Skip to main content
SpringerLink
Log in

Review of Advanced Guidance and Control Methods

  • Chapter
  • First Online:
Advanced Trajectory Optimization, Guidance and Control Strategies for Aerospace Vehicles

Part of the book series: Springer Aerospace Technology ((SAT))

  • 355 Accesses

Abstract

Over the past few decades, how to design a sophisticated guidance and control the (G &C) system for space and aerospace vehicles has been widely researched, which has increasingly drawn attention from all over the world and will continue to do so. As is known to all, there are various model uncertainties and environmental disturbances in G &C system. Therefore, robust and stochastic control-based methods have unsurprisingly played a key role in the system design. Furthermore, a large number of researchers have proposed and successfully established several algorithms which can effectively guide and steer the motion of space/aerospace vehicles. In addition to these stability theory-focused techniques, a major trend in recent years has been the development of optimisation theory- and artificial intelligence (AI)-based controllers for space and aeronautical vehicles in an effort to address the demand for greater system performance. According to related studies, in terms of practical application, these recently established strategies are more advantageous, and they may be suitable for the onboard decision-making system as well. In this chapter, the latest algorithms were analyzed systemically. The chapter begins with a succinct summary of issues with space/aerospace vehicle guidance and control. The discussion of a wide range of scholarly papers pertaining to G &C approaches based on stability theory follows the summary, which examines and explains the potential inherent problems. Then, a summary of different recently proposed optimisation theory-based methods is provided. These methods are expected to generate the optimal guidance and control commands, such as dynamic programming-based, model predictive control-based methods, and other modified versions. This chapter also covered the discussion of their main benefits and inherent drawbacks, which are important in terms of their applications. We also noticed that the combination of AI techniques and the optimal control of vehicle systems has been a new research direction. Therefore, in the following part, we gave a special focus on the recent effort to discuss the feasibility of its application. The key points of the analysis demonstrate how these AI models may be useful for solving space/aerospace vehicle control issues. Finally, a list of potential future study subjects is provided, along with a few concerns for practical implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nagy K (2019) Deep space exploration: the future challenge in engineering. NASA technical report, pp 1–7. JSC-E-DAA-TN67122

    Google Scholar 

  2. Du J, Lei X, Sang J (2019) A space surveillance satellite for cataloging high-altitude small debris. Acta Astronaut 157:268–275. https://doi.org/10.1016/j.actaastro.2019.01.003

    Article  Google Scholar 

  3. Morante D, Sanjurjo Rivo M, Soler M (2018) Multi-objective low-thrust interplanetary trajectory optimization based on generalized logarithmic spirals. J Guid Control Dyn 42(3):476–490. https://doi.org/10.2514/1.G003702

    Article  Google Scholar 

  4. Korzun AM, Dubos GF, Iwata CK, Stahl BA, Quicksall JJ (2010) A concept for the entry, descent, and landing of high-mass payloads at mars. Acta Astronaut 66(7):1146–1159. https://doi.org/10.1016/j.actaastro.2009.10.003

    Article  Google Scholar 

  5. Nishida SI, Kawamoto S, Okawa Y, Terui F, Kitamura S (2009) Space debris removal system using a small satellite. Acta Astronaut 65(1):95–102. https://doi.org/10.1016/j.actaastro.2009.01.041

    Article  Google Scholar 

  6. Chai R, Savvaris A, Tsourdos A, Chai S, Xia Y (2018) Optimal fuel consumption finite-thrust orbital hopping of aeroassisted spacecraft. Aerosp Sci Technol 75:172–182. https://doi.org/10.1016/j.ast.2017.12.026

    Article  Google Scholar 

  7. Chai R, Savvaris A, Chai S (2019) Integrated missile guidance and control using optimization-based predictive control. Nonlinear Dyn 96(2):997–1015. https://doi.org/10.1007/s11071-019-04835-8

    Article  Google Scholar 

  8. Li Q, Yuan J, Zhang B, Gao C (2017) Model predictive control for autonomous rendezvous and docking with a tumbling target. Aerosp Sci Technol 69:700–711. https://doi.org/10.1016/j.ast.2017.07.022

    Article  Google Scholar 

  9. Chung S, Paranjape AA, Dames P, Shen S, Kumar V (2018) A survey on aerial swarm robotics. IEEE Trans Rob 34(4):837–855. https://doi.org/10.1109/TRO.2018.2857475

    Article  Google Scholar 

  10. Bandyopadhyay S, Foust R, Subramanian GP, Chung SJ, Hadaegh FY (2016) Review of formation flying and constellation missions using nanosatellites. J Spacecr Rocket 53(3):567–578. https://doi.org/10.2514/1.A33291

    Article  Google Scholar 

  11. Di Mauro G, Lawn M, Bevilacqua R (2017) Survey on guidance navigation and control requirements for spacecraft formation-flying missions. J Guid Control Dyn 41(3):581–602. https://doi.org/10.2514/1.G002868

    Article  Google Scholar 

  12. Lu P, Brunner CW, Stachowiak SJ, Mendeck GF, Tigges MA, Cerimele CJ (2017) Verification of a fully numerical entry guidance algorithm. J Guid Control Dyn 40(2):230–247. https://doi.org/10.2514/1.G000327

    Article  Google Scholar 

  13. Xia Y, Chen R, Pu F, Dai L (2014) Active disturbance rejection control for drag tracking in mars entry guidance. Adv Space Res 53(5):853–861. https://doi.org/10.1016/j.asr.2013.12.008

