CN107908109A - A kind of hypersonic aircraft reentry stage track optimizing controller based on orthogonal configuration optimization - Google Patents

A kind of hypersonic aircraft reentry stage track optimizing controller based on orthogonal configuration optimization Download PDF

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CN107908109A
CN107908109A CN201711116197.5A CN201711116197A CN107908109A CN 107908109 A CN107908109 A CN 107908109A CN 201711116197 A CN201711116197 A CN 201711116197A CN 107908109 A CN107908109 A CN 107908109A
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刘兴高
刘平
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Zhejiang University ZJU
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Abstract

本发明公开了一种基于正交配置的高超声速飞行器再入段轨迹优化控制器,该控制器由飞行器海拔高度传感器、飞行器速度传感器、飞行器飞行航道倾角传感器、飞行器水平飞行距离传感器、飞行器微控制单元(MCU)、飞行器攻角控制器构成。飞行器MCU根据设定的海拔高度、速度、飞行航道倾角要求自动执行内部正交配置优化算法,得到使高超声速飞行器水平飞行距离最长的轨迹优化控制策略,飞行器MCU将获得的控制策略转换为控制指令发送给飞行器攻角控制器执行。本发明能够根据高超声速飞行器不同的海拔高度、速度、飞行航道倾角和飞行水平距离状态快速地得到轨迹优化控制策略,使高超声速飞行器获得更长的水平飞行距离。

The invention discloses a hypersonic aircraft re-entry segment trajectory optimization controller based on orthogonal configuration. Unit (MCU), aircraft angle of attack controller. The aircraft MCU automatically executes the internal orthogonal configuration optimization algorithm according to the set altitude, speed, and flight path inclination angle requirements, and obtains the trajectory optimization control strategy that makes the horizontal flight distance of the hypersonic aircraft the longest. The aircraft MCU converts the obtained control strategy into a control strategy. The command is sent to the aircraft angle-of-attack controller for execution. The present invention can quickly obtain trajectory optimization control strategies according to different altitudes, speeds, flight path inclination angles and flight horizontal distance states of the hypersonic aircraft, so that the hypersonic aircraft can obtain a longer horizontal flight distance.

Description

一种基于正交配置优化的高超声速飞行器再入段轨迹优化控 制器A trajectory optimization control for hypersonic vehicle re-entry segment based on orthogonal configuration optimization Controller

技术领域technical field

本发明涉及高超声速飞行器再入段轨迹优化领域,主要是一种基于正交配置优化的高超声速飞行器再入段轨迹优化控制器。在高超声速飞行器到达再入段后能够给出速高超声速飞行器轨迹优化控制策略并转换为飞行器攻角控制指令,使高超声速飞行器获得更长的水平飞行距离。The invention relates to the field of trajectory optimization for the re-entry section of a hypersonic vehicle, and mainly relates to a trajectory optimization controller for the re-entry section of a hypersonic vehicle based on orthogonal configuration optimization. After the hypersonic vehicle reaches the re-entry stage, the optimal trajectory control strategy of the hypersonic vehicle can be given and converted into an aircraft angle of attack control command, so that the hypersonic vehicle can obtain a longer horizontal flight distance.

背景技术Background technique

高超声速飞行器是实现远程快速精确打击和全球快速到达的新型飞行器,在未来的军事、政治和经济中具有十分重要的战略地位,已经成为世界航空航天领域一个极其重要的发展方向,研究和发展高超声速飞行器在开发太空和国家安全方面具有非常重要的意义。Hypersonic aircraft is a new type of aircraft that can achieve long-range, rapid and precise strikes and global rapid arrival. It has a very important strategic position in the future military, politics, and economy. It has become an extremely important development direction in the field of aerospace in the world. Supersonic vehicles are of great significance in the development of space and national security.

在高超声速飞行器的研究中,轨迹优化是现代飞行器设计和控制的重要容不仅有利于提高飞行器飞行品质以满足既定任务要求,同时也是完成飞行任务的重要保证和实现机动飞行的必要条件,近些年来一直受到国内外各军事强国的重视,是当前国内外研究的热点和难点。In the study of hypersonic vehicles, trajectory optimization is an important aspect of the design and control of modern aircraft. It is not only conducive to improving the flight quality of the aircraft to meet the established mission requirements, but also an important guarantee for the completion of the flight mission and a necessary condition for the realization of maneuvering flight. Recently, Over the years, it has been valued by various military powers at home and abroad, and it is a hot spot and difficulty in current research at home and abroad.

由于从大气从外缘进入大气层,高度和速度的变化范围很大,高超声速飞行器面临各种严峻的再入环境,再入段轨迹优化技术则是保证高超声速飞行器完成飞行任务的关键,对提高其打击范围和落点精度具有更重要的实用价值。因此,研究高效的高超声速飞行器再入段轨迹优化方法显得尤为重要。Since entering the atmosphere from the outer edge of the atmosphere, the range of altitude and speed varies greatly. Hypersonic vehicles face various severe re-entry environments. The trajectory optimization technology of the re-entry segment is the key to ensure that hypersonic vehicles complete their missions. Its striking range and landing accuracy have more important practical value. Therefore, it is particularly important to study efficient hypersonic vehicle re-entry trajectory optimization methods.

发明内容Contents of the invention

为了使高超声速飞行器获得更长的水平飞行距离,提高高超声速飞行器的打击范围,本发明提供了一种基于正交配置优化的高超声速飞行器再入段轨迹优化控制器。In order to enable the hypersonic vehicle to obtain a longer horizontal flight distance and improve the strike range of the hypersonic vehicle, the present invention provides a re-entry trajectory optimization controller for the hypersonic vehicle based on orthogonal configuration optimization.

本发明的目的是通过以下技术方案来实现的:一种基于正交配置优化的高超声速飞行器再入段轨迹优化控制器,根据高超声速飞行器再入段初始海拔高度、速度、飞行航道倾角和飞行水平距离状态快速地获取轨迹优化控制策略,通过控制飞行器攻角使高超声速飞行器获得更长的水平飞行距离。由飞行器海拔高度传感器、飞行器速度传感器、飞行器飞行航道倾角传感器、飞行器水平飞行距离传感器、飞行器微控制单元(MCU)、飞行器攻角控制器构成。各组成部分均由高超声速飞行器内数据总线连接,所述装置的运行过程包括:The purpose of the present invention is achieved through the following technical solutions: a hypersonic vehicle re-entry section trajectory optimization controller based on orthogonal configuration optimization, according to the hypersonic vehicle re-entry section initial altitude, speed, flight path inclination and flight The horizontal distance state quickly obtains the trajectory optimization control strategy, and the hypersonic vehicle can obtain a longer horizontal flight distance by controlling the aircraft's angle of attack. It consists of an aircraft altitude sensor, an aircraft speed sensor, an aircraft flight path inclination sensor, an aircraft horizontal flight distance sensor, an aircraft micro control unit (MCU), and an aircraft angle of attack controller. Each component is connected by a data bus in the hypersonic vehicle, and the operation process of the device includes:

步骤1):在高超声速飞行器MCU中输入对应于该飞行器的气动系数模型、飞行器性能约束条件、指定优化目标;Step 1): Input the aerodynamic coefficient model corresponding to the aircraft, the performance constraints of the aircraft, and specify the optimization goal in the MCU of the hypersonic aircraft;

