طراحی و بهینه سازی کنترل کننده عصبی برای تنظیم و کنترل ولتاژ خروجی مبدل های DC به DC افزاینده
محورهای موضوعی : مهندسی الکترونیک
محمد زارعی
1
,
مجید مرادی زیرکوهی
2
,
نجمه چراغی شیرازی
3
1 - گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه آزاداسلامی واحد بوشهر
2 - گروه برق، دانشکده فنی، دانشگاه صنعتی بهبهان، بهبهان، ایران
3 - گروه برق، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر، ایران
کلید واژه: : مبدل DC-DC, الگوریتم رقابت استعماری, مبدل بوست, شبکه عصبی,
چکیده مقاله :
مبدل‌های قدرت DC-DC، بطور گسترده در ساخت منابع تغذیه، درایو موتور DC، افزایش/کاهش ولتاژ خروجی پنل-های خورشیدی و موارد متعدد دیگری مورد استفاده قرار می‌گیرند. تغییر ولتاژ DC از یک سطح به سطح دیگر در بسیاری از کاربردهای صنعتی یک ضروت است. کنترل ولتاژ و جریان خروجی این مبدل‌ها، هنگام تغییرات ناگهانی بار و یا ولتاژ منبع ورودی حائز اهمیت است. از این رو یکی از اهداف این مقاله پرداختن به کنترل و تنظیم ولتاژ خروجی مبدل بوست یا افزاینده می‌باشد. کنترل کننده‌ی پیشنهادی برای کنترل سطح ولتاژ DC خروجی مبدل، یک کنترل کننده شبکه عصبی بهینه شده با الگوریتم مبتنی بر رقابت استعماری می‌باشد. عملکرد کنترل کننده پیشنهادی به این صورت است که ابتدا با توجه به اهداف مورد انتظار از سیستم، شبکه عصبی طراحی می‌شود و سپس با تعیین یک تابع معیار مناسب چند هدفه با استفاده از الگوریتم بهینه سازی ساختار شبکه بهینه می‌شود. این کار باعث بهبود عملکرد سیستم کنترل می‌شود. چرا که انتخاب مناسب پارامتر‌های طراحی در عملکرد شبکه عصبی که نقش کنترل کننده را دارد نقش زیادی دارد. نتایج در مقایسه با کنترل کننده PID نشان از برتری روش پیشنهادی دارد.
Due to the many applications of DC to DC converters in electronics, regulating their output voltage is very important. In many applications it is necessary to change the DC voltage from one level to another. DC -DC converters are used for this purpose. The conversion of DC voltage from one level to another is done by switching elements such as transistors and diodes. Recently, the control of these converters has found a special place in scientific texts. Therefore, one of the objectives of this paper is to control and regulate the output voltage of the converter. The controller proposed in this paper to control the DC voltage level of the converter output is an optimized neural network controller with an algorithm based on colonial competition. The proposed controller function is that first the neural network is designed according to the expected goals of the system and then it is optimized by determining a suitable multi-objective benchmark function using the network structure optimization algorithm. This improves the performance of the control system. Because the proper selection of design parameters has a great role in the performance of the neural network that plays the role of controller. The proposed neural network function is to apply the appropriate signal transducer (PWM signal) to the switching elements in order to increase the performance. The results compared to the PID controller indicate the superiority of the proposed method.
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[24] J. M.-T. Wu, J. Zhan, and J. C.-W. Lin, “An ACO-based approach to mine high-utility itemsets,” Knowledge-Based Systems, vol. 116, no. 2, pp. 102-113, 2017.
[25] B. Morales-Castañeda, D. Zaldívar, E. Cuevas, O. Maciel-Castillo, I. Aranguren, and F. Fausto, “An improved simulated annealing algorithm based on ancient metallurgy techniques,” Applied Soft Computing, vol. 84, pp. 105761, 2019.
[26] W. Deng, J. Xu, Y. Song, and H. Zhao, “Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem,” Applied Soft Computing, vol. 2, no. 3, pp. 106724, 2020.
[27] B. Xing, and W.-J. Gao, "Imperialist competitive algorithm," Innovative computational intelligence: A rough guide to 134 clever algorithms, pp. 203-209: Springer, 2014.
[28] S. Hosseini, and A. Al Khaled, “A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research,” Applied Soft Computing, vol. 24, pp. 1078-1094, 2014.
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[30] S. Khorashadizadeh, and M. Mahdian, “Voltage tracking control of DC-DC boost converter using brain emotional learning,” in international conference on control, instrumentation, and automation (ICCIA), 2016, pp. 268-272.
_||_[1] A. Rezaeipanah and S. J. Mirabedini, A. Mobaraki, “An Ensemble Classifier Method for Breast Cancer Detection Using Genetic Algorithm and Multistage Adjustment of Weights in the MLP Neural Network”, Journal of Communication Engineering, vol. 10,no.40, pp. 1-16, 2021 (in Persian).
[2] E. Faraji, M. Mirzaeian, H. Parvin, A. Chamkoorii and M. Mohammadpour, “Short-Term Load Forecasting using an Ensemble of Artificial Neural Networks: Chaharmahal Bakhtiari Case”, Journal of Communication Engineering, vol. 10,no.38, pp. 17-30, 2020 (in Persian).
[3] A. Sedaratnia, M. M. Zirkohi and N.C. Shirazi, “esign of Optimal Sugeno-type fuzzy Controller for Speed Control of DC Motor Including Drive and Chopper Dynamic Considering Multi-Objective Optimization Using Teaching Learning Optimization Algorithm”, Journal of Communication Engineering, vol. 10,no.40, pp. 51-64, 2021 (in Persian).