    Article  Google Scholar 

  14. Liu C, Chen WH (2016) Disturbance rejection flight control for small fixed-wing unmanned aerial vehicles. J Guid Control Dyn 39(12):2810–2819. https://doi.org/10.2514/1.G001958

    Article  Google Scholar 

  15. Hu Q, Meng Y (2017) Adaptive backstepping control for air-breathing hypersonic vehicle with actuator dynamics. Aerosp Sci Technol 67:412–421. https://doi.org/10.1016/j.ast.2017.04.022

    Article  Google Scholar 

  16. Ventura J, Ciarcia M, Romano M, Walter U (2016) Fast and near-optimal guidance for docking to uncontrolled spacecraft. J Guid Control Dyn 40(12):3138–3154. https://doi.org/10.2514/1.G001843

    Article  Google Scholar 

  17. Taheri E, Junkins JL (2018) Generic smoothing for optimal bang-off-bang spacecraft maneuvers. J Guid Control Dyn 41(11):2470–2475. https://doi.org/10.2514/1.G003604

    Article  Google Scholar 

  18. Li D, Ma G, Li C, He W, Mei J, Ge SS (2018) Distributed attitude coordinated control of multiple spacecraft with attitude constraints. IEEE Trans Aerosp Electron Syst 54(5):2233–2245. https://doi.org/10.1109/TAES.2018.2812438

    Article  Google Scholar 

  19. Zhao Z, Cruz G, Bernstein DS (2019) Adaptive spacecraft attitude control using single-gimbal control moment gyroscopes without singularity avoidance. J Guid Control Dyn 42(11):2342–2355. https://doi.org/10.2514/1.G003926

    Article  Google Scholar 

  20. Chen W, Yang J, Guo L, Li S (2016) Disturbance-observer-based control and related methods-an overview. IEEE Trans Industr Electron 63(2):1083–1095. https://doi.org/10.1109/TIE.2015.2478397

    Article  Google Scholar 

  21. Li H, Yan W, Shi Y (2017) Continuous-time model predictive control of under-actuated spacecraft with bounded control torques. Automatica 75:144–153. https://doi.org/10.1016/j.automatica.2016.09.024

    Article  MathSciNet  MATH  Google Scholar 

  22. Bayat F (2019) Model predictive sliding control for finite-time three-axis spacecraft attitude tracking. IEEE Trans Industr Electron 66(10):7986–7996. https://doi.org/10.1109/TIE.2018.2881936

    Article  Google Scholar 

  23. Izzo D, Martens M, Pan B (2019) A survey on artificial intelligence trends in spacecraft guidance dynamics and control. Astrodynamics 3(4):287–299. https://doi.org/10.1007/s42064-018-0053-6

    Article  Google Scholar 

  24. Sánchez-Sánchez C, Izzo D (2018) Real-time optimal control via deep neural networks: Study on landing problems. J Guid Control Dyn 41(5):1122–1135. https://doi.org/10.2514/1.G002357

    Article  Google Scholar 

  25. Li H, Chen S, Izzo D, Baoyin H (2020) Deep networks as approximators of optimal low-thrust and multi-impulse cost in multitarget missions. Acta Astronaut 166:469–481. https://doi.org/10.1016/j.actaastro.2019.09.023

    Article  Google Scholar 

  26. Chai R, Tsourdos A, Savvaris A, Xia Y, Chai S (2020) Real-time reentry trajectory planning of hypersonic vehicles: A two-step strategy incorporating fuzzy multiobjective transcription and deep neural network. IEEE Trans Industr Electron 67(8):6904–6915. https://doi.org/10.1109/TIE.2019.2939934

    Article  Google Scholar 

  27. Furfaro R, Scorsoglio A, Linares R, Massari M (2020) Adaptive generalized zem-zev feedback guidance for planetary landing via a deep reinforcement learning approach. Acta Astronaut 171:156–171. https://doi.org/10.1016/j.actaastro.2020.02.051

    Article  Google Scholar 

  28. Li S, Jiang X (2014) Review and prospect of guidance and control for mars atmospheric entry. Prog Aerosp Sci 69:40–57. https://doi.org/10.1016/j.paerosci.2014.04.001

    Article  Google Scholar 

  29. Shah MZ, Samar R, Bhatti AI (2015) Guidance of air vehicles: a sliding mode approach. IEEE Trans Control Syst Technol 23(1):231–244. https://doi.org/10.1109/TCST.2014.2322773

    Article  Google Scholar 

  30. Kumar SR, Rao S, Ghose D (2012) Sliding-mode guidance and control for all-aspect interceptors with terminal angle constraints. J Guid Control Dyn 35(4):1230–1246. https://doi.org/10.2514/1.55242

    Article  Google Scholar 

  31. Padhi R, Chawla C, Das PG (2014) Partial integrated guidance and control of interceptors for high-speed ballistic targets. J Guid Control Dyn 37(1):149–163. https://doi.org/10.2514/1.61416

    Article  Google Scholar 

  32. Padhi R, Rakesh PR, Venkataraman R (2014) Formation flying with nonlinear partial integrated guidance and control. IEEE Trans Aerosp Electron Syst 50(4):2847–2859. https://doi.org/10.1109/TAES.2014.120719

    Article  Google Scholar 

  33. Wang Q, Ran M, Dong C (2016) Robust partial integrated guidance and control for missiles via extended state observer. ISA Trans 65:27–36. https://doi.org/10.1016/j.isatra.2016.08.017

