CN111538255B - Aircraft control method and system for an anti-swarm drone - Google Patents

Abstract

The application relates to an aircraft control method and system of an anti-bee colony unmanned aerial vehicle, comprising the steps of creating a model of an unmanned aerial vehicle component and an environment component, wherein the unmanned aerial vehicle component is used for simulating performance parameters of the anti-bee colony unmanned aerial vehicle, and the environment component is used for simulating simulation environment parameters; generating a simulation model according to the unmanned aerial vehicle assembly model, the environment assembly model and preset configuration information in an unmanned aerial vehicle controller; setting a flight route of unmanned aerial vehicle flight, and inputting the flight route into the simulation model; and acquiring the simulation flight parameters output by the simulation model in real time, and sending a flight mode switching instruction to the unmanned aerial vehicle assembly when the simulation flight parameters reach a set threshold value. The unmanned aerial vehicle has the advantages that the vertical take-off and landing functions of the unmanned aerial vehicle are realized through the vertical flight mode, the long-endurance and quick-cruising functions of the unmanned aerial vehicle are realized through the horizontal flight mode, and the multifunctional integrated structure is realized, so that the requirement of executing special tasks in a complex environment is met.

Description

A method and system for controlling an anti-swarm UAV aircraft

Technical Field

The present application relates to the technical field of unmanned aerial vehicle flight control, and in particular to an aircraft control method and system for an anti-swarming unmanned aerial vehicle.

Background Art

With the continuous development of aerospace technology, UAVs have been increasingly widely used in many fields such as communication, navigation and positioning, resource exploration, danger detection, scientific research, and military affairs. The flight control system is an important part of UAVs.

Traditional drones are mainly divided into two categories: fixed-wing drones and multi-rotor drones. Fixed-wing drones can achieve long flight time and fast cruising, but it is difficult for them to have vertical take-off and landing functions; multi-rotor drones can achieve vertical take-off and landing functions, but it is difficult for them to have the characteristics of long flight time and fast cruising.

In this case, these two types of UAVs cannot perform some special tasks in some complex environments. Therefore, it is necessary to design a new flight control system and method for UAVs.

Summary of the invention

Based on this, it is necessary to provide an aircraft control method and system for an anti-swarm UAV in response to the above-mentioned technical problems, to ensure that the anti-swarm UAV using the control system and method can achieve multi-functions such as long flight time, fast cruising and vertical take-off and landing.

An anti-swarm UAV aircraft control method, the method comprising:

Creating models of a drone component and an environment component, wherein the drone component is used to simulate performance parameters of an anti-swarm drone, and the environment component is used to simulate simulation environment parameters;

Generate a simulation model according to the drone component model, the environment component model and preset configuration information in the drone controller;

Setting a flight route for the UAV and inputting the flight route into the simulation model;

The simulated flight parameters output by the simulation model are collected in real time. When the simulated flight parameters reach a set threshold, a flight mode switching instruction is sent to the UAV component; the flight modes include: vertical flight mode, horizontal flight mode and transition flight mode.

In one embodiment, the preset configuration information includes at least a route, a waypoint, a position, a speed, an acceleration, a distance, a pitch angle, a yaw angle, a track angle, a sideslip angle, an angle of attack, a force, a control torque, and a moment of inertia; wherein,

The force includes at least the total thrust generated by the four propellers of the drone, the thrust of any propeller, air pressure, lift, and drag;

The control torque at least includes a roll control torque, a pitch control torque, and a yaw control torque;

The thresholds include at least a distance threshold, a track angle threshold and a yaw angle threshold.

In one embodiment, when the UAV component switches from the vertical flight mode to the horizontal flight mode, the simulation flight parameters simultaneously satisfy: the distance to the next preset waypoint is greater than a set distance threshold, the expected track angle to the next preset waypoint is less than a set track angle threshold, and the expected yaw angle threshold to the next preset waypoint is less than a set yaw angle threshold.

In one embodiment, when the UAV component switches from the horizontal flight mode to the vertical flight mode, the simulation flight parameters meet: the distance to the next preset waypoint is less than a set distance threshold or the expected track angle to the next preset waypoint is greater than a set track angle threshold.

In one of the embodiments, the UAV adopts a vertical take-off and landing control mode for vertical flight control, the UAV adopts a fast level flight control mode for horizontal flight control, and the UAV adopts a hybrid control mode for transitional flight control.

In one of the embodiments, when the UAV is in vertical flight mode, it is also necessary to establish a dynamic model and a kinematic model based on the anti-swarm UAV; analyze the functional relationship between the pressure on the UAV component and the air speed, air density, UAV shape and posture according to the dynamic model; and analyze the action type and motion trajectory of the UAV component according to the kinematic model.

