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CLC number: V448; TP273

On-line Access: 2020-05-18

Received: 2019-08-31

Revision Accepted: 2020-02-06

Crosschecked: 2020-03-01

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zheng-yu Song

https://orcid.org/0000-0001-8011-4195

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.5 P.652-674

http://doi.org/10.1631/FITEE.1900458


Survey of autonomous guidance methods for powered planetary landing


Author(s):  Zheng-yu Song, Cong Wang, Stephan Theil, David Seelbinder, Marco Sagliano, Xin-fu Liu, Zhi-jiang Shao

Affiliation(s):  China Academy of Launch Vehicle Technology, Beijing 100076, China; more

Corresponding email(s):   zycalt12@sina.com

Key Words:  Autonomous guidance method, Pinpoint soft landing, Powered descent, Nonlinear programming


Zheng-yu Song, Cong Wang, Stephan Theil, David Seelbinder, Marco Sagliano, Xin-fu Liu, Zhi-jiang Shao. Survey of autonomous guidance methods for powered planetary landing[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 652-674.

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pages="652-674",
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doi="10.1631/FITEE.1900458"
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Abstract: 
This paper summarizes the autonomous guidance methods (AGMs) for pinpoint soft landing on celestial surfaces. We first review the development of powered descent guidance methods, focusing on their contributions for dealing with constraints and enhancing computational efficiency. With the increasing demand for reusable launchers and more scientific returns from space exploration, pinpoint soft landing has become a basic requirement. Unlike the kilometer-level precision for previous activities, the position accuracy of future planetary landers is within tens of meters of a target respecting all constraints of velocity and attitude, which is a very difficult task and arouses renewed interest in AGMs. This paper states the generalized three- and six-degree-of-freedom optimization problems in the powered descent phase and compares the features of three typical scenarios, i.e., the lunar, Mars, and Earth landing. On this basis, the paper details the characteristics and adaptability of AGMs by comparing aspects of analytical guidance methods, numerical optimization algorithms, and learning-based methods, and discusses the convexification treatment and solution strategies for non-convex problems. Three key issues related to AGM application, including physical feasibility, model accuracy, and real-time performance, are presented afterward for discussion. Many space organizations, such as those in the United States, China, France, Germany, and Japan, have also developed free-flying demonstrators to carry out related research. The guidance methods which have been tested on these demonstrators are briefly introduced at the end of the paper.

行星表面动力着陆自主制导方法综述

宋征宇1,5,王聪2,Stephan THEIL3,David SEELBINDER3,Marco SAGLIANO3,刘新福4,邵之江5
1中国运载火箭技术研究院,中国北京市,100076
2北京航天自动控制研究所,中国北京市,100854
3德国宇航中心空间系统与GNC系统研究所,德国不莱梅,28001
4北京理工大学宇航学院,中国北京市,100081
5浙江大学控制科学与工程学院,中国杭州市,310027

摘要:本文总结了天体表面精确软着陆的自主制导方法。首先回顾了动力下降制导方法的发展,重点介绍了其在约束处理和提升计算效率方面的贡献。随着对可重复使用运载器需求的不断增加,以及太空探索带来更多的科学回报,定点软着陆成为一项基本要求。不同于过去任务中公里级的着陆精度,未来行星着陆器在满足全部速度和姿态约束条件下,着陆位置精度要达到10米级,这项任务的困难引起学者对自主制导方法的兴趣。本文讨论了动力下降阶段一般性的3自由度和6自由度优化问题,并对比月球、火星和地球3种典型着陆场景的特点。在此基础上,通过比较解析制导方法、数值优化算法和基于学习的方法,详细阐述自主制导方法的特点和适应性,并讨论非凸问题的凸化处理和求解策略。随后提出自主制导方法工程应用的3个关键问题:物理可行性、模型精度和实时性。最后,简要介绍各国航天组织(包括美国、中国、法国、德国和日本)研发的垂直起降验证飞行器,以及目前在验证飞行器上开展的制导方法试验工作。

关键词:自主制导方法;定点软着陆;动力下降;非线性规划

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Accikmecse B, Blackmore L, 2011. Lossless convexification of a class of optimal control problems with non-convex control constraints. Automatica, 47(2):341-347.

