CN116560401A

CN116560401A - Method for determining control instruction of plane in unmanned plane formation and terminal equipment

Info

Publication number: CN116560401A
Application number: CN202310535355.XA
Authority: CN
Inventors: 陈琪锋; 郝文康; 廖宇新; 戴明哲; 魏才盛; 蔡德恩
Original assignee: Central South University
Current assignee: Central South University
Priority date: 2023-01-30
Filing date: 2023-05-12
Publication date: 2023-08-08

Abstract

The method is suitable for the technical field of unmanned aerial vehicle control, and provides a method for determining a control instruction of a plane in unmanned aerial vehicle formation and terminal equipment, wherein the method obtains intermediate state quantities of a plane and a plane under the action of the control quantity according to a motion model of the unmanned aerial vehicle formation when flying and initial state quantities of the plane and the plane defined in advance; calculating the state error of the wing plane relative to the long plane according to the intermediate state quantity of the long plane and the wing plane; carrying out language variable division on a predefined state error domain and a predefined control instruction error domain to obtain a state error language variable set and a control instruction error language variable set; determining control instruction fuzzy constraint according to membership functions of language variable values in each language variable set and fuzzy control rules; based on the DMPC method, the control command of the plane is determined according to the fuzzy constraint of the control command. The control energy consumption of the bureaucratic plane can be reduced.

Description

Method for determining control instruction of plane in unmanned plane formation and terminal equipment

Technical Field

The application belongs to the technical field of unmanned aerial vehicle control, and particularly relates to a method for determining a control instruction of a plane in unmanned aerial vehicle formation and terminal equipment.

Background

In recent years, the cooperative task execution of a fixed-wing unmanned aerial vehicle (UAV, unmanned Aerial Vehicle) cluster has become an important development trend for unmanned aerial vehicle system application, and the advantages of high cooperation, multiple tasks, low cost and the like of a small-sized multi-unmanned aerial vehicle are paid attention to. Unmanned aerial vehicle formation control technology is widely studied as a sub-problem of multi-unmanned aerial vehicle coordination, and unmanned aerial vehicle formation flying aims at achieving ideal formation by controlling the behaviors of all unmanned aerial vehicles.

Common formation flight strategies include a master-slave method, a virtual structure method, a behavior-based method, a consensus theory and the like. Unmanned aerial vehicle formation can be divided into two types of centralized and distributed according to a communication mode, in the centralized method, a control center is established in formation to realize control of the whole formation, and in the distributed method, the whole formation is completed through information exchange between adjacent individuals. Because the distributed control does not have a control center, the distributed control has better flexibility and expansibility compared with the centralized control. For the formation control method, there are common consistency control, adaptive feedback, sliding mode control, fuzzy control and the like, but these methods are inconvenient to handle constraints, and cost functions and constraint conditions can be designed according to specific requirements by using an optimized method, such as distributed model predictive control (DMPC, distributed Model Predictive Control).

Regarding unmanned aerial vehicle formation control problems, stability and feasibility of formation are generally studied, and little research on energy saving in formation process is performed. In the prior art, parameter setting in a DMPC algorithm is mostly carried out, the weight of a control quantity is increased to reduce the effect of the control quantity, but the rate of state convergence is slowed down, and the energy consumption is increased.

Disclosure of Invention

The embodiment of the application provides a method for determining a plane control instruction in unmanned plane formation and terminal equipment, which can solve the problem of large consumption of plane control energy in the current unmanned plane formation.

In a first aspect, an embodiment of the present application provides a method for determining a control instruction of a bureau in unmanned aerial vehicle formation, including:

obtaining the intermediate state quantity of the long plane and the intermediate state quantity of the bureau under the action of the control quantity according to the motion model of the unmanned plane during formation flight, the predefined initial state quantity of the long plane and the predefined initial state quantity of the bureau;

calculating the state error of the bureau relative to the bureau according to the intermediate state quantity of the bureau and the intermediate state quantity of the bureau;

carrying out language variable division on a predefined state error domain of the plane and a predefined control instruction error domain to obtain a state error language variable set of the plane and a control instruction error language variable set of the plane;

Determining control command fuzzy constraint of the plane according to membership functions corresponding to each language variable value in the state error language variable set, membership functions corresponding to each language variable value in the control command error language variable set and a predefined control command fuzzy control rule;

based on the distributed model predictive control method, the control instruction of the plane is determined according to the fuzzy constraint of the control instruction of the plane.

Optionally, the expression of the motion model when the unmanned aerial vehicle is in flight is as follows:

wherein, (x) _i ，y _i ) Representing the coordinates of the ith plane in the unmanned aerial vehicle formation under the inertial coordinate system, ψ _i Indicating heading angle of ith plane in unmanned plane formation, V _i Representing the speed, omega, of the ith plane in the unmanned plane formation _i Indicating the angular velocity of the ith plane in the unmanned aerial vehicle formation, ψ _i ^c Indicating the heading angle control instruction of the ith plane in unmanned plane formation, V _i ^c Indicating the speed control instruction of the ith plane in the unmanned aerial vehicle formation,and->All represent the inertial time constant, τ, of the autopilot course angle channel _v Inertial time constant representing speed channel, i=1, 2,.. _a ，N _a Representing the total number of bureau in flying formation, V _min Representing the minimum flying speed of the plane, V _max Indicating the maximum flying speed of the plane, a _max Indicating maximum acceleration, ω of the plane _max Indicating the maximum angular velocity of the plane alpha _max Indicating maximum angular acceleration of the plane +.>Indicating the angular acceleration of the ith plane,/->Indicating the acceleration of the ith plane.

Optionally, the intermediate state quantity X of the long machine ₀ ＝[x，y，ψ，V，ω] ^T 。

Optionally, intermediate state quantity of the planeX _i ＝[x _i ，y _i ，ψ _i ，V _i ，ω _i ] ^T The method comprises the steps of carrying out a first treatment on the surface of the Wherein X is _i Representing the intermediate state quantity of the ith plane.

Optionally, calculating the state error of the plane relative to the plane according to the intermediate state quantity of the plane and the intermediate state quantity of the plane, including:

coordinate system P of north east _n OP _e Taking the long machine as a reference point R as an inertial coordinate system to obtain a state quantity X of the reference point R in the inertial coordinate system _R ，X _R ＝[x _R ，y _R ，ψ _R ，V _R ，ω _R ] ^T The method comprises the steps of carrying out a first treatment on the surface of the The position of the reference point and the state quantity of the reference point are determined by the state quantity of the long machine;

establishing a reference point coordinate system X taking a long machine as a reference point in an inertial coordinate system _R OY _R And determines the target point G of the ith plane _i In the reference point coordinate system X _R OY _R Coordinates of (a)

By calculation formula

Obtaining the target point G of the ith plane in the inertial coordinate system _i State quantity of (2) Wherein (1)>

By calculation formula

Obtaining state errorRepresenting the state error of the ith plane relative to the long plane under the coordinate system of the reference point; wherein (1) >Coordinate transformation matrix representing inertial system to reference system, x _i 、y _i 、ψ _i 、V _i Omega, omega _i All represent the state quantity of the ith plane in the reference point coordinate system,/th plane> Indicating the error of the ith plane in the x-direction of the longer plane in the reference point coordinate system,/>Indicating the error of the ith plane in the y-direction of the longer plane in the reference point coordinate system,/>Indicating heading angle error of the ith plane relative to the long plane under the reference point coordinate system,/>Indicating the speed error of the ith plane relative to the longer plane in the reference point coordinate system,/>Indicating the angular velocity error of the ith plane relative to the long plane in the reference point coordinate system.