In one embodiment, the method further comprises:

When the UAV component is controlled in vertical take-off and landing mode, it performs take-off, landing and stability control in emergency situations;

When the UAV components are controlled in a fast level flight mode, adaptive coordinated control is performed to expand the safe flight envelope of the UAV cluster;

When the UAV component adopts hybrid mode switching control, the pitch angle of the UAV is used as a scheduling variable, and the transition flight process is completed according to the value of the pitch angle.

An aircraft control system for an anti-swarm UAV, the system comprising:

A building module, used to create models of a drone component and an environment component, wherein the drone component is used to simulate performance parameters of an anti-swarm drone, and the environment component is used to simulate simulation environment parameters;

A model generation module, used to generate a simulation model according to the drone component model, the environment component model and configuration information preset in the drone controller;

A guidance module, used for setting a flight route for the UAV and inputting the flight route into the simulation model;

The simulation analysis module is used to collect the simulated flight parameters output by the simulation model in real time, and when the simulated flight parameters reach a set threshold, send a flight mode switching instruction to the drone component.

A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

Creating models of a drone component and an environment component, wherein the drone component is used to simulate performance parameters of an anti-swarm drone, and the environment component is used to simulate simulation environment parameters;

Generate a simulation model according to the drone component model, the environment component model and preset configuration information in the drone controller;

Setting a flight route for the UAV and inputting the flight route into the simulation model;

The simulated flight parameters output by the simulation model are collected in real time. When the simulated flight parameters reach a set threshold, a flight mode switching instruction is sent to the UAV component; the flight modes include: vertical flight mode, horizontal flight mode and transition flight mode.

A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

Creating models of a drone component and an environment component, wherein the drone component is used to simulate performance parameters of an anti-swarm drone, and the environment component is used to simulate simulation environment parameters;

Generate a simulation model according to the drone component model, the environment component model and preset configuration information in the drone controller;

Setting a flight route for the UAV and inputting the flight route into the simulation model;

The simulated flight parameters output by the simulation model are collected in real time. When the simulated flight parameters reach a set threshold, a flight mode switching instruction is sent to the UAV component; the flight modes include: vertical flight mode, horizontal flight mode and transition flight mode.

Compared with the prior art, the present invention is beneficial in that:

The present invention provides an aircraft control method, system, computer equipment and storage medium for an anti-swarm UAV. According to the performance parameters of the UAV, the UAV is simulated in the form of a component, thereby constructing a UAV component. In order to ensure system simulation, an environment component is created with the UAV component as the core, and the environment component is used to simulate environmental parameters. Through the above processing, the UAV is converted into data executed by the computer, preset configuration information is input, and the UAV component and the environment component are called to generate a simulation model, set the flight route of the UAV during flight and input it into the simulation model, collect simulated flight parameters in real time and determine when the set threshold is reached to send a flight mode switching instruction; the purpose of switching the flight mode is to realize the vertical take-off and landing function of the UAV through the vertical flight mode, and realize the long flight time and fast cruising function of the UAV through the horizontal flight mode, so as to realize multi-functional integration, thereby meeting the requirements of performing special tasks in complex environments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of an anti-swarm UAV aircraft control method according to the present invention;

FIG2 is a diagram showing the framework of the flight control system of the UAV;

FIG3 is a schematic diagram of the flight mode state of the UAV;

FIG4 is a flow chart showing the selection of the UAV flight mode;

FIG5 is a framework diagram of a dynamic model of the UAV;

FIG6 is a graph showing the relationship between the lift coefficient and the angle of attack of the UAV;

FIG7 is a framework diagram of the total energy control system of the UAV;

8 and 9 are simulation results of the speed and track climb angle control of the UAV;

Figures 10 and 11 are control simulation results of the UAV continuously changing its flight speed;

FIG12 is a framework diagram of the guidance module of the UAV;

FIG13 is a diagram showing switching conditions between the horizontal flight mode and the vertical flight mode of the UAV;

FIG14 is a diagram showing the flight trajectory of the UAV;

FIG15 is a graph showing the change of the flight altitude of the UAV over time;

FIG16 is a graph showing the change of the north position of the UAV over time;

FIG. 17 is a diagram showing the internal structure of the computer device.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

As shown in FIG1 , an anti-swarm UAV aircraft control method mainly comprises the following steps:

S1, creating a model of a drone component and an environment component, wherein the drone component is used to simulate the performance parameters of an anti-swarm drone, and the environment component is used to simulate the simulation environment parameters;

In one of the embodiments, based on the idea of components, in software engineering, reusable software components are used as assembly blocks to construct models of drone components and environment components; for this method, drone components and environment components are basic model components. If complex adversarial simulation is required, a large number of basic model components need to be called. The basic model components can be assembled in coordination, and different basic model components can interact with each other, so that a combined model can be obtained from multiple basic model components;

S2, generating a simulation model according to the drone component model, the environment component model and configuration information preset in the drone controller;

