طراحی کنترل کننده فازی نوع سوگنو بهینه برای کنترل سرعت موتور DC با در نظر گرفتن دینامیک درایو و چاپر با الگوریتم بهینه‌سازی مبتنی برآموزش و یادگیری
محورهای موضوعی : مهندسی الکترونیک
علی صدارت نیا
1
,
مجید مرادی زیرکوهی
2
,
نجمه چراغی شیرازی
3
1 - گروه برق، دانشکده فنی و مهندسی، دانشگاه آزاد واحد بوشهر
2 - گروه برق، دانشکده فنی، دانشگاه صنعتی بهبهان، بهبهان، ایران
3 - گروه برق، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر، ایران
کلید واژه: چاپر, : کنترل کننده فازی نوع سوگنو, الگوریتم بهینه سازی مبتنی بر آموزش و یادگیری, موتور DC,
چکیده مقاله :
با توجه به ساختار ساده موتور‌های DC این موتورها کاربردهای زیاد در صنعت و بخصوص حوزه رباتیک پیدا کرده است. از این رو کنترل دقیق سرعت آنها حائز اهمیت است. در این مقاله برای کنترل سرعت موتو DC با در نظر گرفتن دینامیک درایو و چاپر، کنترل کننده فازی نوع سوگنو پیشنهاد می‌شود. علاوه بر این برای افزایش کارایی کنترل کننده فازی از ضرایب مقیاس دهی غیر خطی استفاده می‌شود. برای کنترل ولتاژ اعمالی به آرمیچر موتور DC از چاپر استفاده می‌شود. با در نظر گرفتن ملاحضات عملی در نظر گرفتن دینامیک درایو چاپر باعث افزایش پیچیدگی سیستم می‌شود. بعد از طراحی کنترل کننده فازی برای افزایش عملکرد سیستم کنترل پارامترهای کنترل کننده فازی با استفاده از الگوریتم مبتنی بر آموزش و یادگیری تنظیم می‌شوند. این الگوریتم جدید بوده و یکی از ویژه‌گی‌های آن تعداد کم پارامترهای آن می‌باشد. نتایج نشان می‌دهد کنترل کننده فازی در مقایسه با کنترل کننده تناسبی- انتگرالی مشتقی کلاسیک دارای عملکرد بهتری در مقابل تغییرات پارامترهای سیستم و اغتشاش دارد. با در نظر گرفتن تابع معیار مناسب مقدار تابع هزینه برای روش پیشنهادی 2/. ولی با کنترل کننده بهینه شده تناسبی- انتگرالی مشتقی حدود 31/. می‌باشد که نشان از برتری 55 درصدی روش پیشنهادی را دارد.
Due to the simple structure of DC motors, these motors have found many applications in industry.Therefore, in this paper, the speed control of DC motor is investigated by considering the dynamics of drive and chopper with Sugeno-type fuzzy controller. A chopper is used to control the voltage applied to the DC motor armature. Considering the dynamics of the chopper drive increases the complexity of the system. After designing the fuzzy controller to increase the performance of the control system, the fuzzy controller parameters are adjusted using a teaching-learning-based optimization algorithm. This algorithm is new and one of its features is its small number of parameters. The results show that the fuzzy controller has better performance against changes in system parameters and uncertainties compared to the classic PID controller. Considering the appropriate criterion function, the value of the cost function for the proposed method is 0.2. But with the optimized PID controller about 0.31 which shows a 55% superiority of the proposed method.
[1] U. A. Bakshi, and M. V. Bakshi, Electrical drives and control: Technical Publications, 2009.
[2] A. Kumar, H. Saraf, and R. Kumar, “Hardware design of closed loop four quadrant dc motor drive with regenerative braking,” in proc. 2018 2nd International Conference on Inventive Systems and Control (ICISC), 2018, pp. 287-292.
[3] H. M. Usman, A. G. Haddad, H. Rehman, and S. Mukhopadhyay, “Comparison of PI and FOPI Based Voltage and Current Controlled DC Motor Drive System,” in proc. 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), 2019, pp. 139-142.
[4] A. Rajasekhar, R. K. Jatoth, and A. Abraham, “Design of intelligent PID/PI λ D μ speed controller for chopper fed DC motor drive using opposition based artificial bee colony algorithm,” Engineering Applications of Artificial Intelligence, vol. 29, pp. 13-32, 2014.
[5] V. H. Haji, and C. A. Monje, “Fractional-order PID control of a chopper-fed DC motor drive using a novel firefly algorithm with dynamic control mechanism,” Soft Computing, vol. 22, no. 18, pp. 6135-6146, 2018.
[6] N. Hemati, J. S. Thorp, and M. C. Leu, “Robust nonlinear control of brushless DC motors for direct-drive robotic applications,” Industrial Electronics, IEEE Transactions on, vol. 37, no. 6, pp. 460-468, 1990.