    Article  Google Scholar 

  34. Luo C, Wang J, Huang H, Wang P (2016) Integrated guidance and control based air-to-air autonomous attack occupation of ucav. Math Probl Eng 2016:6431264. https://doi.org/10.1155/2016/6431264

    Article  MathSciNet  MATH  Google Scholar 

  35. Song H, Zhang T (2016) Fast robust integrated guidance and control design of interceptors. IEEE Trans Control Syst Technol 24(1):349–356. https://doi.org/10.1109/TCST.2015.2431641

    Article  Google Scholar 

  36. Tian B, Fan W, Su R, Zong Q (2015) Real-time trajectory and attitude coordination control for reusable launch vehicle in reentry phase. IEEE Trans Industr Electron 62(3):1639–1650. https://doi.org/10.1109/TIE.2014.2341553

    Article  Google Scholar 

  37. Santoso F, Garratt MA, Anavatti SG (2020) State-of-the-art integrated guidance and control systems in unmanned vehicles: a review. IEEE Syst J 1–12. https://ieeexplore.ieee.org/document/9204847

  38. Liu H, Li J, Hexi B (2006) Sliding mode control for low-thrust earth-orbiting spacecraft formation maneuvering. Aerosp Sci Technol 10(7):636–643. https://doi.org/10.1016/j.ast.2006.04.008

    Article  MATH  Google Scholar 

  39. Sun R, Wang J, Zhang D, Shao X (2017) Neural-network-based sliding-mode adaptive control for spacecraft formation using aerodynamic forces. J Guid Control Dyn 41(3):757–763. https://doi.org/10.2514/1.G003063

    Article  Google Scholar 

  40. Dai J, Gao A, Xia Y (2017) Mars atmospheric entry guidance for reference trajectory tracking based on robust nonlinear compound controller. Acta Astronaut 132:221–229. https://doi.org/10.1016/j.actaastro.2016.12.013

    Article  Google Scholar 

  41. Eshghi S, Varatharajoo R (2018) Nonsingular terminal sliding mode control technique for attitude tracking problem of a small satellite with combined energy and attitude control system (ceacs). Aerosp Sci Technol 76:14–26. https://doi.org/10.1016/j.ast.2018.02.006

    Article  Google Scholar 

  42. Qiao J, Li Z, Xu J, Yu X (2020) Composite nonsingular terminal sliding mode attitude controller for spacecraft with actuator dynamics under matched and mismatched disturbances. IEEE Trans Industr Inf 16(2):1153–1162. https://doi.org/10.1109/TII.2019.2936172

    Article  Google Scholar 

  43. Miao Y, Hwang I, Liu M, Wang F (2019) Adaptive fast nonsingular terminal sliding mode control for attitude tracking of flexible spacecraft with rotating appendage. Aerosp Sci Technol 93:105312. https://doi.org/10.1016/j.ast.2019.105312

    Article  Google Scholar 

  44. Tiwari PM, Janardhanan S, Nabi M (2016) Attitude control using higher order sliding mode. Aerosp Sci Technol 54:108–113. https://doi.org/10.1016/j.ast.2016.04.012

    Article  Google Scholar 

  45. Song Z, Duan C, Wang J, Wu Q (2019) Chattering-free full-order recursive sliding mode control for finite-time attitude synchronization of rigid spacecraft. J Franklin Inst 356(2):998–1020. https://doi.org/10.1016/j.jfranklin.2018.02.013

    Article  MathSciNet  MATH  Google Scholar 

  46. Gui H, Vukovich G (2015) Adaptive integral sliding mode control for spacecraft attitude tracking with actuator uncertainty. J Franklin Inst 352(12):5832–5852. https://doi.org/10.1016/j.jfranklin.2015.10.001

    Article  MathSciNet  MATH  Google Scholar 

  47. Guo Y, Huang B, Song SM, Li AJ (2018) Wang CQ (2018) Robust saturated finite-time attitude control for spacecraft using integral sliding mode. J Guidance, Control, Dyn 42(2):440–446. https://doi.org/10.2514/1.G003520

    Article  Google Scholar 

  48. Li B, Hu Q, Yang Y, Postolache OA (2019) Finite-time disturbance observer based integral sliding mode control for attitude stabilisation under actuator failure. IET Control Theory Appl 13(1):50–58. https://doi.org/10.1049/iet-cta.2018.5477

    Article  MathSciNet  MATH  Google Scholar 

  49. Nazari M, Butcher EA, Sanyal AK (2018) Spacecraft attitude fractional feedback control using rotation matrices and exponential coordinates. J Guid Control Dyn 41(10):2185–2198. https://doi.org/10.2514/1.G002956

    Article  Google Scholar 

  50. Ma Z, Zhu ZH, Sun G (2019) Fractional-order sliding mode control for deployment of tethered spacecraft system. Proc Inst Mech Eng, Part G: J Aerospace Eng 233(13):4721–4734. https://doi.org/10.1177/0954410019830030

    Article  Google Scholar 

  51. Ismail Z, Varatharajoo R, Chak YC (2020) A fractional-order sliding mode control for nominal and underactuated satellite attitude controls. Adv Space Res 66(2):321–334. https://doi.org/10.1016/j.asr.2020.02.022

    Article  Google Scholar 

  52. Kawaguchi J, Ninomiya T, Miyazawa Y (2011) Stochastic approach to robust flight control design using hierarchy-structured dynamic inversion. J Guid Control Dyn 34(5):1573–1576. https://doi.org/10.2514/1.53257

    Article  Google Scholar 

  53. Moncayo H, Perhinschi M, Wilburn B, Wilburn J, Karas O (2012) UAV adaptive control laws using non-linear dynamic inversion augmented with an immunity-based mechanism. In: Guidance, navigation, and control and co-located conferences. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2012-4678

