Parameter identification of DC motor model by flower pollination algorithm
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Published:
Dec 23, 2018
Keywords:
parameter identification DC motor flower pollination algorithm metaheuristic
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Abstract
Analysis and design of the direct current (DC) motor speed control system require its accurate model parameters. This paper proposes the parameter identification of the DC motor model based on the flower pollination algorithm (FPA), one of the metaheuristic optimization search techniques. The FPA firstly proposed in 2012, mimics the pollination process of flowering tree in nature associated with Lévy Flight. Under testing, the DC motor system was excited by the step input to generate the specific level of the motor speed considered as the output of the system. As identification results compared with the model obtained by the cuckoo search (CuS), it was found that the FPA gave the model parameters representing system dynamics superior to the model obtained by the CuS. Details are discussed and shown in the paper.
Article Details
How to Cite
คำปัญญา ด. (2018). Parameter identification of DC motor model by flower pollination algorithm. RMUTSB ACADEMIC JOURNAL, 6(2), 207–219. Retrieved from https://li01.tci-thaijo.org/index.php/rmutsb-sci/article/view/149150
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Research Article
Published manuscript are the rights of their original owners and RMUTSB Academic Journal. The manuscript content belongs to the authors' idea, it is not the opinion of the journal's committee and not the responsibility of Rajamangala University of Technology Suvarnabhumi
References
เดชา พวงดาวเรือง, ไชโย ธรรมรัตน์, อรรถรัตน์ นาวิกาวตาร, และสาโรจน์ หลั่งน้ำทิพย์. (2559). การประยุกต์ขั้นตอนวิธีการผสมเกสรดอกไม้เพื่อออกแบบตัวควบคุม PIDA อย่างเหมาะสม. ใน การประชุมวิชาการทางวิศวกรรมไฟฟ้า ครั้งที่ 39 (EECON-39) (น. 533-538). โรงแรมเดอะรีเจนท์ ชะอำบีช หัวหิน เพชรบุรี: จุฬาลงกรณ์มหาวิทยาลัย.
เดชา พวงดาวเรือง, อรรถรัตน์ นาวิกาวตาร, ไชโย ธรรมรัตน์, และสาโรจน์ หลั่งนํ้าทิพย์. (2560). การสังเคราะห์ตัวควบคุม PID แบบแอนะล็อกด้วยขั้นตอนวิธีการผสมเกสรดอกไม้. ใน การประชุมวิชาการทางวิศวกรรมไฟฟ้า ครั้งที่ 40 (EECON-40) (น. 451-454). โรงแรมเดอะซายน์ ชลบุรี: มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ.
Balasubramani, K., & Marcus, K. (2014). A study on flower pollination algorithm and its applications. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 3, 320-325.
Chittka, L., Thomson, J. D., & Waser, N. M. (1999). Flower constancy, insect psychology, and plant evolution. Naturwissenschaften, 86: 361-377.
Eykhoff, P. (1974). System identification: Parameter and state estimation. New Jersey: John Wiley & Sons.
Goldberg, D. E. (1989). Genetic algorithm in search, optimization, and machine learning. Boston, USA: Addison-Wesley Publishing.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proc. IEEE Int. Conf. Neural Networks (ICNN’95) (pp. 1942-1948). Perth, WA, Australia: IEEE.
Krishnan, R. (2001). Electric motor drives modeling, analysis, and control. New Jersey: Prentice-Hall International.
Ketthong, T., Kiree, C., Tunyasrirut, S., & Puangdownreong, D. (2015). Parameter identification of DC-servo motor by adaptive tabu search. In The International Annual Symposium on Computational Science and Engineering (ANSCSE19) (pp. 79-83). Ubon Ratchathani: Ubon Ratchathani University.
Lastomo, D., Djalal, M. R., Wido, D., & Robandi, I. (2015). Optimization of PID controller design for DC motor based on flower pollination algorithm. In The International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM2015). Aryaduta Hotel, Medan: University of Sumatera Utara.
