CN111538255A - Aircraft control method and system for anti-swarm unmanned aerial vehicle - Google Patents
Aircraft control method and system for anti-swarm unmanned aerial vehicle Download PDFInfo
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Abstract
The application relates to an aircraft control method and system of an anti-swarm unmanned aerial vehicle, which comprises the steps of establishing 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-swarm unmanned aerial vehicle, and the environment component is used for simulating simulation environment parameters; generating a simulation model according to the unmanned aerial vehicle component model, the environment component model and preset configuration information in an unmanned aerial vehicle controller; setting a flight route for the unmanned aerial vehicle to fly, 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 component when the simulation flight parameters reach a set threshold value. The vertical take-off and landing function of the unmanned aerial vehicle is realized through the vertical flight mode, the long-endurance and fast-cruise functions of the unmanned aerial vehicle are realized through the horizontal flight mode, and multifunctional integration is realized, so that the requirement of executing special tasks under complex environments is met.
Description
Technical Field
The application relates to the technical field of flight control of unmanned aerial vehicles, in particular to an aircraft control method and system of an anti-swarm unmanned aerial vehicle.
Background
With the continuous development of aerospace technology, unmanned aerial vehicles have been widely used in many fields such as communication, navigation positioning, resource exploration, dangerous case detection, scientific research, military and the like. Flight control systems are an important component of unmanned aerial vehicles.
The traditional unmanned aerial vehicle mainly comprises a fixed-wing unmanned aerial vehicle and a multi-rotor unmanned aerial vehicle, wherein the fixed-wing unmanned aerial vehicle can realize long-endurance and fast cruise, but is difficult to have a vertical take-off and landing function; many rotor unmanned aerial vehicle can realize the VTOL function, but are difficult to have the characteristics that long endurance and fast cruise concurrently.
In this case, the two types of drones cannot perform special tasks in complex environments, and therefore, a novel flight control system and method for the drones needs to be designed.
Disclosure of Invention
Therefore, in order to solve the technical problems, it is necessary to provide an aircraft control method and system for an anti-swarm unmanned aerial vehicle, so that the anti-swarm unmanned aerial vehicle using the control system and method can integrate multiple functions of long-term flight, fast cruise, vertical take-off and landing, and the like.
An aircraft control method of an anti-swarm 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-swarm unmanned aerial vehicle, and the environment component is used for simulating simulation environment parameters;
generating a simulation model according to the unmanned aerial vehicle component model, the environment component model and preset configuration information in an unmanned aerial vehicle controller;
setting a flight route for the unmanned aerial vehicle to fly, and inputting the flight route into the simulation model;
acquiring simulated flight parameters output by the simulation model in real time, and sending a flight mode switching instruction to the unmanned aerial vehicle component when the simulated flight parameters reach a set threshold value; the flight modes include: a vertical flight mode, a horizontal flight mode, and a transitional flight mode.
In one embodiment, the preset configuration information includes at least a course, waypoints, position, speed, acceleration, distance, pitch angle, yaw angle, track angle, sideslip angle, angle of attack, force, control moment, and moment of inertia; wherein,
the force at least comprises total tension generated by four propellers of the unmanned aerial vehicle, tension of any propeller, air pressure, lift force and resistance;
the control moment at least comprises a roll control moment, a pitch control moment and a yaw control moment;
the threshold values include at least a distance threshold value, a track angle threshold value and a yaw angle threshold value.
In one embodiment, when the drone component switches from a vertical flight mode to a horizontal flight mode, the simulated 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, the simulated flight parameters satisfy when the drone component switches from the horizontal flight mode to the vertical flight mode: 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 embodiment, the unmanned aerial vehicle adopts vertical take-off and landing mode control for vertical flight mode control, the unmanned aerial vehicle adopts fast horizontal flight mode control for horizontal flight mode control, and the unmanned aerial vehicle adopts hybrid mode switching control transition flight mode control.
In one embodiment, the drone also needs to establish a dynamic model and a kinematic model based on the anti-swarm drone in the vertical flight mode; analyzing the functional relation between the pressure borne by the unmanned aerial vehicle component and the air speed, the air density, the shape and the posture 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 kinematics model.
In one embodiment, the method further comprises:
when the unmanned aerial vehicle component adopts the vertical take-off and landing mode control, the stable control of take-off, landing and emergency is executed;
when the unmanned aerial vehicle assembly adopts the fast flat flying mode control, executing self-adaptive coordination control, and expanding the safe flying envelope of the unmanned aerial vehicle cluster;
when the unmanned aerial vehicle subassembly adopts hybrid mode switching control, regard unmanned aerial vehicle's pitch angle as the scheduling variable, accomplish transition flight process according to the value control of pitch angle.
