CN111158395B - Multi-unmanned aerial vehicle tight formation control method based on pigeon swarm optimization - Google Patents

Multi-unmanned aerial vehicle tight formation control method based on pigeon swarm optimization Download PDF

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CN111158395B
CN111158395B CN202010031399.5A CN202010031399A CN111158395B CN 111158395 B CN111158395 B CN 111158395B CN 202010031399 A CN202010031399 A CN 202010031399A CN 111158395 B CN111158395 B CN 111158395B
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徐博
张大龙
王连钊
吴磊
李盛新
金坤明
刘梁
张奂
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Harbin Hachuan Zhiju Innovation Technology Development Co ltd
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Harbin Engineering University
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Abstract

The invention provides a multi-unmanned aerial vehicle compact formation control method based on pigeon swarm optimization, which is characterized in that a mathematical model of pneumatic coupling effect under a compact formation condition is established by analyzing the influence of long-aircraft wingtip eddy currents on wing machines, and an ideal state of multi-unmanned aerial vehicle compact formation is obtained by inputting long-aircraft control instructions and improving an artificial potential field method. And estimating the bureaucratic control quantity which can lead the bureaucratic state quantity at the next moment to be the closest to the bureaucratic control quantity at the ideal state by utilizing an improved pigeon swarm optimization algorithm, thereby completing the formation task. The invention has the significance of providing a multi-unmanned aerial vehicle formation control scheme under the condition of compact formation, and the multi-unmanned aerial vehicle formation control scheme has the advantages of high convergence speed, high steady-state precision and higher engineering application value.

Description

Multi-unmanned aerial vehicle tight formation control method based on pigeon swarm optimization
Technical Field
The invention relates to a multi-unmanned aerial vehicle tight formation control method, in particular to a multi-unmanned aerial vehicle tight formation system in-flight formation control method based on an improved pigeon swarm optimization algorithm and an improved artificial potential field method.
Background
The unmanned aerial vehicles are grouped or arranged according to a certain formation, and the formation is kept unchanged in the whole flying process. The unmanned aerial vehicles are arranged according to a certain formation, and the unmanned aerial vehicles are enabled to keep specified distance, interval and height difference among the unmanned aerial vehicles when flying in formation. The vortex field generated by the wings and the empennage can have great influence on the flight power performance of the airplane which passes through the flow field or flies close to the flow field when the airplane flies, the principle of aerodynamic coupling starts from the formation flight of birds, and the influence is favorable and has disadvantages: the vortex produced by a long wing in the formation flight is beneficial to the wing plane, which can obviously reduce the resistance of the wing plane and increase the lift, thus reducing the fuel consumption of the wing plane and increasing the voyage. However, while generating this benefit, the vortex also brings about not only minor air disturbance to the rear aircraft, but also has a great influence on the flight safety and dynamic characteristics of the rear aircraft, so that in the close formation flight, the problem of aerodynamic coupling between the aircraft must be considered and solved by a proper method, and an accurate mathematical model must be established for the problem. The traditional PID controller utilizes input and actual state errors to perform state control at the next moment, the effect of multi-unmanned aerial vehicle formation control under the tight formation condition is poor, the expected formation cannot be achieved, and the effect will be worse and worse along with the enlargement of the formation scale, so that the problem needs to be researched and how to solve.
Disclosure of Invention
Aiming at the prior art, the invention aims to provide a multi-unmanned aerial vehicle tight formation control method based on pigeon swarm optimization, which avoids the control by using formation errors and improves the control speed and the control precision.