Leontaritis, I. J., & Billings, S. A. (1985a). Input-output parametric models for nonlinear systems (part I: deterministic nonlinear systems). Int. J. Control, 41(2), 303-328.
Leontaritis, I. J., & Billings, S. A. (1985b). Input-output parametric models for nonlinear systems (part II: stochastic nonlinear systems), Int. J. Control, 41(2), 329-344.
Ljung, L. (1987). System identification: Theory for the user. New Jersey: Prentice-Hall.
Ogata, K. (2010). Modern control engineering. New Jersey: Prentice Hall.
Pavlyukevich, I. (2007a). Lévy Flights, non–local search and simulated annealing. Journal of Computational Physics, 226, 1830-1844.
Pavlyukevich, I. (2007b). Cooling down Lévy Flights. Journal of Phys. A: Math, Theor., 40, 12299-12313.
Sandou, G. (2013). Metaheuristic optimization for the design of automatic control laws. New Jersey: Wiley.
Sakib, N., Kabir, Md. W., Rahman, Md. S., & Alam, M. S. (2014). A comparative study of flower pollination algorithm and bat algorithm on continuous optimization problems. International Journal of Applied Information Systems, 7, 13-19.
Talbi, E. G. (2009). Metaheuristics form design to implementation. New Jersey: John Wiley & Sons.
Vas, P. (1993). Parameter estimation, condition monitoring and diagnosis of electrical machines. Oxford, UK: Oxford University Press.
Willmer, P. (2011). Pollination and floral ecology. New Jersey: Princeton University Press.
Yadav, S. P., & Tripathi, V. K. (2006). A case study of DC motor speed control with PID controller through MATLAB. International Journal of Advanced Research in Computer and Communication Engineering, 5(5), 1008-1011.
Yang, X. S. (2010). Engineering optimization, an introduction with metaheuristic applications. New Jersey: John Wiley & Sons.
Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In World Congress on Nature & Biologically Inspired Computing (pp. 210-214). Coimbatore, India: IEEE.
Yang, X. S. & Deb, S. (2010). Engineering optimisation by cuckoo search. Mathematical Modelling and Numerical Optimisation, 1(4), 330-343.
Yang, X. S. (2012). Flower pollination algorithm for global optimization. Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, 7445, 240-249.
Yang, X. S., & Deb, S. (2013). Multiobjective cuckoo search for design optimization. Computers and Operations Research, 40(6), 1616-1624.
Yang, X. S., Karamanoglua, M., & He, X. (2013). Multi-objective flower algorithm for optimization. International Conference on Computational Science, Procedia Computer Science, 18, 861-868.
Yang, X. S. (2014). Cuckoo search and firefly algorithm: overview and analysis. Studied in computational intelligence: Cuckoo search and firefly algorithm theory and applications. London: Springer.
Yang, X. S., Karamanoglua, M., & He, X. (2014). Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim, 46(9), 1222-1237.
Zakian, V. (2005). Control systems design, A New Framework. London: Springer-Verlag.
เดชา พวงดาวเรือง, อรรถรัตน์ นาวิกาวตาร, ไชโย ธรรมรัตน์, และสาโรจน์ หลั่งนํ้าทิพย์. (2560). การสังเคราะห์ตัวควบคุม PID แบบแอนะล็อกด้วยขั้นตอนวิธีการผสมเกสรดอกไม้. ใน การประชุมวิชาการทางวิศวกรรมไฟฟ้า ครั้งที่ 40 (EECON-40) (น. 451-454). โรงแรมเดอะซายน์ ชลบุรี: มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ.
Balasubramani, K., & Marcus, K. (2014). A study on flower pollination algorithm and its applications. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 3, 320-325.
Chittka, L., Thomson, J. D., & Waser, N. M. (1999). Flower constancy, insect psychology, and plant evolution. Naturwissenschaften, 86: 361-377.
Eykhoff, P. (1974). System identification: Parameter and state estimation. New Jersey: John Wiley & Sons.