An aircraft control system for anti-swarm drone, 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 assembly and an environment assembly, the unmanned aerial vehicle assembly is used for simulating performance parameters of an anti-swarm unmanned aerial vehicle, and the environment assembly is used for simulating simulation environment parameters;
the model generation module is used for generating a simulation model according to the unmanned aerial vehicle component model, the environment component model and preset configuration information in the unmanned aerial vehicle controller;
the guidance module is used for setting a flight path of the unmanned aerial vehicle, and inputting the flight path into the simulation model;
and the simulation analysis module is used for 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 component when the simulation flight parameters reach a set threshold value.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
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-swarm unmanned aerial vehicle, and the environment component is used for simulating simulation environment parameters;
generating a simulation model according to the unmanned aerial vehicle component model, the environment component model and preset configuration information in an unmanned aerial vehicle controller;
setting a flight route for the unmanned aerial vehicle to fly, and inputting the flight route into the simulation model;
acquiring simulated flight parameters output by the simulation model in real time, and sending a flight mode switching instruction to the unmanned aerial vehicle component when the simulated flight parameters reach a set threshold value; the flight modes include: a vertical flight mode, a horizontal flight mode, and a transitional flight mode.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out 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-swarm unmanned aerial vehicle, and the environment component is used for simulating simulation environment parameters;
generating a simulation model according to the unmanned aerial vehicle component model, the environment component model and preset configuration information in an unmanned aerial vehicle controller;
setting a flight route for the unmanned aerial vehicle to fly, and inputting the flight route into the simulation model;
acquiring simulated flight parameters output by the simulation model in real time, and sending a flight mode switching instruction to the unmanned aerial vehicle component when the simulated flight parameters reach a set threshold value; the flight modes include: a vertical flight mode, a horizontal flight mode, and a transitional flight mode.
Compared with the prior art, the invention has the advantages that:
according to the aircraft control method, the system, the computer equipment and the storage medium of the anti-swarm unmanned aerial vehicle, the unmanned aerial vehicle is simulated in a component mode according to performance parameters of the unmanned aerial vehicle, so that an unmanned aerial vehicle component is constructed, in order to ensure system simulation, an environment component is created by taking the unmanned aerial vehicle component as a core and is used for simulating environment parameters, the unmanned aerial vehicle is converted into data executed by a computer through the processing, preset configuration information is input and the unmanned aerial vehicle component and the environment component are called, so that a simulation model is generated, a flight route of the unmanned aerial vehicle during flight is set and input into the simulation model, simulation flight parameters are acquired in real time, and a flight mode switching instruction is sent when a set threshold value is reached; the aim at of switching flight mode realizes unmanned aerial vehicle's VTOL function through vertical flight mode, and when realizing unmanned aerial vehicle's long voyage and the function of cruising fast through horizontal flight mode, realize multi-functional integrative integration to satisfy the requirement of carrying out special task under the complex environment.
Drawings
Fig. 1 is a schematic flow chart of an aircraft control method of an anti-swarm unmanned aerial vehicle according to the present invention;
fig. 2 is a frame composition frame diagram of the flight control system of the drone;
fig. 3 is a schematic view of a flight mode state of the drone;
fig. 4 is a flow chart of the selection of the flight mode of the drone;
fig. 5 is a frame diagram of a dynamical model of the drone;
FIG. 6 is a graph of a relationship between lift coefficient and angle of attack of the UAV;
fig. 7 is a block diagram of a total energy control system of the drone;
fig. 8 and 9 show the simulation results of the speed and track climb angle control of the drone;
fig. 10 and 11 show the results of control simulation of the continuous change of the flight speed of the drone;
fig. 12 is a frame diagram of a guidance module of the drone;
fig. 13 is a diagram of the switching conditions between the horizontal flight mode and the vertical flight mode of the drone;
fig. 14 is a flight trajectory diagram of the drone;
FIG. 15 is a graph of the change in flying height of the drone over time;
fig. 16 is a graph of the change in the north position of the drone with time;
fig. 17 is an internal structural view of the computer device.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
An aircraft control method of an anti-swarm drone as shown in fig. 1 mainly includes the following steps:
s1, 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-swarm unmanned aerial vehicle, and the environment component is used for simulating simulation environment parameters;
in one embodiment, based on the idea of components, in software engineering, reusable software components are used as assembly blocks to build a model of unmanned aerial vehicle components and environment components; for the method, the unmanned aerial vehicle component and the environment component are basic model components, if complex antagonistic simulation needs to be carried out, a large number of basic model components need to be called, the basic model components can be assembled in a matched mode, and different basic model components can interact with each other, so that a combined model can be obtained by the multiple basic model components;