步骤2):高超声速飞行器到达再入段后,开启飞行器海拔高度传感器、飞行器速度传感器、飞行器飞行航道倾角传感器和飞行器水平飞行距离传感器,得到高超声速飞行器当前的海拔高度、速度、飞行航道倾角和飞行水平距离状态信息;Step 2): After the hypersonic vehicle arrives at the re-entry section, turn on the altitude sensor of the vehicle, the speed sensor of the vehicle, the inclination sensor of the flight path of the vehicle and the horizontal flight distance sensor of the vehicle, and obtain the current altitude, speed, inclination and inclination of the flight path of the hypersonic vehicle. Flight horizontal distance status information;

步骤3):飞行器MCU根据设定的海拔高度、速度、飞行航道倾角要求自动执行内部正交配置优化算法,得到使高超声速飞行器水平飞行距离最长的轨迹优化控制策略;Step 3): The aircraft MCU automatically executes the internal orthogonal configuration optimization algorithm according to the set altitude, speed, and flight path inclination angle requirements, and obtains the trajectory optimization control strategy that makes the horizontal flight distance of the hypersonic aircraft the longest;

步骤4):高超声速飞行器MCU将获得的轨迹优化控制策略发送给控制策略输出模块,并转换为控制指令发送给飞行器攻角控制器执行。Step 4): The MCU of the hypersonic vehicle sends the obtained trajectory optimization control strategy to the control strategy output module, and converts it into a control command and sends it to the aircraft angle-of-attack controller for execution.

所述的高超声速飞行器MCU部分包括信息采集模块21、初始化模块22、常微分方程组(Ordinary Differential Equations,简称ODE)正交配置模块23、非线性规划(Non-linear Programming,简称NLP)问题求解模块24、控制指令输出模块25。其中,信息采集模块包括飞行器海拔高度和速度采集、飞行器飞行航道倾角和飞行水平距离采集、飞行器海拔高度和速度设定采集、飞行器飞行航道倾角设定采集、飞行器的气动系数模型和性能约束条件以及指定优化目标参数采集五个子模块;NLP求解模块包括寻优方向求解、寻优步长求解、寻优修正、NLP收敛性判断四个子模块。The MCU part of the hypersonic vehicle includes an information collection module 21, an initialization module 22, an Ordinary Differential Equations (Ordinary Differential Equations, referred to as ODE) orthogonal configuration module 23, and a non-linear programming (Non-linear Programming, referred to as NLP) problem solving module. Module 24 , control instruction output module 25 . Among them, the information collection module includes aircraft altitude and speed collection, aircraft flight path inclination and flight horizontal distance collection, aircraft altitude and speed setting collection, aircraft flight path inclination setting collection, aircraft aerodynamic coefficient model and performance constraints, and Five sub-modules are specified for the collection of optimization target parameters; the NLP solution module includes four sub-modules: optimization direction solution, optimization step size solution, optimization correction, and NLP convergence judgment.

高超声速飞行器再入段轨迹优化问题可以描述为The trajectory optimization problem of hypersonic vehicle re-entry segment can be described as

max J[u(t)]=x4(tf)max J[u(t)]=x 4 (t f )

x1(t0)=h0,x2(t0)=v0,x3(t0)=γ0,x4(t0)=r0 x 1 (t 0 )=h 0 , x 2 (t 0 )=v 0 , x 3 (t 0 )=γ 0 , x 4 (t 0 )=r 0

x1(tf)=hf,x2(tf)=vf,x3(tf)=γf x 1 (t f )=h f , x 2 (t f )=v f , x 3 (t f )=γ f

G[u(t),x(t),t]≥0G[u(t),x(t),t]≥0

umin≤u(t)≤umax u min ≤ u(t) ≤ u max

其中t表示时间,x(t)表示高超声速飞行器的状态变量,x1(t)表示飞行器海拔高度、x2(t)表示飞行器速度、x3(t)表示飞行器飞行航道倾角、x4(t)表示飞行器水平飞行距离,u(t)表示高超声速飞行器的攻角控制量,为本问题的控制变量;表示状态变量x(t)的一阶导数,F(x(t),u(t),t)是根据高超声速飞行器再入段三维空间运动方程建立的微分方程组数学模型;t0表示再入段轨迹优化开始的时间点,h0表示优化开始时刻飞行器的初始海拔高度,v0表示优化开始时刻飞行器的初始速度,γ0表示优化开始时刻飞行器的初始飞行航道角,r0表示优化开始时刻飞行器的初始水平飞行距离,tf表示再入段轨迹优化结束时间点,hf表示优化结束时刻飞行器的海拔高度,vf表示优化结束时刻飞行器的速度,γf表示优化结束时刻飞行器的飞行航道角;J[u(t)]表示高超声速飞行器轨迹优化的目标函数即优化结束时刻飞行器的水平飞行距离,G[u(t),x(t),t]是高超声速飞行器再入段过程的约束条件,umin和umax表示攻角控制范围的下限值和上限值。Where t represents time, x(t) represents the state variable of the hypersonic vehicle, x 1 (t) represents the altitude of the aircraft, x 2 (t) represents the speed of the aircraft, x 3 (t) represents the inclination angle of the flight path of the aircraft, x 4 ( t) represents the horizontal flight distance of the aircraft, and u(t) represents the control variable of the angle of attack of the hypersonic vehicle, which is the control variable of this problem; Indicates the first-order derivative of the state variable x(t), F(x(t),u(t),t) is a mathematical model of differential equations established according to the three-dimensional space motion equation of the re-entry section of the hypersonic vehicle; t 0 represents the re-entry The time point when the optimization of the entry segment trajectory starts, h 0 represents the initial altitude of the aircraft at the beginning of optimization, v 0 represents the initial velocity of the aircraft at the beginning of optimization, γ 0 represents the initial flight path angle of the aircraft at the beginning of optimization, r 0 represents the start of optimization The initial horizontal flight distance of the aircraft at time, t f represents the end time point of the re-entry trajectory optimization, h f represents the altitude of the aircraft at the end of optimization, v f represents the speed of the aircraft at the end of optimization, γ f represents the flight of the aircraft at the end of optimization Course angle; J[u(t)] represents the objective function of hypersonic vehicle trajectory optimization, that is, the horizontal flight distance of the vehicle at the end of optimization, and G[u(t),x(t),t] is the hypersonic vehicle re-entry section The constraints of the process, u min and u max represent the lower limit and upper limit of the angle of attack control range.

本发明解决其技术问题所采用的技术方案是:在高超声速飞行器微控制单元(MCU)中集成了正交配置优化算法(Orthogonal collocation,简称OC),在高超声速飞行器到达再入段后能够给出速飞行器攻角的控制指令,使高超声速飞行器获得更长的水平飞行距离。The technical solution adopted by the present invention to solve the technical problem is: an orthogonal configuration optimization algorithm (Orthogonal collocation, referred to as OC) is integrated in the micro control unit (MCU) of the hypersonic vehicle, which can give The control command of the attack angle of the high-speed aircraft enables the hypersonic aircraft to obtain a longer horizontal flight distance.

所述MCU可以视为自动控制信号产生器,该控制器包括气动系数模型、飞行器性能约束条件、指定优化目标设定模块11,高超声速飞行器MCU模块12,飞行器海拔高度传感器13,飞行器速度传感器14,飞行器飞行航道倾角传感器15,飞行器水平飞行距离传感器16,飞行器海拔高度、速度、飞行航道倾角设定模块17,飞行器攻角控制18,所述系统内的各组成部分均由控制器内数据总线连接。The MCU can be regarded as an automatic control signal generator, and the controller includes an aerodynamic coefficient model, aircraft performance constraints, a specified optimization target setting module 11, a hypersonic aircraft MCU module 12, an aircraft altitude sensor 13, and an aircraft speed sensor 14 , aircraft flight path inclination sensor 15, aircraft horizontal flight distance sensor 16, aircraft altitude, speed, flight path inclination angle setting module 17, aircraft angle of attack control 18, each component in the described system is all controlled by the data bus in the controller connect.