In one embodiment, the preset configuration information includes routes, waypoints, positions, speeds, accelerations, distances, pitch angles, yaw angles, track angles, sideslip angles, angles of attack, forces, control torques, and moments of inertia, which are pre-set and stored by the operator through a user terminal; wherein the forces include the total thrust generated by the four propellers of the drone, the thrust of any propeller, air pressure, lift, and drag; the control torques include rolling control torques, pitch control torques, and yaw control torques; the thresholds include at least distance thresholds, track angle thresholds, and yaw angle thresholds;

S3, setting a flight route for the UAV, and inputting the flight route into the simulation model;

In one of the embodiments, the desired route and the desired track angle are output after the simulation model is executed and the simulation is performed based on the current flight attitude and flight altitude of the UAV, the distance and angle relative to obstacles, the location of the destination, and other information;

S4, collecting the simulated flight parameters output by the simulation model in real time, and sending a flight mode switching instruction to the drone component when the simulated flight parameters reach a set threshold; switching the flight mode when the flight parameters reach the set threshold, and returning to step S2 when the flight modes do not reach the set threshold;

In one embodiment, the method further comprises:

When the UAV component is controlled in vertical take-off and landing mode, it performs take-off, landing and stability control in emergency situations;

When the UAV components are controlled in a fast level flight mode, adaptive coordinated control is performed to expand the safe flight envelope of the UAV cluster;

When the UAV component adopts hybrid mode switching control, the pitch angle of the UAV is used as a scheduling variable, and the transition flight process is completed according to the value of the pitch angle;

In one of the embodiments, the flight mode is mainly composed of three parts, namely, a vertical flight mode, a horizontal flight mode and a transition flight mode between the two; the transition process between the horizontal flight mode and the vertical flight mode is called a mode switching process, which includes a vertical-to-horizontal transition flight mode and a horizontal-to-vertical transition flight mode, and the mode switching process is a bridge to achieve vertical take-off and landing and horizontal high-speed cruising; the threshold includes a distance threshold, a track angle threshold and a yaw angle threshold.

More specifically, in the process of switching between flight modes, in order to ensure the safety and stability of the mode switching process, as shown in Figures 3 and 4:

When the drone component switches from vertical flight mode to horizontal flight mode, the required judgment conditions must be met at the same time:

1) The distance d to the next waypoint is greater than the set distance threshold d ^* , that is, d≥d ^* ;

2) The expected track angle γ to the next waypoint is less than the set track angle threshold γ ^* , that is, γ ≤ γ ^* ;

3) The expected yaw angle χ to the next waypoint is less than the set yaw angle threshold χ ^* , that is, χ≤χ ^* .

When the drone component switches from horizontal flight mode to vertical flight mode, the required judgment conditions only need to meet any one of the following:

1) The distance d to the next waypoint is less than the set distance threshold d ^* , that is, d<d ^* ;

2) The expected track angle γ to the next waypoint is greater than the set track angle threshold γ ^* , that is, γ>γ ^* .

More specifically, regarding vertical take-off and landing mode control technology:

When the UAV component is in vertical flight mode, a pressure distribution will be generated around it, and the pressure is a function of air speed, air density, shape and attitude of the UAV. Based on this, a dynamic model based on the anti-swarm UAV is established, as shown in Figure 5, where the module on the left is the controller, and the module in the virtual box on the right is the UAV dynamic model, which can be divided into three modules: aerodynamic model, rotational motion model, and translational motion model. The signal bus in the figure contains the 6-DOF motion parameters (position and attitude) of the aircraft and their derivatives (speed and angular velocity), a total of 12 motion parameter signals. Pressure can be modeled with a lift, a drag and a torque, which can usually be expressed as:

Where _Flift represents lift, _Fdrag represents drag, _Mair represents moment, _CL , _CD , _Cm are drag coefficients, s is the wing area, and c is the average chord of the wing.

Usually, the equations of these forces and moments are nonlinear. When the UAV flies at a small angle of attack and sideslip angle, linear equations can be used for approximation. However, the anti-swarm UAV needs to cover a very large range of vertical take-off and landing and horizontal flight during flight, and the fuselage needs to tilt 90°. In order to more accurately establish the relationship between lift, drag and angle of attack within this range, the lift and drag expressions in the above formula are rewritten as follows:

Where α is the angle of attack. When the angle of attack exceeds the threshold of the stall state, the wing is like a flat plate, and its lift coefficient can be expressed as:

C _{L, plate} = 2sign(α)sin ² αcosα

Therefore, the following lift model is used in the simulation, which includes the lift effect of ordinary linear lift and the lift effect in the stall state:

In the formula,

Where M and _α0 are positive constants that determine the dividing point and conversion rate of the mixing function.

The linear lift coefficient part can be expressed by the following equation:

Furthermore, AR·b ² /S is the aspect ratio of the wing, b is the wingspan, and S is the wing area.