s2, generating a simulation model according to the unmanned aerial vehicle component model, the environment component model and the preset configuration information in the unmanned aerial vehicle controller;
in one embodiment, the preset configuration information includes a course, a waypoint, a position, a speed, an acceleration, a distance, a pitch angle, a yaw angle, a track angle, a sideslip angle, an attack angle, a force, a control moment, a rotational inertia and the like, and is preset and stored for an operator through a user terminal; the force comprises the total tension generated by four propellers of the unmanned aerial vehicle, the tension of any propeller, air pressure, lift force and resistance; the control moment comprises a roll control moment, a pitch control moment and a yaw control moment; the threshold value at least comprises a distance threshold value, a track angle threshold value and a yaw angle threshold value;
s3, setting a flight path of the unmanned aerial vehicle, and inputting the flight path into the simulation model;
in one embodiment, the information such as the current flight attitude and flight height of the unmanned aerial vehicle, the distance and angle relative to the obstacle, the position of the destination and the like is combined, and after a simulation model is executed, a desired air route and a desired track angle are output after calculation and simulation;
s4, acquiring simulated flight parameters output by the simulation model in real time, and sending a flight mode switching instruction to the unmanned aerial vehicle component when the simulated flight parameters reach a set threshold value; switching the flight mode when the flight parameters reach the set threshold value, and returning to the step S2 when the flight parameters do not reach the set threshold value;
in one embodiment, the method further comprises:
when the unmanned aerial vehicle component adopts the vertical take-off and landing mode control, the stable control of take-off, landing and emergency is executed;
when the unmanned aerial vehicle assembly adopts the fast flat flying mode control, executing self-adaptive coordination control, and expanding the safe flying envelope of the unmanned aerial vehicle cluster;
when the unmanned aerial vehicle assembly is subjected to hybrid mode switching control, the pitch angle of the unmanned aerial vehicle is used as a scheduling variable, and the transitional flight process is controlled and completed according to the value of the pitch angle;
in one embodiment, the flight mode mainly comprises three parts, namely a vertical flight mode, a horizontal flight mode and a transition flight mode between the vertical flight mode and the horizontal flight mode; the transition process between the horizontal flight mode and the vertical flight mode is called as a mode switching process and comprises a vertical and horizontal transition flight mode and a vertical transition flight mode, and the mode switching process is a bridge for realizing vertical take-off and landing and horizontal high-speed cruising; the threshold values include a distance threshold value, a track angle threshold value and a yaw angle threshold value.
More specifically, in order to ensure the safety and smoothness of the mode switching process during the mode switching process of each flight mode, as shown in fig. 3 to 4:
when the unmanned aerial vehicle subassembly switches into the horizontal flight mode from vertical flight mode, required judgement condition needs to satisfy simultaneously:
1) the distance d from the next waypoint is larger than a set distance threshold value d*I.e. d.gtoreq.d*；
2) The expected track angle gamma to the next waypoint is less than the set track angle threshold gamma*I.e. gamma is less than or equal to gamma*；
3) The desired yaw angle χ to the next waypoint is less than the set yaw angle threshold χ*I.e. χ ≦ χ*。
When the unmanned aerial vehicle subassembly switches into vertical flight mode from horizontal flight mode, required judgement condition only need satisfy following arbitrary one:
1) the distance d to the next waypoint is less than a set distance threshold d*I.e. d < d*；
2) The expected track angle gamma to the next waypoint is larger than the set track angle threshold gamma*I.e. gamma > gamma*。
More specifically, regarding the vertical landing mode control technique:
the drone assembly, in vertical flight mode, produces a pressure distribution around it that is a function of air velocity, air density, drone shape and attitude. Accordingly, a dynamic model based on the anti-swarm unmanned aerial vehicle is established, as shown in fig. 5, the module on the left side is a controller, the module in the virtual frame on the right side is an unmanned aerial vehicle dynamic model, which can be further divided into three modules, namely an aerodynamic model, a rotational motion model and a translational motion model, the signal bus in the figure contains the motion parameters (position and attitude) of the 6 degrees of freedom of the aircraft and derivatives (speed and angular velocity) thereof, the signals of the 12 paths of motion parameters are counted, and the pressure can be modeled by a lift force, a resistance force and a moment, which can be generally expressed as:
in the formula, FliftDenotes lift force, FdragRepresents the resistance, MairIndicating moment, CL、CD、CmFor drag coefficient, s is the airfoil area and c is the average chord of the airfoil.
In general, the equations of these forces and moments are nonlinear, and when the drone flies at a small angle of attack and a small angle of sideslip, the linear equations can be used for approximation, however, the anti-bee-colony drone needs to cover a very large range of vertical take-off and landing and horizontal flight during flight, the fuselage needs to be tilted by 90 degrees, and in order to be able to more accurately establish the relationship between lift, drag and angle of attack in the range, the expressions of lift and drag in the above equation are rewritten as follows:
where α is the angle of attack, and when the angle of attack exceeds the stall threshold, the wing behaves like a flat plate, and the lift coefficient can be expressed as:
CL，plate＝2sign(α)sin2αcosα
therefore, the following lift model is used in the simulation, which contains the normal linear lift effect and the lift effect at stall:
in the formula,
wherein M and α0The demarcation point and the conversion rate of the mixing function are determined as positive constants.
