A hypersonic aircraft formation control method, system, device and medium

Abstract

The present invention discloses a hypersonic aircraft formation control method, system, equipment and medium, which relates to the field of aircraft control technology, including: obtaining expected formation reference information, formation initial information and formation state output information; designing a speed direction cooperative controller according to the formation state output information; taking the minimum average deformation degree of the formation in the flight phase as the goal, determining the objective function according to the expected formation reference information, formation initial information and formation state output information; according to the objective function, using a particle swarm ant colony hybrid algorithm and a sequential quadratic programming method to optimize the control parameters of the formation initial information and the speed direction cooperative controller to obtain a final optimization result; determining the optimized speed direction cooperative controller according to the final optimization result, and determining the attack angle control command and the roll angle control command of the follower aircraft according to the optimized speed direction cooperative controller. The present invention can realize the precise control of the formation flight of hypersonic aircraft.

Description

Technical Field

[0001] The present invention relates to the field of aircraft control technology, and in particular to a hypersonic aircraft formation control method, system, equipment and medium. Background Art

[0002] Formation flight has been a research topic in the field of aeronautics and space engineering for decades. Hypersonic vehicles can improve the burst probability and multi-task adaptability of the entire system through formation transformation and reconstruction during the gliding phase. Realizing formation flight of hypersonic vehicle systems can also provide other potential benefits, especially in military applications. For example, when a hypersonic vehicle system is ordered to attack the same target simultaneously in a salvo attack, forming a formation flight before the attack will have a better strike effect. At the same time, formation flight control also provides a possible solution to the problem of target tracking and interception, in which case the hypersonic vehicles can form a formation with the target. Compared with the formation flight of conventional aircraft, hypersonic vehicles are highly nonlinear, have multivariable dynamics and multiple flight constraints, cannot obtain thrust, and the speed may change dramatically. In addition, there are certain disturbances, uncertainties and strong coupling between the formation vehicles in the system. Therefore, it will be a difficult task to achieve precise control of the formation flight of multiple hypersonic aircraft systems. In addition, the high-speed flight of formation aircraft requires the controller to respond quickly to changes, which is crucial to the control performance of the system and collision avoidance. Summary of the invention

[0003] The purpose of the present invention is to provide a hypersonic aircraft formation control method, system, equipment and medium to achieve precise control of hypersonic aircraft formation flight.

[0004] To achieve the above-mentioned purpose, the present invention provides the following scheme: a hypersonic aircraft formation control method, the hypersonic aircraft formation control method is applied to a plurality of hypersonic aircraft that adopt a leader-follower mode to perform formation flight; wherein the leader aircraft flies along a set flight trajectory, and the follower aircraft keeps consistent with the flight direction of the leader aircraft under the control of a speed direction collaborative controller; the hypersonic aircraft formation control method comprises: obtaining desired formation reference information, formation initial information and formation status output information; the formation initial information comprises: the initial position and initial speed of each aircraft in the formation; the formation status output information comprises: the distance from the center of the earth, longitude, latitude, flight speed, ballistic inclination and heading angle of each aircraft in the formation; according to the formation status The invention discloses a method for designing a speed direction collaborative controller according to the state output information of the formation; taking the minimum average deformation degree of the formation in the flight stage as the goal, determining the objective function according to the expected formation reference information, the formation initial information and the formation state output information; the objective function represents the relationship between the average deformation degree of the formation in the flight stage and the formation initial information and the control parameters of the speed direction collaborative controller; according to the objective function, optimizing the formation initial information and the control parameters of the speed direction collaborative controller by using a particle swarm ant colony hybrid algorithm and a sequential quadratic programming method to obtain a final optimization result; determining the optimized speed direction collaborative controller according to the final optimization result, and determining the angle of attack control command and the roll angle control command of the follower aircraft according to the optimized speed direction collaborative controller.

[0005] A hypersonic aircraft formation control system, the hypersonic aircraft formation control system is applied to a plurality of hypersonic aircraft that adopt a leader-follower mode for formation flight; wherein the leader aircraft flies along a set flight trajectory, and the follower aircraft keeps the same flight direction as the leader aircraft under the control of a speed direction cooperative controller; the hypersonic aircraft formation control system comprises: a data acquisition module for acquiring desired formation reference information, formation initial information and formation state output information; the formation initial information comprises: the initial position and initial speed of each aircraft in the formation; the formation state output information comprises: the distance from the center of the earth, longitude, latitude, flight speed, ballistic inclination and heading angle of each aircraft in the formation; a controller design module for designing a speed direction cooperative control according to the formation state output information a target function determination module, used to determine the target function according to the desired formation reference information, the formation initial information and the formation state output information with the minimum average deformation degree of the formation in the flight phase as the target; the target function characterizes the relationship between the average deformation degree of the formation in the flight phase and the formation initial information and the control parameters of the speed direction collaborative controller; an optimization solution module, used to optimize the formation initial information and the control parameters of the speed direction collaborative controller according to the target function by using a particle swarm ant colony hybrid algorithm and a sequential quadratic programming method to obtain a final optimization result; a control instruction output module, used to determine an optimized speed direction collaborative controller according to the final optimization result, and determine the angle of attack control instruction and the roll angle control instruction of the follower aircraft according to the optimized speed direction collaborative controller.

[0006] An electronic device includes a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned hypersonic aircraft formation control method.

[0007] A computer-readable storage medium stores a computer program, which implements the above-mentioned hypersonic aircraft formation control method when executed by a processor.

[0008] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the hypersonic aircraft formation control method provided by the present invention optimizes the formation initial information and the control parameters of the speed direction collaborative controller by designing a reasonable speed direction collaborative controller and taking the minimum average deformation degree of the formation in the flight stage as the goal, so that the attack angle control instructions and roll angle control instructions of the follower aircraft output by the controller can make the flight direction of the follower aircraft consistent with that of the leader aircraft during the formation flight, and the flight speed of each aircraft in the entire flight stage is not much different, thereby achieving the desired formation. Therefore, the present invention can realize the precise control of the formation flight of hypersonic aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0010] Figure 1 The present invention provides a flow chart of a hypersonic vehicle formation control method.

[0011] Figure 2 A three-dimensional space formation trajectory diagram of hypersonic aircraft provided by the present invention.

[0012] Figure 3 This is a distance curve diagram between the aircraft before optimization of Example 1.