When the above lift model is adopted, the relationship curve between the lift coefficient and the angle of attack is shown in FIG6 , which can accurately describe the aerodynamic lift within a large angle of attack range, meeting the simulation requirements of this embodiment; the flight control system of the anti-swarm UAV is designed with the nonlinear model as the object, and the dynamic inverse control algorithm can effectively realize the linearization of the nonlinear object and the decoupling between channels, eliminate the nonlinearity of the controlled object, and form a global linearization, which is convenient for intuitive understanding of the UAV attitude, and has good control performance and anti-interference, effectively improving the control accuracy, robustness and adaptability of the aircraft control system and the stability of the attitude adjustment and flight of the UAV.

The drag coefficient _CD is also a nonlinear function of the angle of attack α, and consists of two parts, induced drag and waste drag. Waste drag is caused by factors such as the shear stress caused by air passing over the wing. The waste drag coefficient is generally a constant and is represented by _CDp . For small angles of attack, induced drag is proportional to the square of lift. Combining induced drag and waste drag, we get:

Where e is the Oswald efficiency factor, which can usually be taken as 0.8 to 1.0.

The above model can provide a more accurate description of aerodynamic forces in a larger range of angles of attack. The pitching moment generated by aerodynamic forces is usually also a nonlinear function of the angle of attack and must be determined through specific wind tunnels or flight tests. In the preliminary simulation model, the following linear model is used:

The thrust required for the drone to fly and the control torque required for attitude stability control are provided by four motors, and the expressions are as follows:

Where _Fc represents the total thrust generated by the four propellers, _Lc represents the roll control torque, _Mc represents the pitch control torque, _Nc represents the yaw control torque, _Ti is the thrust of the i-th propeller, _Ωi is the speed of the i-th propeller, l represents the distance from the motor shaft to the longitudinal axis of the drone, _Jr is the propeller gyro effect coefficient, r is the yaw angular velocity of the drone, q is the pitch angular velocity of the drone, and _Qi is the anti-torque generated by the i-th propeller.

Furthermore, according to the three flight modes of the UAV, an attitude control system model based on the UAV is established and the attitude control system model is decoupled and linearized to obtain the actual attitude angle and target attitude angle of the UAV; the attitude control system model includes at least a dynamic model and a kinematic model; ignoring the curvature of the earth, under the assumption of a flat earth, the 6-degree-of-freedom kinematic model equations of the anti-swarm UAV are as follows:

Where u, v, w represent the velocity components of the UAV in the x, y, and z directions in the body coordinate system, respectively; m is the mass of the UAV; p, q, and r represent the angular velocity along the body axis; _Ix , _Iy , and _Iz are the moments of inertia of the UAV in the x, y, and z directions; _Fx , _Fy , and _Fz are the force components in the x, y, and z directions, respectively; L, M, and N are the torque components in the body axis direction; _pN , _pE , and h are the north and east positions and the altitude information of the UAV, respectively; B is the rotation matrix from the northeast ground coordinate system to the UAV carrier system; _gx , _gy , and _gz represent the projection of the local gravity acceleration _g0 in the UAV in the x, y, and z directions, as shown in the following formula:

In particular, for the vertical flight mode, the kinematic model expressed by the vertical Euler angle is as follows:

In the formula,

轴的滚转角，θ_v为绕

轴的俯仰角，ψ_v为绕轴

的偏航角，

为无人机的转动角加速度，

为无人机的俯仰角加速度，

为无人机的偏航角加速度，I_xx、I_yy、I_zz分别为无人机在x、y、z方向的转动惯量。Define the horizontal Euler angle c as the roll angle around the _Xb axis, θ as the pitch angle around the _Yb axis, and ψ as the yaw angle around the _Zb axis; the vertical Euler angle _φv is

The roll angle around the axis, θ _v is

The pitch angle of the axis, ψ _v is the pitch angle around the axis

The yaw angle,

is the angular acceleration of the UAV,

is the pitch acceleration of the drone,

is the yaw acceleration of the UAV, I _xx , I _yy and I _zz are the rotational inertia of the UAV in the x, y and z directions respectively.

In anti-swarm UAV clusters, due to the requirements of formation configuration, the speed and attitude instructions of weapon units need to be frequently adjusted according to the changes in formation. However, for aerial UAV clusters, there is an obvious coupling between the track angle and speed. Only by designing a power compensation system can the coupling between the track angle and speed be removed and the precise control of the track be achieved. To remove the coupling problem between the track angle and speed, the present invention proposes a total energy integrated flight attitude control method for anti-swarm UAVs based on a particle swarm optimization algorithm, which has good control performance.

The total energy control system is shown in Figure 7. The total energy of the anti-swarm UAV during flight can be expressed as follows:

Where _ET is the total energy of the drone, which consists of potential energy and kinetic energy, m is the mass, g is the acceleration of gravity, V is the flight speed, and h is the flight altitude.