And the linear lift coefficient portion can be expressed using the following equation:
further, AR · b2And S is the aspect ratio of the wing, b is the wingspan, and S is the wing area.
When the lift force model is adopted, the relationship curve of the lift force coefficient and the attack angle is shown in fig. 6, the aerodynamic lift force can be accurately described in a large attack angle range, and the simulation requirement of the embodiment is met; the design of the flight control system of the anti-swarm unmanned aerial vehicle is carried out by taking the nonlinear model as an object, the linearization of the nonlinear object and the decoupling between channels can be effectively realized by adopting a dynamic inverse control algorithm, the nonlinearity of the controlled object is eliminated, the global linearization is formed, the visual understanding of the attitude of the unmanned aerial vehicle is facilitated, the control performance and the anti-interference performance are good, and the control precision, the robustness and the self-adaptability of the aircraft control system and the stability of the attitude adjustment and the flight of the unmanned aerial vehicle are effectively improved.
Coefficient of resistance CDAnd is a non-linear function of angle of attack α, consisting of two parts, induced drag and waste drag, respectively, the waste drag being generated by factors such as shear stress caused by air moving across the wing, and the waste drag coefficient being substantially constant, and being determined by the angle of attack
And (4) showing. For small angles of attack, the induced drag is proportional to the square of the lift. Combining the induced resistance and the waste resistance simultaneously to obtain:
wherein e is an Oswald efficiency factor, and is usually preferably 0.8 to 1.0.
The model described above can provide a more accurate aerodynamic description over a larger range of angles of attack. The aerodynamically generated pitching moment is also generally a non-linear function of the angle of attack and must be determined by specific wind tunnel or flight tests. In the preliminary simulation model, the following linear model was used:
required thrust and the required control moment of attitude stable control provide by four motors during unmanned aerial vehicle flight, and its expression is as follows:
in the formula, FcRepresenting the total tension, L, generated by the four propellerscRepresenting roll control moment, McRepresenting the pitching control moment, NcRepresenting yaw control moment, TiIs the tension of the ith propeller, omegaiThe rotating speed of the ith propeller, l represents the distance from the motor shaft to the longitudinal axis of the unmanned aerial vehicle, and JrIs the gyro effect coefficient of the propeller, r is the yaw angular velocity of the unmanned aerial vehicle, Q is the pitch angular velocity of the unmanned aerial vehicle, QiThe reaction torque generated for the ith propeller.
Further, an attitude control system model based on the unmanned aerial vehicle is established according to three flight modes of the unmanned aerial vehicle, decoupling and linearization processing are carried out on the attitude control system model, and an actual attitude angle and a target attitude angle of the unmanned aerial vehicle are obtained; the attitude control system model at least comprises a dynamic model and a kinematic model; ignoring the curvature of the earth, under the assumption of a planar earth, the 6-degree-of-freedom kinematic model equation for an anti-swarm drone is as follows:
in the formula, u, v and w respectively represent the speed components of the unmanned aerial vehicle along the directions of x, y and z under a body coordinate system, m is the mass of the unmanned aerial vehicle, p, q and r represent the rotating angular speed along the body axis, and Ix、Iy、IzFor the inertia of the unmanned aerial vehicle in the x, y, z directions, Fx、Fy、FzForce components in the x, y, and z directions, L, M, N moment components in the body axis direction, pN、pEH is the altitude information of the north and east positions and the unmanned aerial vehicle, B is a rotation matrix from the northeast coordinate system to the unmanned aerial vehicle-mounted system, gx、gy、gzRepresents the local gravitational acceleration g0The projection in the x, y, z direction system of the drone is as follows:
in particular, for the vertical flight mode, the kinematic model expressed in terms of vertical euler angles is as follows:
in the formula,
positioning a horizontal Euler angle c as XbRoll angle of the axis theta about YbPitch angle of the axis, psi, about ZbYaw angle of the shaft; vertical Euler angle phivIs composed of
Roll angle of the shaft, thetavTo be wound around
Pitch angle of the shaft,. psivTo be around a shaft
The angle of yaw of (a) is,
is the rotation angular acceleration of the unmanned aerial vehicle,
for the pitch angular acceleration of the drone,
yaw angular acceleration for unmanned aerial vehicles, Ixx、Iyy、IzzThe rotational inertia of the unmanned aerial vehicle in the x direction, the y direction and the z direction respectively.
