Pseudospectral Convex Optimization based Model Predictive Static Programming for Constrained Guidance

This paper presents a pseudospectral convex optimization-based model predictive static programming (PCMPSP) for the constrained guidance problem. First, the sensitivity relation between the state increment and control correction is reformulated using Legendre-Gauss (LG) and Legendre-Gauss-Radau (LGR...

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Veröffentlicht in:IEEE transactions on aerospace and electronic systems Jg. 59; H. 3; S. 1 - 16
Hauptverfasser: Liu, Xu, Li, Shuang, Xin, Ming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Aerodynamics
Air to surface missiles
Air-to-surface missile
Algorithms
Computational geometry
Constrained guidance
Convexity
Guidance (motion)
Guidance systems
Mathematical programming
Missile control
Model predictive static programming
Nonlinear dynamical systems
Optimal control
Optimization
Performance analysis
Performance indices
Predictive models
Pseudospectral convex optimization
Robustness
Sensitivity
Trajectory
Trajectory optimization
ISSN:0018-9251, 1557-9603
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Zusammenfassung:This paper presents a pseudospectral convex optimization-based model predictive static programming (PCMPSP) for the constrained guidance problem. First, the sensitivity relation between the state increment and control correction is reformulated using Legendre-Gauss (LG) and Legendre-Gauss-Radau (LGR) pseudospectral transcriptions. Second, the convex optimal control problem associated with the trajectory optimization is defined by introducing the quadratic performance index. Third, modifications to the initial guess solution and reference trajectory update are introduced to enhance the accuracy and robustness of the algorithm. Finally, a model predictive guidance law is designed based on the proposed PCMPSP algorithm for the air-to-surface missile guidance with impact angle constraint. The simulation results show that the PCMPSP has lower sensitivity to the initial guess trajectory, higher accuracy, as well as faster convergence speed than existing convex programming methods. Moreover, the robustness of the proposed guidance law to uncertainties is demonstrated through the Monte Carlo campaign.
Bibliographie:ObjectType-Article-1
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ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2022.3211245