所述控制器的运行过程如下:The operation process of the controller is as follows:

步骤1):将所述控制器安装在某型高超声速飞行器上,并在飞行器MCU 12中输入对应于飞行器的气动系数模型、飞行器性能约束条件、指定优化目标参数信息11;Step 1): Install the controller on a certain type of hypersonic aircraft, and input the aerodynamic coefficient model corresponding to the aircraft, the aircraft performance constraints, and the specified optimization target parameter information 11 in the aircraft MCU 12;

步骤2):高超声速飞行器到达再入段后,飞行器海拔高度传感器13、飞行器速度传感器14、飞行器飞行航道倾角传感器15和飞行器水平飞行距离传感器16,获得高超声速飞行器当前的海拔高度、速度、飞行航道倾角和飞行水平距离状态信息;Step 2): After the hypersonic vehicle arrives at the reentry section, the vehicle altitude sensor 13, the vehicle speed sensor 14, the vehicle flight path inclination sensor 15 and the vehicle horizontal flight distance sensor 16 obtain the current altitude, speed, and flight distance of the hypersonic vehicle. Track inclination and flight horizontal distance status information;

步骤3):飞行器MCU12根据飞行器海拔高度、速度、飞行航道倾角设定模块17获取控制目标信息,MCU模块12执行内部的正交配置优化算法,得到使飞行器水平飞行距离最远的轨迹控制策略;Step 3): aircraft MCU12 obtains control target information according to aircraft altitude, speed, and flight path inclination setting module 17, and MCU module 12 executes an internal orthogonal configuration optimization algorithm to obtain the trajectory control strategy that makes the aircraft's horizontal flight distance the farthest;

步骤4):飞行器MCU将获得的控制策略转换为攻角控制指令输出至飞行器攻角控制器模块18;Step 4): The control strategy obtained by the aircraft MCU is converted into an angle-of-attack control command and output to the aircraft angle-of-attack controller module 18;

集成了正交配置优化算法的高超声速飞行器MCU是本发明的核心,其内部包括信息采集模块21、初始化模块22、ODE正交配置模块23、NLP问题求解模块24、控制指令输出模块25。其中,信息采集模块包括当前飞行器海拔高度和速度采集、当前飞行器飞行航道倾角和飞行水平距离采集、飞行器海拔高度和速度设定采集、飞行器飞行航道倾角设定采集、飞行器的气动系数模型和性能约束条件以及指定优化目标参数采集五个子模块;NLP求解模块包括寻优方向求解、寻优步长求解、寻优修正、NLP收敛性判断四个子模块。The hypersonic aircraft MCU integrated with the orthogonal configuration optimization algorithm is the core of the present invention, and its interior includes an information collection module 21, an initialization module 22, an ODE orthogonal configuration module 23, an NLP problem solving module 24, and a control command output module 25. Among them, the information collection module includes current aircraft altitude and speed collection, current aircraft flight path inclination and flight horizontal distance collection, aircraft altitude and speed setting collection, aircraft flight path inclination setting collection, aircraft aerodynamic coefficient model and performance constraints There are five sub-modules for the collection of conditions and specified optimization target parameters; the NLP solution module includes four sub-modules for optimization direction solution, optimization step size solution, optimization correction, and NLP convergence judgment.

所述的高超声速飞行器MCU自动产生攻角控制指令的正交配置优化算法运行步骤如下:The operation steps of the orthogonal configuration optimization algorithm for the hypersonic vehicle MCU to automatically generate the angle of attack control command are as follows:

步骤1):高超声速飞行器到达再入段后,飞行器海拔高度传感器、飞行器速度传感器、飞行器飞行航道倾角传感器和飞行器水平飞行距离传感器开启,信息采集模块21获取高超声速飞行器当前的海拔高度、速度、飞行航道倾角和飞行水平距离状态信息;Step 1): After the hypersonic vehicle arrives at the reentry section, the vehicle altitude sensor, the vehicle speed sensor, the vehicle flight path inclination sensor and the vehicle horizontal flight distance sensor are turned on, and the information collection module 21 acquires the current altitude, speed, Flight path inclination and flight horizontal distance status information;

步骤2):初始化模块22开始运行,设置轨迹优化过程时间的离散段数、攻角控制量的初始猜测值u(0)(t)、状态轨迹的初始值x(0)(t),设定优化精度要求tol,将迭代次数k置零;Step 2): The initialization module 22 starts to run, sets the number of discrete segments of the trajectory optimization process time, the initial guess value u (0) (t) of the angle of attack control variable, the initial value x (0) (t) of the state trajectory, and sets The optimization accuracy requires tol, and the number of iterations k is set to zero;

步骤3):通过ODE正交配置模块23将常微分方程组在时间轴[t0,tf]上全部离散;Step 3): through the ODE orthogonal configuration module 23, all the ordinary differential equations are discretized on the time axis [t 0 ,t f ];

步骤4):通过NLP问题求解模块24获得所需的攻角控制策略和对应状态轨迹,这个过程包括多次内部迭代,每次迭代都要求解寻优方向和寻优步长,并进行寻优修正。对于某一次迭代得到的攻角控制量u(k)(t),如果其对应目标函数值J[u(k)(t)]与前一次迭代的目标函数值J[u(k-1)(t)]之差小于精度要求tol,则判断收敛性满足,并将攻角控制量u(k)(t)作为指令输出到控制策略输出模块25。Step 4): Obtain the required angle of attack control strategy and corresponding state trajectory through the NLP problem solving module 24. This process includes multiple internal iterations, and each iteration requires the solution of the optimization direction and the optimization step size, and performs the optimization fix. For the angle of attack control quantity u (k) (t) obtained in a certain iteration, if its corresponding objective function value J[u (k) (t)] is the same as the objective function value J[u (k-1) of the previous iteration (t)] is less than the accuracy requirement tol, then it is judged that the convergence is satisfied, and the angle of attack control quantity u (k) (t) is output to the control strategy output module 25 as an instruction.

所述的ODE正交配置模块,采用如下步骤实现:Described ODE orthogonal configuration module, adopts following steps to realize:

步骤1):将攻角控制量u(t)、状态轨迹x(t)用M阶基函数的线性组合表示,即:Step 1): The angle of attack control variable u(t) and the state trajectory x(t) are represented by a linear combination of M-order basis functions, namely:

其中N是时间轴[t0,tf]的离散段数,φ(t)是拉格朗日插值基函数,线性组合系数ui,j和si,j分别是u(t)和x(t)在配置点ti,j上的值。Where N is the number of discrete segments of the time axis [t 0 ,t f ], φ(t) is the Lagrangian interpolation basis function, and the linear combination coefficients u i,j and s i,j are u(t) and x( t) the value at configuration point t i,j .

步骤2):由于所有基函数的导函数表达式已知,于是状态轨迹的微分方程组被离散化代数形式:Step 2): Since the derivative function expressions of all basis functions are known, the differential equations of the state trajectory are discretized in algebraic form:

步骤3):用离散化后的微分方程组代替原来微分方程组,将得到待求的NLP问题。Step 3): Replace the original differential equation system with the discretized differential equation system, and the NLP problem to be solved will be obtained.

