Optimal PID controller design through swarm intelligence algorithms for sun tracking system

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.

[1]  Ilhami Colak,et al.  Position Control of a DC Motor Used in Solar Panels with Artificial Neural Network , 2012, 2012 11th International Conference on Machine Learning and Applications.

[2]  Mohammad Abdul Mannan,et al.  Energy Efficient Hybrid Dual Axis Solar Tracking System , 2014 .

[3]  Pierre Borne,et al.  Tuning PID Controller Using Multiobjective Ant Colony Optimization , 2012, Appl. Comput. Intell. Soft Comput..

[4]  Ahmed Rhif.  A Sliding Mode Control for a Sensorless Tracker: Application on a Photovoltaic System , 2012, ArXiv.

[5]  Robert Salat,et al.  Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach , 2014, Neural Computing and Applications.

[6]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[7]  Amir Hossein Gandomi,et al.  Bat algorithm for constrained optimization tasks , 2012, Neural Computing and Applications.

[8]  Ramin Rajabioun,et al.  Cuckoo Optimization Algorithm , 2011, Appl. Soft Comput..

[9]  B. Nagaraj,et al.  Tuning of proportional integral derivative controller based on firefly algorithm , 2013 .

[10]  Iulia Stamatescu,et al.  on Intelligent Manufacturing and Automation , 2013 Design and Implementation of a Solar-Tracking Algorithm , 2014 .

[11]  Ching-Chang Wong,et al.  Optimal PID Controller Design for AVR System , 2009 .

[12]  Gagandeep Kaur,et al.  Swarm Intelligence Based Tuning of Controller Parameters for Concentration Control of Isothermal Continuous Stirred Tank Reactor , 2012 .

[13]  Karen Abrinia,et al.  A review of principle and sun-tracking methods for maximizing solar systems output , 2009 .

[14]  R. Mantegna,et al.  Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[15]  Liu Luoren,et al.  Research of PID Control Algorithm Based on Neural Network , 2011 .

[16]  Cătălin Alexandru.  A Novel Open-Loop Tracking Strategy for Photovoltaic Systems , 2013, TheScientificWorldJournal.

[17]  Bharat Bhushan,et al.  Adaptive control of DC motor using bacterial foraging algorithm , 2011, Appl. Soft Comput..

[18]  M. Clifford,et al.  Design of a novel passive solar tracker , 2004 .

[19]  Khulood AlYahya,et al.  Artificial Bee Colony training of neural networks: comparison with back-propagation , 2014, Memetic Comput..

[20]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[21]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[22]  Rohit Kumar,et al.  Speed control of dc motor using artificial bee colony optimization technique , 2013, 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE).