فناوری در مهندسی هوافضا

فناوری در مهندسی هوافضا

بهبود سیستم کنترل پرواز متحمل عیب با استفاده از تلفیق شبکه عصبی تطبیقی و الگوریتم فوق‌پیچشی با همگرایی سریع

نوع مقاله : علمی پژوهشی

نویسندگان
1 دانشجوی دکتری، دانشکده مهندسی هوافضا، دانشگاه صنعتی مالک اشتر ،تهران، ایران
2 استادیار، دانشکده مهندسی هوافضا، دانشگاه صنعتی مالک اشتر ،تهران، ایران
3 استاد، دانشکده مهندسی هوافضا، دانشگاه صنعتی مالک اشتر ،تهران، ایران
چکیده
در سیستم‌های کنترل پرواز، تضمین عملکرد صحیح و کاهش تأثیرات منفی عوامل غیرخطی، خطاهای سنسور و اغتشاشات محیطی، به‌ویژه در شرایطی که حسگرها دچار نقص یا اطلاعات آن‌ها از دست می‌رود، از اهمیت بالایی برخوردار است. معمولاً برای مقابله با چنین شرایطی، از سیستمی با قابلیت اعتماد بالا و تصمیم‌گیری انسانی استفاده می‌شود، اما این سیستم‌ها در مواجهه با نقص‌های شدید با مشکلاتی روبرو می‌شوند. در این راستا، طراحی کنترل‌کننده‌هایی با تحمل خطا که شامل تخمین و جبران خطا در سطوح مختلف است، ضروری به نظر می‌رسد. در این مقاله، رویکردی نوین در سه گام اصلی برای مقابله با خطای حسگرها تحت تأثیر اغتشاش معرفی شده است. گام نخست، حذف اغتشاش و نویز از خروجی حسگرهای سرعت زاویه‌ای؛ گام دوم، تشخیص و جداسازی خطا (FDI)؛ و گام سوم، طراحی سیستم کنترلی برای کاهش اثرات خطای شناسایی‌شده است. در گام اول، رؤیت‌گری فوق‌پیچشی مبتنی بر مود لغزشی با همگرایی سریع به کار گرفته شده، در گام دوم، تشخیص خطا از طریق رؤیت‌گر عصبی-تطبیقی انجام می‌شود و در نهایت، کنترل‌کننده با روش گام‌به‌عقب و فرمان نرم طراحی می‌گردد. شبیه‌سازی‌ها روی مدل غیرخطی هواپیمای F-18 نشان‌دهنده عملکرد مؤثر این الگوریتم در مدیریت همزمان اغتشاش و نقص حسگرها است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Enhancing Aircraft Fault-Tolerant Control: Hybrid Fusion of Fast Convergence Super-Twisting Observer and Adaptive Neural Network Approaches

نویسندگان English

Javad Naderifar 1
Mostafa Khazaee 2
Seyyed Hossein Sadati 3
1 Faculty of Aerospace Engineering, University of Malek Ashtar, Tehran, Iran
2 Faculty of Aerospace Engineering, University of Malek Ashtar, Tehran, Iran
3 Faculty of Aerospace Engineering, University of Malek Ashtar, Tehran, Iran
چکیده English

Robust performance in flight control systems requires minimizing the impact of nonlinear dynamics, sensor faults, signal loss, and external disturbances. To address these challenges, conventional architectures rely on high-reliability systems supported by human intervention to detect and manage errors that can critically impair aircraft operation. Fault-tolerant control strategies integrate multiple layers of estimation and compensation to manage various fault types. This study proposes a three-stage methodology to mitigate sensor faults in the presence of disturbances. In the first stage, a fast-converging super-twisting sliding mode observer is employed to eliminate disturbances and noise from the outputs of angular velocity sensors. The second stage applies an adaptive neural observer for fault detection and isolation (FDI). In the third stage, a control system based on the backstepping method generates smooth control commands to compensate for the identified faults. The control strategy dynamically adapts based on the fault type and location-whether in sensors, actuators, or other components-ensuring consistent fault mitigation. Simulation results using a nonlinear six-degree-of-freedom F-18 aircraft model validate the effectiveness of the proposed algorithm in simultaneously detecting and compensating for disturbances and faults.