所述的NLP求解模块,采用如下步骤实现:Described NLP solution module, adopts following steps to realize:

步骤1):将攻角控制量u(k-1)(t)作为向量空间中的某个点,记作P1,P1对应的目标函数值就是J[u(k-1)(t)];Step 1): Take the angle of attack control variable u (k-1) (t) as a point in the vector space, denoted as P 1 , and the value of the objective function corresponding to P 1 is J[u (k-1) (t )];

步骤2):从点P1出发,根据选用的NLP算法,构造向量空间中的一个寻优方向向d(k -1)和步长α(k-1)Step 2 ): Starting from point P1, according to the selected NLP algorithm, construct a search direction d (k -1) and step size α (k-1) in the vector space;

步骤3):通过式u(k)(t)=u(k-1)(t)+α(k-1)d(k-1)构造向量空间中对应u(k)的另外一个点P2,使得P2对应的目标函数值J[u(k)(t)]比J[u(k-1)(t)]更优。Step 3): Construct another point P corresponding to u (k) in the vector space through the formula u (k) (t)=u (k-1) (t)+α (k-1) d (k-1) 2 , so that the objective function value J[u (k) (t)] corresponding to P 2 is better than J[u (k-1) (t)].

步骤4):采用寻优校正u(k)(t),得到校正后的点记为点P3,同时令使得P3对应的目标函数值J[u(k)(t)]比J[u(k-1)(t)]更优;Step 4): Use the optimization correction u (k) (t) to obtain the corrected point denoted as point P 3 , and let Make the objective function value J[u (k) (t)] corresponding to P 3 better than J[u (k-1) (t)];

步骤5):如果本次迭代的目标函数值J[u(k)(t)]与上一次迭代的目标函数值J[u(k -1)(t)]的绝对值之差小于精度tol,则判断收敛性满足,将本次迭代得到的控制策略u(k)(t)输出至控制策略输出模块25;如果收敛性不满足,迭代次数k增加1,将u(k)(t)设置为初始值,继续执行步骤2)。Step 5): If the absolute value difference between the objective function value J[u (k) (t)] of this iteration and the objective function value J[u (k -1) (t)] of the previous iteration is less than the accuracy tol , then it is judged that the convergence is satisfied, and the control strategy u (k) (t) obtained in this iteration is output to the control strategy output module 25; if the convergence is not satisfied, the number of iterations k is increased by 1, and u (k) (t) Set as the initial value, proceed to step 2).

本发明的有益效果主要表现在:由于正交配置法具备较精确的拟合能力,可以获得高超声速飞行器再入轨迹优化的精确解;采用了寻优方向求解、寻优步长求解、寻优修正、NLP收敛性判断策略,近似的NLP问题的解将逐渐逼近原问题的最优解;由于该方法不需要反复求解动态方程,可以获得较快的收敛速度,减少获得高超声速飞行器再入轨迹优化最优控制策略的计算时间。本发明能够使高超声速飞行器水平飞行距离更长的轨迹优化攻角控制指令,提高高超声速飞行器打击范围。The beneficial effects of the present invention are mainly manifested in: due to the more accurate fitting ability of the orthogonal configuration method, the precise solution for the reentry trajectory optimization of the hypersonic vehicle can be obtained; Correction, NLP convergence judgment strategy, the solution of the approximate NLP problem will gradually approach the optimal solution of the original problem; since this method does not need to repeatedly solve the dynamic equation, it can obtain a faster convergence speed and reduce the number of hypersonic vehicle reentry trajectories Optimize the calculation time of the optimal control strategy. The invention can optimize the control command of the angle of attack for the trajectory of the hypersonic vehicle with a longer horizontal flight distance, and improve the striking range of the hypersonic vehicle.

附图说明Description of drawings

图1是本发明的结构示意图;Fig. 1 is a structural representation of the present invention;

图2是本发明高超声速飞行器MCU内部模块结构图;Fig. 2 is the structural diagram of the internal module of hypersonic vehicle MCU of the present invention;

图3是实施例1的攻角控制策略曲线图;Fig. 3 is the angle of attack control strategy graph of embodiment 1;

图4是实施例1的攻角控制策略对应的水平飞行距离图。FIG. 4 is a diagram of the horizontal flight distance corresponding to the angle of attack control strategy of Embodiment 1. FIG.

具体实施方式Detailed ways

高超声速飞行器再入段轨迹优化问题可以描述为The trajectory optimization problem of hypersonic vehicle re-entry segment can be described as

max J[u(t)]=x4(tf)max J[u(t)]=x 4 (t f )

x1(t0)=h0,x2(t0)=v0,x3(t0)=γ0,x4(t0)=r0 x 1 (t 0 )=h 0 , x 2 (t 0 )=v 0 , x 3 (t 0 )=γ 0 , x 4 (t 0 )=r 0

x1(tf)=hf,x2(tf)=vf,x3(tf)=γf x 1 (t f )=h f , x 2 (t f )=v f , x 3 (t f )=γ f

G[u(t),x(t),t]≥0G[u(t),x(t),t]≥0

umin≤u(t)≤umax u min ≤ u(t) ≤ u max

其中t表示时间,x(t)表示高超声速飞行器的状态变量,x1(t)表示飞行器海拔高度、x2(t)表示飞行器速度、x3(t)表示飞行器飞行航道倾角、x4(t)表示飞行器水平飞行距离,u(t)表示高超声速飞行器的攻角控制量,为本问题的控制变量;表示状态变量x(t)的一阶导数,F(x(t),u(t),t)是根据高超声速飞行器再入段三维空间运动方程建立的微分方程组数学模型;t0表示再入段轨迹优化开始的时间点,h0表示优化开始时刻飞行器的初始海拔高度,v0表示优化开始时刻飞行器的初始速度,γ0表示优化开始时刻飞行器的初始飞行航道角,r0表示优化开始时刻飞行器的初始水平飞行距离,tf表示再入段轨迹优化结束时间点,hf表示优化结束时刻飞行器的海拔高度,vf表示优化结束时刻飞行器的速度,γf表示优化结束时刻飞行器的飞行航道角;J[u(t)]表示高超声速飞行器轨迹优化的目标函数即优化结束时刻飞行器的水平飞行距离,G[u(t),x(t),t]是高超声速飞行器再入段过程的约束条件,umin和umax表示攻角控制范围的下限值和上限值。Where t represents time, x(t) represents the state variable of the hypersonic vehicle, x 1 (t) represents the altitude of the aircraft, x 2 (t) represents the speed of the aircraft, x 3 (t) represents the inclination angle of the flight path of the aircraft, x 4 ( t) represents the horizontal flight distance of the aircraft, and u(t) represents the control variable of the angle of attack of the hypersonic vehicle, which is the control variable of this problem; Indicates the first-order derivative of the state variable x(t), F(x(t),u(t),t) is a mathematical model of differential equations established according to the three-dimensional space motion equation of the re-entry section of the hypersonic vehicle; t 0 represents the re-entry The time point when the optimization of the entry segment trajectory starts, h 0 represents the initial altitude of the aircraft at the beginning of optimization, v 0 represents the initial velocity of the aircraft at the beginning of optimization, γ 0 represents the initial flight path angle of the aircraft at the beginning of optimization, r 0 represents the start of optimization The initial horizontal flight distance of the aircraft at time, t f represents the end time point of the re-entry trajectory optimization, h f represents the altitude of the aircraft at the end of optimization, v f represents the speed of the aircraft at the end of optimization, γ f represents the flight of the aircraft at the end of optimization Course angle; J[u(t)] represents the objective function of hypersonic vehicle trajectory optimization, that is, the horizontal flight distance of the vehicle at the end of optimization, and G[u(t),x(t),t] is the hypersonic vehicle re-entry section The constraints of the process, u min and u max represent the lower limit and upper limit of the angle of attack control range.