Optionally, the control instruction error includes a firstSpeed control command error of i planeAnd heading angle control instruction error of ith plane +.>

Optionally, the linguistic variables include NB, NM, MS, Z, PS, PM and PB; where NB represents a large negative error, NM represents a medium negative error, MS represents a small negative error, Z represents almost no error, PS represents a small positive error, PM represents a medium positive error, and PB represents a large positive error.

Optionally, the set of state error linguistic variables A state error language variable set representing the ith plane; wherein (1)>

Optionally, the control instruction error language variable set A control instruction error language variable set for representing the ith plane; wherein (1)>

Optionally, determining the control command fuzzy constraint of the plane according to the membership function corresponding to each language variable value in the state error language variable set, the membership function corresponding to each language variable value in the control command error language variable set, and a predefined control command fuzzy control rule, including:

drawing a state error membership image according to a membership function corresponding to each language variable value in the state error language variable set to obtain a first language variable membership set corresponding to the state error; the first language variable membership degree set comprises membership degrees corresponding to each language variable value in the state error language variable set;

drawing a control instruction error membership image according to a membership function corresponding to each language variable value in the control instruction error language variable set to obtain a second language variable membership value corresponding to the control instruction error; the second language variable membership degree set comprises membership degrees corresponding to each language variable value in the control instruction error language variable set;

obtaining a plurality of control instruction error fuzzy sets according to the first language variable membership degree set, the second language variable membership degree set and a predefined fuzzy control rule;

Aggregating the control command error fuzzy sets to obtain a control command error aggregation fuzzy set;

selecting an element set corresponding to the maximum membership degree from the control instruction error aggregation fuzzy set as the maximum control instruction error fuzzy set, and taking a language variable value corresponding to the maximum control instruction error fuzzy set as an optimal language variable;

and determining the boundary of the control command fuzzy constraint of the bureau plane according to the optimal language variable to obtain the control command fuzzy constraint of the bureau plane.

Optionally, the control instruction fuzzy control rule includes:

according toAnd->Determining speed control command error of ith plane>

According toAnd->Determining heading angle control instruction error of ith plane>

Optionally, determining a boundary of a control command fuzzy constraint of the plane according to the optimal language variable to obtain the control command fuzzy constraint of the plane, including:

aiming at the ith plane, the control instruction corresponding to the position with the same membership degree of the optimal language variable and adjacent to the language variable value on the left side of the optimal language variable is used as the left boundary of the fuzzy constraint of the control instruction of the plane

Aiming at the ith plane, the control instruction corresponding to the position with the same membership degree of the optimal language variable and adjacent to the right side language variable value of the optimal language variable is used as the right boundary of the fuzzy constraint of the control instruction of the plane

According to the left boundaryAnd right border->Determining control command ambiguity constraint of ith planeWherein U is ^X Representing the instruction space.

Optionally, the method for controlling the prediction based on the distributed model determines the control command of the wing plane according to the fuzzy constraint of the control command of the wing plane, and includes:

by calculation formula

Obtaining the coordinate system X of the ith plane relative to the jth plane at the reference point _R OY _R State error in (a)Where j=1, 2,.. _a And j is not equal to i;

by calculation formula

Xe _ij ^(R) (τ|t _k )＝Xe _j (τ|t _k )-Xe _i (τ|t _k )

Obtaining control instruction track of ith planeAnd at t the bureau _k Control command at +delta timeDetermining a final control instruction; wherein δ represents the update period, t _k Indicating the moment corresponding to the actual state quantity after k updates,/time> Represents a natural number set, τ e { t } _k +δs}，s＝0，1，K，N ^p -1，N ^p Representing the number of predicted steps δN ^p Representing the prediction time domain, S ^(X) And S is ^(U) All represent normalized matrix, M ₁ ，M ₂ N represents positive definite symmetric matrix, X _i Representing the state quantity of the ith plane, X _-i Representing a state quantity of a plane adjacent to the ith plane, X _i (t _k ) Indicating that the ith plane is at t _k State quantity of time, U _i (t _k ) Indicating that the ith plane is at t _k Control of the time of day->Representing the predicted status track of the ith plane,/->Representing the estimated state trajectory of the ith plane in the predicted time domain,/ >Representing the optimal state track of the ith plane in the forecast domain,/for the plane>Representing the predicted control track of the ith plane,representing i-th plane estimated controlTrack (S)/(S)>Indicating the optimal predicted control trajectory of the ith plane,/-> State quantity transition equation representing the i-th plane from the point tau to the point tau + delta>Representing the predicted state quantity of the ith plane at the time tau + delta,/for the plane>Representing the estimated state quantity of the ith plane at the time τ+δ,/v>Indicating the optimal predicted state quantity of the ith plane at the time tau + delta,/for the plane>The optimal predicted state quantity of the adjacent plane of the ith plane at the tau+delta moment is represented.Indicating the state error of the plane i relative to the plane at time τ, A (τ|t _k ) Representing a coordinate transformation matrix from an inertial system to a reference system at a time tau, X _i (τ|t _k ) Indicating the state quantity of the ith plane at the moment tau +.>State quantity indicating the target position of the ith plane at time tau +.>Indicating the target position of the ith planeControl quantity at time τ, +.>Indicating the status error of the j-th plane at the time tau and the long plane,/>Representing the adjacent plane of the ith plane at t _k Estimated state quantity of time ∈>Representing the adjacent plane of the ith plane at t _k Estimated control amount of time,/->Indicating the status error of the ith and jth plane at tau point,/for the ith and jth plane >Representation ofRank of (a)/(b)>Indicating the control error of the plane i relative to the plane at the moment tau, and indicating norm.

In a second aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the method for determining a control instruction of a plane in unmanned aerial vehicle formation described above when executing the computer program.

The scheme of the application has the following beneficial effects:

in some embodiments of the present application, according to a motion model of an unmanned aerial vehicle formation flying, a predefined initial state quantity of a long machine and a predefined initial state quantity of a bureau, under the action of a control quantity, an intermediate state quantity of the long machine and an intermediate state quantity of a bureau are obtained, then according to the intermediate state quantity of the long machine and the intermediate state quantity of the bureau, a state error of the bureau relative to the long machine is calculated, then a language variable division is performed on a predefined state error domain of the bureau and a predefined control instruction error domain of the bureau, a state error language variable set of the bureau and a control instruction error language variable set of the bureau are obtained, then according to a membership function corresponding to each language variable value in the state error language variable set, a predefined control instruction fuzzy control rule corresponding to each language variable value in the control instruction error language variable set, a control instruction fuzzy constraint of the bureau is determined, and finally, a control instruction of the bureau is determined according to the control instruction fuzzy constraint of the bureau on the control instruction of the bureau. The control command of the wing plane is determined by determining the fuzzy constraint of the control command of the wing plane and then determining the control command of the wing plane based on the distributed model predictive control method, so that the amplitude change of the control quantity can be reduced under the condition that the weight of the control quantity is unchanged, and the energy consumption of the control of the wing plane is reduced.