[0013] Figure 4 This is a speed curve diagram of each aircraft before optimization of Example 1.

[0014] Figure 5 This is a curve diagram of the trajectory inclination angles of each aircraft before optimization of Example 1.

[0015] Figure 6 This is a curve diagram of the trajectory deflection angle of each aircraft before optimization of Example 1.

[0016] Figure 7 This is a distance curve diagram between the aircraft after optimization of Example 1.

[0017] Figure 8 This is a speed curve diagram of each aircraft after optimization of Example 1.

[0018] Fig. 9 This is a curve diagram of the trajectory inclination of each aircraft after optimization in Example 1.

[0019] Fig.10 This is a curve diagram of the trajectory deflection angle of each aircraft after optimization in Example 1.

[0020] Fig.11 This is a curve diagram of the distance between the aircraft before optimization of Example 2.

[0021] Fig.12 This is the speed curve diagram of each aircraft before optimization of Example 2.

[0022] Fig.13 This is a graph of the trajectory inclination angles of each aircraft before optimization in Example 2.

[0023] Fig.14This is a curve diagram of the trajectory deflection angle of each aircraft before optimization of Example 2.

[0024] Fig.15 This is a distance curve diagram between the aircraft after optimization of Example 2.

[0025] Fig.16 This is the speed curve diagram of each aircraft after optimization of Example 2.

[0026] Fig.17 A module diagram of the hypersonic aircraft formation control system provided by the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The purpose of the present invention is to provide a hypersonic aircraft formation control method, system, equipment and medium to achieve precise control of hypersonic aircraft formation flight.

[0029] Specifically, in order to realize the formation flight of multiple hypersonic aircraft, it is necessary for them to maintain the desired relative position at all times during the flight. If the formation is required to be a fixed formation, it is necessary for the magnitude and direction of the flight speed of each hypersonic aircraft to remain completely consistent. However, it should be made clear that the hypersonic aircraft has no propulsion force in the gliding stage, so the flight speed cannot be controlled by changing the acceleration of the aircraft. In order to make the speed and direction of two hypersonic aircraft with large speed differences tend to be consistent, the aircraft with a larger speed can only consume excess energy by flying around to achieve the speed of the two hypersonic aircraft. When the flight speeds of the hypersonic aircraft are equal and the directions are basically the same, the flight directions of each aircraft during the formation flight can be controlled by a reasonably designed direction controller, so that the flight speeds of each aircraft in the entire flight stage can be approximately not different, so that the desired formation can be approximately achieved.

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] The present invention provides a hypersonic aircraft formation control method. The hypersonic aircraft formation control method is applied to a plurality of hypersonic aircraft that perform formation flight in a leader-follower mode, wherein the leader aircraft flies along a set flight trajectory, and the follower aircraft maintains the same flight direction as the leader aircraft under the control of a speed direction cooperative controller.

[0032] like Figure 1 As shown, the hypersonic aircraft formation control method includes the following steps.

[0033] Step S1: Obtaining the desired formation reference information, formation initial information and formation status output information; the formation initial information includes: the initial position and initial speed of each aircraft in the formation; the formation status output information includes: the distance from the center of the earth, longitude, latitude, flight speed, ballistic inclination and heading angle of each aircraft in the formation.

[0034] Step S2: Design a speed direction coordinated controller according to the formation state output information.

[0035] Step S2 specifically includes: determining the trajectory inclination of the leader aircraft as the desired trajectory inclination of the follower aircraft, and determining the heading angle of the leader aircraft as the desired heading angle of the follower aircraft. Designing a trajectory inclination controller according to the formation state output information and the desired trajectory inclination of the follower aircraft. Designing a heading angle controller according to the formation state output information and the desired heading angle of the follower aircraft. Determining the trajectory inclination controller and the heading angle controller as a speed direction coordinated controller.

[0036] Step S3: With the goal of minimizing the average deformation degree of the formation during the flight phase, an objective function is determined according to the desired formation reference information, the formation initial information and the formation state output information; the objective function characterizes the relationship between the average deformation degree of the formation during the flight phase and the formation initial information and the control parameters of the speed direction collaborative controller.

[0037] Step S3 specifically includes: determining the deformation degree of the formation at the initial moment according to the formation initial information and the expected formation reference information. Determining the deformation degree of the formation at the end moment according to the formation state output information and the expected formation reference information. Integrating and averaging the deformation degree of the formation from the initial moment to the end moment to obtain the average deformation degree of the formation in the flight phase. Determining the objective function with the goal of minimizing the average deformation degree of the formation in the flight phase.

[0038] Step S4: According to the objective function, a particle swarm ant colony hybrid algorithm and a sequential quadratic programming method are used to optimize the initial information of the formation and the control parameters of the speed direction collaborative controller to obtain a final optimization result.

[0039] Step S4 specifically includes: according to the objective function, using a particle swarm ant colony hybrid algorithm to optimize the initial information of the formation and the control parameters of the speed direction collaborative controller to obtain a global optimization result. Using the global optimization result as an initial value, using a sequential quadratic programming method to optimize the initial information of the formation and the control parameters of the speed direction collaborative controller to obtain a final optimization result.

[0040] Specifically, according to the objective function, a particle swarm algorithm is first used to search for preliminary optimization results in a global scope, and then the pheromone guidance mechanism of the ant colony algorithm is used to optimize the preliminary optimization results to obtain a global optimization result; wherein the number of particles of the particle swarm algorithm is equal to the number of ants of the ant colony algorithm.

[0041] Step S5: determining an optimized speed direction cooperative controller according to the final optimization result, and determining an angle of attack control instruction and a roll angle control instruction of the follower aircraft according to the optimized speed direction cooperative controller.

[0042] Wherein, determining the angle of attack control instruction and the roll angle control instruction of the follower aircraft according to the optimized speed direction cooperative controller specifically includes: determining the ballistic inclination control instruction and the heading angle control instruction of the follower aircraft according to the optimized speed direction cooperative controller. Limiting the ballistic inclination control instruction and the heading angle control instruction based on the heat flow rate constraint, dynamic pressure constraint and overload constraint of the flight phase. Calculating the lift value and lift coefficient according to the limited ballistic inclination control instruction and the limited heading angle control instruction. Calculating the angle of attack control instruction and the roll angle control instruction of the follower aircraft according to the lift value and the lift coefficient.

