Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry.This paper presents a neural-network-accelerated methodology for optimizing parametr...Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry.This paper presents a neural-network-accelerated methodology for optimizing parametric bank angle profiles in Mars atmospheric entry missions.The methodology includes a universal approach to handling path constraints and a reliable solution method based on the Particle Swarm Optimization(PSO)algorithm.For illustrative purposes,a mission with the objective of maximizing terminal altitude is considered.The original entry optimization problem is converted into optimizing three coefficients for the bank angle profiles with terminal constraints by formulating a parametric Mars entry bank angle profile and constraint handling methods.The parameter optimization problem is addressed using the PSO algorithm,with reliability enhanced by increasing the PSO swarm size.To improve computational efficiency,an enhanced Deep Operator Network(Deep ONet)is used as a dynamics solver to predict terminal states under various bank angle profiles rapidly.Numerical simulations demonstrate that the proposed methodology ensures reliable convergence with a sufficiently large PSO swarm while maintaining high computational efficiency facilitated by the neural-network-based dynamics solver.Compared to the existing methodologies,this methodology offers a streamlined process,the reduced sensitivity to initial guesses,and the improved computational efficiency.展开更多
A formal analysis to footprint problem with effects of angle of attack （AOA） is presented. First a flexible and rapid standardized method for footprint generation is developed. Zero bank angle control strategy and t...A formal analysis to footprint problem with effects of angle of attack （AOA） is presented. First a flexible and rapid standardized method for footprint generation is developed. Zero bank angle control strategy and the maximum crossrange method are used to obtain virtual target set; afterward, closed-loop bank angle guidance law is used to find footprint by solving closest approach problem for each element in virtual target set. Then based on quasi-equilibrium glide condition, the typical inequality reentry trajectory constraints are converted to angle of attack lower boundary constraint. Constrained by the lower boundary, an original and practical angle of attack parametric method is proposed. By using parametric angle of attack profile, optimization algorithm for angle of attack is designed and the impact of angle of attack to footprint is discussed. Simulations with different angle of attack profiles are presented to demonstrate the performance of the proposed footprint solution method and validity of optimal algorithm.展开更多
基金supported in part by the National Defense Basic Scientific Research Program of China(No.JCKY2021603B030)the Shenzhen Fundamental Research Program,China(No.JCYJ20220818102601004)the Science Center Program of National Natural Science Foundation of China(No.62188101)。
文摘Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry.This paper presents a neural-network-accelerated methodology for optimizing parametric bank angle profiles in Mars atmospheric entry missions.The methodology includes a universal approach to handling path constraints and a reliable solution method based on the Particle Swarm Optimization(PSO)algorithm.For illustrative purposes,a mission with the objective of maximizing terminal altitude is considered.The original entry optimization problem is converted into optimizing three coefficients for the bank angle profiles with terminal constraints by formulating a parametric Mars entry bank angle profile and constraint handling methods.The parameter optimization problem is addressed using the PSO algorithm,with reliability enhanced by increasing the PSO swarm size.To improve computational efficiency,an enhanced Deep Operator Network(Deep ONet)is used as a dynamics solver to predict terminal states under various bank angle profiles rapidly.Numerical simulations demonstrate that the proposed methodology ensures reliable convergence with a sufficiently large PSO swarm while maintaining high computational efficiency facilitated by the neural-network-based dynamics solver.Compared to the existing methodologies,this methodology offers a streamlined process,the reduced sensitivity to initial guesses,and the improved computational efficiency.
基金National Natural Science Foundation of China (61174221)
文摘A formal analysis to footprint problem with effects of angle of attack （AOA） is presented. First a flexible and rapid standardized method for footprint generation is developed. Zero bank angle control strategy and the maximum crossrange method are used to obtain virtual target set; afterward, closed-loop bank angle guidance law is used to find footprint by solving closest approach problem for each element in virtual target set. Then based on quasi-equilibrium glide condition, the typical inequality reentry trajectory constraints are converted to angle of attack lower boundary constraint. Constrained by the lower boundary, an original and practical angle of attack parametric method is proposed. By using parametric angle of attack profile, optimization algorithm for angle of attack is designed and the impact of angle of attack to footprint is discussed. Simulations with different angle of attack profiles are presented to demonstrate the performance of the proposed footprint solution method and validity of optimal algorithm.