Other advantages of the present application will be described in detail in the detailed description section that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a method for determining a control command of a plane in unmanned plane formation according to an embodiment of the present application;

fig. 2 is a schematic illustration of unmanned aerial vehicle formation flight provided in an embodiment of the present application;

FIG. 3a is a chart showing the membership image of the linguistic variables for the y-direction error of the plane provided in one embodiment of the present application;

FIG. 3b is a chart showing membership images of linguistic variables for the x-direction error of a plane in accordance with one embodiment of the present invention;

FIG. 3c is a linguistic variable membership image of the course angle error of the plane provided by an embodiment of the present application;

FIG. 3d is a linguistic variable membership image of a speed error of a wing engine according to one embodiment of the present application;

FIG. 4a is a linguistic variable membership image of a corner command error for a plane provided in one embodiment of the present application;

FIG. 4b is a linguistic variable membership image of a speed command error of a wing engine provided in one embodiment of the present application;

fig. 5a is a schematic view of a flight path of a bureau using a DMPC method according to an embodiment of the present application;

fig. 5b is a schematic view of a flight path of the bureau according to an embodiment of the present application when the bureau adopts the fuzzy constraint DMPC method;

FIG. 6a is a schematic diagram illustrating the comparison of the distance variation in the x direction of the DMPC method and the fuzzy constraint DMPC method according to an embodiment of the present application;

FIG. 6b is a schematic diagram illustrating a comparison of y-direction distance variation of a DMPC method and a fuzzy constraint DMPC method according to an embodiment of the present application;

FIG. 6c is a schematic diagram illustrating speed variation of the DMPC method and the fuzzy constraint DMPC method according to an embodiment of the present application;

FIG. 6d is a schematic diagram showing course angle variation contrast of the DMPC method and the fuzzy constraint DMPC method according to an embodiment of the present application;

FIG. 7 is a schematic diagram illustrating energy loss comparison of a DMPC method and a fuzzy constraint DMPC method according to an embodiment of the present application;

FIG. 8a is a schematic diagram of a flight path difference between a DMPC method and a fuzzy constraint DMPC method according to an embodiment of the present disclosure;

FIG. 8b is a schematic diagram of a speed variation accumulated difference of the DMPC method and the fuzzy constraint DMPC method according to an embodiment of the present disclosure;

FIG. 8c is a schematic diagram of the accumulated difference of the angular velocity changes of the DMPC method and the fuzzy constraint DMPC method according to an embodiment of the present application;

FIG. 8d is a schematic diagram of energy consumption difference between the DMPC method and the fuzzy constraint DMPC method according to an embodiment of the present disclosure;

fig. 9 is a schematic structural diagram of a terminal device according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".

In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

Aiming at the problem of large consumption of control energy of a plane in the formation of an unmanned plane at present, the application provides a method and terminal equipment for determining a plane control instruction in the formation of the plane, wherein the method obtains the intermediate state quantity of the plane and the intermediate state quantity of the plane according to a motion model of the unmanned plane in the formation of the plane, a predefined initial state quantity of the plane and the predefined initial state quantity of the plane, calculates the state error of the plane relative to the plane according to the intermediate state quantity of the plane and the intermediate state quantity of the plane, then carries out language variable division on a state error language variable set of the plane and a predefined control instruction error language variable set of the plane, and finally determines the control instruction fuzzy control rule of the plane according to a membership function corresponding to each language variable value in the state error language variable set, a predefined control instruction fuzzy control rule corresponding to each language variable value in the control instruction error language variable set and finally, and the control instruction fuzzy control rule of the plane based on the distributed prediction control method. The control command of the wing plane is determined by determining the fuzzy constraint of the control command of the wing plane and then determining the control command of the wing plane based on the distributed model predictive control method, so that the amplitude change of the control quantity can be reduced under the condition that the weight of the control quantity is unchanged, and the energy consumption of the control of the wing plane is reduced.

As shown in fig. 1, the method for determining a control instruction of a wing plane in unmanned aerial vehicle formation provided by the present application mainly includes the following steps:

and step 11, obtaining the intermediate state quantity of the long plane and the intermediate state quantity of the rear plane under the action of the control quantity according to the motion model, the predefined initial state quantity of the long plane and the predefined initial state quantity of the rear plane when the unmanned plane is formed to fly.

The unmanned aerial vehicle formation comprises a long machine and a bureau, and in an embodiment of the application, the unmanned aerial vehicle formation comprises a long machine and a plurality of bureaus; in another embodiment of the present application, the unmanned aerial vehicle formation comprises a drone and a wing. The plane refers to an unmanned plane which is in formation flight and follows a long plane to perform tasks, and the long plane refers to a unmanned plane with a team in formation flight.

In some embodiments of the present application, the initial state quantity of the long plane and the initial state quantity of the plane may be given initial values according to human experience when determining the flight formation motion model.

In the embodiment of the application, a two-dimensional planar kinematic model of the fixed-wing unmanned aerial vehicle is adopted to describe a motion model of the unmanned aerial vehicle when the unmanned aerial vehicle is in formation flight.

Specifically, the expression of the motion model when the unmanned aerial vehicle is in formation flight is as follows:

Wherein, (x) _i ，y _i ) Representing the coordinates of the ith plane in the inertial coordinate system, ψ _i Indicating the heading angle of the ith plane, V _i Representing the speed, omega, of the ith plane in the unmanned plane formation _i Indicating the angular speed of the ith plane in the unmanned formation.

For the control of the wing plane, in the embodiment of the present application, the autopilot system is used to control the position and the attitude of the wing plane, and the expression of the autopilot model is as follows:

wherein, psi is _i ^c Indicating the heading angle control instruction of the ith plane, V _i ^c Indicating the speed control instruction of the ith plane,and->All represent the inertial time constant, τ, of the autopilot course angle channel _v Inertial time constant representing speed channel, i=1, 2,.. _a ，N _a Indicating the total number of bureau in flight formation.

Considering the state constraint in the actual flight process of the unmanned plane, including the speed constraint, the acceleration constraint, the angular speed constraint and the angular acceleration constraint, the state constraint expression of the ith plane is as follows:

wherein V is _min Representing the minimum flying speed of the plane, V _max Indicating the maximum flying speed of the plane, a _max Indicating maximum acceleration, ω of the plane _max Indicating the maximum angular velocity of the plane alpha _max Indicating the maximum angular acceleration of the plane of the bureau,indicating the angular acceleration of the ith plane,/- >Represent the first _i Acceleration of the personal plane.

From the motion model, the intermediate state quantity X of the long machine can be determined ₀ ＝[x，y，ψ，V，ω] ^T Intermediate state quantity X of plane _i ＝[x _i ，y _i ，ψ _i ，V _i ，ω _i ] ^T . Wherein X is _i Representing the intermediate state quantity of the ith plane.

