Study On Trajectory Optimization And Guidance Approach For Gliding Reentry Vehicle

The gliding reentry vehicle has excellent capabilities such as flexible maneuvering,long-range strike and rapid response,which has unlimited military,political and economic value.It has always been the focus of global attention,leading the world aerospace industry.This thesis focuses on the research of the reentry trajectory optimization and guidance problem.The main work contents are:The gliding reentry vehicle is selected as the research object,and the three-degree-of-freedom motion model is established.the physical quantity is normalized to eliminate the influence of different physical quantities due to different dimensions.According to the process constraints and quasi-equilibrium glide condition,the reentry corridor is designed.Model and analyze atmospheric environment and aerodynamic parameters.Design a reentry trajectory optimization algorithm based on multiple-shooting and particle swarm optimization.Multiple-shooting is used to discrete the gliding reentry segment,and the particle swarm optimization is used to optimize the transformed nonlinear programming problem.Design sets of simulation experiments to verify the feasibility and effectiveness of the algorithm.Design a numerical predictor–corrector guidance algorithm based on quasi-equilibrium glide condition and penalty function.The numerical predictor–corrector guidance is used for the longitudinal guidance,and the heading angle deviation corridor is used for the lateral guidance.The penalty function is used to handle process constraints.quasi-equilibrium glide condition is used to eliminate oscillations.The Monte Carlo simulation with deviation is designed to verify the fast convergence and robustness of the algorithm.Design an LQR trajectory tracking algorithm based on differential evolution algorithm.LQR is used to calculate the feedback gain of the closed loop system and the differential evolution algorithm optimizes the weighting coefficients of the LQR performance indicators.The Monte Carlo simulation with deviation is designed to verify the feasibility and robustness of the algorithm.