[2]Accikmecse B, Ploen SR, 2005. A powered descent guidance algorithm for Mars pinpoint landing. AIAA Guidance, Navigation, and Control Conf and Exhibit, Article 6288.

[3]Accikmecse B, Ploen SR, 2007. Convex programming approach to powered descent guidance for Mars landing. J Guid Contr Dynam, 30(5):1353-1366.

[4]Accikmecse B, Aung M, Casoliva J, et al., 2013a. Flight testing of trajectories computed by G-FOLD: fuel optimal large divert guidance algorithm for planetary landing. 23rd AAS/AIAA Spaceflight Mechanics Meeting, Article 386.

[5]Accikmecse B, Carson JM, Blackmore L, 2013b. Lossless convexification of nonconvex control bound and pointing constraints of the soft landing optimal control problem. IEEE Trans Contr Syst Technol, 21(6):2104-2113.

[6]Akametalu AK, Tomlin CJ, Chen M, 2018. Reachability-based forced landing system. J Guid Contr Dynam, 41(12):2529-2542.

[7]Benito J, Mease KD, 2010. Reachable and controllable sets for planetary entry and landing. J Guid Contr Dynam, 33(3):641-654.

[8]Biegler LT, Zavala VM, 2009. Large-scale nonlinear programming using IPOPT: an integrating framework for enterprise-wide dynamic optimization. Comput Chem Eng, 33(3):575-582.

[9]Blackmore L, 2016. Autonomous precision landing of space rockets. Bridge, 46(4):15-20.

[10]Blackmore L, Accikmecse B, Scharf DP, 2010. Minimum-landing-error powered-descent guidance for Mars landing using convex optimization. J Guid Contr Dynam, 33(4):1161-1171.

[11]Blackmore L, Accikmecse B, Carson JMIII, 2012. Lossless convexification of control constraints for a class of nonlinear optimal control problems. Syst Contr Lett, 61(8):863-870.

[12]Boggs PT, Tolle JW, 1995. Sequential quadratic programming. Acta Numer, 4(4):1-51.

[13]Bomze IM, Demyanov VF, Fletcher R, et al., 2007. Nonlinear Optimization. Springer, Berlin, Germany.

[14]Boyd S, Vandenberghe L, 2004. Convex Optimization. Cambridge University Press, New York, USA.

[15]Boyd S, Parikh N, Chu E, et al., 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn, 3(1):1-122.

[16]Brent RP, 2013. Algorithms for Minimization without Derivatives. Courier Corporation, New York, USA.

[17]Casoliva J, 2013. Spacecraft Trajectory Generation by Successive Approximation for Powered Descent and Cyclers. PhD Thesis, University of California, Irvine, USA.

[18]Chen SZ, Chu LF, Yang XM, et al., 2019. Application of state prediction neural network control algorithm in small reusable rocket. Acta Aeron Astron Sin, 40(3):149-163 (in Chinese).

[19]Chen WF, Shao ZJ, Wang KX, et al., 2010. Convergence depth control for interior point methods. AIChE J, 56(12):3146-3161.

[20]Domahidi A, Zgraggen AU, Zeilinger MN, et al., 2012. Efficient interior point methods for multistage problems arising in receding horizon control. 51st IEEE Conf on Decision and Control, p.668-674.

[21]Domahidi A, Chu E, Boyd S, 2013. ECOS: an SOCP solver for embedded systems. European Control Conf, p.3071-3076.

[22]Dueri D, Jing Z, Accikmecse B, 2014. Automated custom code generation for embedded, real-time second order cone programming. 19th Int Federation of Automatic Control World Congress, p.1605-1612.

