Optimization Of Rocket Flight Parameters Based On Improved Genetic Algorithm

In order to improve the flight performance of the rocket,this paper studies the rocket kinematics model,flight parameter optimization scheme and optimization method.Aiming at the problem that the simple genetic algorithm is easy to converge to the local minimum,combined with the method of penalty function to deal with the constraint conditions,an improved genetic algorithm is designed to realize the optimal design of the rocket flight parameters under the constraint conditions of the maximum range and the minimum takeoff mass,which has an important guiding significance for the overall design of the rocket and the selection of the guidance scheme.The main work is as follows:1.The three degree of freedom kinematic model of the rocket is established.This paper analyzes the flight characteristics and motion law of the rocket,establishes the three degree of freedom kinematic model of the rocket,verifies the rationality of the motion model through mathematical simulation,and provides a verification platform for the optimal design of rocket flight parameters.2.The optimization scheme of rocket flight parameters is designed.This paper analyzes the flight process of the rocket,transforms the process optimization problem of selecting pitch program angle into the static optimization problem of selecting the maximum value of the absolute value of attack angle in subsonic section and the rate of change of pitch angle of the rocket above the second stage by using the variational method,designs the flight parameter optimization scheme,and establishes two optimization mathematical models with the maximum range and the minimum takeoff mass as the optimization objectives.3.An improved genetic algorithm is proposed.When simple genetic algorithm is applied to the optimization of rocket flight parameters with non-monotone and multi peak,it is easy to converge to the problems of local minimum and low search efficiency,combined with the method of penalty function to deal with the constraint conditions,the simple genetic algorithm is improved based on the similarity of self-identification control cross parent,adaptive adjustment of mutation probability and optimal preservation strategy.The "traveling salesman problem" is used to simulate and verify the improved genetic algorithm.The simulation results show that the improved genetic algorithm can break through the local minimum,search new solution space,and find the approximate global optimal solution of the optimization problem with less evolution times.4.The flight parameters of the rocket are optimized and simulated.Taking Minuteman 3rocket as the verification object,Monte Carlo simulation is carried out by using simple genetic algorithm and improved genetic algorithm respectively.Through the statistical analysis of 10 simulation results,the results show that: in the optimization results of maximum range,under the same simulation conditions,the standard deviation of the optimization results of improved genetic algorithm is about 770 times smaller than that of simple genetic algorithm,which indicates that The results show that the standard deviation of the improved genetic algorithm is about 20 times better than that of the simple genetic algorithm,which solves the problem of evolution stagnation in the process of population evolution.Through simulation analysis and comparative test,it can be seen that the improved genetic algorithm proposed in this paper can effectively jump out of local minima,improve the search efficiency,and fully verify the effectiveness of the improved genetic algorithm for rocket flight path optimization.