[0043] Further, calculating the angle of attack control instructions and the roll angle control instructions of the follower aircraft according to the lift value and the lift coefficient specifically includes: comparing the lift coefficient with the maximum lift coefficient; if the lift coefficient is greater than the maximum lift coefficient, calculating the angle of attack control instructions and the roll angle control instructions of the follower aircraft according to the lift value and the maximum lift coefficient; if the lift coefficient is less than or equal to the maximum lift coefficient, calculating the angle of attack control instructions and the roll angle control instructions of the follower aircraft according to the lift value and the lift coefficient.

[0044] The following is a detailed explanation of the five aspects of speed direction collaborative controller design, angle of attack and roll angle calculation, hypersonic vehicle formation optimization design, optimization algorithm and simulation verification and analysis.

[0045] First, the speed direction collaborative controller is designed.

[0046] First, it is assumed that each aircraft adopts the leader-follower mode during the formation flight, in which the flight trajectory of the leader aircraft is a trajectory planned in advance according to the flight requirements, and the formation flight can be achieved by controlling the flight direction of other follower aircraft to be consistent with that of the leader aircraft at all times through the speed direction cooperative controller. Based on the modeling of hypersonic aircraft and the known characteristics of the uncontrollable speed of hypersonic aircraft, each follower aircraft only needs to consider its coordination with the leader aircraft in the pitch direction and yaw direction during the formation cooperative flight. The state variables corresponding to the pitch direction and yaw direction are the ballistic inclination angle and heading angle respectively.

[0047] Considering that there are N hypersonic vehicles in the system, , , , , , They represent the six state variables of the distance from the center of the earth, longitude, latitude, flight speed, ballistic inclination and heading angle of the aircraft i (i=1,...,N) at time t. , , , , , represent the state variables of the leader aircraft respectively. Therefore, the trajectory inclination of the leader aircraft is selected and heading angle The expected ballistic inclination angles of the follower aircraft and the desired heading angle , that is, we have formula (1)-(2).

[0048] (1).

[0049] (2).

[0050] To design the speed direction coordinated controller of the follower aircraft, we can obtain equations (3)-(4).

[0051] (3).

[0052] (4).

[0053] Among them, k1 is the proportional coefficient of the trajectory inclination controller, k2 is the integral coefficient of the trajectory inclination controller, k3 is the proportional coefficient of the heading angle controller, and k4 is the integral coefficient of the heading angle controller. express The first derivative of express The first derivative of , and so on, superscript Both represent first-order derivatives. i represents the number of the aircraft, and t represents the number of the moment.

[0054] The trajectory inclination error , heading angle error , then the above controller can be used to obtain equations (5)-(6).

[0055] (5).

[0056] (6).

[0057] in, express The second-order derivative of express The second derivative of , and so on, superscript Both represent the second-order derivative. 1,i is the proportional coefficient of the trajectory inclination controller of the ith follower aircraft, k 2,i is the integral coefficient of the trajectory inclination controller of the ith follower aircraft, k 3,i is the proportional coefficient of the heading angle controller of the i-th follower aircraft, k 4,i is the integral coefficient of the heading angle controller of the i-th follower aircraft.

[0058] For the trajectory inclination error function e 1,t (t), then: (7).

[0059] The characteristic equation of the second-order system in formula (7) is: (8).

[0060] The Rolls table is as follows: (9).

[0061] Among them, s 2 represents the quadratic term, s 1 represents a first-order term, s 0 represents the 0th-order term, i.e. the constant term. Then the necessary and sufficient condition for the stability of the above system is: k 1,i >0,k 2,i >0 (10).

[0062] Similarly, for the heading angle error function e 2,t (t), when k is satisfied 3,i >0,k 4,i >0, the system is stable, that is, there is e 1,t (t)→0 and e 2,t (t)→0.

[0063] Second, the calculation of the angle of attack and the angle of roll.

[0064] According to the above speed direction coordinated controller, the trajectory inclination control instruction can be and heading angle control commands Calculate the angle of attack corresponding to each hypersonic vehicle and roll angle (For the convenience of description, the following mathematical analysis will take one of the multiple follower aircraft as an example and use replace , and so on for the rest).

[0065] Based on the mathematical model of hypersonic aircraft, the differential equations of trajectory inclination and heading angle are: (11).

[0066] Where V represents the flight speed of the aircraft, m represents the mass of the aircraft, r represents the distance of the aircraft from the center of the earth, and g represents the acceleration of gravity. represents the Earth's rotation speed, represents the ballistic inclination angle, represents the heading angle, represents the tilt angle, represents latitude, and L represents lift. In the short-distance and short-time formation process of the aircraft, the influence of the earth's rotation speed and its quadratic term on the aircraft is very small and can be ignored. At the same time, in the approximate calculation process of the angle of attack and the roll angle, V 2 The / r term has a smaller influence on the calculation results than the gravitational acceleration term, so the simplified differential equations of the trajectory inclination angle and heading angle are shown in Equations (12)-(13).

[0067] (12).

[0068] (13).

[0069] By transformation, we can obtain equations (14)-(15).

[0070] (14).

[0071] (15).

[0072] Square the above equation and add the left and right sides together to get: (16).

[0073] Based on the two-dimensional aerodynamic data of CAV-H, the lift coefficient of the aircraft is positive, so the lift L must be positive, which is consistent with the aerodynamics of the aircraft, that is, (17).

[0074] in, and They represent the trajectory inclination control command and heading angle control command output by the velocity direction cooperative controller, Represents the calculated lift value.

[0075] Considering the heat flux constraint, dynamic pressure constraint and overload constraint of the hypersonic vehicle during flight, it is necessary to ensure that the roll angle is within the roll angle corridor. According to the given roll angle corridor, the range of the roll angle is constrained. , that is, the allowable range of the roll angle is [-60°, 60°]. At the same time, in order to ensure that the aircraft has sufficient lift, the attack angle range with a higher lift-drag ratio of CAV-H is taken as the allowable range of the aircraft attack angle. At this time, at a given Mach number Ma, the maximum and minimum values ​​of the aircraft's lift are: (18).

[0076] Among them, L min Indicates the minimum value of lift, L max represents the maximum value of lift, Ma represents the Mach number, C L represents the lift coefficient of the aircraft, S represents the wing area, Indicates the air density.

[0077] According to equations (12) and (13), we have equations (19)-(20).

[0078] (19).

[0079] (20).