In an anti-swarm unmanned aerial vehicle cluster, due to the requirement of formation configuration, the speed and attitude instructions of weapon units need to be frequently adjusted according to the change of formation, however, for the aerial unmanned aerial vehicle cluster, the coupling between the flight path angle and the speed is obvious, and only by designing a power compensation system, the coupling between the flight path angle and the speed can be removed, so that the accurate control of the flight path is realized. In order to solve the problem of coupling between a flight path angle and a speed, the invention provides a particle swarm optimization algorithm-based total energy comprehensive flight attitude control method for an anti-swarm unmanned aerial vehicle, and the method has good control performance.
Total energy control system as shown in fig. 7, the total energy of the anti-swarm drone in flight can be expressed as follows:
in the formula, ETThe total energy of the unmanned aerial vehicle is composed of potential energy and kinetic energy, m is mass, g is gravitational acceleration, V is flying speed, and h is flying height.
Carrying out dimensionless treatment on the two sides of the formula to obtain the total energy change rate of the unmanned aerial vehicle
Comprises the following steps:
the tangential force equation of the unmanned aerial vehicle centroid motion can be expressed as:
in the formula, T is engine thrust, and gamma is a flight path angle; d is pneumatic resistance, and the formula is arranged to obtain:
therefore, the engine thrust can change the total energy change rate of the unmanned aerial vehicle, and the thrust increment delta T required by the change of the flight attitude of the unmanned aerial vehicle is as follows:
in the formula,
is the desired rate of change of total energy
And actual rate of change of energy
A difference of
It can be seen that the change in thrust changes the rate of change of energy of the drone by a certain proportion. In order to achieve a better attitude control effect and eliminate steady-state errors, a proportional-integral control law can be adopted to form a control quantity of thrust based on the deviation of the energy change rate, and the expression is as follows:
in the formula, TcEngine thrust, K, required to change the attitude of the droneTPIs a proportionality coefficient, KTIIs an integral coefficient. The effect of this control law is to cause a deviation in the rate of change of the total energy due to a change in attitude
Tending to zero.
The total energy change rate describes the total change trend of the potential energy and the kinetic energy of the airplane. To describe the proportional relationship between the two, the distribution ratio of the total energy is defined as follows:
the energy distribution rate converts the kinetic energy and the potential energy of the airplane to each other through the pitching attitude loop. Comprehensively considering the damping terms required for improving the short-period motion quality of the airplane, adopting a control law similar to thrust to express the control quantity of the thrust differential as follows:
in the formula: delta T is the difference in thrust between the propellers; kEPIs a proportionality coefficient; kEIIs an integral coefficient;
for a desired total energy distribution ratio DcAnd actual energy distribution rate
A difference of
KθAnd KqRespectively, the pitch angle and the feedback gain of the pitch angle velocity. The control law has the effects that the deviation of the energy distribution rate tends to zero, and meanwhile, the quality of the short-period attitude of the unmanned aerial vehicle is improved.
Determining an attitude angle deviation value to be adjusted according to a target attitude angle to be adjusted of the unmanned aerial vehicle and a current actual attitude angle of the unmanned aerial vehicle, establishing an Euler attitude model and an error model according to the attitude angle deviation value, wherein the error model has very important significance for optimizing a target and optimizing controller parameters, and a time weighted error absolute value integral index can be selected to serve as a fitness function:
where e (t) is the calculated deviation value after the current position of the particle is introduced into the error model, and e (t) ═ y (t) — y (∞), this definition is different from the conventional error definition because for a system with static error, y (∞) ≠ yc(t) if according to e (t) y (t) -yc(t) defining the deviation value, finally making all integrals infinite and meaningless, and also proving the effectiveness of the model for reducing errors.
Considering the stability of the flight attitude of the unmanned aerial vehicle, the unmanned aerial vehicle is not expected to perform excessive actions instantaneously, and meanwhile, the change rate of the unmanned aerial vehicle is expected to be as small as possible, and the actions are mild, so that the fitness function is changed into the following form:
in the formula, the "control amount" is a change rate of the control amount.
Parameter K in anti-swarm unmanned aerial vehicle attitude control system by adopting particle swarm optimization algorithmTP、KTI、KEPAnd KEIOptimizing, wherein the number of the set particles is 100, the iteration number is 200, and the parameter optimization range is (0, 2)]The optimization result is KTP＝0.6315，KTI＝0.2338，KEP＝1.6983，KEI0.1362. Meanwhile, step instructions of the flight speed and the track climbing angle are given to the unmanned aerial vehicle in the cruising state, and simulation comparison results are shown in fig. 8 to 9.
