A Pseudospectral Model Predictive Solution to Midcourse Guidance for Multistage Interceptor

This article presents an enhanced pseudospectral model predictive static programming (MPSP) approach to address the midcourse guidance problem for an endoatmospheric multistage interceptor. The proposed algorithm incorporates a nonlinear, state-dependent cost function to maximize terminal velocity w...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 61; no. 5; pp. 14433 - 14449
Main Authors: Zhou, Cong, Chen, Sai, Qiao, Hao, Li, Chaoyong, Yan, Xiaodong
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aerospace and electronic systems
Boosting
Computational efficiency
Computational modeling
Constraints
Convex functions
Convexity
Cost function
Free coasting time
Interceptors
Midcourse guidance
model predictive static programming (MPSP)
multi-phase optimization
Multiphase
Optimization
Performance analysis
Performance indices
Prediction algorithms
Predictive models
Terminal velocity
Time factors
variational linearization
ISSN:0018-9251, 1557-9603
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article presents an enhanced pseudospectral model predictive static programming (MPSP) approach to address the midcourse guidance problem for an endoatmospheric multistage interceptor. The proposed algorithm incorporates a nonlinear, state-dependent cost function to maximize terminal velocity while solving a multiphase optimization problem featuring interior-point constraints and free coasting time. Compared to convex optimization or conventional MPSP methods, contributions of the proposed approach are threefold: first, it employs a variational linearization technique to handle complex multiphase optimization, significantly improving computational efficiency; second, it implicitly treats interior-point constraints and eliminates state variables during the process, reducing the number of the optimization variables; and third, it establishes a direct relationship between the state-dependent performance index and the control protocol, obviating the need for partial derivative computations of intermediate variables. Performance of the proposed method is demonstrated through its application to a midcourse guidance scenario that involves a two-stage interceptor. Numerical results show that the proposed approach delivers optimal solutions with high computational efficiency and robustness, accommodating both specified and free terminal time conditions, as well as uncertainties in the operational environment.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2025.3586598