc.titleA Swarm Intelligence Based Scheme for Complete and Fault-tolerant Identification of a Dynamical Fractional Order Process
dc.typetext

Files

Collections

arXiv