Multi-unmanned aerial vehicle formation control method and controller combined with active disturbance rejection

Abstract

The application provides a kind of multi-unmanned aerial vehicle formation control method and controller of combining self-disturbance rejection.The control method comprises: taking desired formation position as input, obtaining acceleration control amount u of any unmanned aerial vehicle based on self-disturbance rejection control ADRCi And corresponding unmanned aerial vehicle state, the unmanned aerial vehicle state includes unmanned aerial vehicle position and speed; taking position and speed information output by self-disturbance rejection control of any single unmanned aerial vehicle as input, obtaining desired acceleration control amount u required for corresponding keeping desired formation shape based on preset consistency control protocol CONi ; acceleration amount u in self-disturbance rejection ADRCi And acceleration control amount u in consistency formation CONi Respectively take weight q1, q2 and obtain control amount u by using the following formula i : u i =q1u ADRCi +q2u CONi ; q1+q2=1; control amount u i As actual control amount in unmanned aerial vehicle dynamics model to realize control on corresponding unmanned aerial vehicle.The technical solution can ensure realizing multi-unmanned aerial vehicle stable formation while making the whole formation have the ability of resisting external disturbance.

Description

Technical Field

[0001] This invention belongs to the field of multi-UAV formation control technology, and relates to a multi-UAV formation control method and controller that combines active disturbance rejection. This method is suitable for multi-UAV formation control that resists environmental interference. Background Technology

[0002] During actual flight, drone formations are subject to external interference such as wind and airflow, which can cause drones to lose their intended formation. This is especially true when the lead drone is maneuvering, requiring wingmen to maintain the desired distance, angle, and speed to follow it. Therefore, maintaining formation stability places higher demands on formation control methods. However, in addition to external wind interference, drone formations also face the problem of drone-model coupling.

[0003] Currently, active disturbance rejection control (ADRC) is widely used in UAV control, as it can suppress disturbances while also addressing the modeling complexity caused by model coupling. However, ADRC methods are primarily used in single-UAV control and have not been applied to multi-UAV formation control. In multi-UAV control, consensus formation control is commonly used; however, consensus formation control cannot effectively suppress the effects of disturbances. Therefore, how to achieve anti-disturbance formation control for multi-UAVs is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art or related art.

[0005] Therefore, the present invention provides a multi-UAV formation control method and controller that combines active disturbance rejection.

[0006] The technical solution of the present invention is as follows:

[0007] According to one aspect, a multi-UAV formation control method incorporating active disturbance rejection is provided, the control method comprising:

[0008] Using the desired formation position as input, the acceleration control quantity u of any UAV is obtained based on active disturbance rejection control. ADRCi and the corresponding drone status, which includes the drone's position and speed;

[0009] Using the position and velocity information output by the active disturbance rejection control of any single UAV as input, the desired acceleration control quantity u required to maintain the desired formation is obtained based on a preset consistency control protocol. CONi ;

[0010] The acceleration metric u in active disturbance rejection ADRCi Acceleration control quantity u in consistent formation CONi The control quantity u is obtained by taking weights q1 and q2 respectively and using the following formula.i :

[0011] u i =q1u ADRCi +q2u CONi ;

[0012] q1 + q2 = 1;

[0013] control quantity u i As the actual control variable in the UAV dynamics model, it is used to control the corresponding UAV.

[0014] Furthermore, the method further includes: in the control variable u i Based on this, the state of the controlled UAV is used as the state input for consistency control and active disturbance rejection control to form a closed-loop control.

[0015] Furthermore, using the desired formation position as input, the acceleration control quantity u of any UAV is obtained based on active disturbance rejection control using the following formula. ADRCi :

[0016] The distance error e between the i-th UAV and the lead aircraft i Find the second derivative;

[0017] The second-order error equation is obtained based on the dynamic model of the i-th UAV and the second-order derivative.

[0018] Design a second-order active disturbance rejection controller based on the second-order error equation;

[0019] Using the desired formation position as input, the acceleration control quantity u is obtained based on the second-order active disturbance rejection controller. ADRCi .