[0080] Based on the value range of the aircraft lift, the trajectory inclination angle and heading angle control commands output by the velocity direction cooperative controller are limited to a certain extent, as shown in equations (21)-(22).

[0081] (twenty one).

[0082] (twenty two).

[0083] The lift of the aircraft is calculated based on the limited trajectory inclination control command, heading angle control command and formula (17): , and calculate the corresponding lift coefficient based on the calculated lift value: (twenty three).

[0084] According to the current Mach number Ma and the calculated lift coefficient The angle of attack command at this speed can be calculated . Due to the existence of the maximum lift coefficient , when the solution is obtained When the current Mach number Ma and the maximum lift coefficient Calculate the corresponding angle of attack command ; When the solution , then according to the current Mach number Ma and the calculated lift coefficient Calculate the angle of attack command at this speed .

[0085] Similarly, the corresponding roll angle command of the hypersonic vehicle can also be calculated , that is: (twenty four).

[0086] Through the above analysis, under the designed speed direction coordinated controller, the angle of attack control instruction of the hypersonic aircraft can be calculated and roll angle control commands .

[0087] Third, optimization design of hypersonic aircraft formation.

[0088] The above-mentioned formation controller based on direction coordination can ensure that when the speed of hypersonic aircraft is the same, each aircraft in the system can maintain a fixed relative distance in the same direction to complete formation flight. However, in actual situations, the speed of hypersonic aircraft during the reentry glide phase is not controllable, or the speed of the aircraft when it reaches the initial position of the formation does not necessarily meet the requirements of size and direction consistency, or each hypersonic aircraft will be subject to different degrees of aerodynamic interference during the reentry glide phase. The difference in aerodynamic force between the aircraft will cause speed differences between the aircraft. This difference will cause the relative distance between the aircraft in the system to gradually increase over time. When this difference is smaller, the change in relative distance is also smaller.

[0089] Generally, the factors that affect the flight speed of a hypersonic aircraft include its initial flight position (x0, y0, z0) and initial flight speed V0. At the same time, the factors that affect the formation control effect include the control parameters k1, k2, k3, and k4 of the speed direction collaborative controller. Therefore, based on the idea of ​​optimization, the above-mentioned influencing factor variables related to the aircraft speed can be used as optimization variables, and a reasonable optimization algorithm can be used for optimization, so as to achieve the multi-hypersonic aircraft system maintaining a good formation flight as much as possible under more appropriate initial conditions and control parameters.

[0090] When using an optimization algorithm to achieve formation optimization, it is necessary to design an objective function of the optimization algorithm, which needs to be able to reflect the maintenance and deformation degree of the system formation. Therefore, considering a system with N hypersonic vehicles, the deformation degree and average deformation degree of the formation during the flight phase are defined as shown in Equations (25)-(26).

[0091] (25).

[0092] (26).

[0093] in, It indicates the degree of deformation of the system formation from the expected formation at time t, represents the relative distance between aircraft i and aircraft j at time t, It represents the expected relative distance between aircraft i and aircraft j. It can be clearly seen from the above formula that when , indicating that the system strictly maintains the desired formation. The smaller it is, the better the formation is maintained and the better the formation effect is. start To the end time t end The real-time formation deformation degree can be integrated to obtain the overall deformation degree of the formation during this period of time, Δt=t end -t start represents the system formation flight time, and J represents the average deformation degree of the formation during the flight phase.

[0094] From the above analysis, we can see that J can be used as a measure of the formation effect in the flight phase, so J can be used as the optimization objective function of the optimization algorithm.

[0095] Assume that the initial position of the formation of hypersonic vehicle i in the local geographic coordinate system is , the position correction amount that needs to be obtained through optimization is , then the corrected initial position of the formation is Need to meet: (27).

[0096] Similarly, the initial flight speed of hypersonic vehicle i is The required speed correction is , the final corrected initial speed of the formation Should meet: (28).

[0097] Based on the above analysis, J is selected as the objective function of the optimization algorithm, and considering the allowable value range of each optimization variable, the hypersonic vehicle system formation optimization problem can be expressed as: (29).

[0098] in, It represents the complex nonlinear analytical function of the formation deformation degree measurement index with respect to each optimization variable, and it is necessary to give the value range of each optimization variable. and They represent the minimum and maximum values ​​of the x-axis position correction of the i-th aircraft, and They represent the minimum and maximum values ​​of the Y-axis position correction of the i-th aircraft, and They represent the minimum and maximum values ​​of the z-axis position correction of the i-th aircraft, and They respectively represent the minimum and maximum values ​​of the speed correction of the ith aircraft, constant represents a constant, and N represents the total number of aircraft.

[0099] Fourth, optimize the algorithm.

[0100] 4.1 Particle swarm optimization algorithm.

[0101] Particle swarm optimization (PSO) is a heuristic algorithm based on cooperative search in a swarm. In the particle swarm optimization algorithm, candidate solutions of the population (called particles) coexist and evolve simultaneously based on knowledge sharing with neighboring particles. When flying in the problem search space, each particle generates a solution using a directed velocity vector. Each particle modifies its velocity to find a better solution (position) by applying its own flight experience (i.e., it has a memory of the best position found in earlier flights) and the experience of neighboring particles (i.e., the best solution of the swarm). The particle updates its position and velocity as follows: (30).

[0102] in, represents the position of particle j when the number of iterations is n, represents the best position of particle j before the number of iterations is less than n, which determines the cognitive contribution of the velocity vector, represents the search speed of particle j when the number of iterations is n, and there is , Indicates the maximum search speed, represents the global optimal position of all particles in the group up to the iteration number n, which determines the social contribution of the velocity vector. r1 and r2 are random numbers uniformly distributed in the interval. c1 and c2 represent the weight parameters of cognitive contribution and social contribution, respectively. w n represents the inertia of the particle, which can be dynamically reduced to gradually reduce the search area, and w n Should meet: (31).

[0103] Among them, w max and w min Respectively represent w n The maximum and minimum possible values, n max Indicates the maximum number of iterations.

[0104] According to equations (30) and (31), particle j can fly to a new position at different speeds. In the same way, all particles in the group will find their new positions and use these new positions to calculate the new speeds of the particles and update their global optimal positions and corresponding optimal points. This process is repeated until the maximum number of iterations n allowed is reached. max Or reach the user-defined iteration stopping standard, thus completing the entire process of the particle swarm algorithm.