And step 12, calculating the state error of the plane relative to the long machine according to the intermediate state quantity of the long machine and the intermediate state quantity of the plane.

The calculation of the state errors of the plane relative to the plane is made for the subsequent determination of the control command fuzzy constraints of the plane.

And 13, dividing the language variables of the predefined state error domains of the plane and the predefined control instruction error domains to obtain a state error language variable set of the plane and a control instruction error language variable set of the plane.

The state error domain of the plane and the control command error domain of the plane represent the state error range of the plane and the control command error range of the plane, respectively.

In the embodiment of the present application, the control command errors include speed control command errors of the ith planeAnd heading angle control instruction error of ith plane +.>

The process of linguistic variable division is a process of blurring the state error domain and the control instruction error domain of each of the wings, and the linguistic variables include NB, NM, MS, Z, PS, PM and PB. Where NB represents a large negative error, NM represents a medium negative error, MS represents a small negative error, Z represents almost no error, PS represents a small positive error, PM represents a medium positive error, and PB represents a large positive error.

Specifically, after language variable division, a state error language variable set of the ith machine is obtained A state error language variable set representing the ith plane; wherein, the liquid crystal display device comprises a liquid crystal display device,

similarly, the control instruction error language variable set of the ith plane A control instruction error language variable set for representing the τ -th plane; wherein (1)>

And step 14, determining the control command fuzzy constraint of the plane according to the membership function corresponding to each language variable value in the state error language variable set, the membership function corresponding to each language variable value in the control command error language variable set and a predefined control command fuzzy control rule.

It should be noted that, when the language variable set is determined, the membership function corresponding to each language variable value is also determined, and generally, the membership function includes a Z-type membership function, a Gaussian (Gaussian) type membership function, and an S-type membership function, where each membership function belongs to common general knowledge, and a form and a calculation process thereof are not described herein.

And 15, based on the distributed model prediction control method, determining the control command of the wing plane according to the control command fuzzy constraint of the wing plane.

The distributed model predictive control method is a method for effectively solving the problem of large-scale system control, and has the advantages that: (1) reducing the computational burden of each subsystem; (2) The expandability of the system can be improved under a plurality of controllers; and (3) the fault tolerance of the system is strong.

In the embodiment of the application, the control command fuzzy constraint is added to the DMPC method, so that the fuzzy constraint DMPC controller is constructed to determine the control command of the plane of the bureau, the amplitude change of the control quantity can be reduced under the condition that the weight of the control quantity of the plane of the bureau is unchanged, the control quantity is in a constant state within a period of time, and the effect of reducing the energy consumption of the control of the plane of the bureau is achieved.

The following describes an exemplary procedure of step 12 (calculating the state error of the plane relative to the plane based on the intermediate state quantity of the plane and the intermediate state quantity of the plane).

Step 12.1, north east coordinate System P _n OP _e Taking the long machine as a reference point R as an inertial coordinate system to obtain a state quantity X of the reference point R in the inertial coordinate system _R ，X _R ＝[x _R ，y _R ，ψ _R ，V _R ，ω _R ] ^T 。

The position of the reference point and the state quantity of the reference point are determined by the state quantity of the long machine.

Step 12.2, establishing a reference point coordinate system X taking the long machine as a reference point in the inertial coordinate system _R OY _R And determines the target point G of the ith plane _i In the reference point coordinate system X _R OY _R Coordinates of (a)

The flight diagram of the ith machine after the inertial coordinate system and the reference point coordinate system are established is shown in fig. 2, the abscissa of fig. 2 represents the coordinate of the inertial coordinate system in the east direction, the ordinate represents the coordinate of the inertial coordinate system in the north direction, and G _i 、G ₁ 、G ₂ 、G ₃ 、G ₅ All representing target points, UAV _i Representing the ith plane.

Step 12.3, through the calculation formula

/>

Obtaining the target point G of the ith plane in the inertial coordinate system _i State quantity of (2)

Wherein, the liquid crystal display device comprises a liquid crystal display device,ψx＝ψ _R ，V _G ＝V _R ，ω _G ＝ω _R 。

step 12.4, through the calculation formula

Obtaining state error

In the above-mentioned method, the step of,representing the state error of the ith plane relative to the long plane under the coordinate system of the reference point; wherein, the liquid crystal display device comprises a liquid crystal display device,coordinate transformation matrix representing inertial system to reference system, x _i 、y _i 、ψ _i 、V _i Omega, omega _i All represent the state quantity of the ith plane in the reference point coordinate system,/th plane> Indicating the error of the ith plane in the x-direction of the longer plane in the reference point coordinate system,/>Indicating the error of the ith plane in the y-direction of the longer plane in the reference point coordinate system,/>Indicating heading angle error of the ith plane relative to the long plane under the reference point coordinate system,/>Indicating the speed error of the ith plane relative to the longer plane in the reference point coordinate system,/>Indicating the angular velocity error of the ith plane relative to the long plane in the reference point coordinate system.

The following describes an exemplary procedure of step 14 (determining the control command fuzzy constraint of the plane according to the membership function corresponding to each language variable value in the state error language variable set, the membership function corresponding to each language variable value in the control command error language variable set, and the predefined control command fuzzy control rule).

And 14.1, drawing a state error membership image according to a membership function corresponding to each language variable value in the state error language variable set to obtain a first language variable membership set corresponding to the state error.

The first language variable membership degree set comprises membership degrees corresponding to each language variable value in the state error language variable set.

Fig. 3a shows a membership image of the y-direction error, the abscissa of which represents the state error of the ith plane in the y-direction, and the ordinate represents membership.

Fig. 3b is a membership image of an x-direction error, the abscissa thereof indicates the state error of the ith plane in the x-direction, and the ordinate thereof indicates membership.

Fig. 3c is a membership image of the heading angle error, the abscissa of which represents the heading angle error of the ith plane and the ordinate represents membership.

Fig. 3d is a membership image of a speed error, with the abscissa representing the speed error of the ith machine and the ordinate representing membership.

The first language variable membership set of the ith plane can be expressed as

And 14.2, drawing a control instruction error membership image according to the membership function corresponding to each language variable value in the control instruction error language variable set, and obtaining a second language variable membership value corresponding to the control instruction error.

The second language variable membership degree set comprises membership degrees corresponding to each language variable value in the control instruction error language variable set.

Fig. 4a is a membership image of a heading angle command error, the abscissa thereof representing the heading angle error of the ith plane, and the ordinate thereof representing membership.

Fig. 4b is a membership image of a speed command error, with the abscissa representing the speed error of the ith machine and the ordinate representing membership.

The second language variable membership set of the ith plane can be expressed as

And 14.3, obtaining a plurality of control instruction error fuzzy sets according to the first language variable membership degree set, the second language variable membership degree set and the predefined fuzzy control rule.

In embodiments of the present application, a defined heading angle control command errorThe fuzzy rules of (2) are shown in the following table:

in embodiments of the present application, a defined speed control command errorThe fuzzy rules of (2) are shown in the following table: />

In the embodiment of the present application, only 7 language variable values are used to describe the state error and the control command error, so the speed control command error of the ith planeAnd speed control command error- >Corresponding to 49 fuzzy sets, respectively. Those skilled in the art will recognize that if N language variable values are used to describe the state error and the control command error, each control command error corresponds to N ² A fuzzy set.