[0020] Furthermore, the distance error between the i-th UAV and the lead aircraft is obtained in the following manner:

[0021] 1) Obtain the actual distance between the i-th UAV and the lead aircraft;

[0022] 2) Transform the desired distance between the i-th UAV and the lead aircraft into the inertial coordinate system;

[0023] 3) The difference between the actual distance obtained in step 1) and the distance obtained in step 2) is used to obtain the distance error e. i .

[0024] Furthermore, the second-order error equation is obtained through the following formula:

[0025]

[0026] Among them, b i Let the coefficient matrix of the preceding term be b i =diag(cosγ)i cosX i cosX i ,cosγ i ), For the "total disturbance" that includes external interference and internal system coupling, γ i Let X be the inclination angle of the i-th UAV's trajectory. i ,Y i Z i Let ω be the three-dimensional position information of the i-th UAV in the inertial coordinate system. i The system output y represents external disturbance. i Let be the distance error between the i-th UAV and the lead aircraft.

[0027] Furthermore, a consistency protocol is designed using the following formula:

[0028]

[0029] Among them, u CONi Let k1 and k2 be the acceleration value of the i-th UAV under consistent control, where k1 and k2 are control parameters, and a ij a is the information flow weight from the j-th drone to the i-th drone in the drone communication topology. i1 This represents the adjacency weight with the lead aircraft, where ζ1 is the lead aircraft's speed, and ζ... i ,ζ j Let e ​​represent the speeds of the i-th and j-th drones, respectively. i Let e ​​be the distance error between the i-th UAV and the lead aircraft. j Let e1 be the distance error between the j-th UAV and the lead aircraft, and set to 0.

[0030] Furthermore, the formation control method also includes optimizing the weights q1 and q2 for active disturbance rejection and consistency using a particle swarm optimization algorithm, including:

[0031] 1.1 Design a fitness function based on the sum of the overshoot of the UAV's X, Y, and Z channels and the convergence time;

[0032] 1.2 Obtaining the control quantity u under given weights q1 and q2 i and the status of the drones under their control;

[0033] 1.3 Calculate the fitness value based on the UAV state obtained in step 1.2 and the fitness function in step 1.1;

[0034] 1.4 Update the particle swarm position and velocity based on the fitness value;

[0035] 1.5 Determine whether the result obtained in step 1.4 meets the termination condition. If yes, select the weights q1 and q2 given in step 1.2 as the optimized weights. If no, update the weights q1 and q2 in step 1.2 and return to step 1.2 based on this. Repeat this process until the particle swarm position and velocity are updated to meet the termination condition.

[0036] Furthermore, the fitness function is designed based on the sum of the overshoots of the UAV's X, Y, and Z channels and the convergence time using the following formula:

[0037] J = min(sum(os) + max(t) c )+S 2 (t c ))

[0038] Where, os = (os X ,os Y ,os Z ) T For the overshoot of the UAV's X, Y, and Z channels, sum(os) is the sum of the overshoots of the UAV's X, Y, and Z channels, t C =(t CX ,t CY ,t CZ ) T Let max(t) be the convergence time of the three channels X, Y, and Z of the UAV. c S represents the maximum convergence time. 2 (t c ) represents the variance of the convergence time.

[0039] According to one aspect, a multi-UAV formation controller combining active disturbance rejection (ADRROC) is provided. The formation controller includes a second-order ADRROC controller and a consistency control system. The second-order ADRROC controller is used to acquire the acceleration control quantity u of any one UAV. ADRCi Based on the UAV state obtained by the second-order active disturbance rejection controller and the preset consistency control protocol, the consistency control system obtains the desired acceleration control quantity u required to maintain the desired formation. CONi The formation controller will also include the acceleration metric u in the self-disturbance response. ADRCi Acceleration control quantity u in consistent formation CONi The control quantity u is obtained by taking weights q1 and q2 respectively and using the following formula. i :

[0040] u i =q1u ADRCi +q2u CONi ;

[0041] q1+q2=1.

[0042] According to another aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0043] The above technical solution proposes a method based on the leader-wingman structure, which uses the acceleration of the UAV as the control variable and introduces active disturbance rejection control in the uniform formation control. It combines active disturbance rejection control and uniform formation, and designs the connection weights of the two control methods. This ensures that the entire formation has the ability to resist external interference while achieving stable formation of multiple UAVs. Attached Figure Description

[0044] The accompanying drawings, which form part of this specification, are provided to further illustrate embodiments of the invention and, together with the textual description, explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0045] Figure 1 A schematic diagram of the structure of a multi-UAV formation controller incorporating active disturbance rejection provided in an embodiment of the present invention is shown;

[0046] Figure 2 This diagram illustrates a flowchart of a multi-UAV formation control method incorporating active disturbance rejection provided by an embodiment of the present invention.