[7] P. M. PeŁczewski, and U. H. Kunz, “The optimal control of a constrained drive system with brushless dc motor,” Industrial Electronics, IEEE Transactions on, vol. 37, no. 5, pp. 342-348, 1990.
[8] V. K. Ummidivarapu, H. K. Voruganti, T. Khajah, and S. P. A. Bordas, “Isogeometric shape optimization of an acoustic horn using the teaching-learning-based optimization (TLBO) algorithm,” Computer Aided Geometric Design,Vol.80, pp. 101881, 2020.
[9] S. Talatahari, N. Taghizadieh, and V. Goodarzimehr, “Hybrid Teaching-Learning-Based Optimization and Harmony Search for Optimum Design of Space Trusses,” Journal of Optimization in Industrial Engineering, vol. 13, no. 1, pp. 177-194, 2020.
[10] R. Rao, “Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems,” Decision Science Letters, vol. 5, no. 1, pp. 1-30, 2016.
[11] R. V. Rao, “Design Optimization of a Robot Manipulator Using TLBO and ETLBO Algorithms,” Teaching Learning Based Optimization Algorithm, pp. 163-169: Springer, 2016.
[12] Z. S. Hasan, and M. Nema Hawas, “Using Teaching Learning Based Optimization to Efficacious and Tuning of UPFC-PODs of Interconnected Systems,” MS&E, vol. 745, no. 1, pp. 012001, 2020.
[13] A. T. Azar, Fuzzy systems: BoD–Books on Demand, 2010.
[14] L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning—II,” Information sciences, vol. 8, no. 4, pp. 301-357, 1975.
[15] L. A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Transactions on systems, Man, and Cybernetics, no. 1, pp. 28-44, 1973.
[16] E. Khan, “Neural fuzzy based intelligent systems and applications,” Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms, pp. 105-140: CRC Press, 2020.
[17] L.-X. Wang, and L.-X. Wang, “A course in fuzzy systems and control: Prentice Hall PTR Upper Saddle River,” NJ, 1997.
[18] R. V. Rao, V. J. Savsani, and D. Vakharia, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011.
[19] A. K. Shukla, P. Singh, and M. Vardhan, “An adaptive inertia weight teaching-learning-based optimization algorithm and its applications,” Applied Mathematical Modelling, vol. 77, pp. 309-326, 2020.
[20] O. T. Chong, W. H. Lim, N. A. M. Isa, K. M. Ang, S. S. Tiang, and C. K. Ang, “A Teaching-Learning-Based Optimization with Modified Learning Phases for Continuous Optimization,” in proc. Science and Information Conference, 2020, pp. 103-124.
[21] W. Chen, X. Chen, J. Peng, M. Panahi, and S. Lee, “Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer,” Geoscience Frontiers, vol.12, no.1, 2021.
[22] S. J. Hammoodi, K. S. Flayyih, and A. R. Hamad, “Design and implementation speed control system of DC Motor based on PID control and Matlab Simulink,” International Journal of Power Electronics and Drive Systems, vol. 11, no. 1, pp. 127, 2020.
[23] A. Latif, A. Z. Arfianto, H. A. Widodo, R. Rahim, and E. T. Helmy, “Motor DC PID System Regulator for Mini Conveyor Drive Based-on Matlab,” Journal of Robotics and Control (JRC), vol. 1, no. 6, pp. 185-190, 2020.
[24] A. Rajasekhar, R. K. Jatoth, and A. Abraham, “Design of intelligent PID/PI λ D μ speed controller for chopper fed DC motor drive using opposition based artificial bee colony algorithm,” Engineering Applications of Artificial Intelligence, vol. 29, no. 2, pp. 13-32, 2014.
[25] A. Rajasekhar, S. Das, and A. Abraham, “Fractional order PID controller design for speed control of chopper fed DC motor drive using artificial bee colony algorithm,” in Nature and Biologically Inspired Computing (NaBIC), 2013 World Congress on, 2013, pp. 259-266.
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[1] U. A. Bakshi, and M. V. Bakshi, Electrical drives and control: Technical Publications, 2009.
[2] A. Kumar, H. Saraf, and R. Kumar, “Hardware design of closed loop four quadrant dc motor drive with regenerative braking,” in proc. 2018 2nd International Conference on Inventive Systems and Control (ICISC), 2018, pp. 287-292.
[3] H. M. Usman, A. G. Haddad, H. Rehman, and S. Mukhopadhyay, “Comparison of PI and FOPI Based Voltage and Current Controlled DC Motor Drive System,” in proc. 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2019 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), 2019, pp. 139-142.