[0105] 4.2 Ant Colony Algorithm.

[0106] Ant colony algorithm is a multi-individual intelligent optimization algorithm that solves complex combinatorial optimization problems by simulating the foraging behavior of ants. Ants are social insects, and their behavior is more inclined to the survival of the colony as a whole rather than the survival of a single individual in the colony. An important and interesting behavior of the ant colony is the indirect cooperative foraging process of ants in the colony. When ants walk from the food source to the nest or from the nest to the food source, they deposit a sub-gesture called pheromone on the ground and form a pheromone trail. Ants can smell the pheromone. When they choose a path to continue at the point where the pheromone trails intersect, they tend to choose a path marked by a high pheromone concentration (a shorter path). At the same time, other ants can also find the location of the food source found by their companions based on the concentration of pheromones. Therefore, the ant colony algorithm is actually an optimization algorithm that simulates the foraging behavior of ants.

[0107] The PSACO algorithm adopted in the present invention is a simple pheromone-guided ant colony search mechanism based on the particle swarm algorithm. The mechanism synchronizes the positions of the particles of the particle swarm algorithm through local action to quickly obtain the feasible domain of the objective function.

[0108] 4.3 Particle swarm and ant colony hybrid algorithm.

[0109] Particle Swarm Ant Colony Optimization (PSACO) is an optimization algorithm that uses the ant colony idea to achieve certain improvements on the particle swarm algorithm. It is based on the common characteristics of the particle swarm optimization algorithm and the ant colony optimization algorithm, such as group survival guided by cooperation and coexistence, and the individual contribution to the search of the target is achieved by sharing local and global information in the group. The implementation of the PSACO algorithm is divided into two stages: the first stage uses the particle swarm algorithm, and the second stage uses the ant colony algorithm. The ant colony algorithm is a local search algorithm, while the particle swarm algorithm is a global optimization algorithm. The particle swarm algorithm is first used to find a better solution in the global scope, that is, the preliminary optimization result, and then the pheromone guidance mechanism of the ant colony algorithm is used to optimize the position found by the particles in the particle swarm algorithm stage. In the PSACO algorithm, a simple pheromone guidance mechanism of the ant colony algorithm is proposed for local search. In the two stages, the number of ants processed by the ant colony algorithm is equal to the number of particles in the particle swarm algorithm. When the current iteration generation is n, the particle swarm algorithm can generate the global best position of all particles in the group. , using pheromone guidance mechanism to locate at the best position Generate a new solution around , and the solution satisfies: (32).

[0110] in, It satisfies Gaussian distribution, and its mean and standard deviation are and ,and satisfy: (33).

[0111] in, is in (10 -2 ,10 -4 ) is a constant in the interval, and d is a constant less than 1. Substituting Calculate the value of the objective function , then let: (34).

[0112] And there are: (35).

[0113] in, represents the position of particle j when the number of iterations is n, and They represent the particle j at position and The objective function value at .

[0114] This pheromone guidance mechanism makes it possible to achieve the global optimal solution of the group at each stage of the ant colony algorithm. There is a trajectory with the highest density everywhere (single pheromone point), and all ants can find the global optimal solution. Search for a better solution in the field of The value of The larger the value of , the larger the search area near the optimal solution. As the algorithm progresses, the scope of the search area decreases. Therefore, the pheromone guidance mechanism of the ant colony algorithm can not only help the particle swarm algorithm to perform global search more efficiently and quickly obtain feasible solution space, but also effectively obtain the optimal or near-optimal solution. The pseudo code of PSACO is shown below.

[0115] Input: particle population size P, number of iterations n max , variable dimension D.

[0116] Output: Global best position , the global optimal objective function value .

[0117] 1. .

[0118] 2. .

[0119] 3. .

[0120] 4. .

[0121] 5. .

[0122] 6. .

[0123] 7. While .

[0124] 8. .

[0125] 9. .

[0126] 10. .

[0127] 11.if .

[0128] 12. .

[0129] 13. .

[0130] 14.if .

[0131] 15. .

[0132] 16. .

[0133] 17. .

[0134] 18.if .

[0135] 19. .

[0136] 20. .

[0137] 21. return .

[0138] Where P represents the number of particles in the group, It represents the optimal objective function value obtained by the particle group after n iterations.

[0139] The particle swarm ant colony hybrid algorithm is an optimization algorithm that uses the ant colony idea to achieve certain improvements on the particle swarm algorithm. It uses the particle swarm optimization algorithm for global optimization, and uses the ant colony algorithm to update the position of particles, so that the feasible solution space can be quickly obtained. Research results show that: (1) The particle swarm optimization algorithm can find reasonable high-quality solutions faster than other evolutionary algorithms; (2) If the particle swarm is in a balanced state, the evolution process of the particle swarm optimization algorithm will stagnate over time. Therefore, as the number of generations increases, the particle swarm optimization algorithm does not have the ability to improve the quality of the solution. At this time, in order to improve the performance of the particle swarm optimization algorithm, the local search for feasible solutions can be achieved based on the pheromone guidance idea of ​​the ant colony algorithm. Studies have shown that the ant colony algorithm not only helps the particle swarm algorithm to search for the global optimal solution more effectively to quickly obtain the feasible solution space, but also can more effectively obtain or approach the optimal solution. It can be used for the optimization of multi-peak continuous functions.

[0140] 4.4 Sequential quadratic programming method.

[0141] Sequential Quadratic Programming (SQP) is a very mature and excellent algorithm for solving small-scale nonlinear problems. In existing simulation software, such as MATLAB, the fmincon function can be used to directly call the SQP function to solve nonlinear problems.

[0142] In view of the above-mentioned formation optimization problem, the present invention will adopt a method combining the PSACO optimization algorithm with the sequential quadratic programming method. Among them, the PSACO optimization algorithm is an optimization algorithm that combines the particle swarm algorithm and the ant colony algorithm. It can find reasonable high-quality solutions more quickly and has better global search performance, but this also often brings a larger amount of calculation. As one of the classical optimization algorithms, the SQP optimization algorithm has good convergence, and its small amount of calculation makes the algorithm have a faster calculation speed, but it is inevitable that the algorithm often obtains a local optimal solution. Therefore, the present invention first uses the PSACO optimization algorithm introduced above, takes the average deformation degree J of the formation in the flight phase as the objective function, and roughly calculates the global optimal result; then uses the result calculated by the PSACO algorithm as the initial value of the SQP optimization algorithm to perform a more detailed optimization solution.