  54. Tal E, Karaman S (2020) Accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness. IEEE Trans Control Syst Technol 1–16 (2020). https://doi.org/10.1109/TCST.2020.3001117

  55. Lu P, van Kampen EJ, de Visser C, Chu Q (2016) Aircraft fault-tolerant trajectory control using incremental nonlinear dynamic inversion. Control Eng Pract 57:126–141. https://doi.org/10.1016/j.conengprac.2016.09.010

    Article  Google Scholar 

  56. Wang X, van Kampen EJ, Chu Q, Lu P (2019) Stability analysis for incremental nonlinear dynamic inversion control. J Guid Control Dyn 42(5):1116–1129. https://doi.org/10.2514/1.G003791

    Article  Google Scholar 

  57. Smeur EJJ, de Croon GCHE, Chu Q (2018) Cascaded incremental nonlinear dynamic inversion for mav disturbance rejection. Control Eng Pract 73:79–90. https://doi.org/10.1016/j.conengprac.2018.01.003

    Article  Google Scholar 

  58. Wang YC, Chen WS, Zhang SX, Zhu JW, Cao LJ (2018) Command-filtered incremental backstepping controller for small unmanned aerial vehicles. J Guid Control Dyn 41(4):954–967. https://doi.org/10.2514/1.G003001

    Article  Google Scholar 

  59. Hu Q, Tan X, Akella MR (2017) Finite-time fault-tolerant spacecraft attitude control with torque saturation. J Guid Control Dyn 40(10):2524–2537. https://doi.org/10.2514/1.G002191

    Article  Google Scholar 

  60. Kim H, Kim HJ (2019) Backstepping-based impact time control guidance law for missiles with reduced seeker field-of-view. IEEE Trans Aerosp Electron Syst 55(1):82–94. https://doi.org/10.1109/TAES.2018.2848319

    Article  Google Scholar 

  61. Zhang J, Yan J, Zhang P (2020) Multi-uav formation control based on a novel back-stepping approach. IEEE Trans Veh Technol 69(3):2437–2448. https://doi.org/10.1109/TVT.2020.2964847

    Article  Google Scholar 

  62. Zhu J, Liu L, Tang G, Bao W (2016) Three-dimensional robust diving guidance for hypersonic vehicle. Adv Space Res 57(2):562–575. https://doi.org/10.1016/j.asr.2015.10.037

    Article  Google Scholar 

  63. Bandyopadhyay S, Chung SJ, Hadaegh FY (2016) Nonlinear attitude control of spacecraft with a large captured object. J Guid Control Dyn 39(4):754–769. https://doi.org/10.2514/1.G001341

    Article  Google Scholar 

  64. Nakka YK, Chung SJ, Allison JT, Aldrich JB, Alvarez-Salazar OS (2019) Nonlinear attitude control of a spacecraft with distributed actuation of solar arrays. J Guid Control Dyn 42(3):458–475. https://doi.org/10.2514/1.G003478

    Article  Google Scholar 

  65. Li G, Wu Y, Xu P (2018) Adaptive fault-tolerant cooperative guidance law for simultaneous arrival. Aerosp Sci Technol 82–83:243–251. https://doi.org/10.1016/j.ast.2018.09.014

    Article  Google Scholar 

  66. Rezaee H, Abdollahi F (2020) Robust attitude alignment in multispacecraft systems with stochastic links failure. Automatica 118:109033. https://doi.org/10.1016/j.automatica.2020.109033

    Article  MathSciNet  MATH  Google Scholar 

  67. Kakihara K, Ozaki N, Ishikawa A, Chikazawa T, Funase R (2020) Tube stochastic optimal control with imperfect information: application to navigation and guidance analyses. American Institute of Aeronautics and Astronautics, AIAA SciTech Forum. https://doi.org/10.2514/6.2020-0961

    Book  Google Scholar 

  68. Dutta A, Raquepas J (2020) Stochastic optimization framework for spacecraft maneuver detection. American Institute of Aeronautics and Astronautics, AIAA SciTech Forum. https://doi.org/10.2514/6.2020-0234

    Book  Google Scholar 

  69. Jiang B, Karimi HR, Yang S, Gao CC, Kao Y (2020) Observer-based adaptive sliding mode control for nonlinear stochastic markov jump systems via t-s fuzzy modeling: applications to robot arm model. IEEE Trans Ind Electron 1–10. https://ieeexplore.ieee.org/document/8960531

  70. Zhang H (2016) A goal programming model of obtaining the priority weights from an interval preference relation. Inf Sci 354(Supplement C):197–210. https://doi.org/10.1016/j.ins.2016.03.015

  71. Yang PF, Fang YW, Yl Wu, Yong XJ (2016) Finite-time convergent terminal guidance law design based on stochastic fast smooth second-order sliding mode. Optik 127(15):6036–6049. https://doi.org/10.1016/j.ijleo.2016.04.037

    Article  Google Scholar 

  72. Chen K (2020) Full state constrained stochastic adaptive integrated guidance and control for stt missiles with non-affine aerodynamic characteristics. Inf Sci 529:42–58. https://doi.org/10.1016/j.ins.2020.03.061

    Article  MathSciNet  MATH  Google Scholar 

  73. Chung SJ, Bandyopadhyay S, Chang I, Hadaegh FY (2013) Phase synchronization control of complex networks of lagrangian systems on adaptive digraphs. Automatica 49(5):1148–1161. https://doi.org/10.1016/j.automatica.2013.01.048