کلیدواژه‌ها English

Fault-tolerant control
disturbance rejection
adaptive neural observer
fast convergence super-twisting
backstepping control
[1] H. K. Khalil, Nonlinear Systems, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2002.
[2] Z. Guo, J. Guo, J. Zhou, and J. Chang, "Robust tracking for hypersonic reentry vehicles via disturbance estimation-triggered control," IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 2, pp. 1279–1289, 2019, https://doi.org/10.1109/TAES.2019.2928605.
[3] X. Qi, L. Chen, Y. Jing, and H. Ma, "Super twisting observer-based arbitrary convergence time sliding mode control for uncertain system," International Journal of Control, Automation, and Systems, vol. 2024, pp. 1–9, 2024, https://doi.org/10.1007/s12555-022-0603-9.
[4] X. Wu and X. Mu, "New design on distributed event-based sliding mode controller for disturbed second-order multiagent systems," IEEE Transactions on Automatic Control, vol. 67, no. 5, pp. 2590–2596, 2021, https://doi.org/10.1109/TAC.2021.3090754.
[5] J. Liu, H. An, Y. Gao, C. Wang, and L. Wu, "Adaptive control of hypersonic flight vehicles with limited angle-of-attack," IEEE/ASME Transactions on Mechatronics, vol. 23, no. 2, pp. 883–894, 2018. https://doi.org/10.1109/TMECH.2018.2800089.
[6] S. Balajiwale, H. Arya, and A. Joshi, "Longitudinal Controller for Hand Launched MAV Based on Supertwisting Algorithm," in AIAA Guidance, Navigation, and Control Conference, 2017, Paper 1045, https://doi.org/10.2514/6.2017-1045.
[7] X. Chang, W. Zhou, P. Zhou, and D.-p. Duan, "A fast convergence super-twisting observer design for an autonomous airship," Asian Journal of Control, vol. 21, no. 2, pp. 429-438, 2019, https://doi.org/10.1002/asjc.1962.
[8] C. C. Z. Gao, "A survey of fault diagnosis and fault-tolerant techniques-Part I: Fault diagnosis with model-based and signal-based approaches," IEEE Transactions on Industrial Electronics, pp. 3757-3767, 2015, https://doi.org/10.1109/TIE.2015.2419013.
[9] I. Z. Y. Sadeghzadeh, "A review on fault-tolerant control for unmanned aerial vehicles (UAVs)," in AIAA Infotech at Aerospace Conference and Exhibit, Louis, Missouri, 2011, Paper 1472, https://doi.org/10.2514/6.2011-1472.
[10] M. M. Polycarpou and A. T. Vemuri, "Learning methodology for failure detection and accommodation," IEEE Control Systems Magazine, vol. 15, pp. 16-24, 1995, https://doi.org/10.1109/37.387613.
[11] X. Zhang, T. Parisini, and M. M. Polycarpou, "Integrated design of fault diagnosis and accommodation schemes for a class of nonlinear systems," in 40th IEEE Conference Decision and Control, Orlando, FL, 2001, pp. 1448-1453, https://doi.org/10.1109/CDC.2001.981098.
[12] M. Gomaa, "Fault tolerant control scheme based on multi-ann faulty models," in International Conference on Electrical, Electronic and Computer Engineering, 2004. ICEEC '04, Cairo, Egypt, vol. 1, 2004, pp. 329-332, https://doi.org/10.1109/ICEEC.2004.1374458.
[13]A. A. Pashilkar, N. Sundararajan, and P. Saratchandran, "A fault-tolerant neural aided controller for aircraft auto-landing," Aerospace Science and Technolog, vol. 10, no. 1, pp. 41-48, 2006, https://doi.org/10.1016/j.ast.2005.05.002.
[14] M. G. Perhinschi, "Integration of sensor and actuator failure detection, identification, and accommodation schemes within fault tolerant control laws," Control and Intelligent Systems, vol. 35, no. 4, pp. 309-318, 2007, https://doi.org/10.2316/Journal.201.2007.4.201-1657.
[15] M. Bodson and J. E. Groszkiewicz, "Multivariable adaptive algorithms for reconfigurable flight control," IEEE Transactions on Control Systems Technology, vol. 5, no. 2, pp. 217-229, 1997, https://doi.org/10.1109/87.556026.
[16] D. Ye, Q. Y. Fan, G. H. Yang, and H. Wang, "Robust H∞ fault-tolerant control for linear systems with fast adaptive fault estimation," IFAC Proceedings Volumes, vol. 47, no. 3, pp. 6753-6757, 2014, https://doi.org/10.3182/20140824-6-ZA-1003.00511.
[17] B. Yu, Y. Zhang, and Y. Qu, "MPC-based FTC with FDD against actuator faults of UAVs," in 15th International Conference on Control, Automation, and Systems (ICCAS), 2015, https://doi.org/10.1109/ICCAS.2015.7364911.
[18] M. Taimoor, X. Lu, H. Maqsood, and C. Sheng, "Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle," Aircraft Engineering and Aerospace Technology, vol. 93, no. 5, pp. 847-861, 2021, https://doi.org/10.1108/AEAT-01-2021-0005.