[0143] Fifth, simulation verification and analysis.

[0144] Embodiment 1: Considering the formation control with deviation value in the initial state.

[0145] Consider a system consisting of N=3 hypersonic vehicles, assuming that the initial altitude of the leader vehicle is The local geographic coordinate system is established with the leader aircraft as the origin, and it is assumed that the formation that the system expects to form is an equilateral triangle with a side length of 10 km, which must satisfy the following requirements: (36).

[0146] Assume that the initial positions of N aircraft in the local geographic coordinate system of the leader aircraft and the initial states of each aircraft are shown in Table 1.

[0147]

[0148] (1) Before optimization: Assume that the initial position of the hypersonic aircraft of the present invention is the intersection point between the aircraft trajectory planning and the formation control. The speeds of the other aircraft at the intersection point are , ballistic inclination , heading angle It is difficult to be exactly the same as the leader aircraft, as shown in Table 1. At the same time, it is also difficult for the remaining aircraft to accurately reach the expected initial position, that is, there is a certain deviation between the initial position of each aircraft and the expected value. Therefore, the local geographic coordinate system is still established with the leader aircraft as the origin, and the position deviations of the remaining aircraft except the leader aircraft and the expected launch position are shown in Table 2.

[0149]

[0150] The simulation verification results show that when the control parameter value of the proportional controller remains unchanged, the larger the control parameter value of the integral controller, the slower the heading angle error of each follower aircraft converges, and the convergence time is much longer than the flight time of the aircraft. When only the proportional controller is considered, there is no steady-state error in the system, that is, there is no need to introduce an integral controller to a certain extent. At the same time, in order to reduce the integral saturation problem that may be caused by the integral controller, the system structure is simplified, and k2=k4=0 is set, that is, the integral controller is not introduced. At this time, the reduction of optimization variables can also reduce the calculation time to a certain extent. Therefore, before the formation optimization, the control parameters of the coordinated direction controller of each aircraft are set to k1=k3=0.2. The flight trajectory of the leader aircraft is given based on the Gaussian pseudospectral method, and the flight time is 424s. Finally, the formation effect of the system during the entire flight phase is examined. The ballistic curves of each hypersonic aircraft in the flight phase obtained by simulation are shown in the following figure. Figure 2 As shown, 1-leader represents the leader aircraft numbered 1, 2-follower and 3-follower represent the follower aircraft numbered 2 and 3 respectively.

[0151] The distance curve, speed curve, trajectory inclination curve and heading angle (i.e. trajectory deviation angle) curve between the aircraft in the entire flight phase are as follows: Figure 3 , Figure 4 , Figure 5 and Figure 6 shown.

[0152] pass Figure 3 From the relative distance relationship of the aircraft at t=424s, it can be seen that during the entire flight process, the maximum relative distance between the aircraft is about 15.5km, and the minimum relative distance is about 9.5km, which is quite different from the expected 10km. Figure 4 The speed change curve shows that the speed difference between the aircraft gradually increases with the passage of time, which is also the reason why the relative distance between the aircraft gradually increases or decreases. During the entire Δt=424s flight process, the formation deformation degree measurement index J=1.9953×10 7 m 2 , the system's formation effect is not good. Figure 5 and Figure 6 The simulation results in show that the directional cooperative controller has a good control effect. It should be noted that the ballistic inclination angles of CAV2 and CAV3 converge to the same as the leader aircraft at almost the same time, but after that, the ballistic inclination angle of CAV2 deviates from that of the leader aircraft to a certain extent. This is mainly because the ballistic inclination angle command is limited to a certain extent. The control ability of the directional cooperative controller is limited, so the ballistic inclination angle of CAV2 takes a long time to converge.

[0153] (2) After optimization: Since the speed of hypersonic vehicles varies over a wide range and the position deviation of the initial position can be offset by the relative distance change caused by the speed difference of the vehicles, it can be used as an optimization variable. At the same time, the formation effect can be improved by optimizing the control parameters of the controller. In order to reduce the amount of calculation and avoid the large steady-state error that may be caused by excessive values ​​of the integral controller control parameters, the integral controller of each vehicle is set to have k2=k4=0, and 0≤k1, k3≤5, -50m / s≤ΔV≤50m / s during the optimization process.

[0154] The PSACO optimization algorithm and the SQP optimization algorithm are used to optimize the initial flight speed of the aircraft and the control parameters of the controller. The optimization results are shown in Table 3.

[0155]

[0156] The relative distance curve, velocity curve, trajectory inclination curve and heading angle (i.e. trajectory deviation angle) curve between the aircraft in the entire flight phase are as follows: Figure 7 , Figure 8 , Fig. 9 and Fig.10 shown.

[0157] Comparing the trajectory curves of the aircraft before and after optimization, it can be clearly seen that at the final moment, the relative position between the aircraft is basically maintained at about 10km, that is, the deformation degree of the formation is smaller than before optimization. At the same time, the speed difference between the aircraft is also significantly reduced, and the convergence speed of the trajectory inclination and heading angle has also been improved to a certain extent. During the entire formation flight, the optimized system has a formation deformation degree measurement index J=1.435×10 5 m 2 , that is, the system's formation effect has been significantly improved.

[0158] Embodiment 2: Formation control considering the presence of aerodynamic interference.

[0159] Assume that the N aircraft in the system will be subject to different aerodynamic disturbances during flight, resulting in certain differences in the lift and drag of each aircraft. Therefore, consider that N=3 aircraft are subject to 0%, 10%, and -10% aerodynamic perturbations respectively. Since this section considers the impact of aerodynamic perturbations on the formation effect, based on the idea of ​​the control variable method, the initial state of each aircraft is made the same as that of the leader aircraft. Assume that the initial state of each aircraft and its initial position in the local geographic coordinate system of the leader aircraft are shown in Table 4.

[0160]

[0161] (1) Before optimization: This section only considers the aerodynamic perturbation and does not consider the impact of the initial position error on the formation effect. The control parameters of the directional coordination controller are set to k1=k3=0.2, k2=k4=0, and the simulation time is still 424s. The formation effect of the system during the entire flight phase is examined. The distance curve, speed curve, trajectory inclination curve, and heading angle (i.e., trajectory deviation angle) curve between the aircraft during the entire flight phase are shown as follows: Fig.11 , Fig.12 , Fig.13 and Fig.14 shown.

