هدایت مقاوم وسایل بازگشت‌پذیرمبتنی بر رگرسیون PLS در حضور عدم قطعیت پارامترهای ورود

نوع مقاله: مقالة‌ تحقیقی‌ (پژوهشی‌)

نویسندگان

1 دانشجو

2 مدیر مرکز ماهواره و فضاپیما، مجتمع دانشگاهی هوافضا، دانشگاه صنعتی مالک اشتر

3 استادیار دانشگاه صنعتی مالک اشتر

چکیده

هدف این مقاله ارائة یک الگوی هدایت و کنترل بهینه‌ برای وسایل بازگشت‌پذیر است که در برابر عدم قطعیت در پارامترهای ورودی مقاوم باشد. روش‌های مختلفی برای طراحی مسیر بهینه و یا کنترل بهینة وسایل بازگشت‌پذیر ارائه شده است، ولی تعداد کمی قابلیت استفاده بر خط را داراست. روش‌هایی نیز که مدعی دارا بودن قابلیت استفاده برخط می‌باشند، عموماً از ساده‌سازی و راه‌حل‌های نزدیک بهینه درون خود استفاده نموده‌اند. در این مقاله سعی شده است تا با استفادة تلفیقی از روش کنترل بهینة غیر‌خطی، روش بهینه‌سازی الگوریتم ژنتیک و روش رگرسیون حداقل مربعات جزیی، الگوریتمی بهینه و مقاوم برای وسایل بازگشت‌پذیر ارائه شود که قابلیت استفاده برخط را داشته باشد. براساس نتایج استخراج شده، نشان داده می‌شود که با استفاده از این روش پیشنهادی، در صورت وجود عدم قطعیت در پارامترهای ورود، ماتریس‌های کنترلی متناسب با هر شرایط اولیة جدید استخراج و با استفاده از روش کنترلی غیرخطی کوادراتیک، وسیلة بازگشت‌پذیر با دقت خوبی به سمت هدف هدایت می‌شود. نتایج آنالیز مونت کارلو نشان می‌دهد که خطای برخورد نسبت به کنترل بهینة غیرخطی کلاسیک 88% بهبود یافته است.

کلیدواژه‌ها


عنوان مقاله [English]

Robust Guidance Algorithmfor Reentry Vehicles based on PLS Regression in the Presence ofInitail Parameter Uncertainties

نویسندگان [English]

  • atefeh hoseinzadeh 1
  • Amirhossain Adami 2
  • Asghar Ebrahimi 3
1 student
2 Satellite & LV center, Aerospace Department, Malek Ashtar University of Technology
3 Associate Professor Malek Ashtar University of Technology
چکیده [English]

The atmospheric re-entry phase is one of the most significantmission steps in the space missions;hence, theguidance and control of reentry vehicles in this phase of mission is important. In this article, a reentry vehicle guidance algorithm has been proposed which has suitable robustness in the presence of initial reentry parameters uncertainties. Here,it has been tried to minimize the landing errors at terminal point using Nonlinear Quadratic Tracking (NQT) and chasing a reference trajectory. In order to define several trajectories with different initial states using evolutionary genetic algorithm with changes in weighting matrices Q and R, it hasbeen tried to reduce the errors of landing at terminal point. The reentry position of the reentry vehicles may be different from the desired ones with respect to several events. In this situation, reentry vehicles start to move in a new trajectory which is not suitable. Therefore, the reentry vehicles should be guided to come back into the desired trajectory or a new optimum trajectory needs to be redesignedto have the same target position on the ground. To do this, we need optimum weighting matrices R and Q for every new trajectory. In this article, this problem has been resolved using partial least squares regression; meanwhile, obtaining the optimal matrices by genetic algorithms needed many times. Also,it is shown that using this method, in the presence of reentry uncertainties, weighting matrices for each new initial condition hasbeen quickly derived. Additionaly,through the matrices obtained and the nonlinear quadratic tracking controller, reentry vehicle was directedto the target with a good accuracy. The Monte Carlo analysis has been used to evaluate the performance of the proposed algoritm. According to the results, the proposed algoritm has a suitable accuracy level and it can generate the online optimum trajectory.

کلیدواژه‌ها [English]

  • Re-entry vehicles
  • Optimal guidance
  • Robustguidance
  • Uncertainty
  • Nonlinear Quadratic Tracking (NQT)
  • Regression
  • Partial Least Squares (PLS)

[1]   Mooij, E., Mease, K. D. and Benito, J., "Robust Re-entry Guidance and Control System Design and Analysis", AIAA Guidance, Navigation and Control Conference and Exhibit, 20 - 23 August 2007, Hilton Head, South Carolina, AIAA 2007-6779.

