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Robust optimal powered descent guidance via model predictive convex programming

作     者:Xiao, Yizheng Gong, Youmin Mei, Jie Ma, Guangfu Wu, Weiren 

作者机构:Harbin Inst Technol Sch Aerosp Sci Shenzhen 518055 Guangdong Peoples R China Harbin Inst Technol Sch Intelligence Sci & Engn Shenzhen 518055 Guangdong Peoples R China Deep Space Explorat Lab Beijing 100089 Peoples R China 

出 版 物:《AEROSPACE SCIENCE AND TECHNOLOGY》 (Aerosp Sci Technol)

年 卷 期:2025年第159卷

核心收录:

学科分类:08[工学] 0825[工学-航空宇航科学与技术] 

基  金:National Defense Basic Scientific Re-search Project [JCKY2021603B030] Science Center Program of National Natural Science Foundation of China 

主  题:Powered descent guidance Convex programming Trajectory optimization Uncertainty propagation Robust optimization 

摘      要:This paper investigates the robust fuel-optimal guidance problem for powered descent landing under uncertainty. A robust optimal control problem (OCP) with stochastic dynamics and constraints is first constructed to ensure both optimality and safety. The polynomial chaos expansion (PCE)-based uncertainty quantification technique is then employed to convert the stochastic OCP into a high-dimensional deterministic OCP, which, while tractable, involves a large number of decision variables and is computationally intensive. To mitigate this issue, the dynamics are convexified within the model predictive convex programming (MPCP) framework, reducing the number of decision variables by establishing the sensitivity relationship between state and control corrections. Furthermore, convexification techniques including lossless convexification and successive convexification are applied to convexify the nonlinear inequality constraints. To enhance computational efficiency, a dimension reduction method is also introduced. After solving the robust OCP through convex optimization, a closed-loop guidance algorithm based on receding horizon strategy is proposed to address navigational errors. Numerical simulations demonstrate the advantages of this guidance algorithm.

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