为：After differentiating both sides of the above equation and non-dimensionalizing it, we can get the total energy change rate of the drone:

for:

The tangential force equation of the UAV center of mass motion can be expressed as:

In the formula, T is the engine thrust, γ is the track angle, and D is the aerodynamic drag. The above formula can be rearranged to obtain:

It can be seen that the engine thrust can change the total energy change rate of the UAV, and the thrust increment ΔT required to change the flight attitude of the UAV is:

为期望的总能量变化率

和实际的能量变化率

之差，即

可见，推力的变化以一定的比例改变无人机的能量变化率。为了达到更好的姿态控制效果，消除稳态误差，可以基于能量变化率的偏差采用比例积分控制律形成推力的控制量，表达式为：In the formula,

is the expected total energy change rate

and the actual rate of energy change

The difference is

It can be seen that the change of thrust changes the energy change rate of the drone in a certain proportion. In order to achieve better attitude control effect and eliminate steady-state error, the proportional-integral control law can be used to form the control amount of thrust based on the deviation of the energy change rate. The expression is:

趋于零。In the formula, _Tc is the engine thrust required to change the attitude of the UAV, _KTP is the proportional coefficient, and _KTI is the integral coefficient. The function of this control law is to reduce the deviation of the total energy change rate caused by the change of flight attitude to

Approaches zero.

The total energy change rate describes the total change trend of the aircraft's potential energy and kinetic energy. In order to describe the proportional relationship between the two, the total energy distribution rate is defined as follows:

The energy distribution rate converts the kinetic energy and potential energy of the aircraft through the pitch attitude loop. Taking into account the damping term required to improve the short-period motion quality of the aircraft, a control law similar to the thrust is used to express the control quantity of the thrust differential as:

为期望的总能量分配率D_c和实际的能量分配率

之差，即

K_θ和K_q分别为俯仰角和俯仰角速度的反馈增益。该控制律的作用是使能量分配率的偏差趋于零，同时改善无人机短周期姿态的品质。Where: ΔδT is the difference in thrust between propellers; K _EP is the proportional coefficient; K _EI is the integral coefficient;

is the desired total energy distribution rate D _c and the actual energy distribution rate

The difference is

K _θ and K _q are the feedback gains of pitch angle and pitch velocity respectively. The function of this control law is to make the deviation of energy distribution rate approach zero and improve the quality of short-period attitude of UAV.

The attitude angle deviation value that needs to be adjusted is determined according to the target attitude angle that the UAV needs to adjust and the current actual attitude angle of the UAV, and then the Euler attitude model and error model are established according to the attitude angle deviation value. The error model is of great significance for the optimization of the target and the optimization of the controller parameters. The time-weighted error absolute value integral index is selected as the fitness function:

In the formula, e(t) is the deviation value calculated after the current position of the particle is brought into the error model, e(t) = y(t) - y(∞). This definition is different from the traditional error definition. The reason is that for a system with static error, y(∞) ≠ y _c (t). If the deviation value is defined according to e(t) = y(t) - y _c (t), all integrals will eventually become infinite and lose their meaning. This also proves the effectiveness of this model in reducing errors.

Considering the stability of the drone's flight attitude, we do not want the drone to perform large movements instantly. At the same time, we hope that its rate of change is as small as possible and the movements are smoother, so the fitness function is changed to the following form:

In the formula, δ is the control quantity and δ' is the rate of change of the control quantity.

The particle swarm optimization algorithm is used to optimize the parameters K _TP , K _TI , K _EP and K _EI in the attitude control system of the anti-swarm UAV. The number of particles is set to 100, the number of iterations is 200, the parameter optimization range is (0, 2], and the optimization results are K _TP = 0.6315, K _TI = 0.2338, K _EP = 1.6983, K _EI = 0.1362. At the same time, the step instructions of flight speed and track climb angle are given to the UAV in the cruising state. The simulation comparison results are shown in Figures 8 and 9.

Through the simulation comparison results before and after optimization, we can clearly see that compared with the artificial experience parameters, the parameters optimized by the particle swarm optimization algorithm have the advantages of small overshoot, fast convergence and high steady-state accuracy. The designed method can simultaneously accurately control the flight speed and track climb angle, and meet the multivariable integrated coordinated control of the track and speed parameters of the weapon unit. In order to further verify the effectiveness of the designed weapon unit integrated flight control method based on total energy, a simulation of continuously changing the flight speed while maintaining the flight altitude is carried out for a certain type of long-flight air weapon unit integrated flight control system. The control parameters are K _TP = 0.5217, K _TI = 0.2135, K _EP = 1.3217, K _EI = 0.1127. The simulation results are shown in Figures 10 and 11. It can be seen from the figure that with the increase of the target flight speed, the height of the anti-swarm UAV fluctuates due to the increase in lift caused by the increase in speed. Under the action of the total energy control method, it can quickly return to the target altitude, and the change amplitude is less than 0.5 meters. As the speed command changes, the throttle can respond quickly and quickly stabilize at the new equilibrium position as the speed stabilizes to the target value; the pitch angle decreases, the angle of attack decreases, the lift coefficient decreases, and the total lift remains unchanged when the speed increases, thereby ensuring that the flight altitude remains unchanged. From the above simulation results, it can be concluded that the attitude control system can make the flight speed track the command value quickly, with high steady-state accuracy, and the fluctuation of the flight altitude is small.