And 14.4, aggregating the plurality of control instruction error fuzzy sets to obtain a control instruction error aggregation fuzzy set.

And 14.5, selecting an element set corresponding to the maximum membership degree from the control instruction error aggregation fuzzy set as the maximum control instruction error fuzzy set, and taking a language variable value corresponding to the maximum control instruction error fuzzy set as an optimal language variable.

And 14.6, determining the boundary of the control command fuzzy constraint of the plane according to the optimal language variable to obtain the control command fuzzy constraint of the plane.

Step 14.6.1, regarding the ith machine, regarding the control instruction corresponding to the position with the same membership degree as the optimal language variable adjacent to the language variable value on the left side of the optimal language variable as the left boundary of the fuzzy constraint of the control instruction of the plane

Step 14.6.2, regarding the ith plane, regarding the control instruction corresponding to the position with the same membership degree as the optimal language variable adjacent to the right language variable value as the right boundary of the fuzzy constraint of the control instruction of the plane

Step 14.6.3, according to left boundaryAnd right border->Determining control command ambiguity constraint of ith plane>

Wherein U is ^X Representing the instruction space.

The following describes an exemplary procedure of step 15 (determining the control command of the wing plane according to the control command fuzzy constraint of the wing plane based on the distributed model predictive control method).

Step 15.1, by calculation formula

Obtaining the coordinate system X of the ith plane relative to the jth plane at the reference point _R OY _R State error in (a)

Where j=1, 2,.. _a And j+.i. The j-th plane here refers to the adjacent plane of the i-th plane.

Step 15.2, by calculation formula

Xe _ij ^（R） (τ|t _k )＝Xe _j (τ|t _k )-Xe _i (τ|t _k )

Obtaining control instruction track of ith planeAnd at t the bureau _k Control command at +delta timeAnd determining the final control instruction.

Wherein δ represents the update period, t _k Indicating the time corresponding to the actual state quantity after k times of updating, represents a natural number set, τ e { t } _k +δs}，s＝0，1，K，N ^p -1，N ^p Representing the number of predicted steps δN ^p Representing the prediction time domain, S ^(X) And S is ^(U) All represent normalized matrix, M ₁ ，M ₂ N represents positive definite symmetric matrix, X _i Representing the state quantity of the ith plane, X _-i Representing a state quantity of a plane adjacent to the ith plane, X _i (t _k ) Indicating that the ith plane is at t _k State quantity of time, U _i (t _k ) Indicating that the ith plane is at t _k Control of the time of day->Representing the predicted status track of the ith plane,/->Representing the estimated state trajectory of the ith plane in the predicted time domain,/>Indicating the optimal state track of the ith plane in the predicted time domain,predictive control track representing the ith plane,/->Representing the i-th plane estimated control trajectory,indicating the optimal predicted control trajectory of the ith plane,/->State quantity transition equation representing the i-th plane from the point tau to the point tau + delta>Representing the predicted state quantity of the ith plane at the time tau + delta,/for the plane>Represents the estimated state quantity of the ith plane at the time tau + delta,indicating the optimal predicted state quantity of the ith plane at the time tau + delta,/for the plane>Representing the optimal predicted state quantity of the adjacent plane of the ith plane at the time tau + delta,/v>Indicating the state error of the plane i relative to the plane at time τ, A (τ|t _k ) Representing a coordinate transformation matrix from an inertial system to a reference system at a time tau, X _i (τ|t _k ) Indicating the state quantity of the ith plane at the moment tau +.>Representing the state quantity of the target position of the ith plane at the moment tau,indicating the control quantity of the target position of the ith plane at the moment tau,/and>indicating the status error of the j-th plane at the time tau and the long plane,/ >Representing the adjacent plane of the ith plane at t _k The estimated state quantity of the moment of time,representing the adjacent plane of the ith plane at t _k Estimated control quantity of time of day,,>indicating the status error of the ith and jth plane at tau point,/for the ith and jth plane>Representation->Rank of (a)/(b)>Indicating the control error of the plane i relative to the plane at the moment tau, and indicating norm.

The analysis of step 15 is as follows:

in embodiments of the present application, given an update period (or sampling period) δ and a predicted time domain t=δn when solving the distributed optimal control problem with the DMPC method ^P (N ^P Representing the predicted number of steps), for ease of calculation, it can be considered that the distributed optimal control problem can be at t _k ＝t ₀ The +δk time is resolved by instantaneous synchronization (t ₀ Indicating the initial time, t _k Representing the time after k instruction updates, k e N, N representing the natural set of numbers).

It should be noted that, a common unmanned aerial vehicle formation generally includes a plurality of bureau planes, and taking an unmanned aerial vehicle formation including a plurality of bureau planes as an example, a description is given to a method for determining a bureau plane control instruction in the unmanned aerial vehicle formation provided in the present application.

For the optimal control problem of the distributed model prediction, the state influence of adjacent plane needs to be considered, namely the state quantity X of the neighbor of each unmanned plane in each prediction domain needs to be calculated _-i An estimation is made. Specifically, the ith plane may accept the estimated control track of the next plane j at a time, and may transmit its estimated control track to the next plane j.

According to the ith plane at t _k State quantity X of time _i (t _k ) And a control amount U _i (t _k ) The ith plane can be obtained from t _k Predicted control trajectory for time-of-day initiation(indicating the control that can be exerted on the ith plane), the ith plane at t _k Optimal predictive control trajectory for time instant>(representing the predicted control trajectory that optimizes the objective function) and the ith machine at t _k Estimated control trajectory +.>(representing the estimation of control quantity applied by adjacent plane to ith plane), in which the variable track is a sequence formed from variable values correspondent to different moments tau in the prediction time domain _k +δs}，s＝0，1，K，N ^p -1. When the ith plane is at τ=t _k At the initial state of the moment->

The state change of the ith plane in the prediction domain can be described by the following expression:

/>

similarly, the predicted state track of the ith plane can be obtainedEstimating state trace +.>Optimal predicted state trajectory +.>At τ=t _k In the case of X, the value of each track is _i (t _k )。

From the above procedure, the estimated control trajectories of the adjacent machines of the ith plane can be defined as The estimated state track of the adjacent plane of the ith plane is +.>

According to the state error model of the ith plane, the ith plane and the long plane j can be determined ^* State error at time τIth plane and long plane j ^* Control error at time τ ∈>Error track of i-th plane and adjacent plane j at time tau>

The objective function of all the control and state quantities of the wing plane can then be obtained, in particular as follows:

wherein S is ^(X) And S is ^(U) All represent normalization matrices that normalize the state quantity error and the control quantity error to [ -1,1]Within the range of M ₁ ，M ₂ N is positive definite symmetrical matrix for ensuring stability of DMPC algorithm and determining weight corresponding to different variables.

And then combining the fuzzy constraint of the control instruction of the ith plane to obtain the t of the ith plane _k Control command for time of dayThe expression of (2) is as follows:

/>

wherein k represents any given constant, x is a state space, U ^X In order to be a space for the instructions,is the fuzzy constraint space of the ith plane.

