Convex Formulation and Efficiency Enhancement for Powered Landing Guidance With Second-Order Cone Probability Constraints

Real-time trajectory optimization is crucial for achieving autonomous guidance in powered landing. However, the existing algorithms often struggle with uncertainties and are unable to handle second-order cone probability constraints, such as control magnitude and direction constraints. In addition,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on aerospace and electronic systems Jg. 61; H. 2; S. 1742 - 1763
Hauptverfasser: Li, Wenbo, Gong, Shengping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Aerospace and electronic systems
Algorithms
Approximation
Constraints
Heuristic algorithms
Landing simulation
Optimal control
Optimization
Real time
Real-time systems
Simulation
Stochastic processes
Trajectory optimization
Uncertainty
ISSN:0018-9251, 1557-9603
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Real-time trajectory optimization is crucial for achieving autonomous guidance in powered landing. However, the existing algorithms often struggle with uncertainties and are unable to handle second-order cone probability constraints, such as control magnitude and direction constraints. In addition, the large-scale and complex nature of these optimization problems hampers real-time performance. This study addresses these shortcomings by presenting a convex formulation suitable for general second-order cone probability constraints and proposing two strategies, conservative approximation and variable reduction, to improve real-time performance. Specifically, the conservative approximation converts spectral norm constraints into second-order cone constraints, while the variable reduction reduces the number of optimization variables. Applied to a powered landing simulation scenario, the proposed approaches effectively handle second-order cone probability constraints and improve real-time performance by two to three orders of magnitude.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2024.3458940