[23]Dueri D, Accikmecse B, Scharf DP, et al., 2017. Customized real-time interior-point methods for onboard powered-descent guidance. J Guid Contr Dynam, 40(2):197-212.

[24]Dumke M, Sagliano M, Saranrittichai P, et al., 2017. EAGLE - environment for autonomous GNC landing experiments. 10th Int ESA Conf on Guidance, Navigation and Control Systems, p.1-25.

[25]Dumont E, Ecker T, Chavagnac C, et al., 2018. CALLISTO - reusable VTVL launcher first stage demonstrator. Space Propulsion Conf, Article 406.

[26]Ebrahimi B, Bahrami M, Roshanian J, 2008. Optimal sliding-mode guidance with terminal velocity constraint for fixed-interval propulsive maneuvers. Acta Astron, 62(10-11):556-562.

[27]Eren U, Dueri D, Accikmecse B, 2015. Constrained reachability and controllability sets for planetary precision landing via convex optimization. J Guid Contr Dynam, 38(11):2067-2083.

[28]Fahroo F, Ross IM, 2008. Pseudospectral methods for infinite-horizon nonlinear optimal control problems. J Guid Contr Dynam, 31(4):927-936.

[29]Furfaro R, Linares R, 2017. Waypoint-based generalized ZEM/ZEV feedback guidance for planetary landing via a reinforcement learning approach. 3rd IAA Conf on Dynamics and Control of Space Systems, p.401-416.

[30]Furfaro R, Selnick S, Cupples ML, et al., 2011. Nonlinear sliding guidance algorithms for precision lunar landing. 21st AAS/AIAA Space Flight Mechanics Meeting, p.945-964.

[31]García CE, Prett DM, Morari M, 1989. Model predictive control: theory and practice—a survey. Automatica, 25(3):335-348.

[32]Gaudet B, Linares R, Furfaro R, 2018. Integrated guidance and control for pinpoint Mars landing using reinforcement learning. Adv Astron Sci, 167:3135-3154.

[33]Ge DT, Cui PY, Zhu SY, 2019. Recent development of autonomous GNC technologies for small celestial body descent and landing. Progr Aerosp Sci, 110:100551.

[34]Gill PE, Murray W, Saunders MA, 2005. SNOPT: an SQP algorithm for large-scale constrained optimization. SIAM Rev, 47(1):99-131.

[35]Giselsson P, Boyd S, 2017. Linear convergence and metric selection for Douglas-Rachford splitting and ADMM. IEEE Trans Autom Contr, 62(2):532-544.

[36]Grant M, Boyd S, Ye Y, 2008. CVX: MATLAB Software for Disciplined Convex Programming. https://cvxr.com/cvx/ [Accessed on Mar. 1, 2020].

[37]Guo YM, Hawkins M, Wie B, 2013. Waypoint-optimized zero-effort-miss/zero-effort-velocity feedback guidance for Mars landing. J Guid Contr Dynam, 36(3):799-809.

[38]Harris MW, Ac{c}ikmec{s} B, 2014. Lossless convexification of non-convex optimal control problems for state constrained linear systems. Automatica, 50(9):2304-2311.

[39]Jerez J, Merkli S, Bennani S, et al., 2017. Forces-RTTO: a tool for on-board real-time autonomous trajectory planning. 10th Int ESA Conf on Guidance, Navigation and Control Systems, p.1-22.

[40]Jiang XQ, Furfaro R, Li S, 2018. Integrated guidance for Mars entry and powered descent using reinforcement learning and Gauss pseudospectral method. 4th IAA Conf on Dynamics and Control of Space Systems, p.761-774.

[41]Jouffe L, 1998. Fuzzy inference system learning by reinforcement methods. IEEE Trans Syst Man Cybern Part C Appl Rev, 28(3):338-355.

[42]Klumpp AR, 1974. Apollo lunar descent guidance. Automatica, 10(2):133-146.

[43]Lee U, Mesbahi M, 2015. Optimal power descent guidance with 6-DoF line of sight constraints via unit dual quaternions. AIAA Guidance, Navigation, and Control Conf, p.1-25.