Compared with artificial experience parameters, the parameters optimized by the particle swarm optimization algorithm have the advantages of small overshoot, high convergence speed and high steady-state precision. In order to further verify the effectiveness of the designed weapon unit comprehensive flight control method based on the total energy, aiming at a certain type of aerial weapon unit comprehensive flight control system during long endurance, the flight speed is continuously changed, and meanwhile, the flight altitude simulation is kept. The control parameter is KTP＝0.5217，KTI＝0.2135，KEP＝1.3217，KEI0.1127. The simulation results are shown in fig. 10 to fig. 11, and it can be seen from the graphs that as the target flight speed increases, the height of the anti-swarm drone fluctuates due to the increase of lift caused by the increase of speed, and the anti-swarm drone can be rapidly recovered to the target height under the action of the total energy control method, and the change amplitude is less than 0.5 meter. To followThe accelerator can quickly respond and quickly stabilize to a new balance position after the speed is stabilized to a target value along with the change of the speed instruction; the pitch angle is reduced, so that the lift coefficient of the attack angle is reduced, and the total lift is kept unchanged under the condition of increasing the speed, thereby ensuring that the flying height is unchanged. From the above simulation results, it can be concluded that: the attitude control system can lead the flying speed to track the instruction value more quickly, the steady-state precision is very high, and the fluctuation of the flying height is small.
Judging the robustness and stability of the unmanned aerial vehicle control system model:
the transition process between the horizontal flight mode and the vertical flight mode is called as a mode switching process and comprises a vertical transition flight mode and a horizontal transition flight mode, and the mode switching process is a bridge for realizing vertical take-off and landing and horizontal high-speed cruising. In the transitional flight process, the pitching angle of the anti-swarm unmanned aerial vehicle is changed within a +/-90-degree large angle, and the airspeed is greatly changed, so that the dynamic model of the unmanned aerial vehicle is changed violently, and the design difficulty of the transitional flight mode controller is increased due to the nonlinearity and the strong coupling of the system. The working state of the anti-swarm unmanned aerial vehicle is greatly changed in the transitional flight process, so that the inner loop adopts a control technology based on nonlinear dynamic inversion in the mode switching process.
More specifically, regarding the fast flying mode control technique:
the anti-swarm unmanned aerial vehicle needs to fly according to a pre-planned flight path or a real-time planned flight path in a fast flat flying mode, the problem of accurately following the path in a wind field and the problem of improving the safety of the flight in the process of multi-machine cooperative close formation flying are solved, the self-adaptive coordination control and performance optimization of all unmanned aerial vehicles under the high-dynamic complex environment condition are realized, the safe flying envelope of the unmanned aerial vehicles is expanded, and the higher coordination performance index and space-time constraint requirements of a cluster system are realized.
In the process, the guidance system plays a crucial role, and as shown in fig. 12, the guidance system outputs an expected track angle according to the current flight state of the unmanned aerial vehicle and a preset waypoint; the guidance strategy corresponding to the horizontal flight mode and the vertical flight mode is different, the guidance in the horizontal flight mode is divided into a lateral guidance part and a longitudinal guidance part which are relatively independent, and the guidance in the vertical flight mode is implemented according to the guidance strategy of multiple rotors.
For the altitude control problem, it is assumed that the control law in the climbing and descending areas will realize that the unmanned aerial vehicle ascends or descends to an altitude holding area; in the altitude hold zone, the elevation of the drone is controlled using the pitch attitude. Assuming that the continuous closed loop has been implemented correctly, the outer loop kinetic equation can be expressed as:
the height error is defined as:
eh·h-hd＝h-hc
the following results were obtained:
applying the final value theorem can obtain:
H is the height of the unmanned plane, ehIs the oswald efficiency factor.
Analysis shows that constant perturbations will be removed. Therefore, the present embodiment can track a constant altitude and an inclined straight path, so that the flying speed tracks the command value faster, and has zero steady-state altitude error, while the fluctuation of the flying altitude is small.
More specifically, regarding the mixed modality switching control technique:
in the mode switching process, the pitch angle of the unmanned aerial vehicle assembly is used as a scheduling variable, the value of the pitch angle can be measured in real time, a corresponding local controller is called according to the value of the pitch angle to complete the whole transition flight process, and then the whole process is divided into two stages, as shown in fig. 13:
at a pitch angle theta > theta*A vertical flight mode is adopted, and the angle theta is less than the angle theta*A horizontal flight mode is used. In order to ensure the safety and stability of the hybrid mode switching process, when the hybrid mode is switched from the vertical take-off and landing mode to the fast level flight mode, the required judgment condition also needs to meet the condition that the distance from the next waypoint is greater than a set threshold value.
