考虑规避与突防的高超声速飞行器智能容错制导控制一体化设计  

Learning-based integrated fault-tolerant guidance and control for hypersonic vehicles considering avoidance and penetration

作　　者：武天才 王宏伦[1,3] 任斌 刘一恒 吴星雨 严国乘 WU Tiancai;WANG Honglun;REN Bin;LIU Yiheng;WU Xingyu;YAN Guocheng(School of Automation Science and Electrical Engineering,Beihang University,Beijing100191,China;Shen Yuan Honors College,Beihang University,Beijing100191,China;Science and Technology on Aircraft Control Laboratory,Beihang University,Beijing100191,China)

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100191 [2]北京航空航天大学沈元学院,北京100191 [3]北京航空航天大学飞行器控制一体化技术重点实验室,北京100191

出　　处：《航空学报》2024年第15期273-293,共21页Acta Aeronautica et Astronautica Sinica

基　　金：国家自然科学基金(62173022);航空科学基金(2018ZC51031);北京航空航天大学沈元学院卓越研究基金(230121205)。

摘　　要：针对再入过程中的禁飞区规避和拦截弹躲避问题,提出一种考虑规避与突防的高超声速飞行器智能容错制导控制一体化设计方法。首先,在纵向制导中,为了提升飞行器的制导性能,基于制导控制一体化设计思想,采用包含姿态容错控制系统的飞行器六自由度模型进行待飞航程的预测;针对随之产生的计算耗时问题,进一步采用深度网络进行预测环节的拟合,以提升算法的实时性;接着,在侧向制导中,为了解决禁飞区规避和拦截弹躲避问题,引入扰动流体动态系统(IFDS)算法进行考虑威胁因素的飞行器期望航向角计算,并结合航向角误差走廊和倾侧角翻转逻辑进行侧向制导指令的求取,使飞行器具备规避与突防能力;然后,考虑到飞行环境的复杂性、飞行器再入段机动能力的差异性和姿态容错控制系统动态特性对制导性能的影响,结合深度强化学习算法进行智能体训练,使其可根据飞行器实时状态进行扰动流体动态系统算法参数的在线决策,提升所提方法的有效性和适应能力。最后,由半物理对比仿真和蒙特卡罗仿真结果可知,所提方法具有较强的规避、突防与制导性能。To address the problem of no-fly zone and interceptor avoidance during the reentry phase,a learning-based integrated fault-tolerant guidance and control method for hypersonic vehicles considering avoidance and penetration is proposed.Firstly,to improve the guidance performance of the vehicle in longitudinal guidance,the six degree of freedom model of the vehicle,which includes the attitude fault-tolerant control system,is adopted based on the integrated design concept of guidance and control to predict the range-to-go.In view of the resulting problem of time-consuming in calculation,the deep network is further used to fit the predicting process,so as to improve the real-timeliness of the algorithm.Then,to solve the problem of no-fly zone and interceptor avoidance in lateral guidance,the Interfered Fluid Dynamical System(IFDS)algorithm is introduced to calculate the expected heading angle of the vehicle considering threat factors,and the heading angle error corridor and the flip logic of the bank angle are used to obtain the command,so that the vehicle has the ability of avoidance and penetration.Next,considering the complexity of flight environment,the difference of maneuvering ability of vehicle during the reentry phase,and the influence of dynamic characteristics of attitude control system on guidance performance,the deep reinforcement learning algorithm is employed to train the agent,so that the agent can make online decisions on key parameters of IFDS algorithm according to the real-time state of vehicle,and improve the effectiveness and adaptability of the proposed guidance method.Finally,the results of semi-physical comparison simulation and Monte Carlo simulation show that the proposed method can achieve high performance in avoidance,penetration and guidance.

关 键 词：高超声速飞行器 制导控制一体化 规避 突防 深度强化学习 

分 类 号：V249.1[航空宇航科学与技术—飞行器设计]