Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Chance-Constrained Control for Safe Spacecraft Autonomy: Convex Programming Approach (2403.04062v3)

Published 6 Mar 2024 in math.OC, cs.RO, cs.SY, and eess.SY

Abstract: This paper presents a robust path-planning framework for safe spacecraft autonomy under uncertainty and develops a computationally tractable formulation based on convex programming. We utilize chance-constrained control to formulate the problem. It provides a mathematical framework to solve for a sequence of control policies that minimizes a probabilistic cost under probabilistic constraints with a user-defined confidence level (e.g., safety with 99.9% confidence). The framework enables the planner to directly control state distributions under operational uncertainties while ensuring the vehicle safety. This paper rigorously formulates the safe autonomy problem, gathers and extends techniques in literature to accommodate key cost/constraint functions that often arise in spacecraft path planning, and develops a tractable solution method. The presented framework is demonstrated via two representative numerical examples: safe autonomous rendezvous and orbit maintenance in cislunar space, both under uncertainties due to navigation error from Kalman filter, execution error via Gates model, and imperfect force models.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. J. A. Starek, B. Açıkmeşe, I. A. Nesnas, and M. Pavone, “Spacecraft Autonomy Challenges for Next-Generation Space Missions,” in Advances in Control System Technology for Aerospace Applications (E. Feron, ed.), Lecture Notes in Control and Information Sciences, pp. 1–48, Berlin, Heidelberg: Springer, 2016.
  2. M. Ono, B. C. Williams, and L. Blackmore, “Probabilistic planning for continuous dynamic systems under bounded risk,” Journal of Artificial Intelligence Research, vol. 46, pp. 511–577, 2013.
  3. K. Okamoto, M. Goldshtein, and P. Tsiotras, “Optimal Covariance Control for Stochastic Systems Under Chance Constraints,” IEEE Control Systems Letters, vol. 2, pp. 266–271, Apr. 2018.
  4. J. Ridderhof, K. Okamoto, and P. Tsiotras, “Chance Constrained Covariance Control for Linear Stochastic Systems With Output Feedback,” in 2020 59th IEEE Conference on Decision and Control (CDC), vol. 2020-Decem, pp. 1758–1763, IEEE, Dec. 2020.
  5. G. Gaias and S. D’Amico, “Impulsive Maneuvers for Formation Reconfiguration Using Relative Orbital Elements,” Journal of Guidance, Control, and Dynamics, vol. 38, no. 6, pp. 1036–1049, 2015.
  6. B. Açıkmeşe and S. R. Ploen, “Convex Programming Approach to Powered Descent Guidance for Mars Landing,” Journal of Guidance Control and Dynamics, vol. 30, no. 5, pp. 1353–1366, 2007.
  7. P. Lu and X. Liu, “Autonomous Trajectory Planning for Rendezvous and Proximity Operations by Conic Optimization,” Journal of Guidance, Control, and Dynamics, vol. 36, pp. 375–389, Mar. 2013.
  8. T. Ito and S. Sakai, “Throttled explicit guidance for pinpoint landing under bounded thrust acceleration,” Acta Astronautica, vol. 176, pp. 438–454, Nov. 2020.
  9. P. Lu, “Predictor-Corrector Entry Guidance for Low-Lifting Vehicles,” Journal of Guidance, Control, and Dynamics, vol. 31, no. 4, pp. 1067–1075, 2008.
  10. R. Furfaro, D. Cersosimo, and D. R. Wibben, “Asteroid precision landing via multiple sliding surfaces guidance techniques,” Journal of Guidance, Control, and Dynamics, vol. 36, no. 4, pp. 1075–1092, 2013.
  11. A. Weiss, M. Baldwin, R. S. Erwin, and I. Kolmanovsky, “Model Predictive Control for Spacecraft Rendezvous and Docking: Strategies for Handling Constraints and Case Studies,” IEEE Transactions on Control Systems Technology, vol. 23, pp. 1638–1647, July 2015.
  12. S. Di Cairano, H. Park, and I. Kolmanovsky, “Model Predictive Control approach for guidance of spacecraft rendezvous and proximity maneuvering,” International Journal of Robust and Nonlinear Control, vol. 22, no. 12, pp. 1398–1427, 2012.
  13. M. V. Kothare, V. Balakrishnan, and M. Morari, “Robust constrained model predictive control using linear matrix inequalities,” Automatica, vol. 32, no. 10, pp. 1361–1379, 1996.
