Optimal PID controller design through swarm intelligence algorithms for sun tracking systemAuthor-Name: Sabir, Mirza Muhammad

Tariq Ali

Applied Mathematics and Computation, 2016, vol. 274, issue C, 690-699

Abstract: Due to depletion of fossil fuel many developing countries like Pakistan are facing energy crisis. Over the last decade Pakistan has suffered greatly economically due to energy crisis and at government level and in private sector a great effort is being made to overcome this energy crisis. In abundantly available energy sources solar energy is one of the cleanest and omnipresent energy source. Solar tracker systems are gaining popularity due to wide spread of solar energy use as an alternative energy source compared to fossil fuel. To obtain optimum energy from solar system accurate tracking of sun is required with respect to azimuth and tilt angle. Usually there are two types of solar trackers one is single axis and other is dual axis. For both types of solar trackers DC motor based servo systems are used for precise movement control. We consider in the present work designing of an optimum proportional-integral-derivative (PID) controller for DC motors of dual axis solar tracker system. The problem is formulated as optimization problem and three swarm intelligence based metaheuristic algorithms namely particle swarm optimization (PSO), firefly algorithm (FFA) and Cuckoo Search Algorithm (CSA) are employed for optimum tuning of PID controller. The study shows that CSA is superior as compared to FFA and PSO due to its faster convergence rate, small variance and standard deviation of design parameters obtained. Also pertaining to performance domain FFA and CSA show better performance as compared to PSO for the problem domain under study.

Keywords: PID tuning; Swarm intelligence; DC motor control (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:274:y:2016:i:c:p:690-699

DOI: 10.1016/j.amc.2015.11.036

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