    Article  MathSciNet  MATH  Google Scholar 

  74. Tsukamoto H, Chung S (2019) Convex optimization-based controller design for stochastic nonlinear systems using contraction analysis. In: 2019 IEEE 58th conference on decision and control (CDC), pp 8196–8203. https://doi.org/10.1109/CDC40024.2019.9028942

  75. Dani AP, Chung S, Hutchinson S (2015) Observer design for stochastic nonlinear systems via contraction-based incremental stability. IEEE Trans Autom Control 60(3):700–714. https://doi.org/10.1109/TAC.2014.2357671

    Article  MathSciNet  MATH  Google Scholar 

  76. Pozo F, Ikhouane F, Rodellar J (2008) Numerical issues in backstepping control: sensitivity and parameter tuning. J Franklin Inst 345(8):891–905. https://doi.org/10.1016/j.jfranklin.2008.05.005

    Article  MathSciNet  MATH  Google Scholar 

  77. Hadaegh FY, Chung S, Manohara HM (2016) On development of 100-gram-class spacecraft for swarm applications. IEEE Syst J 10(2):673–684. https://doi.org/10.1109/JSYST.2014.2327972

    Article  Google Scholar 

  78. Morgan D, Chung SJ, Blackmore L, Acikmese B, Bayard D, Hadaegh FY (2012) Swarm-keeping strategies for spacecraft under j2 and atmospheric drag perturbations. J Guid Control Dyn 35(5):1492–1506. https://doi.org/10.2514/1.55705

    Article  Google Scholar 

  79. Bandyopadhyay S, Chung S, Hadaegh FY (2017) Probabilistic and distributed control of a large-scale swarm of autonomous agents. IEEE Trans Rob 33(5):1103–1123. https://doi.org/10.1109/TRO.2017.2705044

    Article  Google Scholar 

  80. Matsuka K, Feldman AO, Lupu ES, Chung SJ, Hadaegh FY (2020) Decentralized formation pose estimation for spacecraft swarms. Adv Space Res. https://doi.org/10.1016/j.asr.2020.06.016

    Article  Google Scholar 

  81. Foust RC, Lupu ES, Nakka YK, Chung SJ, Hadaegh FY (2020) Autonomous in-orbit satellite assembly from a modular heterogeneous swarm. Acta Astronaut 169:191–205. https://doi.org/10.1016/j.actaastro.2020.01.006

    Article  Google Scholar 

  82. Hou ZS, Wang Z (2013) From model-based control to data-driven control: survey, classification and perspective. Inf Sci 235:3–35. https://doi.org/10.1016/j.ins.2012.07.014

    Article  MathSciNet  MATH  Google Scholar 

  83. Guo Y, Li X, Zhang H, Cai M, He F (2020) Data-driven method for impact time control based on proportional navigation guidance. J Guid Control Dyn 43(5):955–966. https://doi.org/10.2514/1.G004669

    Article  Google Scholar 

  84. Jiang H, Zhou B, Li D, Duan G (2019) Data-driven-based attitude control of combined spacecraft with noncooperative target. Int J Robust Nonlinear Control 29(16):5801–5819. https://doi.org/10.1002/rnc.4693

    Article  MathSciNet  MATH  Google Scholar 

  85. Gao H, Ma G, Lv Y, Guo Y (2019) Forecasting-based data-driven model-free adaptive sliding mode attitude control of combined spacecraft. Aerosp Sci Technol 86:364–374. https://doi.org/10.1016/j.ast.2019.01.004

    Article  Google Scholar 

  86. Gao H, Ma G, Lyu Y, Guo Y (2019) Data-driven model-free adaptive attitude control of partially constrained combined spacecraft with external disturbances and input saturation. Chin J Aeronaut 32(5):1281–1293. https://doi.org/10.1016/j.cja.2019.01.018

    Article  Google Scholar 

  87. Miyazawa Y, Wickramasinghe NK, Harada A, Miyamoto Y (2013) Dynamic programming application to airliner four dimensional optimal flight trajectory. In: Guidance, navigation, and control and co-located conferences. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2013-4969

  88. Sun W, Pan Y, Lim J, Theodorou EA, Tsiotras P (2018) Min-max differential dynamic programming: Continuous and discrete time formulations. J Guid Control Dyn 41(12):2568–2580. https://doi.org/10.2514/1.G003516

    Article  Google Scholar 

  89. Heydari A (2015) Theoretical and numerical analysis of approximate dynamic programming with approximation errors. J Guid Control Dyn 39(2):301–311. https://doi.org/10.2514/1.G001154

    Article  MathSciNet  Google Scholar 

  90. Zappulla R, Park H, Virgili-Llop J, Romano M (2019) Real-time autonomous spacecraft proximity maneuvers and docking using an adaptive artificial potential field approach. IEEE Trans Control Syst Technol 27(6):2598–2605. https://doi.org/10.1109/TCST.2018.2866963

    Article  Google Scholar 

  91. Li H, Sun L, Tan W, Jia B, Liu X (2020) Switching flight control for incremental model-based dual heuristic dynamic programming. J Guid Control Dyn 43(7):1352–1358. https://doi.org/10.2514/1.G004519

    Article  Google Scholar 

  92. Bian T, Jiang ZP (2016) Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design. Automatica 71:348–360. https://doi.org/10.1016/j.automatica.2016.05.003