[0047] Figure 3 This invention provides a three-dimensional flight path diagram of a UAV formation.

[0048] Figure 4 The results of anti-jamming UAV formation performance analysis provided by the embodiments of the present invention are shown.

[0049] In this context, (a) represents △X, (b) represents △Y, and (c) represents △Z. Detailed Implementation

[0050] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0052] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0053] like Figure 1-2 As shown, in one embodiment of the present invention, a multi-UAV formation control method incorporating active disturbance rejection is provided, the control method comprising:

[0054] Step 1: Using the desired formation position as input, obtain the acceleration control quantity u of any UAV based on active disturbance rejection control. ADRCi and the corresponding drone status, which includes the drone's position and speed;

[0055] Step 2: Using the position and velocity information output by the active disturbance rejection control of any single UAV as input, obtain the desired acceleration control quantity u required to maintain the desired formation based on a preset consistency control protocol. CONi ;

[0056] Step 3: The acceleration metric u in active disturbance rejection... ADRCi Acceleration control quantity u in consistent formation CONi The control quantity u is obtained by taking weights q1 and q2 respectively and using the following formula. i :

[0057] u i =q1u ADRCi +q2u CONi ;

[0058] q1 + q2 = 1;

[0059] Step 4: Set the control quantity u i As the actual control variable in the UAV dynamics model, it is used to control the corresponding UAV.

[0060] In this embodiment of the invention, in order to achieve closed-loop control, the method further includes: controlling the variable u i Based on this, the state of the controlled UAV is used as the state input for consistency control and active disturbance rejection control to form a closed-loop control.

[0061] Furthermore, in this embodiment of the invention, the drone formation is a leader-wingman structure, and the drone status described in this embodiment of the invention includes the drone's position and speed.

[0062] As can be seen, the embodiments of the present invention propose a method based on the leader-wingman structure, which uses the acceleration of the UAV as the control variable and introduces active disturbance rejection control in the uniform formation control. It combines active disturbance rejection control and uniform formation, and designs the connection weights of the two control methods. Thus, while ensuring the stable formation of multiple UAVs, the entire formation has the ability to resist external interference.

[0063] In the above embodiments, in order to obtain the acceleration control quantity u of any UAV based on active disturbance rejection control... ADRCi The acceleration control quantity u of any UAV can be obtained based on active disturbance rejection control by using the desired formation position as input in the following formula. ADRCi :

[0064] The distance error e between the i-th UAV and the lead aircraft i Find the second derivative;

[0065] The second-order error equation is obtained based on the dynamic model of the i-th UAV and the second-order derivative.

[0066] Design a second-order active disturbance rejection controller based on the second-order error equation;

[0067] Using the desired formation position as input, the acceleration control quantity u is obtained based on the second-order active disturbance rejection controller. ADRCi .

[0068] In this embodiment of the invention, the distance error between the i-th UAV and the lead aircraft can be obtained in the following manner:

[0069] 1) Obtain the actual distance between the i-th UAV and the lead aircraft;

[0070] 2) Transform the desired distance between the i-th UAV and the lead aircraft into the inertial coordinate system;

[0071] 3) The difference between the actual distance obtained in step 1) and the distance obtained in step 2) is used to obtain the distance error e.i .

[0072] That is, given the expected distance E between the i-th drone and the lead drone. ci =(ΔX) ci ΔY ci ΔZ ci ) T Transform to an inertial coordinate system and subtract the actual distance to obtain the error e. i =ξ i -ξ1-PE ci P is the transformation matrix from the long-range aircraft coordinate system to the inertial coordinate system, and ξ is the transformation matrix. i ξ1 and ξ1 represent the real-time positions of UAV i and the lead aircraft, respectively.

[0073] To transform the second-order system into a second-order error equation, the second derivative of the error between the i-th UAV and the lead aircraft is calculated:

[0074]

[0075] Substitute the dynamic model of the i-th UAV into the above equation and extract the control variable u. ADRCi The relevant parts. The remaining parts and the coupling between the X, Y, and Z channels are considered as interference items.