[0162] from Fig.11 It can be seen that the distance difference between the aircraft is the largest at t=424s, the maximum relative distance is about 19km, and the minimum relative distance is about 5km. During the entire flight process, the formation deformation degree measurement index J=3.356×10 7 m 2 This large fluctuation is mainly caused by the difference in aerodynamic forces between aircraft. At the same time, different aerodynamic forces make the speed change rate of each aircraft different. As time goes by, the speed difference becomes larger and larger. Fig.12 But from Fig.13 and Fig.14 It can be seen from the simulation results that when there is no initial error in the trajectory inclination angle and heading angle, the direction cooperative controller has a better control effect, and the flight direction of each aircraft can be kept consistent with the leader aircraft.

[0163] (2) After optimization: Based on the speed direction coordinated controller and the multi-hypersonic aircraft formation optimization design studied in this invention, more suitable initial conditions and control parameters can be obtained by optimizing the initial flight speed, initial position and control parameters of the controller, so that the multi-hypersonic aircraft system can maintain a good formation flight as much as possible, that is, the optimization variable is (Δx i ,Δy i ,Δz i )、ΔV i , k1, k2, k3, k4, where i=2,...,N. In order to reduce the amount of calculation and avoid the large steady-state error caused by the integral controller, this part still sets k2=k4=0. In the optimization process, 0≤k1, k3≤5, -500m≤Δx,Δy,Δz≤500m, -100m / s≤ΔV≤100m / s.

[0164] The optimization results obtained using the PSACO optimization algorithm and the SQP optimization algorithm are shown in Table 5.

[0165] The optimized and corrected results are used as the new initial state and control parameters. The distance curves and speed curves between the aircraft in the entire flight phase are as follows: Fig.15 and Fig.16 shown.

[0166]

[0167] contrast Fig.11 and Fig.15 It can be clearly seen that after the system optimization, the maximum relative distance between the aircraft is less than 12.5km, and the minimum relative distance is about 8km, which is a significant improvement compared to before the optimization. Fig.12 and Fig.16 From the speed curve, it can be seen that the speed difference between the aircraft in the optimized system has been reduced to a certain extent. At the same time, the formation deformation caused by the difference in aerodynamic force of each aircraft is compensated by the difference in initial speed, which is also the reason why the formation deformation degree is reduced. During the entire flight phase of the optimized formation system, the formation deformation degree measurement index J=3.698×10 6 m 2 , that is, the system's formation effect has been improved to a certain extent.

[0168] In summary, the present invention studies the formation control problem of multiple hypersonic aircraft. First, based on the idea of ​​consistent flight speed direction, a speed direction collaborative controller is designed to make each follower aircraft consistent with the flight direction of the leader aircraft in both the longitudinal and lateral directions, and a calculation method for the aircraft angle of attack and roll angle is given. Then, the factors affecting the degree of deformation of the system formation when the speed directions of each aircraft are consistent are analyzed, and each influencing factor is used as the optimization variable, the overall deformation degree of the formation is used as the optimization objective function, and the formation formation of the system as a whole is optimized by combining the PSACO algorithm with the SQP algorithm. Finally, considering the two situations that the initial state of the aircraft has a certain deviation and the aerodynamic interference of each aircraft to different degrees, relevant simulation verification is carried out, and the formation control effect of the system is given. The results show that the formation control method can achieve the desired formation formation maintenance of multiple hypersonic aircraft, and the simulation effect shows the effectiveness of the method designed by the present invention.

[0169] In order to implement the above method and achieve the corresponding functions and technical effects, a hypersonic aircraft formation control system is provided below. The hypersonic aircraft formation control system is applied to a plurality of hypersonic aircraft that fly in formation in a leader-follower mode; wherein the leader aircraft flies along a set flight trajectory, and the follower aircraft, under the control of a speed direction cooperative controller, keeps the same flight direction as the leader aircraft.

[0170] like Fig.17As shown, the hypersonic aircraft formation control system includes: a data acquisition module 1, which is used to obtain the expected formation reference information, the formation initial information and the formation state output information; the formation initial information includes: the initial position and initial speed of each aircraft in the formation; the formation state output information includes: the distance from the center of the earth, longitude, latitude, flight speed, ballistic inclination and heading angle of each aircraft in the formation. A controller design module 2 is used to design a speed direction collaborative controller according to the formation state output information. An objective function determination module 3 is used to determine the objective function according to the expected formation reference information, the formation initial information and the formation state output information with the minimum average deformation degree of the formation in the flight stage as the goal; the objective function characterizes the relationship between the average deformation degree of the formation in the flight stage and the formation initial information and the control parameters of the speed direction collaborative controller. An optimization solution module 4 is used to optimize the formation initial information and the control parameters of the speed direction collaborative controller according to the objective function using a particle swarm ant colony hybrid algorithm and a sequential quadratic programming method to obtain the final optimization result. The control instruction output module 5 is used to determine the optimized speed direction cooperative controller according to the final optimization result, and determine the angle of attack control instruction and the roll angle control instruction of the follower aircraft according to the optimized speed direction cooperative controller.

[0171] The present invention also provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to run the computer program to enable the electronic device to execute the above-mentioned hypersonic aircraft formation control method. The electronic device may be a server.

[0172] In addition, the present invention also provides a computer-readable storage medium storing a computer program, which implements the above-mentioned hypersonic aircraft formation control method when executed by a processor.

[0173] The present invention designs a multi-hypersonic aircraft formation controller based on the formation control concept of consistent flight speed direction, and considers the process constraints that the aircraft needs to meet during flight, and designs a limiting strategy for directional collaborative control instructions. Then, a suitable optimization algorithm is selected to optimize the initial state and control parameters of the hypersonic aircraft, thereby realizing formation control of multiple hypersonic aircraft. Finally, the proposed formation control method will be simulated and verified for different situations. Compared with the existing hypersonic aircraft formation control method that only considers the two-dimensional plane, the hypersonic aircraft formation control method of the present invention can realize precise control of hypersonic aircraft formation flight.

