A Survey on Convex Optimization for Guidance and Control of Vehicular Systems

会合 航天器 航空航天 轨迹优化 计算机科学 机器人学 下降(航空) 汽车工业 航空航天工程 控制(管理) 凸优化 弹道 系统工程 控制工程 最优控制 工程类 人工智能 正多边形 机器人 数学优化 物理 几何学 数学 天文
作者
Zhenbo Wang
出处
期刊:Cornell University - arXiv [Cornell University]
被引量:2
标识
DOI:10.48550/arxiv.2311.05115
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 scenarios and present tables that summarize similarities and distinctions among the key problems, ideas, and approaches. Where available, we provide comparative analyses and reveal correlations between different applications. Finally, we identify open challenges and issues, discuss potential opportunities, and make suggestions for future research directions.
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