A survey on convex optimization for guidance and control of vehicular systems

被引:16
作者
Wang, Zhenbo [1 ]
机构
[1] Univ Tennessee, Dept Mech Aerosp & Biomed Engn, Knoxville, TN 37996 USA
基金
美国国家科学基金会; 美国国家航空航天局;
关键词
Guidance and control; Optimal control; Trajectory optimization; Convex optimization; Space vehicles; Air vehicles; Ground vehicles; MODEL-PREDICTIVE CONTROL; UNMANNED AERIAL VEHICLES; THRUST TRAJECTORY OPTIMIZATION; POWERED-DESCENT GUIDANCE; CONSTRAINED ATTITUDE-CONTROL; LOSSLESS CONVEXIFICATION; SPACECRAFT REORIENTATION; AUTONOMOUS VEHICLES; ENERGY MANAGEMENT; POLYNOMIAL OPTIMIZATION;
D O I
10.1016/j.arcontrol.2024.100957
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Guidance and control (G&C) technologies play a central role in the development and operation of vehicular systems. The emergence of computational guidance and control (CG&C) and highly efficient numerical algorithms has opened up the great potential for solving complex constrained G&C problems onboard, enabling higher level of autonomous vehicle operations. In particular, convex-optimization-based G&C has matured significantly over the years and many advances continue to be made, allowing the generation of optimal G&C solutions in real-time for many vehicular systems in aerospace, automotive, and other domains. In this paper, we review recent major advances in convex optimization and convexification techniques for G&C of vehicular systems, focusing primarily on three important application fields: (1) Space vehicles for powered descent guidance, small body landing, rendezvous and proximity operations, orbital transfer, spacecraft reorientation, space robotics and manipulation, spacecraft formation flying, and station keeping; (2) Air vehicles including hypersonic/entry vehicles, missiles and projectiles, launch/ascent vehicles, and low-speed air vehicles; and (3) Motion control and powertrain control of ground vehicles. Throughout the paper, we draw figures that illustrate the basic mission concepts and objectives, introduce key equations that characterize the feature of each class of problems and approaches, and present tables that summarize similarities and distinctions among the problems, ideas, and methods. Where available, we provide comparative analyses and reveal correlations between different applications and technical approaches. Finally, we identify open challenges and issues, discuss potential opportunities, and make suggestions for future research directions.
引用
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页数:41
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共 450 条
  • [1] Two-timescale optimization approach for coordinated multi-point design in unmanned aerial vehicle-assisted cellular networks
    Abdelhakam, Mostafa M.
    Elmesalawy, Mahmoud M.
    Ibrahim, Ibrahim I.
    Sayed, Samir G.
    [J]. TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2023, 34 (01):
  • [2] Aci kmese B., 2012, LPI Contributions, P4193
  • [3] Acikmese A.B., 2005, AIAA GUIDANCE NAVIGA, P6288
  • [4] Acikmese B., 2008, AIAAAAS ASTRODYNAMIC, P6426, DOI DOI 10.2514/6.2008-6426
  • [5] Convex programming approach to powered descent guidance for Mars landing
    Acikmese, Behcet
    Ploen, Scott R.
    [J]. JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2007, 30 (05) : 1353 - 1366
  • [6] Açikmese B, 2013, ADV ASTRONAUT SCI, V148, P1867
  • [7] Lossless Convexification of Nonconvex Control Bound and Pointing Constraints of the Soft Landing Optimal Control Problem
    Acikmese, Behcet
    Carson, John M., III
    Blackmore, Lars
    [J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2013, 21 (06) : 2104 - 2113
  • [8] Lossless convexification of a class of optimal control problems with non-convex control constraints
    Acikmese, Behcet
    Blackmore, Lars
    [J]. AUTOMATICA, 2011, 47 (02) : 341 - 347
  • [9] Alonso-Mora J, 2016, IEEE INT CONF ROBOT, P5356, DOI 10.1109/ICRA.2016.7487747
  • [10] Alonso-Mora J, 2015, IEEE INT C INT ROBOT, P4634, DOI 10.1109/IROS.2015.7354037