In order to solve the technical problem, the invention discloses a multi-unmanned aerial vehicle tight formation control method based on pigeon swarm optimization, which comprises the following steps:
the method comprises the following steps: setting control instruction U of unmanned aerial vehicle long machineL＝[VLc ψLc hLc]And formation expected spacing
Figure BDA0002364435880000011
Wherein
Figure BDA0002364435880000012
Representing the desired longitudinal spacing between a prolonged and a bureaucratic machine,
Figure BDA0002364435880000013
representing the desired lateral spacing between a long and a bureaucratic plane,
Figure BDA0002364435880000014
representing the expected distance between the longerons and the bureaucratic machines in the height direction, establishing a compact formation mathematical model, and utilizing the control commands U of the longeronsL＝[VLc ψLc hLc]And current state quantity X of the long machineL＝[VL ψL hL]Calculating the state quantity X of the long machine at the next momentLnextWherein V isLcSpeed control command, psi, representing a longplaneLcRepresenting a course angle control command of the long plane; h isLcHeight control command, V, representing a long machineLRepresenting the speed of the long machine; psiLRepresenting the heading angle of the long plane; h isLRepresents the height of the long machine;
step two: the state X of the long machine at the next momentLnextQuantity of current state of bureaucratic planeFAnd formation expectation roomInputting the distance D into an artificial potential field controller, and calculating the ideal state of the unmanned aerial vehicle formation in the next step, wherein X isF＝[x VF y ψF z ζ]TX, y and z represent the distance between the wing plane and the farm plane; vFThe speed of a representative wing plane; psiFA course angle representing a wing plane; zeta represents the speed difference in the direction of the height of a wing plane and a long plane;
step three: calculating the control quantity of a bureau plane by utilizing an improved pigeon group optimization algorithm;
step four: inputting the control quantity of a bureaucratic machine into a compact formation model of the bureaucratic machine to calculate the next state quantity of the bureaucratic machine;
step five: and repeating the second step to the fourth step until the simulation duration.
The invention also includes:
1. the tight formation mathematical model comprises: the pilot plane automatic pilot model and the six-degree-of-freedom state space model of the wing plane meet the following conditions:
Figure BDA0002364435880000021
Figure BDA0002364435880000022
Figure BDA0002364435880000023
wherein, tauVRepresenting the drone speed time constant; tau isψRepresenting a course time constant of the unmanned aerial vehicle;
Figure BDA0002364435880000027
and
Figure BDA0002364435880000028
representing the unmanned aerial vehicle altitude time constant;
the state space model of six degrees of freedom of the wing plane satisfies:
Figure BDA0002364435880000024
UFc＝[VFc ψFc hFc]Tamount of bureaucratic; z ═ VL ψL hLc]TThe coupling quantity of the long machine is set; the specific elements of each matrix are as follows:
Figure BDA0002364435880000025
Figure BDA0002364435880000026
Figure BDA0002364435880000031
Figure BDA0002364435880000032
in the formula
Figure BDA0002364435880000033
The initial speed of the long machine is set as,
Figure BDA0002364435880000034
is the average aerodynamic pressure, S is the wing area, m is the total mass, V is the air velocity equal to the unmanned aerial vehicle speed,
Figure BDA0002364435880000035
in order to have a one time constant for the height,
Figure BDA0002364435880000036
is the height time constant two;
Figure BDA0002364435880000037
bureau plane velocityTime constant of degree, its value and tauVThe same;
Figure BDA0002364435880000038
as a wing aircraft course time constant, its value and τψThe same is true.
Figure BDA0002364435880000039
Is the component of the lateral force increment coefficient in the y direction;
Figure BDA00023644358800000310
is the component of the incremental coefficient of resistance in the y direction;
Figure BDA00023644358800000311
is the component of the lateral force increment coefficient in the z direction;
Figure BDA00023644358800000312
is the component of the lift delta coefficient in the y-direction.