本发明解决其技术问题所采用的技术方案是:在高超声速飞行器微控制单元(MCU)中集成了正交配置优化算法(Orthogonal collocation,简称OC),在高超声速飞行器到达再入段后能够给出速飞行器攻角的控制指令,使高超声速飞行器获得更长的水平飞行距离。The technical solution adopted by the present invention to solve the technical problem is: an orthogonal configuration optimization algorithm (Orthogonal collocation, referred to as OC) is integrated in the micro control unit (MCU) of the hypersonic vehicle, which can give The control command of the attack angle of the high-speed aircraft enables the hypersonic aircraft to obtain a longer horizontal flight distance.

所述MCU可以视为自动控制信号产生器,该控制器如图1所示,包括气动系数模型、飞行器性能约束条件、指定优化目标设定模块11,高超声速飞行器MCU模块12,飞行器海拔高度传感器13,飞行器速度传感器14,飞行器飞行航道倾角传感器15,飞行器水平飞行距离传感器16,飞行器海拔高度、速度、飞行航道倾角设定模块17,飞行器攻角控制18,所述系统内的各组成部分均由控制器内数据总线连接。The MCU can be regarded as an automatic control signal generator. As shown in Figure 1, the controller includes an aerodynamic coefficient model, aircraft performance constraints, a specified optimization target setting module 11, a hypersonic aircraft MCU module 12, and an aircraft altitude sensor. 13, aircraft speed sensor 14, aircraft flight path inclination sensor 15, aircraft horizontal flight distance sensor 16, aircraft altitude, speed, flight path inclination angle setting module 17, aircraft angle of attack control 18, each component in the described system is Connected by the data bus in the controller.

所述控制器的运行过程如下:The operation process of the controller is as follows:

步骤1):将所述控制器安装在某型高超声速飞行器上,并在飞行器MCU 12中输入对应于飞行器的气动系数模型、飞行器性能约束条件、指定优化目标参数信息11;Step 1): Install the controller on a certain type of hypersonic aircraft, and input the aerodynamic coefficient model corresponding to the aircraft, the aircraft performance constraints, and the specified optimization target parameter information 11 in the aircraft MCU 12;

步骤2):高超声速飞行器到达再入段后,飞行器海拔高度传感器13、飞行器速度传感器14、飞行器飞行航道倾角传感器15和飞行器水平飞行距离传感器16,获得高超声速飞行器当前的海拔高度、速度、飞行航道倾角和飞行水平距离状态信息;Step 2): After the hypersonic vehicle arrives at the reentry section, the vehicle altitude sensor 13, the vehicle speed sensor 14, the vehicle flight path inclination sensor 15 and the vehicle horizontal flight distance sensor 16 obtain the current altitude, speed, and flight distance of the hypersonic vehicle. Track inclination and flight horizontal distance status information;

步骤3):飞行器MCU12根据飞行器海拔高度、速度、飞行航道倾角设定模块17获取控制目标信息,MCU模块12执行内部的正交配置优化算法,得到使飞行器水平飞行距离最远的轨迹控制策略;Step 3): aircraft MCU12 obtains control target information according to aircraft altitude, speed, and flight path inclination setting module 17, and MCU module 12 executes an internal orthogonal configuration optimization algorithm to obtain the trajectory control strategy that makes the aircraft's horizontal flight distance the farthest;

步骤4):飞行器MCU将获得的控制策略转换为攻角控制指令输出至飞行器攻角控制器模块18;Step 4): The control strategy obtained by the aircraft MCU is converted into an angle-of-attack control command and output to the aircraft angle-of-attack controller module 18;

集成了正交配置优化算法的高超声速飞行器MCU是本发明的核心,如图2所示,其内部包括信息采集模块21、初始化模块22、ODE正交配置模块23、NLP问题求解模块24、控制指令输出模块25。其中,信息采集模块包括当前飞行器海拔高度和速度采集、当前飞行器飞行航道倾角和飞行水平距离采集、飞行器海拔高度和速度设定采集、飞行器飞行航道倾角设定采集、飞行器的气动系数模型和性能约束条件以及指定优化目标参数采集五个子模块;NLP求解模块包括寻优方向求解、寻优步长求解、寻优修正、NLP收敛性判断四个子模块。The hypersonic vehicle MCU integrated with the orthogonal configuration optimization algorithm is the core of the present invention, as shown in Figure 2, which includes an information collection module 21, an initialization module 22, an ODE orthogonal configuration module 23, an NLP problem solving module 24, a control Instruction output module 25 . Among them, the information collection module includes current aircraft altitude and speed collection, current aircraft flight path inclination and flight horizontal distance collection, aircraft altitude and speed setting collection, aircraft flight path inclination setting collection, aircraft aerodynamic coefficient model and performance constraints There are five sub-modules for the collection of conditions and specified optimization target parameters; the NLP solution module includes four sub-modules for optimization direction solution, optimization step size solution, optimization correction, and NLP convergence judgment.

所述的高超声速飞行器MCU自动产生攻角控制指令的正交配置优化算法运行步骤如下:The operation steps of the orthogonal configuration optimization algorithm for the hypersonic vehicle MCU to automatically generate the angle of attack control command are as follows:

步骤1):高超声速飞行器到达再入段后,飞行器海拔高度传感器、飞行器速度传感器、飞行器飞行航道倾角传感器和飞行器水平飞行距离传感器开启,信息采集模块21获取高超声速飞行器当前的海拔高度、速度、飞行航道倾角和飞行水平距离状态信息;Step 1): After the hypersonic vehicle arrives at the reentry section, the vehicle altitude sensor, the vehicle speed sensor, the vehicle flight path inclination sensor and the vehicle horizontal flight distance sensor are turned on, and the information collection module 21 acquires the current altitude, speed, Flight path inclination and flight horizontal distance status information;

步骤2):初始化模块22开始运行,设置轨迹优化过程时间的离散段数、攻角控制量的初始猜测值u(0)(t)、状态轨迹的初始值x(0)(t),设定优化精度要求tol,将迭代次数k置零;Step 2): The initialization module 22 starts to run, sets the number of discrete segments of the trajectory optimization process time, the initial guess value u (0) (t) of the angle of attack control variable, the initial value x (0) (t) of the state trajectory, and sets The optimization accuracy requires tol, and the number of iterations k is set to zero;

步骤3):通过ODE正交配置模块23将常微分方程组在时间轴[t0,tf]上全部离散;Step 3): through the ODE orthogonal configuration module 23, all the ordinary differential equations are discretized on the time axis [t 0 ,t f ];

步骤4):通过NLP问题求解模块24获得所需的攻角控制策略和对应状态轨迹,这个过程包括多次内部迭代,每次迭代都要求解寻优方向和寻优步长,并进行寻优修正。对于某一次迭代得到的攻角控制量u(k)(t),如果其对应目标函数值J[u(k)(t)]与前一次迭代的目标函数值J[u(k-1)(t)]之差小于精度要求tol,则判断收敛性满足,并将攻角控制量u(k)(t)作为指令输出到控制策略输出模块25。Step 4): Obtain the required angle of attack control strategy and corresponding state trajectory through the NLP problem solving module 24. This process includes multiple internal iterations, and each iteration requires the solution of the optimization direction and the optimization step size, and performs the optimization fix. For the angle of attack control quantity u (k) (t) obtained in a certain iteration, if its corresponding objective function value J[u (k) (t)] is the same as the objective function value J[u (k-1) of the previous iteration (t)] is less than the accuracy requirement tol, then it is judged that the convergence is satisfied, and the angle of attack control quantity u (k) (t) is output to the control strategy output module 25 as an instruction.