[0076]

[0077] The second-order error equation is obtained by rearranging:

[0078]

[0079] Among them, b i Let the coefficient matrix of the preceding term be b i =diag(cosγ) i cosX i cosX i ,cosγ i ), For the "total disturbance" that includes external interference and internal system coupling, γ i Let X be the inclination angle of the i-th UAV's trajectory. i ,Y i Z i Let ω be the three-dimensional position information of the i-th UAV in the inertial coordinate system. i The system output y represents external disturbance. i Let be the distance error between the i-th UAV and the lead aircraft. Based on this second-order error equation, an active disturbance rejection controller can be designed.

[0080] In the above embodiments, in order to better unify the overall state of the drone swarm, a consistency protocol can be designed using the following formula:

[0081]

[0082] Among them, u CONi Let k1 and k2 be the acceleration value of the i-th UAV under consistent control, where k1 and k2 are control parameters and both are greater than 0, and a ij a is the information flow weight from the j-th drone to the i-th drone in the drone communication topology. i1 This represents the adjacency weight with the lead aircraft, where ζ1 is the lead aircraft's speed, and ζ... i ,ζ j Let e ​​represent the speeds of the i-th and j-th drones, respectively. i Let e ​​be the distance error between the i-th UAV and the lead aircraft. j Let e1 be the distance error between the j-th UAV and the lead aircraft, and set to 0.

[0083] For example, taking the three-degree-of-freedom dynamic model of the i-th UAV as an example, the following equation is used:

[0084]

[0085] Among them, X i ,Y i Z i V represents the three-dimensional position information of UAV i in the inertial coordinate system. i For the speed of drone i, χ i Let γi be the heading angle of UAV i, γi be the trajectory tilt angle of UAV i, g be the acceleration due to gravity, and u be the acceleration due to gravity. xi u yi u zi These are the tangential acceleration, horizontal component of normal acceleration, and vertical component of normal acceleration of UAV i, respectively, with an interference amount ω added to them. xi ω yi ω zi .

[0086] Convert it to the position quantity ξ i =(X i ,Y i Z i ) T ∈R 3×1 and velocity quantity The second-order equation is given. The control variable is the acceleration u. i =(u xi ,u yi ,u zi ) T ∈R 3×1

[0087]

[0088] The goal of consistent formation control is to converge the positions of all wingmen to the desired positions, given the desired distance E between the i-th UAV and the lead UAV. ci =(ΔX) ci ΔY ci ΔZ ci ) T Transform to an inertial coordinate system and subtract the actual distance to obtain the error e. i =ξ i -ξ1-PE ci ξ1 represents the position of the lead aircraft. If the positional error between any wingmen approaches zero, it means that all UAVs have reached their set positions and completed the formation task.

[0089]

[0090] Where P is the transformation matrix from the lead aircraft's coordinate system to the inertial coordinate system, and χ1 and γ1 are the lead aircraft's heading angle and track inclination angle, respectively.

[0091]

[0092] Considering the impact of speed and position errors with the lead aircraft and other wingmen on formation, the control protocol is designed as follows:

[0093]

[0094] The acceleration u of the drone according to the consistency control protocol CONi Adjustments are made to control the status of each UAV to achieve multi-UAV formation. After obtaining the UAV status under anti-interference control, the acceleration control quantity and the corresponding UAV status under consistent control can be calculated based on this consistency control protocol.

[0095] In the above embodiments, in order to better ensure the stable formation of multiple UAVs while enabling the entire formation to resist external interference, as an important aspect of the present invention, the weights for self-disturbance resistance and consistency are optimized using a particle swarm optimization algorithm. The optimization process specifically includes:

[0096] 1.1 Design a fitness function based on the sum of the overshoot of the UAV's X, Y, and Z channels and the convergence time;

[0097] 1.2 Obtaining the control quantity u under given weights q1 and q2 i and the status of the drones under their control;

[0098] 1.3 Calculate the fitness value based on the UAV state obtained in step 1.2 and the fitness function in step 1.1;

[0099] 1.4 Update the particle swarm position and velocity based on the fitness value;

[0100] 1.5 Determine whether the result obtained in step 1.4 meets the termination condition. If yes, select the weights q1 and q2 given in step 1.2 as the optimized weights. If no, update the weights q1 and q2 in step 1.2 and return to step 1.2 based on this. Repeat this process until the particle swarm position and velocity are updated to meet the termination condition.

