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

Abstract

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

A trajectory optimization control for hypersonic vehicle re-entry segment based on orthogonal configuration optimization Controller

technical field

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.

Background technique

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.

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.

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.

Contents of the invention

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.

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:

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;

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;

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;

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.

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.

The trajectory optimization problem of hypersonic vehicle re-entry segment can be described as

max J[u(t)]＝x ₄ (t _f )

x ₁ (t ₀ )=h ₀ , x ₂ (t ₀ )=v ₀ , x ₃ (t ₀ )=γ ₀ , x ₄ (t ₀ )=r ₀

x ₁ (t _f )=h _f , x ₂ (t _f )=v _f , x ₃ (t _f )=γ _f

G[u(t),x(t),t]≥0

u _min ≤ u(t) ≤ u _max

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.

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.

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.

The operation process of the controller is as follows:

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;

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;

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;

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;

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.

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:

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;

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;

Step 3): through the ODE orthogonal configuration module 23, all the ordinary differential equations are discretized on the time axis [t ₀ ,t _f ];

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.

Described ODE orthogonal configuration module, adopts following steps to realize:

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:

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 .

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:

Step 3): Replace the original differential equation system with the discretized differential equation system, and the NLP problem to be solved will be obtained.

Described NLP solution module, adopts following steps to realize:

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 )];

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;

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)].

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)];

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).

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.

Description of drawings

Fig. 1 is a structural representation of the present invention;

Fig. 2 is the structural diagram of the internal module of hypersonic vehicle MCU of the present invention;

Fig. 3 is the angle of attack control strategy graph of embodiment 1;

FIG. 4 is a diagram of the horizontal flight distance corresponding to the angle of attack control strategy of Embodiment 1. FIG.

Detailed ways

The trajectory optimization problem of hypersonic vehicle re-entry segment can be described as

max J[u(t)]＝x ₄ (t _f )

x ₁ (t ₀ )=h ₀ , x ₂ (t ₀ )=v ₀ , x ₃ (t ₀ )=γ ₀ , x ₄ (t ₀ )=r ₀

x ₁ (t _f )=h _f , x ₂ (t _f )=v _f , x ₃ (t _f )=γ _f

G[u(t),x(t),t]≥0

u _min ≤ u(t) ≤ u _max

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.

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.

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.

The operation process of the controller is as follows:

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;

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;

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;

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;

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.

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:

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;

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;

Step 3): through the ODE orthogonal configuration module 23, all the ordinary differential equations are discretized on the time axis [t ₀ ,t _f ];

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.

Described ODE orthogonal configuration module, adopts following steps to realize:

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:

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 .

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:

Step 3): Replace the original differential equation system with the discretized differential equation system, and the NLP problem to be solved will be obtained.

Described NLP solution module, adopts following steps to realize:

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 )];

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;

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)].

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)];

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).

Example 1

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)]＝x ₄ (t _f )

C _L ＝-0.15+3.44u(t)

C _D ＝0.29-1.51u(t)+5.87u(t) ²

x ₁ (0)=80×10 ³ , x ₁ (t _f )=24×10 ³

x ₂ (0)＝6.4×10 ³ , x ₂ (t _f )＝760

x ₃ (0)=-0.052, x ₃ (t _f )=-0.08

x ₄ (0) = 0

-15≤u(t)≤30

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:

G[u(t),x(t),t] is used to represent the constraints of the hypersonic vehicle re-entry process, which is:

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.

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:

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;;

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;

Step 3): through the ODE orthogonal configuration module 23, all the ordinary differential equations are discretized on the time axis [t ₀ ,t _f ];

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.

Described ODE orthogonal configuration module, adopts following steps to realize:

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:

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 .

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:

Step 3): Replace the original differential equation system with the discretized differential equation system, and the NLP problem to be solved will be obtained.

Described NLP solution module, adopts following steps to realize:

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 )];

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;

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)].

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)];

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).

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.

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,t_f] 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,t_f] discrete hop count, φ (t) is Lagrange's interpolation basic function, linear combination coefficient u_i,jWith s_i,jIt is u (t) and x (t) respectively in collocation point t_i,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).