Keywords
How to Cite
Abstract
Maqolada nolinear, ko‘p o‘zgaruvchili va vaqt bo‘yicha xususiyatlari o‘zgarib turadigan texnologik jarayonlarni boshqarishda neyron tarmoqlarga asoslangan adaptiv modellar va algoritmlardan foydalanish masalalari ko‘rib chiqiladi. Tadqiqotda feed-forward neural network, NARX, recurrent neural network, LSTM, GRU hamda physics-informed neural network modellarining texnologik jarayonlarni identifikatsiyalash va prognozlashdagi imkoniyatlari tahlil qilindi. Neyron modelni model predictive control bilan integratsiyalash, model xatosini real vaqt rejimida kuzatish, parametrlar driftini aniqlash va event-triggered online learning asosida modelni yangilash tamoyillari ishlab chiqildi. Adaptiv boshqaruv arxitekturasida sensor ma’lumotlarini filtrlash, holatlarni baholash, neyron prediktor, cheklovli optimallashtirish, xavfsizlik nazorati va rezerv boshqaruvchidan foydalanish nazarda tutiladi. Tahlil shuni ko‘rsatadiki, neyron tarmoqning yuqori prognoz aniqligi o‘z-o‘zidan samarali boshqaruvni kafolatlamaydi; yopiq kontur barqarorligi, hisoblash vaqti, constraintlarni bajarish va operating regiondan tashqaridagi noaniqliklar ham birgalikda nazorat qilinishi zarur.
References
1. Ren, Y. M., Alhajeri, M. S., Luo, J., Chen, S., Abdullah, F., Wu, Z., & Christofides, P. D. (2022). A tutorial review of neural network modeling approaches for model predictive control. Computers & Chemical Engineering, 165, 107956. https://doi.org/10.1016/j.compchemeng.2022.107956
2. Morato, M. M., & Felix, M. S. (2024). Data science and model predictive control: A survey of recent advances on data-driven MPC algorithms. Journal of Process Control, 144, 103327.
3. Xu, Q., Dong, J., Peng, K., & Yang, X. (2024). A novel method of neural network model predictive control integrated process monitoring and applications to hot rolling process. Expert Systems with Applications, 237, 121682.
4. Model Predictive Control using Physics Informed Neural Networks for Process Systems. (2024). IFAC-PapersOnLine, 58(14), 775-780.
5. Al Bannoud, M., da Silva, C. A. M., & Martins, T. D. (2024). Applications of metaheuristic optimization algorithms in model predictive control for chemical engineering processes: A systematic review. Annual Reviews in Control, 58, 100973.
6. Turan, E. M., & Jäschke, J. (2024). Closed-loop training of static output feedback neural network controllers for large systems: A distillation case study. Journal of Process Control, 143, 103302.
7. Sancı, M. E., & Günel, G. Ö. (2024). Neural network based adaptive inverse optimal control for non-affine nonlinear systems. Neural Processing Letters, 56, 46.
8. Panzer, M., & Gronau, N. (2024). Designing an adaptive and deep learning based control framework for modular production systems. Journal of Intelligent Manufacturing, 35, 4113-4136.
9. Narendra, K. S., & Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1), 4-27.
10. Åström, K. J., & Wittenmark, B. (2008). Adaptive Control (2nd ed.). Dover Publications.
11. Haykin, S. (2009). Neural Networks and Learning Machines (3rd ed.). Pearson.
12. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
13. Rawlings, J. B., Mayne, D. Q., & Diehl, M. (2017). Model Predictive Control: Theory, Computation, and Design (2nd ed.). Nob Hill Publishing.
14. Camacho, E. F., & Bordons, C. (2007). Model Predictive Control (2nd ed.). Springer.
15. Ljung, L. (1999). System Identification: Theory for the User (2nd ed.). Prentice Hall.
This work is licensed under a Creative Commons Attribution 4.0 International License.