Although the conventional DMPC algorithm can reduce the effect of the control amount by increasing the weight of the control amount, this causes the convergence time to become long. And a DMPC control algorithm with fuzzy constraint is adopted, so that the amplitude change of the control quantity can be reduced under the condition that the weight of the control quantity is unchanged, and the control quantity is in a constant state within a period of time, thereby achieving the purpose of reducing the control quantity and further reducing the energy consumption.

In order to verify the effectiveness of the method for determining the control instruction of the bureau in the unmanned aerial vehicle formation, in the embodiment of the application, a simulation experiment is further performed on the method, and the process is as follows:

in the simulation, six unmanned aerial vehicles are considered to form a train, wherein one unmanned aerial vehicle is used as a long plane (UAV 0), and the other five unmanned aerial vehicles are used as plane (UAV 1, UAV2, UAV3, UAV4 and UAV 5), and initial values of position coordinates, course angles, speeds and reference position coordinates of the unmanned aerial vehicles relative to a reference point are shown in the following table:

sequence number	Position (m)	Speed (m/s)	Course angle (rad)	Desired position (m)
					UAV0	(0，0)	35	pi/2	-
UAV1	(0，230)	35	pi/2	(0，200)
					UAV2	(240，10)	35	pi/2	(190，61)
UAV3	(-340，150)	35	pi/2	(-190，61)
					UAV4	(40，180)	35	pi/2	(117，-161)
UAV5	(-320，-260)	35	pi/2	(-117，-161)

For a single machine model of the unmanned aerial vehicle, the self-driving driver parameter tau of the unmanned aerial vehicle _v ＝1s， Unmanned aerial vehicle's state constraint V _min ＝15m/s，V _max ＝45m/s，a _max ＝7m/s，ω _max ＝0.1rad/s，α _max =0.1 rad/s. For simulation parameters of unmanned aerial vehicle formation flight control, a prediction control domain N of model prediction control ^p ＝15，M ₁ ＝2M ₂ ＝diag[15，15，5，1，0]，N＝diag[5，1]The method comprises the steps of carrying out a first treatment on the surface of the The change range of membership function in fuzzy control, the input quantity includes position error, course angle error and speed error of-500 (m), pi/2-pi/2 (rad), 20-20 (m/S), output quantity of-pi/6 (rad), 15-15 (m/S) and normalized matrix S is set ^(X) ＝diag[500，500，pi/2，20，1] ^-1 And S is ^(U) ＝diag[pi/2，20] ^-1 . Simulation Time time=90 s, simulation step=0.5 s.

The simulation experiment comprises comparison of a DMPC method and a fuzzy DMPC method and Latin hypercube sampling comparison.

The DMPC method is compared with the fuzzy constraint DMPC method.

In the simulation experiment embodiment, a long machine is responsible for tracking a track, and a plane flies according to a formation control algorithm, so as to seek the difference of a DMPC method and a fuzzy constraint DMPC method in energy consumption.

When the DMPC method and the fuzzy constraint DMPC method are used for controlling the plane to fly, the flight track of the plane is shown in fig. 5a and 5b respectively, the abscissa in the figures (fig. 5a and 5 b) represents the coordinate in the middle east direction of the inertial coordinate system, and the ordinate represents the coordinate in the north direction. It is observed that each of the wing planes constantly adjusts its own position to form the desired formation. By comparing fig. 5a and fig. 5b, it can be found that the track generated by the DMPC method generates larger fluctuation, while the DMPC method with fuzzy constraint is smoother in the formation process of the flight formation, and the flight track is smoother.

In order to more intuitively show the simulation effect, the state change of the UAV5 with a larger position error is selected as an observation object. The state trajectories of the UAV5 under the control of two methods (DMPC method and fuzzy constraint DMPC method) are shown in fig. 6a, 6b, 6c, and 6d, respectively, wherein the abscissa of fig. 6a represents a unit time and the ordinate represents a distance in the x direction; the abscissa of fig. 6b represents a unit time, and the ordinate represents a distance in the y-direction; the abscissa of fig. 6c represents a unit time, and the ordinate represents a speed; the abscissa of fig. 6d represents a unit time, and the ordinate represents a heading angle.

In order to better compare the DMPC method with the fuzzy constraint DMPC method, the following four comparison indices are defined:

1. flight distance I _S ，The flight path represents the total path the drone is flying from the start to the end of the formation.

2. Cumulative value of speed variation I _V ，The speed change integrated value represents an integration of the absolute value of the speed change amount of the unmanned aerial vehicle.

3. Cumulative value of angle change I _Ψ ，The angle change integrated value represents an integration of the absolute value of the angle change amount.

4. Energy consumption I _Qcsm ，The energy consumption represents the work done to overcome the resistance. Wherein the resistance is proportional to the square of the speed, i.e. F _drag ＝K _drag y ² For easy calculation, the resistance coefficient K _drag Let 1 be the value.

By calculation, the flight path, the speed change cumulative value and the angle change cumulative value of the DMPC method are 3266.75m,20.92m/s and 0.71rad respectively, while the change of the DMPC method with fuzzy constraint is 3263.76m,12.03m/s and 0.48rad respectively, the change of the DMPC method with fuzzy constraint is 99.9 percent, 57.5 percent and 67.6 percent of the change of the energy loss of the DMPC method and the DMPC method with fuzzy constraint is shown as figure 7, the abscissa of figure 7 shows unit time, the ordinate shows energy, and the figure 7 shows that the process of overcoming resistance acting by the DMPC method with fuzzy constraint is gentle, the speed of the DMPC method just started increases more, the acting resistance is overcome more, and the rising trend is shown; because the speed of the DMPC method is increased and the speed of the DMPC method is reduced by a large extent, the speed can be the same at about 18s, and at the later time, the speed of the DMPC method is lower than that of the fuzzy constraint DMPC method, so that the fuzzy constraint DMPC method overcomes the small resistance to do work, and a difference curve shows a tendency of reduction and finally tends to be stable; when the energy consumption difference is stable, the value is larger than zero, which indicates that the fuzzy constraint DMPC method has the advantage of saving energy.

And (II) Latin hypercube sampling comparison.

In order to further compare the fuzzy constraint DMPC method and the DMPC method, a Latin hypercube sampling (LHS, latin hypercube sampling) method (which is a method for approximate random sampling from multi-element parameter distribution, belongs to a layered sampling technology, is commonly used for computer experiments or Monte Carlo integration and the like) is adopted to acquire the initial position of the unmanned aerial vehicle, the target points in the formation topological structure are tracked from different initial positions, and the difference of the DMPC algorithm and the fuzzy constraint DMPC algorithm in terms of flight path, speed change cumulative value and angle change cumulative value and energy consumption is simulated and compared.

The long aircraft flies from the origin along the direction of Pe, the target point G of the formation auxiliary aircraft is on the vertex of a regular pentagon which is formed by taking the long aircraft as the center, and the sampling rule is that each unmanned aircraft is sampled 1000 times within the range of about 500m respectively by taking the respective target point G as the center, so that the formation flight test of 1000 times is completed.