When an Euler attitude model of the unmanned aerial vehicle is processed, the Euler attitude model is generally decomposed into a longitudinal subsystem and a lateral subsystem, obviously, state quantities such as a rolling angle, a yaw angle, a sideslip angle and the like of the lateral subsystem of the unmanned aerial vehicle are kept unchanged in the transitional flight process, and only a pitch angle, an airspeed and an attack angle of the lateral subsystem are changed. In order to facilitate analysis and design of a transition flight mode controller, simplification processing needs to be performed on a mathematical model of a transition flight mode, a lateral subsystem model of the unmanned aerial vehicle is omitted and is used as an interference quantity to be processed, and only a longitudinal subsystem model of the unmanned aerial vehicle is analyzed, so that a six-degree-of-freedom model of the unmanned aerial vehicle can be simplified into a two-degree-of-freedom model. According to the simplified model, the whole transition flight process can be regarded as being completed in a two-dimensional plane, namely a traditional longitudinal plane. The pitch angle and the airspeed of the unmanned aerial vehicle are greatly changed in the whole transition flight process, and in order to ensure the smooth implementation of the transition flight process, a wide airspace without barriers around the switching point is required to be selected. In the transitional flight process, the longitudinal motion of the unmanned aerial vehicle is mainly concerned, and then the longitudinal model of the unmanned aerial vehicle needs to be further analyzed, wherein the longitudinal dynamic model is as follows:
mVa＝D+T cos a-mg sin g
mVag＝L+T sin a-mg cos g
qv＝G2pr+Tm/Jy
wherein L, D, T represents lift, drag and propeller drag, g represents flight path angle, and g is q-a, TmIs the pitching moment.
Through the analysis of the system dynamics model in the transitional flight process, the pitch angle and the airspeed are mainly controlled in the transitional flight process, the roll angle and the yaw angle are relatively kept constant in the transitional flight process, and the key of the transitional flight control is to control the pitch angle q and the airspeed Va. The transition flight controller does not adopt a new controller structure, and is controlled by state scheduling of a pitch angle on the basis of the original vertical flight controller and the original horizontal flight controller, so that the design difficulty of the controller is simplified.
In order to verify the method of the embodiment, a flight trajectory as shown in fig. 14 is set, in the figure, 11 is a reference trajectory, 12 is a vertical takeoff trajectory, 13 is a transition flight trajectory, 14 is a horizontal flight trajectory, and 15 is a vertical landing trajectory, the vertical takeoff is firstly performed, the vertical flight mode climbs to a safe height of 20m, then the horizontal flight mode is performed, the vertical flight mode climbs to a height of 100m, and simultaneously the target point outside 4km is flown, in the process, an initial lateral deviation of 10m is set, and when the target ground is close to the ground, the vertical flight mode is switched to and landed. The change curves of the flight height and the north position of the unmanned aerial vehicle along with time are shown in fig. 15 and 16, in fig. 15, 21 is a vertical takeoff track, 22 is a transition flight track, 23 is a horizontal flight track, and 24 is a vertical landing track; in fig. 16, 31 is a vertical takeoff trajectory, 32 is a transitional flight trajectory, 33 is a horizontal flight trajectory, and 34 is a vertical landing trajectory; . Compared with the preset flight path, the steady-state error of the altitude tracking is less than 1m in the horizontal flight mode, and the steady-state value of the lateral deviation of the linear flight path tracking approaches zero, which shows the effectiveness of the method in the embodiment.
The method solves the technical problems of a vertical take-off and landing mode control technology, a rapid flat flying mode control technology and a mixed mode switching control technology in the traditional unmanned aerial vehicle flight control method; the vertical take-off and landing function of the unmanned aerial vehicle is realized through the vertical flight mode, the long-endurance and fast-cruise functions of the unmanned aerial vehicle are realized through the horizontal flight mode, and multifunctional integration is realized.
In one embodiment, as shown in fig. 2, an aircraft control system for an anti-swarm drone is provided, which does not contain any controllable aerodynamic surface (such as ailerons, elevators, and rudders), and its main structure adopts a conventional four-rotor and fixed wing combination, a lifting surface is installed on the motor arm of each rotor, and when the drone flies horizontally, the additional wing takes an X-configuration and provides lift, and thrust required for the drone during flying and torque required for attitude stability control are provided by four motors. In this embodiment, the aircraft control system mainly includes:
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 assembly and an environment assembly, the unmanned aerial vehicle assembly is used for simulating performance parameters of an anti-swarm unmanned aerial vehicle, and the environment assembly is used for simulating simulation environment parameters;
the model generation module is used for generating a simulation model according to the unmanned aerial vehicle component model, the environment component model and preset configuration information in the unmanned aerial vehicle controller;
the guidance module is used for setting a flight path of the unmanned aerial vehicle, and inputting the flight path into the simulation model;
and the simulation analysis module is used for 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 component when the simulation flight parameters reach a set threshold value.