    Article  MathSciNet  MATH  Google Scholar 

  93. Zhou Y, van Kampen EJ, Chu Q (2018) Incremental approximate dynamic programming for nonlinear adaptive tracking control with partial observability. J Guid Control Dyn 41(12):2554–2567. https://doi.org/10.2514/1.G003472

    Article  Google Scholar 

  94. Mu C, Wang D, He H (2017) Novel iterative neural dynamic programming for data-based approximate optimal control design. Automatica 81:240–252. https://doi.org/10.1016/j.automatica.2017.03.022

    Article  MathSciNet  MATH  Google Scholar 

  95. Ozaki N, Campagnola S, Funase R, Yam CH (2017) Stochastic differential dynamic programming with unscented transform for low-thrust trajectory design. J Guid Control Dyn 41(2):377–387. https://doi.org/10.2514/1.G002367

    Article  Google Scholar 

  96. Zhang H, Hu B, Wang X, Xu J, Wang L, Sun Q, Zhao Z (2020) An action dependent heuristic dynamic programming approach for algal bloom prediction with time-varying parameters. IEEE Access 8:26235–26246. https://doi.org/10.1109/ACCESS.2020.2971244

    Article  Google Scholar 

  97. He S, Shin HS, Tsourdos A (2019) Computational guidance using sparse gauss-hermite quadrature differential dynamic programming. IFAC-PapersOnLine 52(12):13–18. https://doi.org/10.1016/j.ifacol.2019.11.062

    Article  MathSciNet  Google Scholar 

  98. Eren U, Prach A, Kocer BB, Rakovic SV, Kayacan E, Acikmese B (2017) Model predictive control in aerospace systems: Current state and opportunities. J Guid Control Dyn 40(7):1541–1566. https://doi.org/10.2514/1.G002507

    Article  Google Scholar 

  99. Chai R, Savvaris A, Tsourdos A, Chai S, Xia Y (2018) Optimal tracking guidance for aeroassisted spacecraft reconnaissance mission based on receding horizon control. IEEE Trans Aerosp Electron Syst 54(4):1575–1588. https://doi.org/10.1109/TAES.2018.2798219

    Article  Google Scholar 

  100. Chai R, Savvaris A, Tsourdos A, Chai S, Xia Y (2017) Improved gradient-based algorithm for solving aeroassisted vehicle trajectory optimization problems. J Guid Control Dyn 40(8):2093–2101. https://doi.org/10.2514/1.G002183

    Article  Google Scholar 

  101. Sachan K, Padhi R (2019) Waypoint constrained multi-phase optimal guidance of spacecraft for soft lunar landing. Unmanned Syst 07(02):83–104. https://doi.org/10.1142/S230138501950002X

    Article  Google Scholar 

  102. Maity A, Oza HB, Padhi R (2014) Generalized model predictive static programming and angle-constrained guidance of air-to-ground missiles. J Guid Control Dyn 37(6):1897–1913. https://doi.org/10.2514/1.G000038

    Article  Google Scholar 

  103. Mondal S, Padhi R (2018) Angle-constrained terminal guidance using quasi-spectral model predictive static programming. J Guid Control Dyn 41(3):783–791. https://doi.org/10.2514/1.G002893

    Article  Google Scholar 

  104. Mondal S, Padhi R (2018) State and input constrained missile guidance using spectral model predictive static programming. American Institute of Aeronautics and Astronautics, AIAA SciTech Forum. https://doi.org/10.2514/6.2018-1584

    Book  Google Scholar 

  105. Luo J, Jin K, Wang M, Yuan J, Li G (2017) Robust entry guidance using linear covariance-based model predictive control. Int J Adv Rob Syst 14(1):1729881416687503. https://doi.org/10.1177/1729881416687503

    Article  Google Scholar 

  106. Hong H, Maity A, Holzapfel F, Tang S (2019) Model predictive convex programming for constrained vehicle guidance. IEEE Trans Aerosp Electron Syst 55(5):2487–2500. https://doi.org/10.1109/TAES.2018.2890375

    Article  Google Scholar 

  107. Mammarella M, Capello E, Lorenzen M, Dabbene F, Allgower F (2017) A general sampling-based smpc approach to spacecraft proximity operations. In: 2017 IEEE 56th annual conference on decision and control (CDC), pp 4521–4526. https://doi.org/10.1109/CDC.2017.8264326

  108. Mammarella M, Capello E, Park H, Guglieri G, Romano M (2018) Tube-based robust model predictive control for spacecraft proximity operations in the presence of persistent disturbance. Aerosp Sci Technol 77:585–594. https://doi.org/10.1016/j.ast.2018.04.009

    Article  Google Scholar 

  109. He X, Chen W, Yang L (2020) Suboptimal impact-angle-constrained guidance law using linear pseudospectral model predictive spread control. IEEE Access 8:102040–102050. https://doi.org/10.1109/ACCESS.2020.2996752

    Article  Google Scholar 

  110. Mesbah A (2016) Stochastic model predictive control: an overview and perspectives for future research. IEEE Control Syst Mag 36(6):30–44. https://doi.org/10.1109/MCS.2016.2602087

    Article  MathSciNet  MATH  Google Scholar 

  111. Morgan D, Chung SJ, Hadaegh FY (2014) Model predictive control of swarms of spacecraft using sequential convex programming. J Guid Control Dyn 37(6):1725–1740. https://doi.org/10.2514/1.G000218

    Article  Google Scholar 

  112. Morgan D, Subramanian GP, Chung SJ, Hadaegh FY (2016) Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming. Int J Robot Res 35(10):1261–1285. https://doi.org/10.1177/0278364916632065