The comparison simulation results of the fuzzy constraint DMPC method and the DMPC method are shown in fig. 8a, 8b, 8c and 8d, and the following four indexes, namely, the difference of flight distance, the difference of accumulated values of speed change, the difference of accumulated values of angle change and the difference of energy loss are compared from two aspects of single-plane (taking UAV5 as an example) and unmanned aerial vehicle formation, wherein the difference is the simulation result of subtracting the fuzzy constraint DMPC algorithm from the DMPC algorithm.

As can be seen from fig. 8a, 8b, 8c and 8d, the abscissa of fig. 8a represents the unmanned aerial vehicle number and the ordinate represents the distance; the abscissa of fig. 8b represents the unmanned number and the ordinate represents the speed; the abscissa of fig. 8c represents the unmanned number and the ordinate represents the angle; the abscissa of fig. 8d represents the unmanned aerial vehicle number, the ordinate represents the energy, and the values of the indexes (the difference between flight paths, the difference between the integrated values of the speed change, the difference between the integrated values of the angle change and the energy loss) of the fuzzy constraint DMPC method are generally superior to those of the DMPC method. If the two methods are compared from the formation perspective, the advantage of the fuzzy constraint DMPC method is more obvious.

According to four evaluation indexes, statistics are respectively carried out from simulation results of 1000 times of formation flight tests and 1000 times of single machine flight, and the statistical results are shown in the following table:

/>

since the initial positions of the sample points are different but have the same initial velocity and angle, only the angle and standard deviation of the velocity change are considered. From the table above, it can be seen that:

1) As can be seen in statistics of formation flights, each index modeling the constraint DMPC algorithm in each simulation experiment is superior to the DMPC algorithm;

2) In the statistics of single machine flight, the absolute value is small and the proportion of the sample points with the difference value smaller than 0 is also small, wherein the flight distance and the angle change are smaller than 7%, the flight speed is smaller than 21%, the energy loss difference is smaller than 0 and only accounts for 0.2%, and the minimum value is-42.23 and is far smaller than the average value;

3) The standard deviation of the blur-constrained DPMC angle change and the speed change amount is about half that of the DMPC method.

Statistical results show that the DMPC algorithm with fuzzy constraint has obvious effect in reducing the flight path, the angle change cumulative value and the speed change cumulative value, and meanwhile, as can be seen from the standard deviation, the fluctuation of the speed change cumulative value and the angle change cumulative value of the DMPC method using the fuzzy constraint for the whole sample space is relatively gentle.

Therefore, the method for determining the machine control instruction in the unmanned aerial vehicle formation has the following advantages:

1) The control command fuzzy constraint which is suitable for the error state of the plane is designed according to the defined fuzzy rule, and is used as the control quantity constraint for the DMPC controller, so that the variation amplitude of speed and course angle is reduced, and the energy consumption of the plane in the formation process is reduced.

2) The statistical result of Latin supersquare sampling comparison simulation shows that the probability of saving energy in single machine and formation (6 frames) flight by the method for determining the machine control instruction in unmanned aerial vehicle formation provided by the application is 99.8% and 100% respectively, and the method has good performance advantage in the whole sample space from the aspect of statistical significance.

As shown in fig. 9, an embodiment of the present application provides a terminal device, as shown in fig. 9, a terminal device D10 of the embodiment includes: at least one processor D100 (only one processor is shown in fig. 9), a memory D101 and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, the processor D100 implementing the steps in any of the various method embodiments described above when executing the computer program D102.

Specifically, when the processor D100 executes the computer program D102, according to the motion model of the unmanned plane during formation, the predefined initial state quantity of the long plane and the predefined initial state quantity of the bureau plane, under the action of the control quantity, the intermediate state quantity of the long plane and the intermediate state quantity of the bureau plane are obtained, then according to the intermediate state quantity of the long plane and the intermediate state quantity of the bureau plane, the state error of the relative long plane is calculated, and then the state error language variable set of the pre-defined machine and the control instruction error language variable set of the pre-defined control instruction error language variable set of the bureau plane are obtained, and then according to the membership function corresponding to each language variable value in the state error language variable set, the control instruction fuzzy control rule corresponding to each language variable value in the control instruction error language variable set, the control instruction constraint of the bureau plane is determined, and finally, the control instruction constraint of the bureau plane is determined according to the control instruction constraint of the distributed model. The control command of the wing plane is determined by determining the fuzzy constraint of the control command of the wing plane and then determining the control command of the wing plane based on the distributed model predictive control method, so that the amplitude change of the control quantity can be reduced under the condition that the weight of the control quantity is unchanged, and the energy consumption of the control of the wing plane is reduced.

The processor D100 may be a central processing unit (CPU, central Processing Unit), the processor D100 may also be other general purpose processors, digital signal processors (DSP, digital Signal Processor), application specific integrated circuits (ASIC, application Specific Integrated Circuit), off-the-shelf programmable gate arrays (FPGA, field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory D101 may in some embodiments be an internal storage unit of the terminal device D10, for example a hard disk or a memory of the terminal device D10. The memory D101 may also be an external storage device of the terminal device D10 in other embodiments, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal device D10. Further, the memory D101 may also include both an internal storage unit and an external storage device of the terminal device D10. The memory D101 is used for storing an operating system, an application program, a boot loader (BootLoader), data, other programs, etc., such as program codes of the computer program. The memory D101 may also be used to temporarily store data that has been output or is to be output.

It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein again.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to the terminal equipment described above, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

While the foregoing is directed to the preferred embodiments of the present application, it should be noted that modifications and adaptations to those embodiments may occur to one skilled in the art and that such modifications and adaptations are intended to be comprehended within the scope of the present application without departing from the principles set forth herein.

Claims

1. A method for determining control instructions of a plane in unmanned aerial vehicle formation, comprising:

obtaining the intermediate state quantity of the long plane and the intermediate state quantity of the plane under the action of the control quantity according to a motion model of the unmanned plane during formation flight, a predefined initial state quantity of the long plane and a predefined initial state quantity of the plane;

calculating the state error of the plane relative to the long machine according to the intermediate state quantity of the long machine and the intermediate state quantity of the plane;

determining control command fuzzy constraint of a plane according to the membership function corresponding to each language variable value in the state error language variable set, the membership function corresponding to each language variable value in the control command error language variable set and a predefined control command fuzzy control rule;

2. The method of claim 1, wherein the expression of the model of motion of the unmanned aerial vehicle while the unmanned aerial vehicle is flying is as follows:

wherein, (x) _i ,y _i ) Representing the coordinate, psi, of the ith plane in the unmanned plane formation under the inertial coordinate system _i Indicating the heading angle of the ith plane, V _i Indicating the speed, omega of the ith plane _i Indicating the angular velocity of the ith plane, ψ _i ^c Indicating the heading angle control instruction of the ith plane, V _i ^c Indicating the speed control instruction of the ith plane,and->All represent the inertial time constant, τ, of the autopilot course angle channel _v Inertial time constant representing speed channel, i=1, 2,.. _a ，N _a Representing the total number of plane in the flight formation, V _min Representing the minimum flying speed of the plane, V _max Indicating the maximum flying speed of the plane, a _max Indicating maximum acceleration, ω of the plane _max Indicating the maximum angular velocity of the plane alpha _max Indicating maximum angular acceleration of the plane +.>Indicating the angular acceleration of the ith plane,/->Indicating the acceleration of the ith plane.