[0101] In this embodiment of the invention, the fitness function is designed based on the sum of the overshoots of the UAV's X, Y, and Z channels and the convergence time using the following formula:

[0102] J = min(sum(os) + max(t) c )+S 2 (t c ))

[0103] Where, os = (os X ,os Y ,os Z ) T For the overshoot of the UAV's X, Y, and Z channels, sum(os) is the sum of the overshoots of the UAV's X, Y, and Z channels, t C =(t CX ,t CY ,t CZ ) T Let max(t) be the convergence time of the three channels X, Y, and Z of the UAV. c S represents the maximum convergence time. 2 (t c ) represents the variance of the convergence time.

[0104] For example, by optimizing the weights for active disturbance rejection and consistency using the particle swarm optimization algorithm, the actual position difference between the wingman and the lead aircraft in the lead aircraft's coordinate system is set to ξ. i -ξ1=(ΔX,ΔY,ΔZ) T The expectation is that during the formation process, the positional difference between each drone and the lead drone can be minimized by the overshoot os = (os X ,os Y ,os Z ) T and convergence time t C =(t CX ,t CY ,t CZ ) T Converging to the expected value, and the os and t of the X, Y, and Z channels. C The differences are not significant; therefore, the above evaluation function is designed for the particle swarm algorithm.

[0105] like Figure 1As shown, another embodiment of the present invention designs a multi-UAV formation controller, which includes a second-order active disturbance rejection controller and a consistency control system. The second-order active disturbance rejection controller is used to obtain the acceleration control quantity u of any UAV. ADRCi Based on the UAV state obtained by the second-order active disturbance rejection controller and the preset consistency control protocol, the consistency control system obtains the desired acceleration control quantity u required to maintain the desired formation. CONi The formation controller will also include the acceleration metric u in the self-disturbance response. ADRCi Acceleration control quantity u in consistent formation CONi The control quantity u is obtained by taking weights q1 and q2 respectively and using the following formula. i :

[0106] u i =q1u ADRCi +q2u CONi ;

[0107] q1+q2=1.

[0108] In this embodiment of the invention, for a multi-UAV formation system, the acceleration of each UAV is controlled according to a consensus protocol to adjust the UAV state and make it tend towards the desired trajectory. To compensate for the defects in the anti-disturbance performance of consensus control, this embodiment of the invention introduces a parameter-optimized active disturbance rejection controller (ADRC) into the formation controller. That is, the ADRC can be designed based on the second-order error equation in the above embodiment.

[0109] like Figure 1 As shown, for the active disturbance rejection controller, with the desired formation position Eci as the system input, x 1i x 2i The following are the tracking results of the tracking differentiator (TD) for a given value and its derivative, respectively: z 1i z 2i z 3i For the extended state observer (ESO), the equation for the second-order error is e. i , The estimated value of g. LSEF is a linear feedback controller, whose function is to find a suitable linear combination form to obtain the error feedback law.

[0110] Furthermore, preferably, in this embodiment of the invention, the control parameters of the extended state observer are optimized by particle swarm optimization based on the classic active disturbance rejection controller structure, and the position and velocity information output by the active disturbance rejection control of a single UAV are used as input to the consistency control system. The desired acceleration required to maintain the desired formation is obtained through the consistency control protocol.

[0111] Taking the i-th UAV as an example, the acceleration metric u in active disturbance rejection is... ADRCiThe control quantity u is obtained by summing the weights q1 and q2 (q1+q2=1) of the control quantity uCONi of the formation. i .

[0112] u i =q1u ADRCi +q2u CONi

[0113] will u i As the actual control variable in the UAV dynamics model, the controlled UAV state is used as the state input for consistency and active disturbance rejection to form a closed-loop control. The formation control method of the above embodiment is implemented based on this formation controller, and the specific process is as follows: Figure 2 As shown.

[0114] Furthermore, another embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0115] The technical effects of a multi-UAV formation control method combining active disturbance rejection are described below with reference to the accompanying drawings and specific embodiments.