    Article  Google Scholar 

  113. Foust R, Chung SJ, Hadaegh FY (2019) Optimal guidance and control with nonlinear dynamics using sequential convex programming. J Guid Control Dyn 43(4):633–644. https://doi.org/10.2514/1.G004590

    Article  Google Scholar 

  114. Wang Z (2019) Optimal trajectories and normal load analysis of hypersonic glide vehicles via convex optimization. Aerosp Sci Technol 87:357–368. https://doi.org/10.1016/j.ast.2019.03.002

    Article  Google Scholar 

  115. Wang Z, McDonald ST (2020) Convex relaxation for optimal rendezvous of unmanned aerial and ground vehicles. Aerosp Sci Technol 99:105756. https://doi.org/10.1016/j.ast.2020.105756

    Article  Google Scholar 

  116. Guiggiani A, Kolmanovsky I, Patrinos P, Bemporad A (2015) Fixed-point constrained model predictive control of spacecraft attitude. In: 2015 American control conference (ACC), pp 2317–2322. https://doi.org/10.1109/ACC.2015.7171078

  117. Fleming J, Kouvaritakis B, Cannon M (2015) Robust tube mpc for linear systems with multiplicative uncertainty. IEEE Trans Autom Control 60(4):1087–1092. https://doi.org/10.1109/TAC.2014.2336358

    Article  MathSciNet  MATH  Google Scholar 

  118. Yayla M, Kutay AT (2017) Adaptive model predictive control of uncertain systems with input constraints. American Institute of Aeronautics and Astronautics, AIAA SciTech Forum. https://doi.org/10.2514/6.2017-1494

    Book  Google Scholar 

  119. Esfahani NR, Khorasani K (2016) A distributed model predictive control (mpc) fault reconfiguration strategy for formation flying satellites. Int J Control 89(5):960–983. https://doi.org/10.1080/00207179.2015.1110753

    Article  MathSciNet  MATH  Google Scholar 

  120. Amini MR, Kolmanovsky I, Sun J (2020) Hierarchical mpc for robust eco-cooling of connected and automated vehicles and its application to electric vehicle battery thermal management. IEEE Trans Control Syst Technol 1–13. https://ieeexplore.ieee.org/document/9027885

  121. Kumar R, Wenzel MJ, Ellis MJ, ElBsat MN, Drees KH, Zavala VM (2019) Hierarchical mpc schemes for periodic systems using stochastic programming. Automatica 107:306–316. https://doi.org/10.1016/j.automatica.2019.05.054

    Article  MathSciNet  MATH  Google Scholar 

  122. Capuano V, Kim K, Harvard A, Chung SJ (2020) Monocular-based pose determination of uncooperative space objects. Acta Astronaut 166:493–506. https://doi.org/10.1016/j.actaastro.2019.09.027

    Article  Google Scholar 

  123. Harvard A, Capuano V, Shao EY, Chung SJ (2020) Spacecraft pose estimation from monocular images using neural network based keypoints and visibility maps. AIAA SciTech Forum. American Institute of Aeronautics and Astronautics (2020). https://doi.org/10.2514/6.2020-1874

  124. Capuano V, Harvard A, Lin Y, Chung SJ (2019) Dgnss-vision integration for robust and accurate relative spacecraft navigation. In: Proceedings of the 32nd international technical meeting of the satellite division of the institute of navigation (ION GNSS+ 2019), Miami, Florida, pp 2923–2939. https://doi.org/10.33012/2019.16961

  125. Lee S, Capuano V, Harvard A, Chung SJ (2020) Fast uncertainty estimation for deep learning based optical flow. In: Proceedings of the 2020 IEEE/RSJ international conference on intelligent robots and systems (IROS)

    Google Scholar 

  126. Villa J, Bandyopadhyay S, Morrell B, Hockman B, Lubey D, Harvard A, Chung SJ, Bhaskaran S, Nesnas IA (2020) Optical navigation for autonomous approach of unexplored small bodies. In: Proceedings of the 43rd annual AAS guidance, navigation and control conference, pp. AAS 20–125

    Google Scholar 

  127. Chai R, Savvaris A, Tsourdos A, Chai S, Xia Y (2019) A review of optimization techniques in spacecraft flight trajectory design. Prog Aerosp Sci 109:100543. https://doi.org/10.1016/j.paerosci.2019.05.003

    Article  Google Scholar 

  128. Pontani M, Conway BA (2013) Optimal finite-thrust rendezvous trajectories found via particle swarm algorithm. J Spacecr Rocket 50(6):1222–1234. https://arc.aiaa.org/doi/abs/10.2514/1.A32402?journalCode=jsr

  129. Chai R, Savvaris A, Tsourdos A, Chai S, Xia Y (2019) Trajectory optimization of space maneuver vehicle using a hybrid optimal control solver. IEEE Trans Cybern 49(2):467–480. https://doi.org/10.1109/TCYB.2017.2778195

    Article  Google Scholar 

  130. Wang Z, Grant MJ (2018) Minimum-fuel low-thrust transfers for spacecraft: a convex approach. IEEE Trans Aerosp Electron Syst 54(5):2274–2290. https://doi.org/10.1109/TAES.2018.2812558

    Article  Google Scholar 

  131. Englander JA, Conway BA (2016) Automated solution of the low-thrust interplanetary trajectory problem. J Guid Control Dyn 40(1):15–27. https://doi.org/10.2514/1.G002124

    Article  Google Scholar 

  132. Cao X, Shi P, Li Z, Liu M (2018) Neural-network-based adaptive backstepping control with application to spacecraft attitude regulation. IEEE Trans Neural Netw Learn Syst 29(9):4303–4313. https://doi.org/10.1109/TNNLS.2017.2756993