[19] A. Abbaspour, K. K. Yen, P. Forouzannezhad, and A. Sargolzaei, "A neural adaptive approach for active fault-tolerant control design in UAV," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 9, pp. 3401-3411, 2020, https://doi.org/10.1109/TSMC.2018.2850701.
[20] Y. Wang, J. Yang, X. Yang, and T. Wang, "Adaptive neural network-based fault tolerant control for a three degrees of freedom helicopter," International Journal of Control, vol. 96, pp. 1-17, 2021, https://doi.org/10.1080/00207179.2021.1984583.
[21] F. W. Alsaade, Q. Yao, M. S. Alzahrani, A. S. Alzahrani, and H. Jahanshahi, "Indirect-neural-approximation-based fault-tolerant integrated attitude and position control of spacecraft proximity operations," Sensors, vol. 22, no. 5, 2022, Art. no. 1726, https://doi.org/10.3390/s22051726.
[22] Q. Chen, S. Xie, and X. He, "Neural-network-based adaptive singularity-free fixed-time attitude tracking control for spacecrafts," IEEE Transactions on Cybernetics, vol. 51, no. 10, pp. 5032-5045, 2021, https://doi.org/10.1109/TCYB.2020.3024672.
[23] J. Y. Gong, B. Jiang, and Q. K. Shen, "Adaptive fault-tolerant neural control for large-scale systems with actuator faults," International Journal of Control, Automation and Systems, vol. 17, pp. 1421–1431, 2019, https://doi.org/10.1007/s12555-018-0729-y.
[24] S. Mokhtari, A. Abbaspour, K. K. Yen, and A. Sargolzaei, "Neural network-based active fault-tolerant control design for unmanned helicopter with additive faults," Remote Sensing, vol. 13, no. 12, 2021, Art. no. 2396, https://doi.org/10.3390/rs13122396.
[25] R. C. Nelson, Flight Stability and Automatic Control, 2nd ed. McGraw-Hill, 1998.
[26] S. A. Snell, D. F. Enns, and W. L. Garrard Jr., "Nonlinear inversion flight control for a supermaneuverable aircraft," Journal of Guidance, Control, and Dynamics, vol. 15, no. 4, pp. 976-984, 1992, https://doi.org/10.2514/3.20932.
[27] B. L. Stevens and F. L. Lewis, Aircraft Control and Simulation, John Wiley & Sons Inc., 1992.
[28] S. A. Snell, D. F. Enns, and W. L. Garrard Jr., "Nonlinear inversion flight control for a supermaneuverable aircraft," Journal of Guidance, Control, and Dynamics, vol. 15, no. 4, pp. 976-984, 1992, https://doi.org/10.2514/3.20932.
[29] K. Funahashi and Y. Nakamura, "Approximation of dynamic systems by continuous time recurrent neural networks," Neural Networks, vol. 6, pp. 801-806, 1993, https://doi.org/10.1016/S0893-6080(05)80125-X.
[30] S. H. Sadati, M. Sabzehparvar, M. B. Menhaj, and M. Bahrami, "Backstepping controller design using neural networks for a fighter aircraft," European Journal of Control, vol. 13, no. 5, pp. 516-526, 2007, https://doi.org/10.3166/ejc.13.516-526.
[31] N. E. Wu, "Robust feedback design with optimized diagnostic performance," IEEE Transactions on Automatic Control, vol. 42, no. 9, pp. 1264-1268, 1997, https://doi.org/10.1109/9.623089.
[32] M. L. Tyler and M. Morari, "Optimal and robust design of integrated control and diagnostic modules," in IEEE Conference on Decision and Control, 1999, pp. 3703-3708, https://doi.org/10.1109/CDC.1999.832057.
[33] J. Chen, R. J. Patton, and H. Y. Zhang, "Design of unknown input observer and robust fault detection filters," International Journal of Control (IJC), vol. 63, no. 1, pp. 85–105, 1996, https://doi.org/10.1080/00207179608921833.
[34] H. Seguchi and T. Ohtsuka, "Nonlinear receding horizon control of an underactuated hovercraft," International Journal of Robust and Nonlinear Control, vol. 13, no. 3–4, pp. 381–398, 2003, https://doi.org/10.1109/CACSD-CCA-ISIC.2006.4776714.
[35] J. Chen and R. J. Patton, Robust Model-Based Fault Diagnosis for Dynamic Systems, Kluwer Academic, 1999.
[36] X. Y. Gou, J. K. Liu, and Q. Z. Zhang, "Adaptive backstepping fault-tolerant control for hypersonic aircraft with unknown control direction under actuator and sensor faults," The Aeronautical Journal, vol. 128, no. 1324, pp. 1183–1203, 2024, https://doi.org/10.2514/3.20932.
[37] N. S. Abdul-Jaleel and M. S. Shaker, "Robust integration of fault estimation and sliding mode fault-tolerant control for interconnected systems against sensor fault," Asian Journal of Control, vol. 26, 2024, https://doi.org/10.1017/aer.2023.93.

  • تاریخ دریافت 04 شهریور 1403
  • تاریخ بازنگری 12 آبان 1403
  • تاریخ پذیرش 13 آبان 1403
  • تاریخ اولین انتشار 13 آبان 1403