Affiliation:
1. National University of Defense Technology, 410073 Changsha, People’s Republic of China
Abstract
Sequential second-order cone programming (SSOCP) is commonly used in aerospace applications for solving nonlinear trajectory optimization problems. The SSOCP possesses good real-time performance. However, one long-standing challenge is its unguaranteed convergence. In this paper, we theoretically analyze the descent property of the [Formula: see text] penalty function in the SSOCP. Using Karush–Kuhn–Tucker conditions, we obtain two important theoretical results: 1) the [Formula: see text] penalty function of the original nonlinear problem always descends along the iteration direction; 2) a sufficiently small trust region can decrease the [Formula: see text] penalty function. Based on these two results, we design an improved trust region shrinking algorithm with theoretically guaranteed convergence. In numerical simulations, we verify the proposed algorithm using a reentry trajectory optimization problem.
Funder
National Natural Science Foundation of China Youth Fund
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Space and Planetary Science,Aerospace Engineering,Control and Systems Engineering
Cited by
1 articles.
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