Active-set pseudospectral model predictive static programming for midcourse guidance

This paper focuses on the midcourse guidance law for the interceptor to engage the hypersonic gliding vehicles (HGVs). Firstly, an active-set pseudospectral model predictive static programming (APS-MPSP) algorithm is presented, which entails two major improvements: the application of pseudospectral...

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Veröffentlicht in:Aerospace science and technology Jg. 134; S. 108137
Hauptverfasser: Zhou, Cong, He, Lei, Yan, Xiaodong, Meng, Fanqin, Li, Chaoyong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Masson SAS 01.03.2023
Schlagworte:
Active-set
Midcourse guidance
Model Predictive Static Programming (MPSP)
Pseudospectral discretization
Trajectory optimization
Pseudospectral discretization
Model Predictive Static Programming (MPSP)
Midcourse guidance
Trajectory optimization
Active-set
ISSN:1270-9638, 1626-3219
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Zusammenfassung:This paper focuses on the midcourse guidance law for the interceptor to engage the hypersonic gliding vehicles (HGVs). Firstly, an active-set pseudospectral model predictive static programming (APS-MPSP) algorithm is presented, which entails two major improvements: the application of pseudospectral discretion and handling of control constraint using active-set iteration strategy. Except inheriting the high efficiency of the MPSP, this new algorithm can address the control constraint directly. Secondly, the optimal predictive guidance design is established, which embeds the target prediction into the guidance framework by formulating a simultaneous nonlinear optimal problem. Employing the APS-MPSP algorithm to solve this optimal problem efficiently, the predictive guidance command that ensures interceptor reach an appropriate predicted interception point (PIP) can be obtained directly. This strategy has the merits of no need to precompute the PIP or time-to-go and high prediction accuracy. The reliability and efficiency of the proposed methods are verified by numerical examples and comparisons with already methods.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2023.108137