The embodiment realizes the take-off and landing functions of the unmanned aerial vehicle and the stable control under emergency conditions by the vertical take-off and landing mode control technology; the self-adaptive coordination control and performance optimization of the unmanned aerial vehicle cluster under the high-dynamic complex environment condition are realized through a rapid flat flying mode control technology, and the safe flying envelope of the flying platform is expanded; and the pitch angle of the unmanned aerial vehicle is used as a scheduling variable through a hybrid mode switching control technology, and a corresponding local controller is called according to the value of the pitch angle to complete the whole transitional flight process. The four-rotor wing reinforced fixed wing combination is adopted, so that the vertical take-off and landing function of the multi-rotor wing unmanned aerial vehicle can be realized, and the fixed wing unmanned aerial vehicle has the advantages of long endurance and rapid cruise, so that the requirement of executing special tasks in a complex environment is met.
In one embodiment, as shown in FIG. 17, 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 by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store base model component data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of flight control for anti-swarm drones.
Those skilled in the art will appreciate that the architecture shown in fig. 17 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program:
s1, 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-swarm unmanned aerial vehicle, and the environment component is used for simulating simulation environment parameters;
s2, generating a simulation model according to the unmanned aerial vehicle component model, the environment component model and the preset configuration information in the unmanned aerial vehicle controller;
s3, setting a flight path of the unmanned aerial vehicle, and inputting the flight path into the simulation model;
s4, acquiring simulated flight parameters output by the simulation model in real time, and sending a flight mode switching instruction to the unmanned aerial vehicle component when the simulated flight parameters reach a set threshold value; the flight modes include: a vertical flight mode, a horizontal flight mode, and a transitional flight mode.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method in the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. 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. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An aircraft control method of an anti-swarm 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-swarm unmanned aerial vehicle, and the environment component is used for simulating simulation environment parameters;
generating a simulation model according to the unmanned aerial vehicle component model, the environment component model and preset configuration information in an unmanned aerial vehicle controller;
setting a flight route for the unmanned aerial vehicle to fly, and inputting the flight route into the simulation model;
acquiring simulated flight parameters output by the simulation model in real time, and sending a flight mode switching instruction to the unmanned aerial vehicle component when the simulated flight parameters reach a set threshold value; the flight modes include: a vertical flight mode, a horizontal flight mode, and a transitional flight mode.
2. The flight control method of anti-swarm drone of claim 1, wherein the preset configuration information includes at least course, waypoint, position, speed, acceleration, distance, pitch angle, yaw angle, track angle, sideslip angle, angle of attack, force, control moment and moment of inertia; wherein,
the force at least comprises total tension generated by four propellers of the unmanned aerial vehicle, tension of any propeller, air pressure, lift force and resistance;
the control moment at least comprises a roll control moment, a pitch control moment and a yaw control moment;
the threshold values include at least a distance threshold value, a track angle threshold value and a yaw angle threshold value.
3. The method of claim 2, wherein the simulated flight parameters satisfy simultaneously when the drone component switches from a vertical flight mode to a 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.
4. The method of claim 2, wherein the simulated flight parameters satisfy when the drone component switches from horizontal flight mode to vertical flight mode: 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.
5. The method of claim 1, wherein the drone is controlled in vertical flight mode using vertical take-off and landing mode control, in horizontal flight mode using fast flat flight mode control, and in transition flight mode using hybrid mode switching control.
6. The method for controlling the flying vehicle of the anti-swarm unmanned aerial vehicle according to claim 5, wherein the unmanned aerial vehicle is further required to establish a dynamic model and a kinematic model based on the anti-swarm unmanned aerial vehicle in a vertical flight mode; analyzing the functional relation between the pressure borne by the unmanned aerial vehicle component and the air speed, the air density, the shape and the posture 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 kinematics model.
7. The method of claim 5, further comprising:
when the unmanned aerial vehicle component adopts the vertical take-off and landing mode control, the stable control of take-off, landing and emergency is executed;
when the unmanned aerial vehicle assembly adopts the fast flat flying mode control, executing self-adaptive coordination control, and expanding the safe flying envelope of the unmanned aerial vehicle cluster;
when the unmanned aerial vehicle subassembly adopts hybrid mode switching control, regard unmanned aerial vehicle's pitch angle as the scheduling variable, accomplish transition flight process according to the value control of pitch angle.
8. An aircraft control system for anti-swarm drone, 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 assembly and an environment assembly, the unmanned aerial vehicle assembly is used for simulating performance parameters of an anti-swarm unmanned aerial vehicle, and the environment assembly is used for simulating simulation environment parameters;
the model generation module is used for generating a simulation model according to the unmanned aerial vehicle component model, the environment component model and preset configuration information in the unmanned aerial vehicle controller;
the guidance module is used for setting a flight path of the unmanned aerial vehicle, and inputting the flight path into the simulation model;
and the simulation analysis module is used for 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 component when the simulation flight parameters reach a set threshold value.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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