Calculating the state quantity X of the long machine at the next momentLnextThe method specifically comprises the following steps: will be long machine control instruction ULAnd current state quantity X of long machineL＝[VL ψL hL]Inputting the model of the automatic pilot of the pilot machine to obtain the next time state X of the pilot machineLnext。
2. In the second step, the motion equation of the unmanned aerial vehicle in the artificial potential field controller is expressed as the following formula:
Figure BDA00023644358800000313
in the formula xiA position vector representing an ith drone; v. ofiRepresenting a velocity vector of an ith drone; m isiRepresenting the mass of the ith drone; u. ofiA control vector representing an ith drone; k is a radical ofiviRepresenting the velocity damping vector of the ith robot, where uiRepresented by the formula:
ui＝αi+βi+γi+kivi
in the formula of alphaiRepresenting the speed consistency control quantity of the ith unmanned aerial vehicle and the adjacent unmanned aerial vehicle; beta is aiRepresenting the distance potential field control quantity of the ith unmanned aerial vehicle and the adjacent unmanned aerial vehicle; gamma rayiRepresenting the formation speed consistency control quantity of the ith unmanned aerial vehicle and the multi-unmanned aerial vehicle system; v. ofendA set system formation speed is set; vijRepresenting the set distance potential field function, the control quantity can be expressed as:
Figure BDA0002364435880000041
in the formula KvRepresenting a velocity feedback gain factor; kpRepresenting a potential field feedback gain factor, wherein a distance potential field function VijThe settings were as follows:
Figure BDA0002364435880000042
in the formula xijNamely the distance between two adjacent unmanned aerial vehicles at present;
the state quantity of the leader at the next moment can be used for obtaining the formation at the next moment, namely the ideal state quantity X of the leader at the next momentFnextThat is, the control quantity u currently suffered by the wing plane is calculated by the speed difference and the position difference between the next moment of the lead plane and the current moment of the wing plane and the stable speed difference between the wing plane and the set formationiAnd then the position, the speed and the course of the wing plane at the next moment are calculated by the unmanned plane motion equation.
3. In the third step, the calculation of the amount of controlling of the bureaucratic machines by utilizing the improved pigeon group optimization algorithm is as follows: selecting particle X as bureaucratic control quantity in improved pigeon group algorithm
Figure BDA0002364435880000043
Updating the set X, and updating the rule as follows:
updating rules of quantum particle swarms:
Figure BDA0002364435880000044
Figure BDA0002364435880000045
and (3) improving a landmark operator updating rule:
β＝round(1+rand)
Figure BDA0002364435880000046
Figure BDA0002364435880000047
wherein α is the contraction-expansion coefficient; beta is a learning factor; xpbestRepresenting individual history optimal; xgbestRepresents global history optimality; xmbestRepresenting the optimal average of individual history, and outputting X after iteration is completedgbestBureau of bureau plane control UFcAnd Nc is the current iteration number.
4. The input of the controlling quantity of the wing plane into the compact formation of the wing plane model in the fourth step is concretely as follows:
will find the UFcA state space model with six degrees of freedom and with bureaucratic machines:
Figure BDA0002364435880000051
thus obtaining the actual state quantity X 'at the next moment of the wing plane'Fnext。
The invention has the beneficial effects that: the invention provides a new compact formation control scheme based on an unmanned aerial vehicle state space equation under compact formation and by considering the difference between an expected formation state and an actual formation state, and realizes formation control by taking the consistency difference of the two states as control input. Compared with the traditional PID controller scheme, the method avoids the control by utilizing the formation error, improves the control speed and the control precision, and can complete the high-precision compact formation task in a larger unmanned aerial vehicle range. The invention provides a new scheme for the formation control of multiple unmanned aerial vehicles under the condition of tight formation, and has higher engineering application value.
Drawings
Fig. 1 is a schematic diagram in an example of the present invention.
Fig. 2 is a flowchart of the pigeon flock algorithm in the present example.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
The invention provides a multi-unmanned aerial vehicle compact formation system control method based on an improved pigeon swarm optimization algorithm and an improved artificial potential field method. And estimating the bureaucratic control quantity which can lead the bureaucratic state quantity at the next moment to be the closest to the bureaucratic control quantity at the ideal state by utilizing an improved pigeon swarm optimization algorithm, thereby completing the formation task. The invention has the significance of providing a multi-unmanned aerial vehicle formation control scheme under the condition of compact formation, and the multi-unmanned aerial vehicle formation control scheme has the advantages of high convergence speed, high steady-state precision and higher engineering application value.
