Rocket trajectory optimization method based on improved genetic algorithm

Abstract

The invention discloses a rocket trajectory optimization method based on an improved genetic algorithm. The rocket trajectory optimization method comprises the following steps: step 1) converting an optimal control problem of rocket trajectory optimization into a parameter optimization problem of a maximum value of a rocket subsonic segment attack angle absolute value and a rocket pitch angle change rate through a variational method; 2) performing binary coding on the maximum value of the absolute value of the attack angle of the rocket subsonic segment and the change rate of the rocket pitchangle to generate a primary population; 3) calculating individual fitness in the population by taking the maximum range as an optimization target; 4) for equality and inequality constraints in the rocket flight process, adjusting the fitness of population individuals by using a penalty function; 5) reserving the optimal individual in the population by utilizing the population fitness obtained by calculation; 6) selecting two male parents from the population, self-identifying cross male parents by utilizing a similarity threshold, reselecting the male parents higher than the set male parent similarity threshold, and carrying out cross operation on the male parents lower than the set similarity threshold; 7) according to the diversity of the population and the algebra of population evolutionstagnation, adjusting the mutation probability, then selecting individuals to be mutated, and carrying out mutation operation; The rocket flight path optimization method has the advantages that the global search capability is higher, the excessive similarity between population individuals and optimal individuals is avoided, the optimization performance of the genetic algorithm is improved, and the performance of a rocket flight path optimization scheme is ensured.

Description

technical field [0001] The invention belongs to the field of rocket guidance, and relates to a trajectory optimization method for designing an active section of a rocket, in particular to a rocket trajectory optimization method based on an improved genetic algorithm. Background technique [0002] The development level of aerospace technology is an important manifestation of a country's comprehensive national strength and an important measure of a country's high-tech research and innovation capabilities. With the development of science and technology, the development of aerospace industry has entered a period of high-speed development. As an important branch of rocket design, rocket trajectory optimization provides theoretical guidance for the overall design of the rocket, which runs through the entire rocket design. process. [0003] Although the simple genetic algorithm has a strong global search ability in the solution process, due to the serious nonlinearity of the aerod...

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Problems solved by technology

[0003] Although the simple genetic algorithm has a strong global search ability in the solution process, due to the serious nonlinearity of the aerodynamic and atmospheric parameters involved in the calculation equation of the rocket trajectory, the optimization parameters, structural parameters and aerodynamic coefficients, pressure core coefficients, drag Coupling exists between coefficients, etc., resulting in the rocket flight parameter optimization problem being a non-monotone and multi-peak function, which is easy to converge to a local minimum point, so that the performance level of the design optimization scheme cannot be fully improved

The main reason is that in the iterative process of the simple genetic algorithm, individuals with low fitness have a low probability of being selected, and may even be eliminated, while individuals with high fitness have a high probability of being selected, so there will be a large number of individuals in the later stage of population evolution. Individuals are concentrated at a certain extreme point, the diversity of the population is reduced, a large number of individuals are similar to the optimal individual, and inbreeding occurs in the population, making it difficult to introduce new species no matter how many times of selection, crossover, and mutation operations. individual

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