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. / 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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:dut/oai:ir.dut.ac.za:10321/488 |
| Date | January 2008 |
| Creators | Pillay, Nelendran |
| Contributors | Govender, Poobalan |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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