Rocket boosting section trajectory optimization method based on particle swarm optimization

Abstract

The invention discloses a rocket boosting section trajectory optimization method based on a particle swarm algorithm. The method comprises the following steps: establishing a three-degree-of-freedom motion model of a rocket boosting section trajectory according to motion parameters of a rocket boosting section; constructing a rocket boosting section flight program angle model according to the rocket boosting section three-degree-of-freedom motion model; establishing a rocket boosting section trajectory optimization model based on the rocket boosting section flight program angle model; and based on a particle swarm algorithm and the preset initial value of the rocket, calculating to obtain an optimal solution of the rocket boosting section trajectory optimization model. The solid carrier rocket trajectory optimization design problem can be effectively solved, the consumption of the last boosting stage liquid propellant is reduced by 11.1%, the convergence speed is high, the number of configuration parameters is small, and engineering implementation is easy.

Description

technical field [0001] The disclosure belongs to the technical field of ballistic optimization, and in particular relates to a particle swarm algorithm-based ballistic optimization method for booster section of a rocket. Background technique [0002] Ballistic optimization is an important part of the overall optimal design of solid launch vehicles, and plays an extremely important role in the entire life cycle of the launch vehicle. In the actual flight trajectory design stage, trajectory optimization design can effectively improve the quality of payloads entering orbit and reduce launch costs. As the trajectory optimization design of the ascent section of solid launch vehicles is a complex nonlinear programming problem, there are many equality constraints and inequality constraints, and the constraint conditions and objective functions often have no clear analytical expressions, which makes traditional optimization algorithms face severe challenges. Unable to maximize the ...

AI Technical Summary

Problems solved by technology

The particle swarm optimization algorithm is also an optimization tool based on iteration, but there is no operation such as selection, crossover, and mutation in the algorithm implementation process, and it has the advantages of few parameters to be set, fast convergence speed, and easy implementation. Applications in the field are gaining more and more attention

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