[44]Lee U, Mesbahi M, 2017. Constrained autonomous precision landing via dual quaternions and model predictive control. J Guid Contr Dynam, 40(2):292-308.

[45]Liu XF, 2019. Fuel-optimal rocket landing with aerodynamic controls. J Guid Contr Dynam, 42(1):65-77.

[46]Liu XF, Lu P, 2014. Solving nonconvex optimal control problems by convex optimization. J Guid Contr Dynam, 37(3):750-765.

[47]Lu P, 2017. Introducing computational guidance and control. J Guid Contr Dynam, 40(2):193.

[48]Lu P, 2018. Propellant-optimal powered descent guidance. J Guid Contr Dynam, 41(4):813-826.

[49]Lu P, Liu XF, 2013. Autonomous trajectory planning for rendezvous and proximity operations by conic optimization. J Guid Contr Dynam, 36(2):375-389.

[50]Luenberger DG, Ye YY, 1984. Linear and Nonlinear Programming. Springer, New York, USA

[51]Ma L, Shao ZJ, Chen WF, et al., 2016. Trajectory optimization for lunar soft landing with a Hamiltonian-based adaptive mesh refinement strategy. Adv Eng Softw, 100:266-276.

[52]Ma L, Wang KX, Shao ZJ, et al., 2017. Trajectory optimization for planetary multi-point powered landing. IFAC-PapersOnLine, 50(1):8291-8296.

[53]Ma L, Wang KX, Xu ZH, et al., 2018a. Trajectory optimization for lunar rover performing vertical takeoff vertical landing maneuvers in the presence of terrain. Acta Astron, 146:289-299.

[54]Ma L, Wang KX, Xu ZH, et al., 2018b. Trajectory optimization for powered descent and landing of reusable rockets with restartable engines. 69th Int Astronautical Congress, Article 44 659.

[55]Ma L, Wang KX, Xu ZH, et al., 2019. Multi-point powered descent guidance based on optimal sensitivity. Aerosp Sci Technol, 86:465-477.

[56]Malyuta D, Reynolds TP, Szmuk M, et al., 2019. Discretization performance and accuracy analysis for the rocket powered descent guidance problem. AIAA Scitech 2019 Forum, Article 925.

[57]Mao YQ, Szmuk M, Accikmecse B, 2016. Successive convexification of non-convex optimal control problems and its convergence properties. 55th Conf on Decision and Control, p.3636-3641.

[58]Mao YQ, Dueri D, Szmuk M, et al., 2017. Successive convexification of non-convex optimal control problems with state constraints. IFAC-PapersOnLine, 50(1):4063-4069.

[59]Mao YQ, Szmuk M, Accikmecse B, 2018. Successive convexification: a superlinearly convergent algorithm for non-convex optimal control problems. https://arxiv.org/abs/1804.06539v1

[60]Mattingley J, Boyd S, 2012. CVXGEN: a code generator for embedded convex optimization. Opt Eng, 13(1):1-27.

[61]Mayne DQ, Rawlings JB, Rao CV, et al., 2000. Constrained model predictive control: stability and optimality. Automatica, 36(6):789-814.

[62]McHenry RL, de Long AJ, Cockrell BF, et al., 1979. Space shuttle ascent guidance, navigation, and control. J Astron Sci, 27:1-38.

[63]Meditch J, 1964. On the problem of optimal thrust programming for a lunar soft landing. IEEE Trans Autom Contr, 9(4):477-484.

[64]Monchaux D, Rmili B, Hassin J, et al., 2018. FROG, a rocket for GNC demonstrations. 69th Int Astronautical Congress, Article 43 308.

[65]Najson F, Mease KD, 2006. Computationally inexpensive guidance algorithm for fuel-efficient terminal descent. J Guid Contr Dynam, 29(4):955-964.