    Article  Google Scholar 

  133. Huang Y, Li S, Sun J (2019) Mars entry fault-tolerant control via neural network and structure adaptive model inversion. Adv Space Res 63(1):557–571. https://doi.org/10.1016/j.asr.2018.09.016

    Article  Google Scholar 

  134. Zhou N, Kawano Y, Cao M (2019) Neural network-based adaptive control for spacecraft under actuator failures and input saturations. IEEE Trans Neural Netw Learn Syst 1–15. https://ieeexplore.ieee.org/document/8894505

  135. Shi G, Shi X, O’Connell M, Yu R, Azizzadenesheli K, Anandkumar A, Yue Y, Chung S (2019) Neural lander: stable drone landing control using learned dynamics. In: 2019 international conference on robotics and automation (ICRA), pp 9784–9790. https://doi.org/10.1109/ICRA.2019.8794351

  136. Tsukamoto H, Chung S (2021) Neural contraction metrics for robust estimation and control: a convex optimization approach. IEEE Control Syst Lett 5(1):211–216. https://doi.org/10.1109/LCSYS.2020.3001646

    Article  MathSciNet  Google Scholar 

  137. Riviere B, Honig W, Yue Y, Chung S, (2020) Glas: global-to-local safe autonomy synthesis for multi-robot motion planning with end-to-end learning. IEEE Robot Autom Lett 5(3):4249–4256. https://ieeexplore.ieee.org/document/9091314

  138. Izzo D, Tailor D, Vasileiou T (2020) On the stability analysis of deep neural network representations of an optimal state-feedback. IEEE Trans Aerospace Electron Syst 1–9. https://ieeexplore.ieee.org/document/9149837

  139. Chai R, Tsourdos A, Savvaris A, Chai S, Xia Y, Chen CLP (2019) Six-dof spacecraft optimal trajectory planning and real-time attitude control: a deep neural network-based approach. IEEE Trans Neural Netw Learn Syst 1–9. https://ieeexplore.ieee.org/document/8939337

  140. Peng H, Bai X (2018) Artificial neural network-based machine learning approach to improve orbit prediction accuracy. J Spacecr Rocket 55(5):1248–1260. https://doi.org/10.2514/1.A34171

    Article  Google Scholar 

  141. Cheng L, Wang Z, Song Y, Jiang F (2020) Real-time optimal control for irregular asteroid landings using deep neural networks. Acta Astronaut 170:66–79. https://doi.org/10.1016/j.actaastro.2019.11.039

    Article  Google Scholar 

  142. Peng H, Bai X (2018) Exploring capability of support vector machine for improving satellite orbit prediction accuracy. J Aerospace Inf Syst 15(6):366–381. https://doi.org/10.2514/1.I010616

    Article  Google Scholar 

  143. Li W, Huang H, Peng F (2015) Trajectory classification in circular restricted three-body problem using support vector machine. Adv Space Res 56(2):273–280. https://doi.org/10.1016/j.asr.2015.04.017

    Article  Google Scholar 

  144. Gaudet B, Linares R, Furfaro R (2020) Adaptive guidance and integrated navigation with reinforcement meta-learning. Acta Astronaut 169:180–190. https://doi.org/10.1016/j.actaastro.2020.01.007

    Article  Google Scholar 

  145. Gaudet B, Linares R, Furfaro R (2018) Deep reinforcement learning for six degree-of-freedom planetary powered descent and landing. arXiv:1810.08719

  146. Liu S, Hou Z, Tian T, Deng Z, Li Z (2019) A novel dual successive projection-based model-free adaptive control method and application to an autonomous car. IEEE Trans Neural Netw Learn Syst 30(11):3444–3457. https://doi.org/10.1109/TNNLS.2019.2892327

    Article  MathSciNet  Google Scholar 

  147. Choi J, Huhtala K (2016) Constrained global path optimization for articulated steering vehicles. IEEE Trans Veh Technol 65(4):1868–1879. https://doi.org/10.1109/TVT.2015.2424933

    Article  Google Scholar 

  148. Shen C, Shi Y, Buckham B (2017) Integrated path planning and tracking control of an auv: A unified receding horizon optimization approach. IEEE/ASME Trans Mechatron 22(3):1163–1173. https://doi.org/10.1109/TMECH.2016.2612689

    Article  Google Scholar 

  149. Shen C, Shi Y, Buckham B (2018) Trajectory tracking control of an autonomous underwater vehicle using lyapunov-based model predictive control. IEEE Trans Industr Electron 65(7):5796–5805. https://doi.org/10.1109/TIE.2017.2779442

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Automation, Beijing Institute of Technology, Beijing, China

    Runqi Chai, Lingguo Cui & Senchun Chai

  2. Vanke School of Public Health, Tsinghua University, Beijing, China

    Kaiyuan Chen

  3. School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedfordshire, UK

    Gokhan Inalhan & Antonios Tsourdos

Authors
  1. Runqi Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaiyuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lingguo Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Senchun Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gokhan Inalhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Antonios Tsourdos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Runqi Chai .

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chai, R., Chen, K., Cui, L., Chai, S., Inalhan, G., Tsourdos, A. (2023). Review of Advanced Guidance and Control Methods. In: Advanced Trajectory Optimization, Guidance and Control Strategies for Aerospace Vehicles. Springer Aerospace Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-4311-1_6

Download citation

Publish with us

Policies and ethics

Search

Navigation