Regarding the robustness and stability judgment of the UAV control system model:

The transition process between horizontal flight mode and vertical flight mode is called mode switching process, which includes vertical-to-horizontal transition flight mode and horizontal-to-vertical transition flight mode. The mode switching process is a bridge to achieve vertical take-off and landing and horizontal high-speed cruising. During the transition flight, the pitch angle of the anti-swarm UAV has a large-angle maneuver of ±90°, and the airspeed has also changed significantly, resulting in drastic changes in the UAV's dynamic model. The nonlinearity and strong coupling of the system make the design of the transition flight mode controller more difficult. During the transition flight, the working state of the anti-swarm UAV changes significantly. Therefore, during the mode switching process, the inner loop adopts a control technology based on nonlinear dynamic inversion.

More specifically, regarding the fast level flight mode control technology:

In the fast level flight mode, the anti-swarm UAV needs to fly according to the pre- or real-time planned track. It is necessary to solve the problem of accurately following the path in the wind field and improving the flight safety in the process of multiple drones coordinating and flying in close formation. It is necessary to realize adaptive coordinated control and performance optimization of each UAV under highly dynamic and complex environmental conditions, expand the safe flight envelope of the UAV, and achieve higher coordination performance indicators and time and space constraints for the cluster system.

In this process, the guidance system plays a vital role. As shown in Figure 12, the guidance system outputs the expected track angle according to the current flight status of the UAV and the preset waypoints. The guidance strategies corresponding to the horizontal flight mode and the vertical flight mode are different. The guidance of the horizontal flight mode is divided into two relatively independent parts: lateral guidance and longitudinal guidance, while the guidance of the vertical flight mode is implemented according to the guidance strategy of the multi-rotor.

For the altitude control problem, it is assumed that the control laws in the climb and descent areas will achieve the drone's ascent or descent to the altitude hold area; in the altitude hold area, the pitch attitude is used to control the drone's altitude. Assuming that the continuous closed loop has been correctly implemented, the outer loop dynamic equation can be expressed as:

Define the height error as:

e _h ·hh _d =hh ^c

available:

Applying the final value theorem, we get:

对

right

Where h is the altitude of the drone and e _h is the Oswald efficiency factor.

The analysis shows that the constant disturbance will be removed. Therefore, the present embodiment can track a constant altitude and inclined straight line path, so that the flight speed tracks the command value faster, and has zero steady-state altitude error, while the fluctuation of the flight altitude is small.

More specifically, regarding the hybrid mode switching control technology:

In the process of mode switching, the pitch angle of the drone component is used as a scheduling variable, and its value can be measured in real time. According to the value of the pitch angle, the corresponding local controller is called to complete the entire transition flight process, and the entire process is divided into two stages, as shown in FIG13:

When the pitch angle θ＞θ ^*, the vertical flight mode is adopted, and when the pitch angle θ＜θ ^*, the horizontal flight mode is adopted. In order to ensure the safety and smoothness of the hybrid mode switching process, when switching from the vertical take-off and landing mode to the fast level flight mode, the required judgment condition also needs to be satisfied that the distance to the next waypoint is greater than the set threshold.

When dealing with the UAV Euler attitude model, we usually decompose it into the longitudinal subsystem and the lateral subsystem. Obviously, during the transition flight, the roll angle, yaw angle and sideslip angle of the UAV lateral subsystem remain unchanged, only the pitch angle, airspeed and angle of attack of the lateral subsystem change. In order to facilitate the analysis and design of the transition flight mode controller, it is necessary to simplify the mathematical model of the transition flight mode. Here we ignore the lateral subsystem model of the UAV and treat it as an interference quantity, and only analyze its longitudinal subsystem model. Therefore, the six-degree-of-freedom model of the UAV can be simplified to a two-degree-of-freedom model. According to the simplified model, we can regard the entire transition flight process as completed in a two-dimensional plane, that is, the traditional longitudinal plane. During the entire transition flight process, the pitch angle and airspeed of the UAV have undergone significant changes. In order to ensure the smooth implementation of the transition flight process, the switching point must be selected in a wide airspace without obstacles around. During the transition flight process, we mainly focus on the longitudinal movement of the UAV. Next, we need to further analyze the longitudinal model of the UAV. Its longitudinal dynamic model is:

mV _a = D + Tcosa-mgsing

mV _a g＝L+Tsina-mgcosg

q _v =G ₂ pr+T _m /J _y

Where L, D, T are lift, drag and propeller thrust respectively, g is the flight path angle and g=qa, and _Tm is the pitching moment.