[0116] Three UAVs were selected for formation flight simulation tests, with UAV1 as the lead aircraft and UAV2 and UAV3 as wingmen. The initial states of the three aircraft are shown in the table below. After 30 seconds, the formation was set to be affected by a wind interference of magnitude 5 sint, and the formation entered a turning state with a radius of 25m between 60 and 100 seconds. Simulations were conducted to verify both the consistent formation and the anti-interference formation method described in this paper.

[0117] Table 1 Initial State

[0118]

[0119]

[0120] Construct a long-range coordinate system with the long-range aircraft as the origin. The desired distance matrix in the long-range coordinate system is D = (E c1 ...E cn ) and the communication matrix A = [a ij ]∈R n×n Set them as follows:

[0121]

[0122] The parameters of the active disturbance rejection controller were optimized. To facilitate faster formation stabilization, parameter optimization was performed on both straight and turning segments. The particle swarm inertia factor was set to 0.6, and the acceleration constants c1 and c2 were both 2. The optimized parameters are shown in the table below.

[0123] Table 2 Active Disturbance Rejection Control Parameters

[0124]

[0125] With consistency control parameters k1=2 and k2=2, and influence weight q2=0.4, the formation effects of consistency and the control method described in this invention are obtained, as shown below. Figure 3 The thin and thick lines indicate that the uniform formation deviates significantly from the desired position after being affected by interference, and the track oscillates after formation. After introducing active disturbance rejection control, the system can quickly reach the desired formation, and can also converge quickly after the introduction of a given amount of interference, almost unaffected by the interference.

[0126] For the two methods (consistency and the method of this invention), the distance between the two wingmen relative to the lead aircraft in the lead aircraft's coordinate system is simulated. Figure 4 UAV2 and UAV3 are two wingmen, distinguished by dashed and straight lines, respectively. The simulation curve for the control method described in this embodiment of the invention is as follows. Figure 4 Medium-thick line group. Under uniform control, the drone formation speed is relatively slow, and after 30 seconds of interference, it will be affected by the interference and enter an oscillating state. After adding self-disturbance rejection, the drone's performance can quickly reach stability and remain unaffected by interference.

[0127] Simulation results show that the method of this invention can effectively reduce system settling time and overshoot, enabling each wingman to approach the ideal formation position with similar time and state. The control method of this invention has great practical application value.

[0128] As can be seen, the method of this invention embeds active disturbance rejection control into consistent formation control, and optimizes the control weights of active disturbance rejection and consistency through particle swarm optimization, thereby enhancing the wind disturbance resistance performance of individual units while achieving fast and stable formation.

[0129] The features described and / or illustrated above with respect to one embodiment may be used in the same or similar manner in one or more other embodiments, and / or in combination with or in lieu of features in other embodiments.

[0130] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, whole, step, or component, but does not exclude the presence or addition of one or more other features, wholes, steps, components, or combinations thereof.

[0131] The methods described above in this invention can be implemented in hardware or in combination with software. This invention relates to computer-readable programs that, when executed by a logic component, enable the logic component to implement the aforementioned apparatus or constituent parts, or to implement the various methods or steps described above. This invention also relates to storage media for storing the above programs, such as hard disks, magnetic disks, optical disks, DVDs, flash memory, etc.

[0132] Many features and advantages of these embodiments are apparent from this detailed description, and therefore the appended claims are intended to cover all such features and advantages of these embodiments that fall within their true spirit and scope. Furthermore, since many modifications and alterations will readily occur to those skilled in the art, the embodiments of the invention are not intended to be limited to the precise structures and operations illustrated and described, but rather to encompass all suitable modifications and equivalents falling within their scope.

[0133] The parts of this invention not described in detail are techniques known to those skilled in the art.