[4] A. Rajasekhar, R. K. Jatoth, and A. Abraham, “Design of intelligent PID/PI λ D μ speed controller for chopper fed DC motor drive using opposition based artificial bee colony algorithm,” Engineering Applications of Artificial Intelligence, vol. 29, pp. 13-32, 2014.
[5] V. H. Haji, and C. A. Monje, “Fractional-order PID control of a chopper-fed DC motor drive using a novel firefly algorithm with dynamic control mechanism,” Soft Computing, vol. 22, no. 18, pp. 6135-6146, 2018.
[6] N. Hemati, J. S. Thorp, and M. C. Leu, “Robust nonlinear control of brushless DC motors for direct-drive robotic applications,” Industrial Electronics, IEEE Transactions on, vol. 37, no. 6, pp. 460-468, 1990.
[7] P. M. PeŁczewski, and U. H. Kunz, “The optimal control of a constrained drive system with brushless dc motor,” Industrial Electronics, IEEE Transactions on, vol. 37, no. 5, pp. 342-348, 1990.
[8] V. K. Ummidivarapu, H. K. Voruganti, T. Khajah, and S. P. A. Bordas, “Isogeometric shape optimization of an acoustic horn using the teaching-learning-based optimization (TLBO) algorithm,” Computer Aided Geometric Design,Vol.80, pp. 101881, 2020.
[9] S. Talatahari, N. Taghizadieh, and V. Goodarzimehr, “Hybrid Teaching-Learning-Based Optimization and Harmony Search for Optimum Design of Space Trusses,” Journal of Optimization in Industrial Engineering, vol. 13, no. 1, pp. 177-194, 2020.
[10] R. Rao, “Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems,” Decision Science Letters, vol. 5, no. 1, pp. 1-30, 2016.
[11] R. V. Rao, “Design Optimization of a Robot Manipulator Using TLBO and ETLBO Algorithms,” Teaching Learning Based Optimization Algorithm, pp. 163-169: Springer, 2016.
[12] Z. S. Hasan, and M. Nema Hawas, “Using Teaching Learning Based Optimization to Efficacious and Tuning of UPFC-PODs of Interconnected Systems,” MS&E, vol. 745, no. 1, pp. 012001, 2020.
[13] A. T. Azar, Fuzzy systems: BoD–Books on Demand, 2010.
[14] L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning—II,” Information sciences, vol. 8, no. 4, pp. 301-357, 1975.
[15] L. A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Transactions on systems, Man, and Cybernetics, no. 1, pp. 28-44, 1973.
[16] E. Khan, “Neural fuzzy based intelligent systems and applications,” Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms, pp. 105-140: CRC Press, 2020.
[17] L.-X. Wang, and L.-X. Wang, “A course in fuzzy systems and control: Prentice Hall PTR Upper Saddle River,” NJ, 1997.
[18] R. V. Rao, V. J. Savsani, and D. Vakharia, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011.
[19] A. K. Shukla, P. Singh, and M. Vardhan, “An adaptive inertia weight teaching-learning-based optimization algorithm and its applications,” Applied Mathematical Modelling, vol. 77, pp. 309-326, 2020.
[20] O. T. Chong, W. H. Lim, N. A. M. Isa, K. M. Ang, S. S. Tiang, and C. K. Ang, “A Teaching-Learning-Based Optimization with Modified Learning Phases for Continuous Optimization,” in proc. Science and Information Conference, 2020, pp. 103-124.
[21] W. Chen, X. Chen, J. Peng, M. Panahi, and S. Lee, “Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer,” Geoscience Frontiers, vol.12, no.1, 2021.
[22] S. J. Hammoodi, K. S. Flayyih, and A. R. Hamad, “Design and implementation speed control system of DC Motor based on PID control and Matlab Simulink,” International Journal of Power Electronics and Drive Systems, vol. 11, no. 1, pp. 127, 2020.
[23] A. Latif, A. Z. Arfianto, H. A. Widodo, R. Rahim, and E. T. Helmy, “Motor DC PID System Regulator for Mini Conveyor Drive Based-on Matlab,” Journal of Robotics and Control (JRC), vol. 1, no. 6, pp. 185-190, 2020.
[24] A. Rajasekhar, R. K. Jatoth, and A. Abraham, “Design of intelligent PID/PI λ D μ speed controller for chopper fed DC motor drive using opposition based artificial bee colony algorithm,” Engineering Applications of Artificial Intelligence, vol. 29, no. 2, pp. 13-32, 2014.
[25] A. Rajasekhar, S. Das, and A. Abraham, “Fractional order PID controller design for speed control of chopper fed DC motor drive using artificial bee colony algorithm,” in Nature and Biologically Inspired Computing (NaBIC), 2013 World Congress on, 2013, pp. 259-266.