Through the above analysis of the system dynamics model during the transition flight, it can be seen that the pitch angle and airspeed are mainly controlled during the transition flight. During this process, the roll angle and yaw angle remain relatively constant. The key to transition flight control is to control the pitch angle q and airspeed _Va . The transition flight controller does not adopt a new controller structure, but is controlled by the state scheduling of the pitch angle based on the original vertical flight controller and horizontal flight controller, thereby simplifying the design difficulty of the controller.

In order to verify the method described in this embodiment, a flight trajectory as shown in Figure 14 is set. In the figure, 11 is a reference trajectory, 12 is a vertical take-off trajectory, 13 is a transition flight trajectory, 14 is a horizontal flight trajectory, and 15 is a vertical landing trajectory. First, take off vertically and climb to a safe height of 20m in vertical flight mode, then turn to level flight and climb to an altitude of 100m, and fly to a target point 4km away at the same time. In this process, an initial lateral deviation of 10m is set. When approaching the air above the destination, switch to vertical flight mode and land. The change curves of the flight altitude and north position of the drone over time are shown in Figures 15 and 16. In Figure 15, 21 is a vertical take-off trajectory, 22 is a transition flight trajectory, 23 is a horizontal flight trajectory, and 24 is a vertical landing trajectory; in Figure 16, 31 is a vertical take-off trajectory, 32 is a transition flight trajectory, 33 is a horizontal flight trajectory, and 34 is a vertical landing trajectory;. Compared with the preset track, it can be seen that in the horizontal flight mode, the steady-state error of altitude tracking is less than 1m, and the steady-state value of the lateral deviation of the straight track tracking is close to zero, which shows the effectiveness of the method described in this embodiment.

This embodiment solves the problems of vertical take-off and landing mode control technology, fast level flight mode control technology and mixed mode switching control technology in traditional UAV flight control methods; the vertical take-off and landing function of the UAV is realized through the vertical flight mode, and the long flight time and fast cruising function of the UAV is realized through the horizontal flight mode, thereby realizing multi-functional integrated integration.

In one embodiment, as shown in FIG2 , an aircraft control system for an anti-swarm UAV is provided, which does not contain any manipulable aerodynamic surfaces (such as ailerons, elevators, and rudders). Its main structure adopts a traditional quad-rotor plus a fixed wing combination, and a lifting surface is installed on the motor arm of each rotor. When it is flying horizontally, the additional wing is in an X configuration and provides lift. The thrust required for the flight of the UAV and the torque required for attitude stability control are provided by four motors. In this embodiment, the aircraft control system mainly includes:

A building module, used to create models of a drone component and an environment component, wherein the drone component is used to simulate performance parameters of an anti-swarm drone, and the environment component is used to simulate simulation environment parameters;

A model generation module, used to generate a simulation model according to the drone component model, the environment component model and configuration information preset in the drone controller;

A guidance module, used for setting a flight route for the UAV and inputting the flight route into the simulation model;

The simulation analysis module is used to collect the simulated flight parameters output by the simulation model in real time, and when the simulated flight parameters reach a set threshold, send a flight mode switching instruction to the drone component.

This embodiment realizes the take-off and landing functions of the drone and the stable control in emergency situations through the vertical take-off and landing mode control technology; realizes the adaptive coordinated control and performance optimization of the drone cluster under highly dynamic and complex environmental conditions through the fast level flight mode control technology, and expands the safe flight envelope of the flight platform; uses the pitch angle of the drone as the scheduling variable through the hybrid mode switching control technology, and calls the corresponding local controller according to the value of the pitch angle to complete the entire transition flight process. The present invention adopts a combination of four rotors and fixed wings to realize the vertical take-off and landing function of multi-rotor drones, and has the advantages of long flight time and fast cruising of fixed-wing drones, thereby meeting the requirements of performing special tasks in complex environments.

In one embodiment, as shown in FIG14 , a computer device is provided, which may be a server. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store basic model component data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the flight control method of the anti-swarm UAV is implemented.

Those skilled in the art will understand that the structure shown in FIG. 14 is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method in the above embodiment are implemented:

S1, creating a model of a drone component and an environment component, wherein the drone component is used to simulate the performance parameters of an anti-swarm drone, and the environment component is used to simulate the simulation environment parameters;

S2, generating a simulation model according to the drone component model, the environment component model and configuration information preset in the drone controller;

S3, setting a flight route for the UAV, and inputting the flight route into the simulation model;

S4, collecting the simulated flight parameters output by the simulation model in real time, and when the simulated flight parameters reach a set threshold, sending a flight mode switching instruction to the drone component; the flight modes include: vertical flight mode, horizontal flight mode and transition flight mode.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims (8)