  14. Y. Kuwata, A. Richards, and J. How, “Robust receding horizon control using generalized constraint tightening,” in American Control Conference, (New York, NY), pp. 4482–4487, Institute of Electrical and Electronics Engineers (IEEE), July 2007.
  15. C. Buckner and R. Lampariello, “Tube-Based Model Predictive Control for the Approach Maneuver of a Spacecraft to a Free-Tumbling Target Satellite,” in 2018 Annual American Control Conference (ACC), pp. 5690–5697, June 2018.
  16. C. E. Oestreich, R. Linares, and R. Gondhalekar, “Tube-Based Model Predictive Control with Uncertainty Identification for Autonomous Spacecraft Maneuvers,” Journal of Guidance, Control, and Dynamics, vol. 46, no. 1, pp. 6–20, 2023.
  17. 1999.
  18. 2010.
  19. V. Szebehely, Theory of Orbit: The Restricted Problem of Three Bodies. Academic Press Inc., 1967.
  20. C. R. Gates, “A simplified model of midcourse maneuver execution errors,” tech. rep., Jet Propulsion Laboratory, California Institute of Technology (Report No. 32-504), Pasadena, CA, Oct. 1963.
  21. K. Oguri and J. W. McMahon, “Robust Spacecraft Guidance Around Small Bodies Under Uncertainty: Stochastic Optimal Control Approach,” Journal of Guidance, Control, and Dynamics, vol. 44, pp. 1295–1313, July 2021.
  22. N. Kumagai and K. Oguri, “Robust NRHO Station-keeping Planning with Maneuver Location Optimization under Operational Uncertainties,” in AAS/AIAA Astrodynamics Specialist Conference, (Big Sky, MT), 2023.
  23. K. Oguri, M. Ono, and J. W. McMahon, “Convex Optimization over Sequential Linear Feedback Policies with Continuous-time Chance Constraints,” in IEEE 58th Conference on Decision and Control (CDC), pp. 6325–6331, Dec. 2019.
  24. M. Szmuk, T. P. Reynolds, and B. Açıkmeşe, “Successive Convexification for Real-Time Six-Degree-of-Freedom Powered Descent Guidance with State-Triggered Constraints,” Journal of Guidance, Control, and Dynamics, vol. 43, pp. 1399–1413, Aug. 2020.
  25. D. Aleti, K. Oguri, and N. Kumagai, “Chance-Constrained Output-Feedback Control without History Feedback: Application to NRHO Stationkeeping,” in AAS/AIAA Astrodynamics Specialist Conference, (Big Sky, MT), 2023.
  26. A. Nemirovski and A. Shapiro, “Convex Approximations of Chance Constrained Programs,” SIAM Journal on Optimization, vol. 17, no. 4, pp. 969–996, 2006.
  27. K. Oguri and G. Lantoine, “Stochastic Sequential Convex Programming for Robust Low-thrust Trajectory Design under Uncertainty,” in AAS/AIAA Astrodynamics Specialist Conference, 2022.
  28. J. Ridderhof, J. Pilipovsky, and P. Tsiotras, “Chance-constrained Covariance Control for Low-Thrust Minimum-Fuel Trajectory Optimization,” in AAS/AIAA Astrodynamics Specialist Conference, (South Lake Tahoe, CA (Virtual)), AAS, Aug. 2020.
  29. Y. Mao, M. Szmuk, and B. Acikmese, “Successive convexification of non-convex optimal control problems and its convergence properties,” in IEEE 55th Conference on Decision and Control (CDC), pp. 3636–3641, IEEE, Dec. 2016.
  30. K. Oguri, “Successive Convexification with Feasibility Guarantee via Augmented Lagrangian for Non-Convex Optimal Control Problems,” in 2023 62nd IEEE Conference on Decision and Control (CDC), (Singapore, Singapore), pp. 3296–3302, IEEE, Dec. 2023.
  31. J. A. Christian and S. B. Robinson, “Noniterative Horizon-Based Optical Navigation by Cholesky Factorization,” Journal of Guidance, Control, and Dynamics, vol. 39, pp. 2757–2765, Dec. 2016.
  32. D. C. Qi and K. Oguri, “Analysis of Autonomous Orbit Determination in Various Near-Moon Periodic Orbits,” The Journal of the Astronautical Sciences, 2023.
  33. T. A. Pavlak, Trajectory Design and Orbit Maintenance Strategies in Multi-Body Dynamical Regimes. PhD thesis, Purdue University, 2013.
Citations (3)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now