مکانیک هوافضا

مکانیک هوافضا

بهبود عملکرد موشک آشیانه‌یاب در سامانه‌های پدافند با کنترل پیش‌بین و شبکه عصبی

نوع مقاله : گرایش دینامیک، ارتعاشات و کنترل

نویسندگان
1 دانشجوی دکتری، دانشگاه خواجه نصیر، تهران، ایران
2 استادیار، دانشکده افسری امام علی (ع)، تهران، ایران
چکیده
در سامانه‌های هدایت موشکی، دستیابی به دقت بالا و عملکرد مطلوب مستلزم طراحی یکپارچه و بهینه‌سازی هم‌زمان اجزای مختلف سیستم است. این پژوهش روشی نوین برای ارتقای کارایی سیستم هدایت موشک ارائه می‌دهد که مبتنی بر کنترل‌کننده پیش‌بین مدل (MPC) با بهره‌گیری از یک شناساگر عصبی است. در این روش، با هدف کاهش خطای هدایت و مدت زمان پرواز، مسیر حرکت موشک نسبت به هدف به‌صورت دقیق شبیه‌سازی و بهینه‌سازی می‌شود. کنترل‌کننده طراحی‌شده با تکیه بر یادگیری دینامیک سیستم از طریق شبکه عصبی، قادر است مدل‌سازی دقیقی از سیستم بدون نیاز به در اختیار داشتن مدل تحلیلی دقیق انجام دهد. این ویژگی نه‌تنها موجب کاهش وابستگی به مدل‌های سنتی می‌شود، بلکه هزینه‌های طراحی و پیاده‌سازی سیستم را نیز به‌طور محسوسی کاهش می‌دهد. نتایج شبیه‌سازی‌ها نشان می‌دهد که کنترل‌کننده پیشنهادی در مقایسه با روش‌های مرسومی همچون PID، و کنترل‌کننده مقاوم مود لغزشی در کاهش فاصله عدم برخورد و کاهش زمان رسیدن به هدف عملکرد مؤثرتری از خود نشان می‌دهد.

چکیده تصویری

بهبود عملکرد موشک آشیانه‌یاب در سامانه‌های پدافند با کنترل پیش‌بین و شبکه عصبی
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Performance Enhancement of Homing Missiles in Air Defense Systems through Model Predictive Control Integrated with Neural Networks

نویسندگان English

MohamadMahdi Soori 1
kazem Imani 2
1 PhD Student, Khajeh Nasir University, Tehran, Iran
2 Assistant Professor, Imam Ali Officer University, Tehran, Iran
چکیده English

Achieving high precision and optimal performance in missile guidance systems necessitates a unified design framework with simultaneous optimization of multiple system components and constraints. This paper presents an innovative and effective approach to improving the overall performance of missile guidance by employing a Model Predictive Controller (MPC) integrated with a neural network-based identifier. The proposed method focuses on accurate three-dimensional modeling and real-time trajectory optimization between the missile and its maneuvering target, aiming to minimize guidance errors and reduce total flight time. By leveraging a neural network to learn the nonlinear dynamics of the system, the controller eliminates the need for an exact analytical model, significantly decreasing dependency on traditional modeling techniques. This not only enhances modeling flexibility and adaptability but also contributes to lower development and implementation costs in practical scenarios. Simulation results confirm that the proposed MPC-based controller significantly outperforms conventional strategies such as PID & SMC in terms of miss distance reduction and faster interception under dynamic conditions.

کلیدواژه‌ها English

Neural Network
Based Model Predictive Control Intelligent Missile Control Simulation and Analysis of Air Defense Systems Guidance System Performance Optimization