[66]Nonaka S, 2018. Flight demonstration by reusable rocket vehicle RV-X. 28th Workshop on JAXA Astrodynamics and Flight Mechanics, SA6000135029.

[67]Pascucci CA, Bennani S, Bemporad A, 2015. Model predictive control for powered descent guidance and control. European Control Conf, p.1388-1393.

[68]Ploen S, Accikmecse B, Wolf A, 2006. A comparison of powered descent guidance laws for Mars pinpoint landing. AIAA/AAS Astrodynamics Specialist Conf and Exhibit, Article 6676.

[69]Prakash R, Burkhart PD, Chen A, et al., 2008. Mars science laboratory entry, descent, and landing system overview. IEEE Aerospace Conf, p.1-18.

[70]Sagliano M, 2018a. Pseudospectral convex optimization for powered descent and landing. J Guid Contr Dynam, 41(2):320-334.

[71]Sagliano M, 2018b. Generalized hp pseudospectral convex programming for powered descent and landing. AIAA Guidance, Navigation, and Control Conf, Article 1870.

[72]Sagliano M, Mooij E, 2018. Optimal drag-energy entry guidance via pseudospectral convex optimization. AIAA Guidance, Navigation, and Control Conf, Article 1315.

[73]Sagliano M, Dumke M, Theil S, 2019a. Simulations and flight tests of a new nonlinear controller for the EAGLE lander. J Spacecr Rock, 56(1):259-272.

[74]Sagliano M, Tsukamoto T, Hernandez J, et al., 2019b. Guidance and control strategy for the CALLISTO flight experiment. 8th EUCASS Conf for Aeronautics and Aerospace Sciences, Article 284.

[75]Sánchez-Sánchez C, Izzo D, 2018. Real-time optimal control via deep neural networks: study on landing problems. J Guid Contr Dynam, 41(5):1122-1135.

[76]Sato S, Tsukamoto T, Yamamoto T, et al., 2018. The study of navigation, guidance, and control system of reusable vehicle experiment (RV-X). 28th Workshop on JAXA Astrodynamics and Flight Mechanics, SA6000135030.

[77]Scharf DP, Regehr MW, Vaughan GM, et al., 2014. ADAPT demonstrations of onboard large-divert guidance with a VTVL rocket. IEEE Aerospace Conf, p.1-18.

[78]Scharf DP, Accikmecse B, Dueri D, et al., 2017. Implementation and experimental demonstration of onboard powered-descent guidance. J Guid Contr Dynam, 40(2):213-229.

[79]Schulman J, Wolski F, Dhariwal P, et al., 2017. Proximal policy optimization algorithms. https://arxiv.org/abs/1707.06347

[80]Seelbinder D, 2017. On-board Trajectory Computation for Mars Atmospheric Entry based on Parametric Sensitivity Analysis of Optimal Control Problems. PhD Thesis, Universität Bremen, Bremen, Germany.

[81]Song ZY, Zhao DJ, Lv XG, 2015. Terminal attitude-constrained guidance and control for lunar soft landing. Adv Astron Sci, 153:137-147.

[82]Sostaric R, Rea J, 2005. Powered descent guidance methods for the Moon and Mars. AIAA Guidance, Navigation, and Control Conf and Exhibit, Article 6287.

[83]Stellato B, Banjac G, Goulart P, et al., 2018. OSQP: an operator splitting solver for quadratic programs. https://arxiv.org/abs/1711.08013v2

[84]Szmuk M, Accikmecse B, 2016. Successive convexification for fuel-optimal powered landing with aerodynamic drag and non-convex constraints. AIAA Guidance, Navigation, and Control Conf, Article 378.

[85]Szmuk M, Accikmecse B, 2018. Successive convexification for 6-DoF Mars rocket powered landing with free-final-time. AIAA Guidance, Navigation, and Control Conf, Article 617.

[86]Szmuk M, Eren U, Accikmecse B, 2017. Successive convexification for Mars 6-DoF powered descent landing guidance. AIAA Guidance, Navigation, and Control Conf, Article 1500.