[2]     Halbe, O., Raja, R.S. Radhakant Padhi, G.,Robust Reentry Guidance of a Reusable Launch Vehicle Using Model Predictive Static Programming,” Journal of Guidance, Control, and Dynamics, Vol. 37, No. 1, 2014, pp. 134-148.

[3]     Sun, Z, Liao, X. H., Stewart, F., Li, Bin and Song, Y. D., “Neuro-Robust Reentry Path Control of Reusable Launch Vehicles,” International Journal of Computational Intelligence Research, Vol. 2, No. 1, 2006, pp. 76-80.

[4]     Lu, P., "Predictor-Corrector Entry Guidance for Low-Lifting Vehicles,” Journal of Guidance, Control, and Dynamics, Vol. 31, No. 4, July–August 2008.

[5]     Ashok Joshi, K., Sivan, S. and Amma, S., “Predictor–Corrector Reentry Guidance Algorithm with Path Constraints for Atmospheric Entry Vehicles,” Journal of Guidance, Control, and Dynamics, Vol. 30, No. 5, September–October 2007.

[6]     Xue, S. and Lu, P.,“Constrained Predictor-Corrector Entry Guidance,” AIAA Guidance, Navigation, and Control Conference, 10-13 August 2009, Chicago, Illinois, AIAA 2009-5767.

[7]     Morio, V., Cazaurang, F. and Vernis, Ph., “Flatness-based Hypersonic Reentry Guidance of a Lifting-body Vehicle,” Control Engineering Practice, Vol. 17, 2009, pp. 588–596.

[8]     Poustini, M.J., Esmaelzadeh, R. and Adami, A.H., “A new Approach to Trajectory Optimization Based on Direct Transcription and Differential Flatness,” Acta Astronautica, Vol, 107, 2015, pp. 1-13.

[9]     Harl, N. and Balakrishnan, S.N., “Reentry Terminal Guidance Through Sliding Mode Control,” Journal of Guidance, Control, and Dynamics, Vol. 33, No. 1, 2010, pp. 186-199.

[10]  Shaferman, V. and Shima, T., “Linear Quadratic Guidance Laws for Imposing a Terminal Intercept Angle,” Journal of Guidance, Control, and Dynamics, Vol. 31, No. 5, 2008, pp. 1507-1518.

[11]  Chawla, C., Sarmah, P., Padhi, R., “Suboptimal Reentry Guidance of a Reusable Launch Vehicle using Pitch Plane Maneuver,” Aerospace Science and Technology, Vol. 14, 2010, pp. 377–386.

[12]  Abbasi, D., “Optimal Reentry Guidance Based on Singular Perturbation,” (M.Sc. Thesis), Aerospace Department, Amirkabir University of Technology, Tehran, 2009, (in persian).

[13]  Poustini, M., “Reentry Trajectory Optimization using Direct Method,” (M.Sc. Thesis), Aerospace Department, Malek-ashtar University of Technology, Tehran, 2014, (in persian).

[14]  Barghandan, M., Optimal Guidance and Control of Reentry Vehicle using Combined Methods, (M.Sc. Thesis), Aerospace Department, Tehran, Malek-ashtar University of Technology, 2014, (in persian).

[15]  Jamilnia, R., Developing of Combined On-line Trajectory Optimization, (PhD Thesis), Aerospace Department, Tehran, Amirkabir University of Technology, 2012, (in persian).

[16]  Muylaert, j, et al. "Flight Experiments for Hypersonic Vehicle Development Expert,” s.l. : RTO AVT Lecture Series on Critical Technologies for Hypersonic Vehicle Developmen, 2004.

[17]              Naidu, D. S., Optimal Control Systems, Idaho, USA: CRC Press, 2002.

[18]              hossienzadeh, A., Adami A.H. and Ebrahimi, A. “Nonlinear Optimal Control of Reentry Vehicles Based on Deriving the State and Control Depended Systematic Matrixes in the State Space Form,” Journal of Space Sciences and Technology, under Review, (in persian).

[19]              shahini, A., Partial Least Squares, (M.Sc. Thesis), Mathematics Science Department, Ferdosi University, Mashhad, 2010, (in persian).