Smiley face

[1]     Zarchan, P. , Tactical and strategic missile guidance. 2012: American Institute of Aeronautics and Astronautics, Inc.
[2]     Palumbo, N. F. , R. A. Blauwkamp, and J. M. Lloyd, Basic principles of homing guidance. Johns Hopkins APL Technical Digest, 2010. 29(1): p. 25-41.
[3]     Harl, N. and S. Balakrishnan, Reentry terminal guidance through sliding mode control. Journal of guidance, control, and dynamics, 2010. 33(1): p. 186-199. doi. org/10. 2514/1. 42654
[4]     Wang, X. H. , C. P. Tan, and L. P. Cheng, Impact time and angle constrained integrated guidance and control with application to salvo attack. Asian Journal of Control, 2020. 22(3): p. 1211-1220. https: //doi. org/10. 1002/asjc. 1991
[5]     Tian, J. , et al. , Integrated guidance and control for missile with narrow field-of-view strapdown seeker. ISA transactions, 2020. 106: p. 124-137. https: //doi. org/10. 1016/j. isatra. 2020. 06. 012
[6]     Sinha, A. , S. R. Kumar, and D. Mukherjee. Integrated guidance and control for dual control interceptors under impact time constraint. in AIAA Scitech 2021 Forum. 2021. https: //doi. org/10. 2514/6. 2021-1463
[7]     Guo, J. , Q. Peng, and Z. Guo, SMC-based integrated guidance and control for beam riding missiles with limited LBPU. IEEE Transactions on Aerospace and Electronic Systems, 2021. 57(5): p. 2969-2978. 10. 1109/TAES. 2021. 3069035
[8]     Zhao, B. , Z. Feng, and J. Guo, Integral barrier Lyapunov functions-based integrated guidance and control design for strap-down missile with field-of-view constraint. Transactions of the Institute of Measurement and Control, 2021. 43(6): p. 1464-1477. https: //doi. org/10. 1177/014233122098132
[9]     Li, Z. , et al. , Field-to-View Constrained Integrated Guidance and Control for Hypersonic Homing Missiles Intercepting Supersonic Maneuvering Targets. Aerospace, 2022. 9(11): p. 640. https: //doi. org/10. 3390/aerospace9110640
[10]  Fu, Z. , K. Zhang, and S. Yang, Research on three‐dimensional integrated guidance and control design with multiple constraints. International Journal of Aerospace Engineering, 2022. 2022(1): p. 6296770. https: //doi. org/10. 1155/2022/6296770
[11]  Zhou, H. and X. Zhao, Robust Integrated Guidance and Control Design for Angle Penetration Attack of Multimissiles. International Journal of Aerospace Engineering, 2022. 2022(1): p. 9391236. https: //doi. org/10. 1155/2022/9391236
[12]  Liang, X. , et al. , Adaptive NN control of integrated guidance and control systems based on disturbance observer. Journal of the Franklin Institute, 2023. 360(1): p. 65-86. https: //doi. org/10. 1016/j. jfranklin. 2022. 11. 040
[13]  Li, Z. , et al. , Three-dimensional approximate cooperative integrated guidance and control with fixed-impact time and azimuth constraints. Aerospace Science and Technology, 2023. 142: p. 108617. https: //doi. org/10. 1016/j. ast. 2023. 108617
[14]  Dong, Y. , et al. , Research on the Integrated Design of Missile Guidance Control Considering the Angle of Attack Constraint. Academic Journal of Engineering and Technology Science, 2023. 6(2): p. 7-16. DOI: 10. 25236/AJETS. 2023. 060202
[15]  Tang, X. , et al. , Integrated guidance and control with impact angle and general field-of-view constraints. Aerospace Science and Technology, 2024. 144: p. 108809. https: //doi. org/10. 1016/j. ast. 2023. 108809
[16]  Guo, J. , Y. Zhou, and M. Zhou, Adaptive control law based integrated guidance and control design for missile with the radome error compensation. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2024. 238(4): p. 361-371. https: //doi. org/10. 1177/0954410023122407
[17]  S. Ma, A. Li و Z. Wang, “Integrated Guidance and Control for Homing Missiles with Terminal Angular Constraint in Three Dimension Space", IEEE International Conference on Artificial Intelligence and Information Systems, Dalian, China, 2020. DOI: 10. 1109/ICAIIS49377. 2020. 9194808
[18]  A. Bemporad, "Recent advances in embedded model predictive control," Michigan Engineering, 14 July 2016. [Online]. Available: https: //www. youtube. com/watch?v=ugeCx1sytNU. [Accessed 1 June 2019].
[19]   Y. Li and F. Yang, "Optimal neural network control," in Control Problems, Toronto, Springer, 2009, pp. 37-42.
[20]  T. A. Tutunji, "Parametric system identification using neural networks," Applied Soft Computing, vol. 47, p. 251–261, 2016.
[21]  J. L. Garriga and M. Soroush, "Model predictive control tuning methods: A review," Industrial & Engineering Chemistry Research, vol. 49, no. 8, pp. 3505-3515, 2010.
[22]  R. Rajamani, "Vehicle dynamics and control," in Mechanical Engineering Series, Springer, 2012, pp. 87-109 & 241-162.
[23]  J. H. Lee, "Model predictive control: review of the three decades of development," International Journal of Control, Automation and Systems, vol. 9, p. 415–424, 2011.
[24]  A. Zheng and M. Morari, "Stability of model predictive control with mixed constraints,"IEEE Transactions on Automatic Control, vol. 40, no. 10, pp. 1818-1823, 1995.
 
دوره 21، شماره 4 - شماره پیاپی 82
زمستان
زمستان 1404
صفحه 1-16

  • تاریخ دریافت 17 مهر 1404
  • تاریخ بازنگری 22 آبان 1404
  • تاریخ پذیرش 08 آذر 1404
  • تاریخ انتشار 01 بهمن 1404