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dc.contributor.advisorGovender, Poobalan
dc.contributor.authorPillay, Nelendran
dc.date.accessioned2009-12-14T06:08:38Z
dc.date.available2009-12-14T06:08:38Z
dc.date.issued2008
dc.identifier.other325423
dc.identifier.urihttp://hdl.handle.net/10321/488
dc.descriptionThesis submitted in compliance with the requirements for the Master's Degree in Technology: Electrical Engineering - Light Current, Durban University of Technology, Department of Electronic Engineering, 2008.en_US
dc.description.abstractLinear control systems can be easily tuned using classical tuning techniques such as the Ziegler-Nichols and Cohen-Coon tuning formulae. Empirical studies have found that these conventional tuning methods result in an unsatisfactory control performance when they are used for processes experiencing the negative destabilizing effects of strong nonlinearities. It is for this reason that control practitioners often prefer to tune most nonlinear systems using trial and error tuning, or intuitive tuning. A need therefore exists for the development of a suitable tuning technique that is applicable for a wide range of control loops that do not respond satisfactorily to conventional tuning. Emerging technologies such as Swarm Intelligence (SI) have been utilized to solve many non-linear engineering problems. Particle Swarm Optimization (PSO), developed by Eberhart and Kennedy (1995), is a sub-field of SI and was inspired by swarming patterns occurring in nature such as flocking birds. It was observed that each individual exchanges previous experience, hence knowledge of the “best position” attained by an individual becomes globally known. In the study, the problem of identifying the PID controller parameters is considered as an optimization problem. An attempt has been made to determine the PID parameters employing the PSO technique. A wide range of typical process models commonly encountered in industry is used to assess the efficacy of the PSO methodology. Comparisons are made between the PSO technique and other conventional methods using simulations and real-time control.en_US
dc.description.sponsorshipNational Research Foundationen_US
dc.format.extent207 pen_US
dc.language.isoenen_US
dc.subjectPID controllersen_US
dc.subjectSwarm intelligenceen_US
dc.subjectAutomatic controlen_US
dc.subjectTuning--Electronic equipmenten_US
dc.titleA particle swarm optimization approach for tuning of SISO PID control loopsen_US
dc.typeThesisen_US


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