بهینه سازی ضرایب کنترل کننده تناسبی انتگرالی مشتقی کنترل دور موتور بدون جاروبک با الگوریتم بهینه سازی چرخه آب
محورهای موضوعی : مهندسی الکترونیک
حبیب اله خدادادی
1
(مهندسی برق، دانشکده فنی مهندسی، دانشگاه آزاد اسلامی، بوشهر، ایران)
مصطفی اسمعیل بیک
2
(گروه برق، دانشکده فنی مهندسی ، دانشگاه آزاد اسلامی ، بوشهر ، ایران)
نجمه چراغی شیرازی
3
(گروه برق، دانشکده فنی مهندسی ، دانشگاه آزاد اسلامی ، بوشهر ، ایران)
کلید واژه: الگوریتم چرخه آب, موتور بدون جاروبک, کنترل سرعت, کنترل کننده تناسبی انتگرالی مشتقی,
چکیده مقاله :
در دهه های گذشته تعداد زیادی از کنترل کننده های سرعت برای کنترل سرعت موتورهای بدون جاروبک طراحی شده اند. به طور معمول ، کنترل کننده مشتقی انتگرالی تناسبی انتخاب بهینه برای کنترل سرعت موتورهای بدون جاروبک است که می توان با طراحی پارامترهای سیستم کنترل کننده مشتقی انتگرالی تناسبی، سرعت موتورهای بدون جاروبک را کنترل کرد. روشهای زیادی برای بدست آوردن بهینه پارامترهای کنترل کننده مشتقی انتگرالی تناسبی وجود دارد. یکی از روشهای که بسیار زیاد از آن برای بدست آوردن و طراحی پارامترهای کنترل کننده مشتقی انتگرالی تناسبی استفاده شده است الگوریتم های بهینه سازی هستند. الگوریتم بهینه سازی چرخه آب یک الگوریتم بهینه سازی است که با استفاده از آن کار طراحی و بهینه سازی پارامتر های کنترل کننده مشتقی انتگرالی تناسبی انجام می گیرد. در این مقاله ضریب مشتقی برابر 0 ضریب انتگرالی برابر0.2259937 و ضریب تناسبی0.00188894 بدست آمده است که معیار پایداری برابر0.01038433 ، زمان صعود برابر0.00962 ثانیه ، زمان نشست نمودار برابر0.01492 ثانیه، زمان اوج برابر0.01817 ثانیه و کمترین مقدار نشست نمودار برابر0.9059 را بدست می آورد. این نتایج در مقایسه با نتایج بدست آمده از الگوریتم های ازدحام ذرات و ژنتیک و... قرار گرفته است و بهتر بودن روش چرخه آب را نمایش می دهد.
Brushless motors are widely used in industrial, domestic and electronic equipment today due to their high reliability, high efficiency, low maintenance and many other advantages. But they also have disadvantages, including electronic commutation, and this requires a speed controller (speed) for this type of engine. In recent decades, a large number of speed controllers have been designed to control the speed of brushless motors. Typically, a derivative-integral-proportional controller is the optimal choice for controlling the speed of brushless motors. By designing the parameters of the derivative-integral-proportional control system, the speed of the brushless motors can be controlled. There are many ways to obtain the optimal derivative-integral-proportional control parameters. One of the methods that has been widely used to obtain and design derivative-integral-proportional control parameters are optimization algorithms. Water cycle optimization algorithm is an optimization algorithm that is used to optimize derivative-integral-proportional control parameters. In this paper, the derivative coefficient equal to 0, the integral coefficient equal to 0.2259937 and the proportional coefficient equal to 0.00188894 are obtained which gives the stability index equal to 0.01038433, the rise time equal to0.00962 , settling time equal to 0.01492 , peak time equal to 0.01817 , settling min equal to 0.9059. These results are compared with the results obtained from particle swarm algorithms and genetics and show that the water cycle method is better.
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[2] A. Sadollah, H. Eskandar, H. M. Lee, D. G. Yoo, and J. H. Kim, “Water cycle algorithm: A detailed standard code,” SoftwareX, vol. 5, pp. 37–43, 2015.
[3] A. Sadollah, H. Eskandar, and J. H. Kim, “Water cycle algorithm for solving constrained multi-objective optimization problems,” Appl. Soft Comput. J., vol. 27, pp. 279–298, 2015.
[4] A. Sadollah, H. Eskandar, A. Bahreininejad, and J. H. Kim, “Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems,” Appl. Soft Comput. J., vol. 30, no. 6, pp. 58–71, 2015.
[5] D. Potnuru, K. Alice Mary, and C. Sai Babu, “Experimental implementation of Flower Pollination Algorithm for speed controller of a BLDC motor,” Ain Shams Eng. J., vol. 10, no. 2, pp. 287–295, 2019.
[6] K. Premkumar and B. V. Manikandan, “Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System,” Appl. Soft Comput. J., vol. 32, pp. 403–419, 2015.
[7] B. N. Kommula and V. R. Kota, “Direct instantaneous torque control of Brushless DC motor using firefly Algorithm based fractional order PID controller,” J. King Saud Univ. - Eng. Sci., vol. 22, no. 18, pp. 6135–6146, 2018.
[8] H. E. A. Ibrahim, F. N. Hassan, and A. O. Shomer, “Optimal PID control of a brushless DC motor using PSO and BF techniques,” Ain Shams Eng. J., vol. 5, no. 2, pp. 391–398, 2014.
[9] K. Premkumar and B. V. Manikandan, “Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor,” Eng. Sci. Technol. an Int. J., vol. 19, no. 2, pp. 818–840, 2016.
