A Survey on Convex Optimization for Guidance and Control of Vehicular Systems
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Nov 2023.
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025Decision-Focused Learning for Complex System Identification: HVAC Management System Applicationcited by 0
- 2026SAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV Decisioncited by 0
- 2026Optimality-Informed Neural Networks for Lunar Landing Trajectory Optimizationcited by 0
- 2026Optimality-Informed Neural Networks for Lunar Landing Trajectory Optimization2 Jul 2026
- 2026SAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV Decision27 Feb 2026
- 2025Decision-Focused Learning for Complex System Identification: HVAC Management System Application24 Jan 2025