[4] J. D. Irwin, and M. H. Rashid, “Power Electronics handbook,” Book, Auburn University, 2001.
[5] E. M. Navarro-López, D. Cortés, and C. Castro, “Design of practical sliding-mode controllers with constant switching frequency for power converters,” Electric Power Systems Research, vol. 79, no. 5, pp. 796-802, 2009.
[6] H. Li, and X. Ye, “Sliding-mode PID control of DC-DC converter,” in Industrial Electronics and Applications (ICIEA) IEEE Conference on, 2010, pp. 730-734.
[7] R.-J. Wai, and L.-C. Shih, “Design of voltage tracking control for DC–DC boost converter via total sliding-mode technique,” IEEE Transactions on Industrial Electronics, vol. 58, no. 6, pp. 2502-2511, 2011.
[8] V. Raviraj, and P. C. Sen, “Comparative study of proportional-integral, sliding mode, and fuzzy logic controllers for power converters,” IEEE Transactions on Industry Applications, vol. 33, no. 2, pp. 518-524, 1997.
[9] H. Lam, T. Lee, F. H. Leung, and P. K. Tam, “Fuzzy control of DC-DC switching converters: stability and robustness analysis/sup 1,” in Industrial Electronics Society, Annual Conference of the IEEE, 2001, pp. 899-902.
[10] C. Elmas, O. Deperlioglu, and H. H. Sayan, “Adaptive fuzzy logic controller for DC–DC converters,” Expert Systems with Applications, vol. 36, no. 2, pp. 1540-1548, 2009.
[11] S. C. Lin, and C. C. Tsai, “Adaptive backstepping control with integral action for PWM buck DC‐DC converters,” Journal of the Chinese Institute of Engineers, vol. 28, no. 6, pp. 977-984, 2005.
[12] J. Fermeiro, J. Pombo, M. Calado, and S. Mariano, “A new controller for DC-DC converters based on particle swarm optimization,” Applied Soft Computing, vol. 52, pp. 418-434, 2017.
[13] N. Bouarroudj, D. Boukhetala, V. Feliu-Batlle, F. Boudjema, B. Benlahbib, and B. Batoun, “Maximum power point tracker based on fuzzy adaptive radial basis function neural network for PV-system,” Energies, vol. 12, no. 14, pp. 2827, 2019.
[14] S. Shoja-Majidabad, and A. Hajizadeh, “Decentralized adaptive neural network control of cascaded DC–DC converters with high voltage conversion ratio,” Applied Soft Computing, vol. 86, pp. 105878, 2020.
[15] H. Hamdi, C. B. Regaya, and A. Zaafouri, “Real-time study of a photovoltaic system with boost converter using the PSO-RBF neural network algorithms in a MyRio controller,” Solar Energy, vol. 183, no. 3, pp. 1-16, 2019.
[16] E. Atashpaz-Gargari, and C. Lucas, “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition,” in IEEE congress on evolutionary computation, 2007, pp. 4661-4667.
[17] D. Lei, Y. Yuan, J. Cai, and D. Bai, “An imperialist competitive algorithm with memory for distributed unrelated parallel machines scheduling,” International Journal of Production Research, vol. 58, no. 2, pp. 597-614, 2020.
[18] T. Instruments, “Understanding boost power stages in switchmode power supplies,” Texas Instruments, Mar, 1999.
[19] N. Mohan, and T. M. Undeland, Power electronics: converters, applications, and design: John Wiley & Sons, 2007.
[20] T. Vijayakumar, “Comparative study of capsule neural network in various applications,” Journal of Artificial Intelligence, vol. 1, no. 01, pp. 19-27, 2019.
[21] M. Anthony, and P. L. Bartlett, Neural network learning: Theoretical foundations: cambridge university press, 2009.
[22] S. Mirjalili, “Genetic algorithm,” Evolutionary algorithms and neural networks, pp. 43-55: Springer, 2019.
[23] J. C. Bansal, “Particle swarm optimization,” Evolutionary and swarm intelligence algorithms, pp. 11-23: Springer, 2019.
[24] J. M.-T. Wu, J. Zhan, and J. C.-W. Lin, “An ACO-based approach to mine high-utility itemsets,” Knowledge-Based Systems, vol. 116, no. 2, pp. 102-113, 2017.
[25] B. Morales-Castañeda, D. Zaldívar, E. Cuevas, O. Maciel-Castillo, I. Aranguren, and F. Fausto, “An improved simulated annealing algorithm based on ancient metallurgy techniques,” Applied Soft Computing, vol. 84, pp. 105761, 2019.
[26] W. Deng, J. Xu, Y. Song, and H. Zhao, “Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem,” Applied Soft Computing, vol. 2, no. 3, pp. 106724, 2020.
[27] B. Xing, and W.-J. Gao, "Imperialist competitive algorithm," Innovative computational intelligence: A rough guide to 134 clever algorithms, pp. 203-209: Springer, 2014.
[28] S. Hosseini, and A. Al Khaled, “A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research,” Applied Soft Computing, vol. 24, pp. 1078-1094, 2014.
[29] M. H. Rashid, Power electronics handbook: devices, circuits and applications: Academic press, 2010.
[30] S. Khorashadizadeh, and M. Mahdian, “Voltage tracking control of DC-DC boost converter using brain emotional learning,” in international conference on control, instrumentation, and automation (ICCIA), 2016, pp. 268-272.