[10] S. Srikanth and G. Raghu Chandra, “Modeling and PID control of the brushless DC motor with the help of Genetic Algorithm,” IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM,. pp. 639–644, 2012.
[11] M. K. Merugumalla and P. K. Navuri, “PSO and firefly algorithms based control of BLDC motor drive,” in 2nd International Conference on Inventive Systems and Control (ICISC), 2018, pp. 994–999.
[12] I. Anshory, I. Robandi, and Wirawan, “Monitoring and optimization of speed settings for Brushless Direct Current (BLDC) using Particle Swarm Optimization (PSO),” in IEEE Region 10 Symposium (TENSYMP), 2016, pp. 243–248.
[13] F. Aymen, O. Berkati, S. Lassaad, and M. N. Srifi, “BLDC Control Method Optimized by PSO Algorithm,” in 2019 International Symposium on Advanced Electrical and Communication Technologies, ISAECT, 2019, pp. 1–5.
[14] S. B. Murali and P. M. Rao, “Adaptive sliding mode control of BLDC motor using cuckoo search algorithm,” in 2nd International Conference on Inventive Systems and Control (ICISC), 2018, pp. 989–993.
[15] K. S. Rama Rao and A. H. Bin Othman, “Design optimization of a BLDC motor by Genetic Algorithm and Simulated Annealing,” in 2007 International Conference on Intelligent and Advanced Systems, 2007, pp. 854–858.
[16] O. Gulbas, Y. Hames, and M. Furat, “Comparison of PI and Super-twisting Controller Optimized with SCA and PSO for Speed Control of BLDC Motor,” in International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2020, pp. 1–7.
[17] C. Mun Ong, Dynamic Simulation of Electric Machinery: Using MATLAB/SIMULIN, Illustrate, no. October. Prentice Hall PTR, 1998.
_||_[1] H. Eskandar, A. Sadollah, A. Bahreininejad, and M. Hamdi, “Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems,” Comput. Struct, vol. 110–111, pp. 151–166, 2012.
[2] A. Sadollah, H. Eskandar, H. M. Lee, D. G. Yoo, and J. H. Kim, “Water cycle algorithm: A detailed standard code,” SoftwareX, vol. 5, pp. 37–43, 2015.
[3] A. Sadollah, H. Eskandar, and J. H. Kim, “Water cycle algorithm for solving constrained multi-objective optimization problems,” Appl. Soft Comput. J., vol. 27, pp. 279–298, 2015.
[4] A. Sadollah, H. Eskandar, A. Bahreininejad, and J. H. Kim, “Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems,” Appl. Soft Comput. J., vol. 30, no. 6, pp. 58–71, 2015.
[5] D. Potnuru, K. Alice Mary, and C. Sai Babu, “Experimental implementation of Flower Pollination Algorithm for speed controller of a BLDC motor,” Ain Shams Eng. J., vol. 10, no. 2, pp. 287–295, 2019.
[6] K. Premkumar and B. V. Manikandan, “Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System,” Appl. Soft Comput. J., vol. 32, pp. 403–419, 2015.
[7] B. N. Kommula and V. R. Kota, “Direct instantaneous torque control of Brushless DC motor using firefly Algorithm based fractional order PID controller,” J. King Saud Univ. - Eng. Sci., vol. 22, no. 18, pp. 6135–6146, 2018.
[8] H. E. A. Ibrahim, F. N. Hassan, and A. O. Shomer, “Optimal PID control of a brushless DC motor using PSO and BF techniques,” Ain Shams Eng. J., vol. 5, no. 2, pp. 391–398, 2014.
[9] K. Premkumar and B. V. Manikandan, “Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor,” Eng. Sci. Technol. an Int. J., vol. 19, no. 2, pp. 818–840, 2016.
[10] S. Srikanth and G. Raghu Chandra, “Modeling and PID control of the brushless DC motor with the help of Genetic Algorithm,” IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM,. pp. 639–644, 2012.
[11] M. K. Merugumalla and P. K. Navuri, “PSO and firefly algorithms based control of BLDC motor drive,” in 2nd International Conference on Inventive Systems and Control (ICISC), 2018, pp. 994–999.
[12] I. Anshory, I. Robandi, and Wirawan, “Monitoring and optimization of speed settings for Brushless Direct Current (BLDC) using Particle Swarm Optimization (PSO),” in IEEE Region 10 Symposium (TENSYMP), 2016, pp. 243–248.
[13] F. Aymen, O. Berkati, S. Lassaad, and M. N. Srifi, “BLDC Control Method Optimized by PSO Algorithm,” in 2019 International Symposium on Advanced Electrical and Communication Technologies, ISAECT, 2019, pp. 1–5.
[14] S. B. Murali and P. M. Rao, “Adaptive sliding mode control of BLDC motor using cuckoo search algorithm,” in 2nd International Conference on Inventive Systems and Control (ICISC), 2018, pp. 989–993.
[15] K. S. Rama Rao and A. H. Bin Othman, “Design optimization of a BLDC motor by Genetic Algorithm and Simulated Annealing,” in 2007 International Conference on Intelligent and Advanced Systems, 2007, pp. 854–858.
[16] O. Gulbas, Y. Hames, and M. Furat, “Comparison of PI and Super-twisting Controller Optimized with SCA and PSO for Speed Control of BLDC Motor,” in International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2020, pp. 1–7.
[17] C. Mun Ong, Dynamic Simulation of Electric Machinery: Using MATLAB/SIMULIN, Illustrate, no. October. Prentice Hall PTR, 1998.