Fig. 1 shows a schematic diagram of a multi-unmanned aerial vehicle system formation control scheme based on an improved pigeon swarm algorithm and an artificial potential field method, which is provided by the invention, and mainly aims at solving the problems that multi-unmanned aerial vehicle formation has strong coupling, strong nonlinearity and the like under a tight formation condition. The method comprises the following steps:
the method comprises the following steps: and setting a long-machine control instruction of the unmanned aerial vehicle and an expected formation interval, and establishing a compact formation mathematical model. And calculating the state quantity of the long machine at the next moment by using the control instruction of the long machine and the current state quantity of the long machine. As shown in FIG. 1, before the formation begins, a control command U of the captain of the unmanned aerial vehicle needs to be setL＝[VLc ψLc hLc]Wherein the formation takes place at the desired spacing
Figure BDA0002364435880000061
And a mathematical model during tight formation, including a longplane autopilot model:
Figure BDA0002364435880000062
Figure BDA0002364435880000063
Figure BDA0002364435880000064
in the formula tauVRepresenting the drone speed time constant; tau isψRepresenting a course time constant of the unmanned aerial vehicle;
Figure BDA0002364435880000065
and
Figure BDA0002364435880000066
representing the unmanned aerial vehicle altitude time constant; vLRepresenting the speed of the long machine; psiLRepresenting the heading angle of the long plane; h isLRepresents the height of the long machine; vLcRepresenting a speed control command of the long machine; psiLcRepresenting a course angle control command of the long plane; h isLcRepresenting height control instructions for a long machine. Will be long machine control instruction ULAnd current state quantity X of long machineL＝[VL ψL hL]Inputting the model of the automatic pilot of the pilot machine to obtain the next time state X of the pilot machineLnext，
Figure BDA0002364435880000067
Represents the desired longitudinal spacing between a longplane and a bureaucratic plane;
Figure BDA0002364435880000068
represents the lateral desired spacing between a long and a bureaucratic plane;
Figure BDA0002364435880000069
representing the desired spacing in the direction of height between a longeron and a bureaucratic machine.
Spatial model of six degrees of freedom of wing plane:
Figure BDA00023644358800000610
in the formula XF＝[x VF y ψF z ζ]TThe quantity of a wing plane state, x, y and z represent the distance between the wing plane and the long plane; vFThe speed of a representative wing plane; psiFA course angle representing a wing plane; zeta represents the speed difference in the direction of the height of a wing plane and a long plane. U shapeFc＝[VFc ψFchFc]TAmount of bureaucratic; z ═ VL ψL hLc]TIs the coupling quantity of the long machine. The specific elements of each matrix are as follows:
Figure BDA00023644358800000611
Figure BDA00023644358800000612
Figure BDA0002364435880000071
Figure BDA0002364435880000072
in the formula
Figure BDA0002364435880000073
For the initial speed of the long machine, the specific parameters in the formula adopt F-16 aircraft model parameters.
Figure BDA0002364435880000074
Is the average aerodynamic pressure, S is the wing area, m is the total mass, V is the air velocity equal to the unmanned aerial vehicle speed,
Figure BDA0002364435880000075
in order to have a one time constant for the height,
Figure BDA0002364435880000076
is the height time constant two;
Figure BDA0002364435880000077
a rate time constant of a wing aircraft, the value of which corresponds to τVThe same; tau isψFAs a wing aircraft course time constant, its value and τψThe same is true.
Figure BDA0002364435880000078
The component of the incremental coefficient of lateral force in the y direction is 0.0033;
Figure BDA0002364435880000079
the component of the incremental drag coefficient in the y-direction is-0.000782;
Figure BDA00023644358800000710
is the component of the lateral force increment coefficient in the z direction, and has the value of-0.0011;
Figure BDA00023644358800000711
the component of the lift increment coefficient in the y-direction is given a value of-0.0077.
TABLE 1F-16 parameter Table for unmanned aerial vehicle
Figure BDA00023644358800000712
Figure BDA0002364435880000081
Step two: the state X of the long machine at the next momentLnextCurrent state of bureaucratic plane XFAnd inputting the expected formation distance D into an artificial potential field controller, and calculating the ideal state of the unmanned aerial vehicle formation in the next step.