所述的ODE正交配置模块,采用如下步骤实现:Described ODE orthogonal configuration module, adopts following steps to realize:

步骤1):将攻角控制量u(t)、状态轨迹x(t)用M阶基函数的线性组合表示,即:Step 1): The angle of attack control variable u(t) and the state trajectory x(t) are represented by a linear combination of M-order basis functions, namely:

其中N是时间轴[t0,tf]的离散段数,φ(t)是拉格朗日插值基函数,线性组合系数ui,j和si,j分别是u(t)和x(t)在配置点ti,j上的值。Where N is the number of discrete segments of the time axis [t 0 ,t f ], φ(t) is the Lagrangian interpolation basis function, and the linear combination coefficients u i,j and s i,j are u(t) and x( t) the value at configuration point t i,j .

步骤2):由于所有基函数的导函数表达式已知,于是状态轨迹的微分方程组被离散化代数形式:Step 2): Since the derivative function expressions of all basis functions are known, the differential equations of the state trajectory are discretized in algebraic form:

步骤3):用离散化后的微分方程组代替原来微分方程组,将得到待求的NLP问题。Step 3): Replace the original differential equation system with the discretized differential equation system, and the NLP problem to be solved will be obtained.

所述的NLP求解模块,采用如下步骤实现:Described NLP solution module, adopts following steps to realize:

步骤1):将攻角控制量u(k-1)(t)作为向量空间中的某个点,记作P1,P1对应的目标函数值就是J[u(k-1)(t)];Step 1): Take the angle of attack control variable u (k-1) (t) as a point in the vector space, denoted as P 1 , and the value of the objective function corresponding to P 1 is J[u (k-1) (t )];

步骤2):从点P1出发,根据选用的NLP算法,构造向量空间中的一个寻优方向向d(k -1)和步长α(k-1)Step 2 ): Starting from point P1, according to the selected NLP algorithm, construct a search direction d (k -1) and step size α (k-1) in the vector space;

步骤3):通过式u(k)(t)=u(k-1)(t)+α(k-1)d(k-1)构造向量空间中对应u(k)的另外一个点P2,使得P2对应的目标函数值J[u(k)(t)]比J[u(k-1)(t)]更优。Step 3): Construct another point P corresponding to u (k) in the vector space through the formula u (k) (t)=u (k-1) (t)+α (k-1) d (k-1) 2 , so that the objective function value J[u (k) (t)] corresponding to P 2 is better than J[u (k-1) (t)].

步骤4):采用寻优校正u(k)(t),得到校正后的点记为点P3,同时令使得P3对应的目标函数值J[u(k)(t)]比J[u(k-1)(t)]更优;Step 4): Use the optimization correction u (k) (t) to obtain the corrected point denoted as point P 3 , and let Make the objective function value J[u (k) (t)] corresponding to P 3 better than J[u (k-1) (t)];

步骤5):如果本次迭代的目标函数值J[u(k)(t)]与上一次迭代的目标函数值J[u(k -1)(t)]的绝对值之差小于精度tol,则判断收敛性满足,将本次迭代得到的控制策略u(k)(t)输出至控制策略输出模块25;如果收敛性不满足,迭代次数k增加1,将u(k)(t)设置为初始值,继续执行步骤2)。Step 5): If the absolute value difference between the objective function value J[u (k) (t)] of this iteration and the objective function value J[u (k -1) (t)] of the previous iteration is less than the accuracy tol , then it is judged that the convergence is satisfied, and the control strategy u (k) (t) obtained in this iteration is output to the control strategy output module 25; if the convergence is not satisfied, the number of iterations k is increased by 1, and u (k) (t) Set as the initial value, proceed to step 2).

实施例1Example 1

高超声速飞行器到达再入段空域,高超声速飞行器海拔高度传感器、速度传感器、飞行航道倾角传感器、水平飞行距离传感器和MCU均已开启。信息采集模块立即采集飞行器进入再入段时的初始海拔高度、速度、飞行航道倾角和水平飞行距离,设当前初始时刻t0=0s,海拔高度传感器传入MCU的海拔高度为h0=80 000m,速度传感器传入MCU的速度为v0=6400m/s,飞行航道倾角传感器传入MCU的飞行航道倾角为γ0=-0.052rad,水平飞行距离传感器感器传入MCU的水平飞行距离为r0=0m;终值时刻tf高超声速飞行器需要满足的条件为海拔高度设定为hf=24000m,速度设定为vf=760m/s,飞行航道倾角设定为γf=-0.08rad;结合飞行器的三维空间运动方程、气动系数模型、飞行器性能约束条件和指定优化目标,得到该问题的数学模型如下:When the hypersonic vehicle arrives in the airspace of the re-entry segment, the hypersonic vehicle’s altitude sensor, speed sensor, flight path inclination sensor, horizontal flight distance sensor and MCU are all turned on. The information collection module immediately collects the initial altitude, speed, flight path inclination and horizontal flight distance of the aircraft when it enters the re-entry segment. Assuming the current initial moment t 0 =0s, the altitude of the altitude sensor transmitted to the MCU is h 0 =80 000m , the velocity of the speed sensor into the MCU is v 0 =6400m/s, the flight path inclination angle of the flight path inclination sensor into the MCU is γ 0 =-0.052rad, and the horizontal flight distance of the horizontal flight distance sensor into the MCU is r 0 =0m; the final value time t f hypersonic vehicle needs to meet the conditions that the altitude is set to h f =24000m, the speed is set to v f =760m/s, and the flight path inclination is set to γ f =-0.08rad ; Combining the aircraft's three-dimensional space motion equation, aerodynamic coefficient model, aircraft performance constraints and specified optimization objectives, the mathematical model of the problem is obtained as follows:

max J[u(t)]=x4(tf)max J[u(t)]=x 4 (t f )

CL=-0.15+3.44u(t)C L =-0.15+3.44u(t)

CD=0.29-1.51u(t)+5.87u(t)2 C D =0.29-1.51u(t)+5.87u(t) 2

x1(0)=80×103,x1(tf)=24×103 x 1 (0)=80×10 3 , x 1 (t f )=24×10 3

x2(0)=6.4×103,x2(tf)=760x 2 (0)=6.4×10 3 , x 2 (t f )=760

x3(0)=-0.052,x3(tf)=-0.08x 3 (0)=-0.052, x 3 (t f )=-0.08

x4(0)=0x 4 (0) = 0

-15≤u(t)≤30-15≤u(t)≤30

其中L表示升力,D表示阻力,CL表示升力系数,CD表示阻力系数。为了便于表述,采用F(x(t),u(t),t)表示高超声速飞行器再入段三维空间运动方程建立的微分方程组数学模型,即:Among them, L represents the lift force, D represents the drag force, C L represents the lift coefficient, and C D represents the drag coefficient. For the convenience of expression, F(x(t), u(t), t) is used to represent the mathematical model of differential equations established by the three-dimensional space motion equation of the hypersonic vehicle re-entry section, namely:

采用G[u(t),x(t),t]表示高超声速飞行器再入段过程的约束条件,为:G[u(t),x(t),t] is used to represent the constraints of the hypersonic vehicle re-entry process, which is:

此外,J[u(t)]表示高超声速飞行器轨迹优化的目标函数即优化结束时刻飞行器的水平飞行距离。In addition, J[u(t)] represents the objective function of hypersonic vehicle trajectory optimization, that is, the horizontal flight distance of the vehicle at the end of optimization.