3. The method according to claim 2, wherein the intermediate state quantity X of the long machine ₀ ＝[x，y，ψ，V，ω] ^T ；

Intermediate state quantity X of said plane _i ＝[x _i ，y _i ，ψ _i ，V _i ，ω _i ] ^T The method comprises the steps of carrying out a first treatment on the surface of the Wherein X is _i Representing an intermediate state quantity of the ith plane;

the calculating the state error of the plane relative to the long machine according to the intermediate state quantity of the long machine and the intermediate state quantity of the plane comprises the following steps:

coordinate system P of north east _n OP _e As an inertial coordinate system, and taking the long machine as a reference point R to obtain a state quantity X of the reference point R in the inertial coordinate system _R ，X _R ＝[x _R ，y _R ，ψ _R ，V _R ，ω _R ] ^T The method comprises the steps of carrying out a first treatment on the surface of the The position of the reference point and the state quantity of the reference point are determined by the state quantity of the long machine;

establishing a reference point coordinate system X taking the long machine as a reference point in the inertial coordinate system _R OY _R And determines the target point G of the ith plane _i In the reference point coordinate system X _R OY _R Coordinates of (a)

By calculation formula

Obtain the inertial sitting of the ith planeTarget point G in the target system _i State quantity of (2) Wherein (1)>

By calculation formula

Obtaining the state errorRepresenting the state error of the ith plane relative to the long plane under the coordinate system of the reference point; wherein (1)>Coordinate transformation matrix representing inertial system to reference system, x _i 、y _i 、ψ _i 、V _i Omega, omega _i All represent the state quantity of the ith plane in the reference point coordinate system,/th plane> Indicating the error of the ith plane in the x-direction of the longer plane in the reference point coordinate system,/>Representation ofError of the ith plane in the reference point coordinate system relative to the y-direction of the long plane,/->Indicating heading angle error of the ith plane relative to the long plane under the reference point coordinate system,/>Indicating the speed error of the ith plane relative to the longer plane in the reference point coordinate system,/>Indicating the angular velocity error of the ith plane relative to the long plane in the reference point coordinate system.

4. A method of determining according to claim 3, wherein said control command errors include speed control command errors of an i-th wingAnd heading angle control instruction error of ith plane +.>

5. The method of claim 4, wherein the linguistic variables include NB, NM, MS, Z, PS, PM and PB; where NB represents a large negative error, NM represents a medium negative error, MS represents a small negative error, Z represents almost no error, PS represents a small positive error, PM represents a medium positive error, PB represents a large positive error;

The state error linguistic variable setA state error language variable set representing the ith plane; wherein (1)>

The control instruction error language variable set A control instruction error language variable set for representing the ith plane; wherein (1)>

6. The method according to claim 5, wherein determining the control command fuzzy constraint of the plane according to the membership function corresponding to each language variable value in the state error language variable set, the membership function corresponding to each language variable value in the control command error language variable set, and a predefined control command fuzzy control rule includes:

aggregating the plurality of control instruction error fuzzy sets to obtain a control instruction error aggregation fuzzy set;

selecting an element set corresponding to the maximum membership degree from the control instruction error aggregation fuzzy set as a maximum control instruction error fuzzy set, and taking a language variable value corresponding to the maximum control instruction error fuzzy set as an optimal language variable;

and determining the boundary of the control command fuzzy constraint of the plane according to the optimal language variable to obtain the control command fuzzy constraint of the plane.

7. The method according to claim 6, wherein the controlling the command fuzzy control rule includes:

according toAnd->Determining speed control command error of ith plane>

According toAnd->Determining the ithCourse angle control instruction error of the assistant machine>

8. The method according to claim 7, wherein determining the boundary of the control command fuzzy constraint of the plane according to the optimal language variable, to obtain the control command fuzzy constraint of the plane, comprises:

Aiming at the ith plane, the control instruction corresponding to the position with the same membership degree as the optimal language variable is used as the right boundary of the fuzzy constraint of the control instruction of the plane, wherein the language variable value of the right side of the adjacent optimal language variable

According to the left boundaryAnd the right border->Determining control command ambiguity constraint of ith planeWherein U is ^X Representing the instruction space.

9. The determining method according to claim 8, wherein the determining the control command of the wing plane according to the control command fuzzy constraint of the wing plane based on the distributed model prediction control method comprises:

by calculation formula

Obtaining control instruction track of ith planeAnd at t the bureau _k Control command at +delta timeDetermining a final control instruction; wherein δ represents the update period, t _k Indicating the moment corresponding to the actual state quantity after k updates,/time> Represents a natural number set, τ e { t } _k +δs}，s＝0，1，K，N ^p -1，N ^p Representing the number of predicted steps δN ^p Representing the prediction time domain, S ^(X) And S is ^(U) All represent normalized matrix, M ₁ ，M ₂ N represents positive definite symmetric matrix, X _i Representing the state quantity of the ith plane, X _-i Representing a state quantity of a plane adjacent to the ith plane, X _i (t _k ) Indicating that the ith plane is at t _k State quantity of time, U _i (t _k ) Indicating that the ith plane is at t _k Control of the time of day->Representing the predicted status track of the ith plane in the predicted time domain +.>Representing the estimated state trajectory of the i-th plane in the prediction horizon,representing the optimal state track of the ith plane in the forecast domain,/for the plane>Predictive control track representing the ith plane,/->Represents the i-th plane estimated control trajectory,/->Indicating the optimal predicted control trajectory of the ith plane,/-> State quantity transition equation representing the i-th plane from the point tau to the point tau + delta>Represents the predicted state quantity of the ith plane at the time tau + delta,representing the estimated state quantity of the ith plane at the time τ+δ,/v>Indicating the optimal predicted state quantity of the ith plane at the time tau + delta,/for the plane>Representing the optimal predicted state quantity of the adjacent plane of the ith plane at the time tau + delta,/v >Representing the state error of the ith plane relative to the longer plane at time tau, A (tau|t _k ) Representing a coordinate transformation matrix from an inertial system to a reference system at a time tau, X _i (τ|t _k ) Representing the state quantity of the ith plane at the moment tau,state quantity indicating the target position of the ith plane at time tau +.>Indicating the control quantity of the target position of the ith plane at the moment tau,/and>indicating the state error of the j-th plane at the time tau and the long plane,representing the adjacent plane of the ith plane at t _k Estimated state quantity of time ∈>Representing the adjacent plane of the ith plane at t _k Estimated control amount of time,/->Indicating the status error of the ith and jth plane at tau point,/for the ith and jth plane>Representation->Rank of (a)/(b)>Indicating the control error of the plane i relative to the plane at time tau,I.I representing the norm.

10. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements a method for determining the control instructions of a plane in a unmanned aerial vehicle formation as claimed in any one of claims 1 to 9 when the computer program is executed.