The equation of motion of the drone in the artificial potential field controller is expressed as:
Figure BDA0002364435880000082
in the formula xiA position vector representing an ith drone; v. ofiRepresenting a velocity vector of an ith drone; m isiRepresenting the mass of the ith drone; u. ofiA control vector representing an ith drone; k is a radical ofiviRepresenting the velocity damping vector for the ith robot. Wherein u isiRepresented by the formula:
ui＝αi+βi+γi+kivi
in the formula of alphaiRepresenting the speed consistency control quantity of the ith unmanned aerial vehicle and the adjacent unmanned aerial vehicle; beta is aiRepresenting the distance potential field control quantity of the ith unmanned aerial vehicle and the adjacent unmanned aerial vehicle; gamma rayiRepresenting the formation speed consistency control quantity of the ith unmanned aerial vehicle and the multi-unmanned aerial vehicle system; v. ofendA set system formation speed is set; vijRepresenting the set distance potential field function, the control quantity can be expressed as:
Figure BDA0002364435880000083
in the formula KvRepresenting a velocity feedback gain factor; kpRepresenting the potential field feedback gain factor. Wherein the distance potential field function VijThe settings were as follows:
Figure BDA0002364435880000084
in the formula xijNamely the distance between two adjacent unmanned aerial vehicles at present. The state quantity of the leader at the next moment can be used for obtaining the formation at the next moment, namely the ideal state quantity X of the leader at the next momentFnextThat is, the control quantity u currently suffered by the wing plane is calculated by the speed difference and the position difference between the next moment of the lead plane and the current moment of the wing plane and the stable speed difference between the wing plane and the set formationiAnd then the position, the speed and the course of the wing plane at the next moment are calculated by the unmanned plane motion equation.
Step three: and calculating the control quantity of the bureaucratic machines by utilizing an improved pigeon swarm optimization algorithm.
Selecting particle X as bureaucratic control quantity in improved pigeon group algorithm
Figure BDA0002364435880000085
Updating the set X according to the flow of FIG. 2, the updating rule is as follows:
1 quantum particle swarm updating rule:
Figure BDA0002364435880000091
Figure BDA0002364435880000092
2, improving the landmark operator updating rule:
β＝round(1+rand)
Figure BDA0002364435880000093
Figure BDA0002364435880000094
wherein α is the contraction-expansion coefficient; beta is a learning factor; xpbestRepresenting individual history optimal; xgbestRepresents global history optimality; xmbestRepresenting the optimal average of the individual history. X output when iteration is completedgbestBureau of bureau plane control UFc。
f(XF)＝(X′Fnext-XFnext)·(X′Fnext-XFnext)T，f(XF) The method is used for improving the fitness function of the pigeon group algorithm. X'Fnext、XFnextThe state quantities of a wing plane at the current moment and the ideal state quantity of a wing plane at the next moment, respectively, can be calculated from the flow chart in fig. 2 as a fitness function f (X)F) Amount of wing-plane control of the hourly space UFc＝[VFc ψFc hFc]In the formula VFcControlling quantity, psi, of wing aircraft speedFcAmount of control of course angle of bureaucratic machine, hFcThe amount of the bureaucratic plane is controlled.
The method comprises the following specific steps:
Figure BDA0002364435880000095
Figure BDA0002364435880000101
the flow chart is shown in fig. 2, where the first loop is a compass operator loop and the second loop is a landmark operator loop. Each cycle updates the particles according to its specific update rule and then finds the one that minimizes the fitness function, i.e., is optimal. The fitness function value is optimized through continuous circulation, namely the difference between the actual state quantity of the representative wing plane and the ideal state quantity is minimum.
Step four: the control quantity of the bureaucratic plane is input into a tight formation model of the bureaucratic plane to calculate the next state quantity of the bureaucratic plane.
Will find the UFcA state space model with six degrees of freedom and with bureaucratic machines:
Figure BDA0002364435880000102
thus obtaining the actual state quantity X 'at the next moment of the wing plane'Fnext
Step five: and repeating the second step to the fourth step until the simulation duration.
Simulation verification:
simulation conditions are as follows: the desired formation pitch is set to [60ft,23.5ft,0ft ]; the initial formation state bureaucratic wing plane states are all [0ft/s,0 degrees, 0ft ]; the status of constant speed stable bureaucratic plane is [825ft/s,0 deg., 45000ft ]. The sampling period is 0.02s, and the simulation time is set to be 60 s; the long machine control can be divided into two stages: the first stage is that the heading control instruction of the first 15s long aircraft is uniformly reduced from zero to minus thirty degrees, and then the aircraft flies stably for 5 s; the second phase increases uniformly from minus thirty degrees to zero degrees during 20s to 35s and holds the control command for the simulation duration.
