Open AccessFast convex optimization of vehicle trajectories based on improved trust domain
Author(s) -
Jin Li,
Yudong Wang,
Liang Zhao
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2220/1/012002
Subject(s) - trajectory optimization , computer science , mathematical optimization , trajectory , optimization problem , convex optimization , variable (mathematics) , process (computing) , control theory (sociology) , regular polygon , mathematics , algorithm , optimal control , control (management) , mathematical analysis , physics , geometry , astronomy , artificial intelligence , operating system
The re-entry process of lift vehicles faces a variety of complex constraints and path limitations, so fast and accurate trajectory optimization for them is an important topic. This paper proposes a fast trajectory generation method based on improved sequential convex optimization, which simplifies the equations of motion with energy as the independent variable, takes the sine and cosine function of the bank angle as the control variable, and convexities multiple constraints to obtain a computational model that satisfies the optimization conditions. On this basis, the "jitter" phenomenon, which is easy to occur in the optimization process, is improved, and the method of variable trust domain and weight function is proposed to reduce the jump problem in the iterative process, and also to improve the terminal accuracy requirement. Finally, numerical simulations show that the algorithm can efficiently and rapidly solve the re-entry trajectory optimization problem under the inclusion of no-fly zone.