Goldberg, D. E. (1989). Genetic algorithm in search, optimization, and machine learning. Boston, USA: Addison-Wesley Publishing.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proc. IEEE Int. Conf. Neural Networks (ICNN’95) (pp. 1942-1948). Perth, WA, Australia: IEEE.
Krishnan, R. (2001). Electric motor drives modeling, analysis, and control. New Jersey: Prentice-Hall International.
Ketthong, T., Kiree, C., Tunyasrirut, S., & Puangdownreong, D. (2015). Parameter identification of DC-servo motor by adaptive tabu search. In The International Annual Symposium on Computational Science and Engineering (ANSCSE19) (pp. 79-83). Ubon Ratchathani: Ubon Ratchathani University.
Lastomo, D., Djalal, M. R., Wido, D., & Robandi, I. (2015). Optimization of PID controller design for DC motor based on flower pollination algorithm. In The International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM2015). Aryaduta Hotel, Medan: University of Sumatera Utara.
Leontaritis, I. J., & Billings, S. A. (1985a). Input-output parametric models for nonlinear systems (part I: deterministic nonlinear systems). Int. J. Control, 41(2), 303-328.
Leontaritis, I. J., & Billings, S. A. (1985b). Input-output parametric models for nonlinear systems (part II: stochastic nonlinear systems), Int. J. Control, 41(2), 329-344.
Ljung, L. (1987). System identification: Theory for the user. New Jersey: Prentice-Hall.
Ogata, K. (2010). Modern control engineering. New Jersey: Prentice Hall.
Pavlyukevich, I. (2007a). Lévy Flights, non–local search and simulated annealing. Journal of Computational Physics, 226, 1830-1844.
Pavlyukevich, I. (2007b). Cooling down Lévy Flights. Journal of Phys. A: Math, Theor., 40, 12299-12313.
Sandou, G. (2013). Metaheuristic optimization for the design of automatic control laws. New Jersey: Wiley.
Sakib, N., Kabir, Md. W., Rahman, Md. S., & Alam, M. S. (2014). A comparative study of flower pollination algorithm and bat algorithm on continuous optimization problems. International Journal of Applied Information Systems, 7, 13-19.
Talbi, E. G. (2009). Metaheuristics form design to implementation. New Jersey: John Wiley & Sons.
Vas, P. (1993). Parameter estimation, condition monitoring and diagnosis of electrical machines. Oxford, UK: Oxford University Press.
Willmer, P. (2011). Pollination and floral ecology. New Jersey: Princeton University Press.
Yadav, S. P., & Tripathi, V. K. (2006). A case study of DC motor speed control with PID controller through MATLAB. International Journal of Advanced Research in Computer and Communication Engineering, 5(5), 1008-1011.
Yang, X. S. (2010). Engineering optimization, an introduction with metaheuristic applications. New Jersey: John Wiley & Sons.
Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In World Congress on Nature & Biologically Inspired Computing (pp. 210-214). Coimbatore, India: IEEE.
Yang, X. S. & Deb, S. (2010). Engineering optimisation by cuckoo search. Mathematical Modelling and Numerical Optimisation, 1(4), 330-343.
Yang, X. S. (2012). Flower pollination algorithm for global optimization. Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, 7445, 240-249.
Yang, X. S., & Deb, S. (2013). Multiobjective cuckoo search for design optimization. Computers and Operations Research, 40(6), 1616-1624.
Yang, X. S., Karamanoglua, M., & He, X. (2013). Multi-objective flower algorithm for optimization. International Conference on Computational Science, Procedia Computer Science, 18, 861-868.
Yang, X. S. (2014). Cuckoo search and firefly algorithm: overview and analysis. Studied in computational intelligence: Cuckoo search and firefly algorithm theory and applications. London: Springer.
Yang, X. S., Karamanoglua, M., & He, X. (2014). Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim, 46(9), 1222-1237.
Zakian, V. (2005). Control systems design, A New Framework. London: Springer-Verlag.