As can be seen from tables 2(a) to 2(c), the stable formation error x direction of the scheme of the invention can reach 0.15 inch at most under the condition of compact formation; a maximum of 0.08 inches in the y-direction; the z direction can be up to 0.3 inches.
Table 2(a) unmanned plane state at 15s
Figure BDA0002364435880000111
Table 2(b) unmanned plane state at 35s
Figure BDA0002364435880000112
TABLE 2(c) unmanned plane State at 60s
Figure BDA0002364435880000113
The specific implementation mode of the invention also comprises:
the method comprises the following steps: and setting a long-machine control instruction of the unmanned aerial vehicle and an expected formation interval, and establishing a compact formation mathematical model.
Step two: and inputting the control instruction of the long-distance unmanned aerial vehicle and the expected formation distance into an artificial potential field controller, and calculating the ideal state of the unmanned aerial vehicle formation in the next step.
Step three: and calculating the control quantity of the bureaucratic machines by utilizing an improved pigeon swarm optimization algorithm.
Step four: the control quantity of the bureaucratic plane is input into a tight formation model of the bureaucratic plane to calculate the next state quantity of the bureaucratic plane.
Step five: and repeating the second step to the fourth step until the simulation duration.

Claims (3)

1. A multi-unmanned aerial vehicle tight formation control method based on pigeon swarm optimization is characterized by comprising the following steps:
the method comprises the following steps: setting control instruction U of unmanned aerial vehicle long machineL＝[VLc ψLc hLc]And formation expected spacing
Figure FDA0002979883230000011
Wherein
Figure FDA0002979883230000012
Representing the desired longitudinal spacing between a prolonged and a bureaucratic machine,
Figure FDA0002979883230000013
representing the desired lateral spacing between a long and a bureaucratic plane,
Figure FDA0002979883230000014
representing the expected distance between the longerons and the bureaucratic machines in the height direction, establishing a compact formation mathematical model, and utilizing the control commands U of the longeronsL＝[VLc ψLc hLc]And current state quantity X of the long machineL＝[VL ψL hL]Calculating the state quantity X of the long machine at the next momentLnextWherein V isLcSpeed control command, psi, representing a longplaneLcRepresenting a course angle control command of the long plane; h isLcHeight control command, V, representing a long machineLRepresenting the speed of the long machine; psiLRepresenting the heading angle of the long plane; h isLRepresents the height of the long machine;
step two: the state X of the long machine at the next momentLnextQuantity of current state of bureaucratic planeFInputting the expected formation distance D into an artificial potential field controller, and calculating the ideal state of the unmanned aerial vehicle formation in the next step, wherein XF＝[x VF y ψF z ζ]TX, y and z represent the distance between the wing plane and the farm plane; vFThe speed of a representative wing plane; psiFA course angle representing a wing plane; zeta represents the speed difference in the direction of the height of a wing plane and a long plane;
step three: calculating the control quantity of a bureau plane by utilizing an improved pigeon group optimization algorithm;
step four: inputting the control quantity of a bureaucratic machine into a compact formation model of the bureaucratic machine to calculate the next state quantity of the bureaucratic machine;
step five: repeatedly executing the second step to the fourth step until the simulation duration;
step three, the calculation of the bureaucratic control amount by utilizing the improved pigeon swarm optimization algorithm is specifically as follows: selecting particle X as bureaucratic control quantity in improved pigeon group algorithm
Figure FDA0002979883230000015
Updating the set X, and updating the rule as follows:
updating rules of quantum particle swarms:
Figure FDA0002979883230000016
Figure FDA0002979883230000017
u～U(0,1)
and (3) improving a landmark operator updating rule:
β＝round(1+rand)
Figure FDA0002979883230000018
Figure FDA0002979883230000019
wherein α is the contraction-expansion coefficient; beta is a learning factor; xpbestRepresenting individual history optimal; xgbestRepresents global history optimality; xmbestRepresenting the optimal average of individual history, and outputting X after iteration is completedgbestBureau of bureau plane control UFcAnd Nc is the current iteration number.