[87]Szmuk M, Reynolds T, Accikmecse B, et al., 2019. Successive convexification for 6-DoF powered descent guidance with compound state-triggered constraints. AIAA Scitech 2019 Forum, Article 926.

[88]Toh KC, Tutuncu RH, Todd MJ, 2004. On the implementation of SDPT3 (version 3.1) - a MATLAB software package for semidefinite-quadratic-linear programming. IEEE Int Conf on Robotics and Automation, p.290-296.

[89]Topcu U, Casoliva J, Mease KD, 2005. Fuel efficient powered descent guidance for Mars landing. AIAA Guidance, Navigation, and Control Conf and Exhibit, Article 6286.

[90]Topcu U, Casoliva J, Mease KD, 2007. Minimum-fuel powered descent for Mars pinpoint landing. J Spacecr Rock, 44(2):324-331.

[91]Tsiotras P, Mesbahi M, 2017. Toward an algorithmic control theory. J Guid Contr Dynam, 40(2):194-196.

[92]Wang C, Song ZY, 2018a. Convex model predictive control for rocket vertical landing. 37th Chinese Control Conf, p.9837-9842.

[93]Wang C, Song ZY, 2018b. Rapid trajectory optimization for lunar soft landing with hazard avoidance. Adv Astron Sci, 161:885-900.

[94]Wang JB, Cui NG, 2018. A pseudospectral-convex optimization algorithm for rocket landing guidance. AIAA Guidance, Navigation, and Control Conf, Article 1871.

[95]Wang KX, Shao ZJ, Zhang ZJ, et al., 2007. Convergence depth control for process system optimization. Ind Eng Chem Res, 46(23):7729-7738.

[96]Wenzel A, 2017. On-board Convex Optimization for Powered Descent Landing of EAGLE. PhD Theis, Lulea University of Technology, Lulea, Sweden.

[97]Wenzel A, Sagliano M, Seelbinder D, 2018. Performance analysis of real-time optimal guidance methods for vertical take-off, vertical landing vehicles. 69th Int Astronautical Congress, Article 44 498.

[98]Wright SJ, 1997. Primal-Dual Interior-Point Methods. Society for Industrial and Applied Mathematics, Philadelphia, USA.

[99]Yang RQ, Liu XF, 2019. Comparison of convex optimization-based approaches to solve nonconvex optimal control problems. AIAA Scitech 2019 Forum, Article 1666.

[100]Zeilinger MN, Raimondo DM, Domahidi A, et al., 2014. On real-time robust model predictive control. Automatica, 50(3):683-694.

[101]Zhang B, Tang S, Pan BF, 2016. Multi-constrained suboptimal powered descent guidance for lunar pinpoint soft landing. Aerosp Sci Technol, 48:203-213.

[102]Zhang HH, Guan YF, Huang XY, et al., 2014a. Guidance navigation and control for Chang’E-3 powered descent. Sci Sin Technol, 44(4):377-384.

[103]Zhang HH, Liang J, Huang XY, et al., 2014b. Autonomous hazard avoidance control for Chang’E-3 soft landing. Sci Sin Technol, 44(6):559-568.

[104]Zhang Y, Guo YN, Ma GF, et al., 2017. Collision avoidance ZEM/ZEV optimal feedback guidance for powered descent phase of landing on Mars. Adv Space Res, 59(6):1514-1525.

[105]Zhao DJ, Song ZY, 2017. Reentry trajectory optimization with waypoint and no-fly zone constraints using multiphase convex programming. Acta Astron, 137:60-69.

[106]Zhao DJ, Jiang BY, Lv XG, 2015. Terminal attitude-constrained optimal feedback guidance for pinpoint planetary landing. Adv Astron Sci, 153:1689-1696.

[107]Zhou LY, Xia YQ, 2014. Improved ZEM/ZEV feedback guidance for Mars powered descent phase. Adv Space Res, 54(11):2446-2455.

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