高超声速飞行器MCU自动产生攻角控制指令的正交配置优化算法如图2所示,其运行步骤如下:The orthogonal configuration optimization algorithm for the hypersonic vehicle MCU to automatically generate the angle of attack control command is shown in Figure 2, and its operation steps are as follows:

步骤1):高超声速飞行器到达再入段后,飞行器海拔高度传感器、飞行器速度传感器、飞行器飞行航道倾角传感器和飞行器水平飞行距离传感器开启,信息采集模块21获取初始时刻t0=0s时高超声速飞行器海拔高度h0=80 000m,速度为v0=6400m/s,飞行航道倾角为γ0=-0.052rad,水平飞行距离传感器感器水平飞行距离设置为r0=0m;终值时刻tf高超声速飞行器海拔高度要求设定为hf=24000m,速度要求设定为vf=760m/s,飞行航道倾角要求设定为γf=-0.08rad;;Step 1): After the hypersonic vehicle arrives at the reentry stage, the vehicle altitude sensor, the vehicle speed sensor, the vehicle flight path inclination sensor and the vehicle horizontal flight distance sensor are turned on, and the information collection module 21 acquires the hypersonic vehicle at the initial time t 0 =0s Altitude h 0 =80 000m, speed v 0 =6400m/s, flight path inclination angle γ 0 =-0.052rad, horizontal flight distance sensor sensor horizontal flight distance is set to r 0 =0m; final value moment t f high The supersonic vehicle altitude requirement is set to h f =24000m, the speed requirement is set to v f =760m/s, and the flight path inclination angle requirement is set to γ f =-0.08rad;;

步骤2):初始化模块22开始运行,设置轨迹优化过程时间的离散段数为10、攻角控制量的初始猜测值u(0)(t)=0.5,设定优化精度要求tol=10-8,将迭代次数k置零;Step 2): The initialization module 22 starts to run, the number of discrete segments of the trajectory optimization process time is set to 10, the initial guess value of the angle of attack control variable u (0) (t) = 0.5, and the optimization accuracy requirement tol = 10 −8 is set, Set the number of iterations k to zero;

步骤3):通过ODE正交配置模块23将常微分方程组在时间轴[t0,tf]上全部离散;Step 3): through the ODE orthogonal configuration module 23, all the ordinary differential equations are discretized on the time axis [t 0 ,t f ];

步骤4):通过NLP问题求解模块24获得所需的攻角控制策略和对应状态轨迹,这个过程包括多次内部迭代,每次迭代都要求解寻优方向和寻优步长,并进行寻优修正。对于某一次迭代得到的攻角控制量u(k)(t),如果其对应目标函数值J[u(k)(t)]与前一次迭代的目标函数值J[u(k-1)(t)]之差小于精度要求10-8,则判断收敛性满足,并将攻角控制量u(k)(t)作为指令输出到控制策略输出模块25。Step 4): Obtain the required angle of attack control strategy and corresponding state trajectory through the NLP problem solving module 24. This process includes multiple internal iterations, and each iteration requires the solution of the optimization direction and the optimization step size, and performs the optimization fix. For the angle of attack control quantity u (k) (t) obtained in a certain iteration, if its corresponding objective function value J[u (k) (t)] is the same as the objective function value J[u (k-1) of the previous iteration (t)] is less than the accuracy requirement of 10 -8 , then it is judged that the convergence is satisfied, and the angle of attack control value u (k) (t) is output to the control strategy output module 25 as an instruction.

所述的ODE正交配置模块,采用如下步骤实现:Described ODE orthogonal configuration module, adopts following steps to realize:

步骤1):将攻角控制量u(t)、状态轨迹x(t)用3阶基函数的线性组合表示,即:Step 1): The angle of attack control variable u(t) and the state trajectory x(t) are represented by the linear combination of the third-order basis functions, namely:

其中N是时间轴[t0,tf]的离散段数,φ(t)是拉格朗日插值基函数,线性组合系数ui,j和si,j分别是u(t)和x(t)在配置点ti,j上的值。Where N is the number of discrete segments of the time axis [t 0 ,t f ], φ(t) is the Lagrangian interpolation basis function, and the linear combination coefficients u i,j and s i,j are u(t) and x( t) the value at configuration point t i,j .

步骤2):由于所有基函数的导函数表达式已知,于是状态轨迹的微分方程组被离散化代数形式:Step 2): Since the derivative function expressions of all basis functions are known, the differential equations of the state trajectory are discretized in algebraic form:

步骤3):用离散化后的微分方程组代替原来微分方程组,将得到待求的NLP问题。Step 3): Replace the original differential equation system with the discretized differential equation system, and the NLP problem to be solved will be obtained.

所述的NLP求解模块,采用如下步骤实现:Described NLP solution module, adopts following steps to realize:

步骤1):将攻角控制量u(k-1)(t)作为向量空间中的某个点,记作P1,P1对应的目标函数值就是J[u(k-1)(t)];Step 1): Take the angle of attack control variable u (k-1) (t) as a point in the vector space, denoted as P 1 , and the value of the objective function corresponding to P 1 is J[u (k-1) (t )];

步骤2):从点P1出发,根据选用的NLP算法,构造向量空间中的一个寻优方向向d(k -1)和步长α(k-1)Step 2 ): Starting from point P1, according to the selected NLP algorithm, construct a search direction d (k -1) and step size α (k-1) in the vector space;

步骤3):通过式u(k)(t)=u(k-1)(t)+α(k-1)d(k-1)构造向量空间中对应u(k)的另外一个点P2,使得P2对应的目标函数值J[u(k)(t)]比J[u(k-1)(t)]更优。Step 3): Construct another point P corresponding to u (k) in the vector space through the formula u (k) (t)=u (k-1) (t)+α (k-1) d (k-1) 2 , so that the objective function value J[u (k) (t)] corresponding to P 2 is better than J[u (k-1) (t)].

步骤4):采用寻优校正u(k)(t),得到校正后的点记为点P3,同时令使得P3对应的目标函数值J[u(k)(t)]比J[u(k-1)(t)]更优;Step 4): Use the optimization correction u (k) (t) to obtain the corrected point denoted as point P 3 , and let Make the objective function value J[u (k) (t)] corresponding to P 3 better than J[u (k-1) (t)];

步骤5):如果本次迭代的目标函数值J[u(k)(t)]与上一次迭代的目标函数值J[u(k -1)(t)]的绝对值之差小于精度10-8,则判断收敛性满足,将本次迭代得到的控制策略u(k)(t)输出至控制策略输出模块25;如果收敛性不满足,迭代次数k增加1,将u(k)(t)设置为初始值,继续执行步骤2)。Step 5): If the absolute value difference between the objective function value J[u (k) (t)] of this iteration and the objective function value J[u (k -1) (t)] of the previous iteration is less than the accuracy of 10 -8 , then it is judged that the convergence is satisfied, and the control strategy u (k) (t) obtained in this iteration is output to the control strategy output module 25; if the convergence is not satisfied, the number of iterations k is increased by 1, and u (k) ( t) is set as the initial value, and proceeds to step 2).

