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A particle swarm optimization approach for tuning of SISO PID control loops

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dc.contributor.advisor Govender, Poobalan
dc.contributor.author Pillay, Nelendran
dc.date.accessioned 2009-12-14T06:08:38Z
dc.date.available 2009-12-14T06:08:38Z
dc.date.issued 2008
dc.identifier.other 325423
dc.identifier.uri http://hdl.handle.net/10321/488
dc.description Thesis 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.abstract Linear 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.sponsorship National Research Foundation en_US
dc.format.extent 207 p en_US
dc.language.iso en en_US
dc.subject PID controllers en_US
dc.subject Swarm intelligence en_US
dc.subject Automatic control en_US
dc.subject Tuning--Electronic equipment en_US
dc.title A particle swarm optimization approach for tuning of SISO PID control loops en_US
dc.type Thesis en_US


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