2. The multi-unmanned aerial vehicle tight formation control method based on pigeon flock optimization according to claim 1, characterized in that: the tight formation mathematical model comprises: the pilot plane automatic pilot model and the six-degree-of-freedom state space model of the wing plane meet the following conditions:
Figure FDA0002979883230000021
Figure FDA0002979883230000022
Figure FDA0002979883230000023
wherein, tauVRepresenting the drone speed time constant; tau isψRepresenting a course time constant of the unmanned aerial vehicle;
Figure FDA0002979883230000024
and
Figure FDA0002979883230000025
representing the unmanned aerial vehicle altitude time constant;
the state space model of six degrees of freedom of the bureaucratic plane meets the following requirements:
Figure FDA0002979883230000026
UFc＝[VFc ψFc hFc]Tamount of bureaucratic; z ═ VL ψL hLc]TThe coupling quantity of the long machine is set; the specific elements of each matrix are as follows:
Figure FDA0002979883230000027
Figure FDA0002979883230000028
Figure FDA0002979883230000031
Figure FDA0002979883230000032
in the formula
Figure FDA0002979883230000033
Figure FDA0002979883230000034
The initial speed of the long machine is set as,
Figure FDA0002979883230000035
is the average aerodynamic pressure, S is the wing area, m is the total mass, V is the air velocity equal to the unmanned aerial vehicle speed,
Figure FDA0002979883230000036
in order to have a one time constant for the height,
Figure FDA0002979883230000037
is the height time constant two;
Figure FDA0002979883230000038
a rate time constant of a wing aircraft, the value of which corresponds to τVThe same;
Figure FDA0002979883230000039
as a wing aircraft course time constant, its value and τψThe same;
Figure FDA00029798832300000310
is the component of the lateral force increment coefficient in the y direction;
Figure FDA00029798832300000311
is the component of the incremental coefficient of resistance in the y direction;
Figure FDA00029798832300000312
is the component of the lateral force increment coefficient in the z direction;
Figure FDA00029798832300000313
is the component of the lift increment coefficient in the y direction;
the state quantity X of the long machine at the next moment is calculatedLnextThe method specifically comprises the following steps: will be long machine control instruction ULAnd current state quantity X of long machineL＝[VL ψL hL]Inputting the model of the automatic pilot of the pilot machine to obtain the next time state X of the pilot machineLnext。
3. The multi-unmanned aerial vehicle tight formation control method based on pigeon flock optimization according to claim 1, characterized in that: and step two, the motion equation of the unmanned aerial vehicle in the artificial potential field controller is expressed as the following formula:
Figure FDA00029798832300000314
in the formula xiA position vector representing an ith drone; v. ofiRepresenting a velocity vector of an ith drone; m isiRepresenting the mass of the ith drone; u. ofiA control vector representing an ith drone; k is a radical ofiviRepresenting the velocity damping vector of the ith robot, where uiRepresented by the formula:
ui＝αi+βi+γi+kivi
in the formula of alphaiRepresenting the speed consistency control quantity of the ith unmanned aerial vehicle and the adjacent unmanned aerial vehicle; beta is aiRepresenting the distance potential field control quantity of the ith unmanned aerial vehicle and the adjacent unmanned aerial vehicle; gamma rayiRepresenting the formation speed consistency control quantity of the ith unmanned aerial vehicle and the multi-unmanned aerial vehicle system; v. ofendA set system formation speed is set; vijRepresenting the set distance potential field function, the control quantity can be expressed as:
Figure FDA0002979883230000041
in the formula KvRepresenting a velocity feedback gain factor; kpRepresenting a potential field feedback gain factor, wherein a distance potential field function VijThe settings were as follows:
Figure FDA0002979883230000042
in the formula xijNamely the distance between two adjacent unmanned aerial vehicles at present;
the state quantity of the leader at the next moment can be used for obtaining the formation at the next moment, namely the ideal state quantity X of the leader at the next momentFnextThat is, the control quantity u currently suffered by the wing plane is calculated by the speed difference and the position difference between the next moment of the lead plane and the current moment of the wing plane and the stable speed difference between the wing plane and the set formationiGo forward and go forwardAnd the position, the speed and the course of the wing plane at the next moment are calculated by the unmanned plane motion equation.
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