[0174] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0175] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A hypersonic vehicle formation control method, characterized in that: The hypersonic aircraft formation control method is applied to a plurality of hypersonic aircraft that perform formation flight in a leader-follower mode; wherein the leader aircraft flies along a set flight trajectory, and the follower aircraft, under the control of a speed direction cooperative controller, keeps the same flight direction as the leader aircraft; the hypersonic aircraft formation control method comprises: Obtaining desired formation reference information, formation initial information and formation status output information; the formation initial information includes: initial position and initial speed of each aircraft in the formation; the formation status output information includes: geocentric distance, longitude, latitude, flight speed, ballistic inclination and heading angle of each aircraft in the formation; Designing a speed direction coordinated controller according to the formation state output information specifically includes: Determine the ballistic inclination angle of the leader aircraft as the desired ballistic inclination angle of the follower aircraft, and determine the heading angle of the leader aircraft as the desired heading angle of the follower aircraft; designing a trajectory inclination controller according to the formation state output information and the desired trajectory inclination of the follower aircraft; Designing a heading angle controller according to the formation state output information and the desired heading angle of the follower aircraft; Determine the trajectory inclination controller and the heading angle controller as a velocity direction coordinated controller; Taking the minimum average deformation degree of the formation in the flight phase as the goal, determining the objective function according to the desired formation reference information, the formation initial information and the formation state output information; the objective function represents the relationship between the average deformation degree of the formation in the flight phase and the formation initial information and the control parameters of the speed direction collaborative controller; According to the objective function, a particle swarm ant colony hybrid algorithm and a sequential quadratic programming method are used to optimize the initial information of the formation and the control parameters of the speed direction collaborative controller to obtain a final optimization result; An optimized speed direction cooperative controller is determined according to the final optimization result, and an angle of attack control instruction and a roll angle control instruction of the follower aircraft are determined according to the optimized speed direction cooperative controller.

2. The hypersonic vehicle formation control method according to claim 1, characterized in that: Taking the minimum average deformation degree of the formation in the flight phase as the goal, determining the objective function according to the expected formation reference information, the formation initial information and the formation state output information, specifically includes: Determining the degree of formation deformation at the initial moment according to the formation initial information and the expected formation reference information; Determine the formation deformation degree at the end time according to the formation state output information and the expected formation reference information; The formation deformation degree from the initial moment to the end moment is integrated and averaged to obtain the average deformation degree of the formation in the flight phase; The objective function is determined with the goal of minimizing the average deformation degree of the formation in the flight phase.

3. The hypersonic vehicle formation control method according to claim 1, characterized in that: According to the objective function, the particle swarm ant colony hybrid algorithm and the sequential quadratic programming method are used to optimize the initial information of the formation and the control parameters of the speed direction collaborative controller to obtain the final optimization result, which specifically includes: According to the objective function, a particle swarm ant colony hybrid algorithm is used to optimize the initial information of the formation and the control parameters of the speed direction collaborative controller to obtain a global optimization result; Taking the global optimization result as the initial value, the sequential quadratic programming method is used to optimize the initial information of the formation and the control parameters of the speed direction collaborative controller to obtain the final optimization result.

4. The hypersonic vehicle formation control method according to claim 3, characterized in that: According to the objective function, a particle swarm ant colony hybrid algorithm is used to optimize the initial information of the formation and the control parameters of the speed direction collaborative controller to obtain a global optimization result, which specifically includes: According to the objective function, a particle swarm algorithm is first used to search for preliminary optimization results in a global scope, and then the pheromone guidance mechanism of the ant colony algorithm is used to optimize the preliminary optimization results to obtain a global optimization result; wherein the number of particles in the particle swarm algorithm is equal to the number of ants in the ant colony algorithm.

5. The hypersonic vehicle formation control method according to claim 1, characterized in that: Determining the attack angle control instruction and the roll angle control instruction of the follower aircraft according to the optimized speed direction cooperative controller specifically includes: Determine the trajectory inclination control instruction and the heading angle control instruction of the follower aircraft according to the optimized speed direction cooperative controller; Limiting the trajectory inclination control instruction and the heading angle control instruction based on the heat flow rate constraint, dynamic pressure constraint and overload constraint during the flight phase; Calculate the lift value and lift coefficient according to the limited trajectory inclination control command and the limited heading angle control command; An attack angle control instruction and a roll angle control instruction of the follower aircraft are calculated according to the lift value and the lift coefficient.

6. The hypersonic vehicle formation control method according to claim 5, characterized in that: Calculating the attack angle control instruction and the roll angle control instruction of the follower aircraft according to the lift value and the lift coefficient specifically includes: comparing the lift coefficient with a maximum lift coefficient; If the lift coefficient is greater than the maximum lift coefficient, calculating the attack angle control instruction and the roll angle control instruction of the follower aircraft according to the lift value and the maximum lift coefficient; If the lift coefficient is less than or equal to the maximum lift coefficient, an attack angle control instruction and a roll angle control instruction of the follower aircraft are calculated according to the lift value and the lift coefficient.

7. A hypersonic vehicle formation control system, characterized in that: The hypersonic aircraft formation control system is applied to a plurality of hypersonic aircraft that perform formation flight in a leader-follower mode; wherein the leader aircraft flies along a set flight trajectory, and the follower aircraft, under the control of a speed direction cooperative controller, keeps the same flight direction as the leader aircraft; the hypersonic aircraft formation control system comprises: A data acquisition module is used to acquire desired formation reference information, formation initial information and formation status output information; the formation initial information includes: initial position and initial speed of each aircraft in the formation; the formation status output information includes: distance from the center of the earth, longitude, latitude, flight speed, ballistic inclination and heading angle of each aircraft in the formation; A controller design module, used for designing a speed direction coordinated controller according to the formation state output information; An objective function determination module is used to determine an objective function according to the desired formation reference information, the formation initial information and the formation state output information with the goal of minimizing the average deformation degree of the formation during the flight phase; the objective function represents the relationship between the average deformation degree of the formation during the flight phase and the formation initial information and the control parameters of the speed direction collaborative controller; An optimization solution module is used to optimize the initial information of the formation and the control parameters of the speed direction collaborative controller according to the objective function by using a particle swarm ant colony hybrid algorithm and a sequential quadratic programming method to obtain a final optimization result; A control instruction output module is used to determine an optimized speed direction cooperative controller according to the final optimization result, and to determine an angle of attack control instruction and a roll angle control instruction of a follower aircraft according to the optimized speed direction cooperative controller.

8. An electronic device, characterized in that: It includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the hypersonic aircraft formation control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the hypersonic aircraft formation control method as described in any one of claims 1 to 6.

Images