最后,飞行器MCU将获得的优化轨迹作为指令输出到控制策略输出模块,转换为控制指令发送给攻角控制器,完成轨迹优化的执行。图3是实施例1的攻角控制策略曲线图;图4是实施例1的攻角控制策略对应的水平飞行距离图。Finally, the aircraft MCU outputs the obtained optimized trajectory as a command to the control strategy output module, converts it into a control command and sends it to the angle-of-attack controller to complete the execution of trajectory optimization. Fig. 3 is a curve diagram of the angle of attack control strategy of embodiment 1; Fig. 4 is a graph of horizontal flight distance corresponding to the angle of attack control strategy of embodiment 1.

以上内容是结合具体的优选实施方式对本发明所作的进一步详细说明,不能认定本发明的具体实施只限于这些说明。对于本发明所属技术领域的普通技术人员来说,在不脱离发明构思的前提下,还可以做出若干简单推演或替换,都应当视为属于本发明的保护范围。The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be assumed that the specific implementation of the present invention is limited to these descriptions. For those of ordinary skill in the technical field of the present invention, without departing from the concept of the invention, some simple deduction or replacement can also be made, which should be regarded as belonging to the protection scope of the present invention.

Claims (1)

1. a kind of hypersonic aircraft reentry stage track optimizing controller based on orthogonal configuration optimization, flies according to hypersonic The initial height above sea level of row device reentry stage, speed, flight path angle and flight horizontal distance state rapidly obtain track optimizing Control strategy, by controlling Aircraft Angle of Attack to make hypersonic aircraft obtain longer horizontal flight distance.It is characterized in that: It is horizontal winged by aircraft altitude height sensor, aircraft speed sensor, aircraft flight navigation channel obliquity sensor, aircraft Row distance sensor, aircraft micro-control unit (MCU), Aircraft Angle of Attack controller are formed.Each part is by high ultrasound Data/address bus connects in fast aircraft, and the operational process of described device includes:
Step 1):Input corresponds to the Aerodynamic Parameter Model of the aircraft, aircraft performance about in hypersonic aircraft MCU Beam condition, specify optimization aim;
Step 2):After hypersonic aircraft reaches reentry stage, aircraft altitude height sensor, aircraft speed sensing are opened Device, aircraft flight navigation channel obliquity sensor and the horizontal flying distance sensor of aircraft, it is current to obtain hypersonic aircraft Height above sea level, speed, flight path angle and flight horizontal distance status information;
Step 3):Aircraft MCU requires automated execution inner orthogonal according to the height above sea level, speed, flight path angle of setting Configuration optimization algorithm, obtains making hypersonic aircraft horizontal flight apart from longest track optimizing control strategy;
Step 4):The track optimizing control strategy of acquisition is sent to control strategy output module by hypersonic aircraft MCU, and Be converted to control instruction and be sent to the execution of Aircraft Angle of Attack controller.
The hypersonic aircraft MCU parts include information acquisition module 21, initialization module 22, ordinary differential system (Ordinary Differential Equations, abbreviation ODE) orthogonal configuration module 23, Non-Linear Programming (Non-linear Programming, abbreviation NLP) problem solver module 24, control instruction output module 25.Wherein, information acquisition module includes flying Row device height above sea level and speed acquisition, aircraft flight navigation channel inclination angle and the collection of flight horizontal distance, aircraft altitude height and Speed setting collection, the collection of aircraft flight navigation channel angle set, the Aerodynamic Parameter Model of aircraft and performance constraints with And specified predetermined optimizing target parameter gathers five submodules;NLP, which solves module, to be included search direction solution, the solution of optimizing step-length, seeks Excellent amendment, NLP convergences judge four submodules.
The hypersonic aircraft MCU automatically generates the orthogonal configuration optimization algorithm operating procedure of angle of attack control instruction such as Under:
Step 1):Hypersonic aircraft reach reentry stage after, aircraft altitude height sensor, aircraft speed sensor, Aircraft flight navigation channel obliquity sensor and the horizontal flying distance sensor of aircraft are opened, and information acquisition module 21 obtains superb The current height above sea level of velocity of sound aircraft, speed, flight path angle and flight horizontal distance status information;
Step 2):Initialization module 22 brings into operation, set the discrete hop count of track optimizing process time, angle of attack controlled quentity controlled variable just Beginning conjecture value u(0)(t), setting optimization required precision tol, by iterations k zero setting;
Step 3):By ODE orthogonal configurations module 23 by ordinary differential system in time shaft [t0,tf] on all it is discrete;
Step 4):Required angle of attack control strategy and corresponding states track, this process are obtained by NLP problem solver modules 24 Including multiple inner iterative, each iteration will solve search direction and optimizing step-length, and carry out optimizing amendment.For certain once The angle of attack controlled quentity controlled variable u that iteration obtains(k)(t), if it corresponds to target function value J [u(k)(t)] with the target letter of preceding an iteration Numerical value J [u(k-1)(t)] difference is less than required precision tol, then judges convergence sexual satisfaction, and by angle of attack controlled quentity controlled variable u(k)(t) it is used as and refers to Order is output to control strategy output module 25.
The ODE orthogonal configuration modules, are realized using following steps:
Step 1):Angle of attack controlled quentity controlled variable u (t), state trajectory x (t) are represented with the linear combination of M rank basic functions, i.e.,:
<mrow> <mi>u</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;ap;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msub> <mi>u</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <msubsup> <mi>&amp;phi;</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mrow> <mo>(</mo> <mi>M</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>N</mi> </mrow>
<mrow> <mi>x</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;ap;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msub> <mi>s</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <msubsup> <mi>&amp;phi;</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mrow> <mo>(</mo> <mi>M</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>N</mi> </mrow>
Wherein N is time shaft [t0,tf] discrete hop count, φ (t) is Lagrange's interpolation basic function, linear combination coefficient ui,jWith si,jIt is u (t) and x (t) respectively in collocation point ti,jOn value.
Step 2):Since the derived function expression formula of all basic functions is it is known that then the differential equation group of state trajectory is discretized Quantic:
<mrow> <mover> <mi>x</mi> <mo>&amp;CenterDot;</mo> </mover> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;ap;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msub> <mi>s</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <msubsup> <mover> <mi>&amp;phi;</mi> <mo>&amp;CenterDot;</mo> </mover> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mrow> <mo>(</mo> <mi>M</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>N</mi> </mrow>
Step 3):Original differential equation group is replaced with the differential equation group after discretization, NLP problems to be asked will be obtained.
The NLP solves module, is realized using following steps:
Step 1):By angle of attack controlled quentity controlled variable u(k-1)(t) as some point in vector space, it is denoted as P1, P1Corresponding target function value It is exactly J [u(k-1)(t)];
Step 2):From point P1Set out, according to the NLP algorithms of selection, construct a search direction in vector space to d(k-1)With Step-length α(k-1)
Step 3):Pass through formula u(k)(t)=u(k-1)(t)+α(k-1)d(k-1)U is corresponded in construction vector space(k)Another point P2, So that P2Corresponding target function value J [u(k)(t)] than J [u(k-1)(t)] it is more excellent.
Step 4):U is corrected using optimizing(k)(t), the point after being correctedIt is denoted as point P3, with seasonSo that P3Corresponding target function value J [u(k)(t)] than J [u(k-1)(t)] it is more excellent;
Step 5):If the target function value J [u of current iteration(k)(t)] with the target function value J [u of last iteration(k-1) (t)] difference of absolute value is less than precision tol, then judges convergence sexual satisfaction, the control strategy u that current iteration is obtained(k)(t) it is defeated Go out to control strategy output module 25;If convergence is unsatisfactory for, iterations k increases by 1, by u(k)(t) initial value is arranged to, Continue to execute step 2).
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