Claims

1. A multi-UAV formation control method combining active disturbance rejection, characterized in that, The control method includes: Using the desired formation position as input, the acceleration control quantity of any UAV is obtained based on active disturbance rejection control. and the corresponding drone status, which includes the drone's position and speed; Using the position and velocity information output by the active disturbance rejection control of any single UAV as input, the desired acceleration control quantity required to maintain the desired formation is obtained based on a preset consistency control protocol. ; Acceleration metric in active disturbance rejection Acceleration control quantity in consistent formation Take weights respectively , The control quantity is obtained using the following formula. : ； ； control quantity As the actual control quantity in the UAV dynamics model to achieve control of the corresponding UAV; Using the desired formation position as input, the acceleration control quantity of any UAV is obtained based on active disturbance rejection control (ADRC) in the following manner. : For the first Distance error between the drone and the lead aircraft Find the second derivative; Based on the The dynamic model of the UAV and the second derivative are used to obtain the second-order error equation; Design a second-order active disturbance rejection controller based on the second-order error equation; Using the desired formation position as input, the acceleration control quantity is obtained based on the second-order active disturbance rejection controller. ; Obtain the number in the following manner Distance error between the drone and the lead aircraft: 1) Obtain the first The actual distance between the drone and the lead aircraft; 2) No. The desired distance between the drone and the lead aircraft is converted to an inertial coordinate system. 3) The difference between the actual distance obtained in step 1) and the distance obtained in step 2) is used to obtain the distance error. .

2. The multi-UAV formation control method combining active disturbance rejection as described in claim 1, characterized in that, The method further includes: in the control quantity Based on this, the state of the controlled UAV is used as the state input for consistency control and active disturbance rejection control to form a closed-loop control.

3. The multi-UAV formation control method combining active disturbance rejection as described in claim 1, characterized in that, The second-order error equation is obtained through the following formula: ,in, The coefficient matrix of the preceding term is , The "total disturbance" includes both external interference and internal system coupling. For the first The tilt angle of the drone's flight path. For the first The three-dimensional position information of the drone in the inertial coordinate system. External interference, system output For the first Distance error between the drone and the lead aircraft.

4. A multi-UAV formation control method combining active disturbance rejection as described in claim 1 or 3, characterized in that, Design a consistency protocol using the following formula: ,in, For consistency control The acceleration value of the drone. All of these are control parameters. It is the first in the UAV communication topology. The drone to the Information flow weight of drones This indicates the adjacency weight with the lead aircraft. For the speed of the lead aircraft, The first drones and the first The speed of the drone For the first The distance error between the drone and the lead aircraft For the first Distance error between the drone and the lead aircraft Set it to 0.

5. The multi-UAV formation control method combined with active disturbance rejection as described in claim 4, characterized in that, The formation control method also includes using a particle swarm optimization algorithm to assign weights to active disturbance rejection and consistency. , Optimizations include: 1.1 Design a fitness function based on the sum of the overshoot of the UAV's X, Y, and Z channels and the convergence time; 1.2 Given weights , The control quantity is obtained under the condition and the status of the drones under their control; 1.3 Calculate the fitness value based on the UAV state obtained in step 1.2 and the fitness function in step 1.1; 1.4 Update the particle swarm position and velocity based on the fitness value; 1.5 Determine whether the result obtained in step 1.4 meets the termination condition. If so, select the weight given in step 1.

2. , Use this as the optimized weight; otherwise, update the weight in step 1.

2. , Then proceed to step 1.2 and repeat this process until the particle swarm position and velocity are updated to meet the termination condition.

6. The multi-UAV formation control method combining active disturbance rejection according to claim 5, characterized in that, The fitness function is designed based on the sum of the overshoots of the UAV's X, Y, and Z channels and the convergence time, using the following formula: ,in, For overshooting the X, Y, and Z channels of the drone, For the sum of the overshoot of the X, Y, and Z channels of the UAV, The convergence time for the X, Y, and Z channels of the drone. The maximum convergence time. The variance of the convergence time.

7. A multi-UAV formation controller incorporating active disturbance rejection, characterized in that, The formation controller is used to implement the method according to any one of claims 1-6, the formation controller includes a second-order active disturbance rejection controller and a consistency control system, the second-order active disturbance rejection controller is used to obtain the acceleration control quantity of any one UAV. Based on the corresponding UAV state obtained by the second-order active disturbance rejection controller and the preset consistency control protocol, the consistency control system obtains the desired acceleration control amount required to maintain the desired formation. The formation controller will also measure acceleration in active disturbance rejection. Acceleration control quantity in consistent formation Take weights respectively , The control quantity is obtained using the following formula. : ； 。 8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that... When the processor executes the computer program, it implements the method of any one of claims 1-6.

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