1. A method of controlling an aircraft of a antifungus drone, the method comprising:

creating a model of an unmanned aerial vehicle component and an environment component, wherein the unmanned aerial vehicle component is used for simulating performance parameters of the anti-bee colony unmanned aerial vehicle, and the environment component is used for simulating environment parameters;

generating a simulation model according to the unmanned aerial vehicle assembly model, the environment assembly model and preset configuration information in an unmanned aerial vehicle controller;

setting a flight route of unmanned aerial vehicle flight, and inputting the flight route into the simulation model;

collecting simulation flight parameters output by the simulation model in real time, and sending a flight mode switching instruction to the unmanned aerial vehicle assembly when the simulation flight parameters reach a set threshold value; the flight mode includes: a vertical flight mode, a horizontal flight mode, and a transitional flight mode;

the preset configuration information at least comprises a route, a route point, a position, a speed, acceleration, distance, pitch angle, yaw angle, track angle, sideslip angle, attack angle, force, control moment and moment of inertia; wherein,,

the force at least comprises the total pulling force generated by four propellers of the unmanned aerial vehicle, the pulling force of any propeller, air pressure, lifting force and resistance;

the control moment at least comprises a rolling control moment, a pitching control moment and a yawing control moment;

the threshold value at least comprises a distance threshold value, a track angle threshold value and a yaw angle threshold value;

the simulation flight parameters simultaneously satisfy when the unmanned aerial vehicle assembly is switched from the vertical flight mode to the horizontal flight mode: the distance to the next preset waypoint is greater than the set distance threshold, the desired track angle to the next preset waypoint is less than the set track angle threshold, and the desired yaw angle threshold to the next preset waypoint is less than the set yaw angle threshold.

2. The method of claim 1, wherein the simulation flight parameters are satisfied when the unmanned aerial vehicle is switched from a horizontal flight mode to a vertical flight mode: the distance to the next preset waypoint is less than the set distance threshold or the desired track angle to the next preset waypoint is greater than the set track angle threshold.

3. The method for controlling an aircraft by using an anti-bee colony unmanned aerial vehicle according to claim 1, wherein the unmanned aerial vehicle performs vertical flight mode control by using vertical take-off and landing mode control, the unmanned aerial vehicle performs horizontal flight mode control by using rapid horizontal flight mode control, and the unmanned aerial vehicle performs transition flight mode control by using hybrid mode switching control.

4. The method for controlling the aircraft of the antifungus unmanned aerial vehicle according to claim 3, wherein the unmanned aerial vehicle is further required to establish a dynamic model and a kinematic model based on the antifungus unmanned aerial vehicle in a vertical flight mode; analyzing the functional relation between the pressure born by the unmanned aerial vehicle component and the air speed, the air density, the shape and the gesture of the unmanned aerial vehicle according to the dynamic model; and analyzing the action type and the motion trail of the unmanned aerial vehicle component according to the kinematic model.

5. A method of controlling an aircraft of a antifungus drone of claim 3, further comprising:

when the unmanned aerial vehicle unit adopts vertical take-off and landing mode control, stable control under the conditions of take-off, landing and emergency is executed;

when the unmanned aerial vehicle assembly adopts the rapid plane flight mode control, executing self-adaptive coordination control, and expanding a safe flight envelope of the unmanned aerial vehicle cluster;

when the unmanned aerial vehicle is subjected to mixed mode switching control, the pitch angle of the unmanned aerial vehicle is used as a scheduling variable, and the transitional flight process is controlled according to the value of the pitch angle.

6. An anti-swarm unmanned aerial vehicle aircraft control system, the system comprising:

the system comprises a building module, a simulation module and a simulation module, wherein the building module is used for building a model of an unmanned aerial vehicle component and an environment component, the unmanned aerial vehicle component is used for simulating performance parameters of the anti-bee colony unmanned aerial vehicle, and the environment component is used for simulating simulation environment parameters;

the model generation module is used for generating a simulation model according to the unmanned aerial vehicle assembly model, the environment assembly model and preset configuration information in the unmanned aerial vehicle controller; the preset configuration information at least comprises a route, a route point, a position, a speed, acceleration, distance, pitch angle, yaw angle, track angle, sideslip angle, attack angle, force, control moment and moment of inertia; the force at least comprises total pulling force generated by four propellers of the unmanned aerial vehicle, pulling force of any propeller, air pressure, lifting force and resistance; the control moment at least comprises a rolling control moment, a pitching control moment and a yawing control moment; the threshold values include at least a distance threshold value, a track angle threshold value, and a yaw angle threshold value; the simulation flight parameters simultaneously satisfy when the unmanned aerial vehicle assembly is switched from the vertical flight mode to the horizontal flight mode: the distance to the next preset waypoint is greater than a set distance threshold, the expected track angle to the next preset waypoint is less than a set track angle threshold, and the expected yaw angle threshold to the next preset waypoint is less than a set yaw angle threshold;

the guidance module is used for setting a flight route of unmanned aerial vehicle flight and inputting the flight route into the simulation model;

and the simulation analysis module is used for collecting the simulation flight parameters output by the simulation model in real time, and sending a flight mode switching instruction to the unmanned aerial vehicle assembly when the simulation flight parameters reach a set threshold value.

7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 5 when the computer program is executed.

8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 5.

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