A method for controlling a wingman in a drone formation, a terminal device, and a medium

Abstract

The application is suitable for the technical field of unmanned aerial vehicle control, and provides a control method for wingmen in an unmanned aerial vehicle formation, a terminal device and a medium. Actual state quantities of the unmanned aerial vehicle formation are collected; estimated state quantities of a lead aircraft are obtained according to the actual state quantities; expected state quantities of the wingmen are calculated according to the estimated state quantities and expected distances; first state errors and second state errors are respectively calculated according to the expected state quantities and the actual state quantities; an expected energy function of the wingmen is constructed according to the first state errors and the second state errors; a control law of each wingman is designed according to the expected energy function and a stability condition of the unmanned aerial vehicle formation; and the wingmen in the unmanned aerial vehicle formation are controlled according to the control law. The application can improve the robustness and stability of the unmanned aerial vehicle formation, reduce the calculation complexity, and improve the calculation efficiency.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of unmanned aerial vehicle control, and particularly relates to a control method for a subordinate unmanned aerial vehicle in an unmanned aerial vehicle formation, a terminal device and a medium. BACKGROUND

[0002] In recent years, distributed control of unmanned aerial vehicle formation has been extensively researched. Compared with centralized formation, in distributed formation, each unmanned aerial vehicle only exchanges information with adjacent unmanned aerial vehicles, thereby having better flexibility and scalability.

[0003] For the control method of unmanned aerial vehicle formation, the traditional control method is linearization near the dynamic equilibrium point of the unmanned aerial vehicle based on small perturbation method, such as linear quadratic control. The performance and stability of the closed-loop system depend on the rate of change of parameters. Unmanned aerial vehicle formation flight is a complex nonlinear system, and the traditional linearization control method is difficult to meet the high-performance flight requirements. In order to obtain the performance and stability of the closed-loop system in a larger range, commonly used nonlinear control methods include dynamic inverse control and adaptive control. The dynamic inverse controller needs an accurate system model. Due to the existence of system modeling error and external disturbance, it is difficult to guarantee the robustness and stability of the system. Adaptive control has good robustness and fault tolerance, and can adapt to a series of uncertainties in the system, but this method sets the state feedback control calculator to calculate complex, which will limit its application in small unmanned aerial vehicles and other low-cost platforms.

[0004] The port Hamilton system (PHS, Port-Hamiltonian system) theory provides a systematic framework for modeling and analysis of multi-physical systems, has a clear structure and explicit physical meaning, and starts from the perspective of energy and power, so that the designed system control law has natural interpretability. For the complexity and high dimensionality of unmanned aerial vehicle formation control, it has special significance to prove the stability of the system from the energy angle. PHS is a system with input, output and dissipation force. Its characteristics are a scalar function representing the total energy of the system and a pair of matrix functions describing how energy is distributed and dissipated. Since the passivity of PHS can be directly derived from power balance, and passivity is closely related to (Lyapunov) stability, modeling the system as PHS helps to design energy-based nonlinear controllers and use existing energy shaping techniques, such as interconnection and damping assignment (IDA, interconnection and damping assignment) based passive control (PBC, passivity-based control).

[0005] PHS has been widely studied in nonlinear control theory, involving various disciplines such as electric motors, unmanned boats and drones. Fahmi et al. studied the stability problem of a fixed-wing aircraft in horizontal flight, modeled the aircraft as a port Hamiltonian system and designed a nonlinear control law using the passivity of the system, so that the aircraft is stabilized to the specified inertial velocity. LV et al. studied the trajectory tracking problem of unmanned surface vessels, and proposed a passive controller based on robust state error interconnection and damping assignment. Woolsey et al. used the energy-casimir method to establish the asymptotic stability control law of the underactuated underwater vehicle under the action of viscous force and torque. Rashad et al. studied the interactive control problem of a fully actuated unmanned aerial vehicle in the port Hamiltonian framework, and designed a controller based on the energy balance passive control technology to achieve the aerial hovering of the unmanned aerial vehicle. Yüksel et al. proposed a new control method based on interconnection and damping assignment for the aerial physical interaction problem of a quadrotor unmanned aerial vehicle. Mahony et al. studied the aerial robot control method based on the port Hamiltonian framework, and developed a visual energy port model to improve the overall control performance. Fu Baizeng et al. studied the active anti-interference control problem in view of the influence of interference on the port Hamiltonian system. Peng Mingxiang et al. studied the synchronization control problem of fully actuated and underactuated mechanical systems based on the passive control theory of port Hamiltonian systems.

[0006] Although experts in the field have conducted a large number of studies on the control method of unmanned aerial vehicle formation, the current control method of unmanned aerial vehicle formation cannot quickly and stably fly in the expected formation due to the poor robustness and complex calculation process. SUMMARY

[0007] The present application provides a control method for a subordinate aircraft in an unmanned aerial vehicle formation, a terminal device and a medium, which can solve the problem that the current control method for unmanned aerial vehicle formation has poor robustness and complex calculation process, and the unmanned aerial vehicle formation cannot quickly and stably fly in the expected formation.

[0008] In a first aspect, the present application provides a control method for a subordinate aircraft in an unmanned aerial vehicle formation, comprising:

[0009] Collecting actual state quantities in the flight process of the unmanned aerial vehicle formation; the unmanned aerial vehicle formation comprises N subordinate aircrafts and a leader aircraft;

[0010] For each of the N subordinate aircrafts, obtaining an estimated state quantity of the leader aircraft according to the actual state quantity of the subordinate aircraft and the actual state quantity of the leader aircraft;

[0011] According to the estimated state quantity and the expected distance between the subordinate aircraft and the leader aircraft set in advance, calculating an expected state quantity of the subordinate aircraft;

[0012] According to the desired state quantity and the actual state quantity, a first state error of the wingman and a second state error between each wingman in the UAV formation are calculated respectively; the first state error represents a state error of the wingman relative to the long plane;

[0013] According to the first state error and the second state error, an expected energy function of the wingman in the UAV formation is constructed;

[0014] According to the expected energy function and a pre-set UAV formation stability condition, a control law of each wingman in the N wingmen is designed;

[0015] According to the control law, the wingman in the UAV formation is controlled.

[0016] Optionally, for each wingman in the N wingmen, an estimated state quantity of the long plane is obtained according to the actual state quantity of the wingman and the actual state quantity of the long plane, including:

[0017] For each wingman h in the UAV formation which directly communicates with the long plane, the estimated state quantity of the long plane is obtained through a calculation formula

[0018]

[0019] The derivative of the estimated state quantity of the long plane is obtained Wherein, represents the derivative of the actual state quantity of the long plane, z R represents the pre-acquired actual state quantity of the long plane, represents the estimation of the actual state of the long plane by the wingman h, The initial value of z h is determined by z h represents the pre-acquired actual state quantity of the wingman h which directly communicates with the long plane, represents the position estimation of the long plane by the wingman h, represents the heading angle estimation of the long plane by the wingman h, represents the speed estimation of the long plane by the wingman h, represents the angular velocity estimation of the long plane by the wingman h, γ represents the dependence degree between the wingmen, γ>0, a hj represents the coefficient in the adjacency matrix, the adjacency matrix represents a matrix composed of the communication relationship between the UAVs, a hj = (0, 1), a hj = 1 indicates that the wingman h and the wingman j can communicate with each other, a hj = 0 indicates that the wingman h and the wingman j cannot communicate with each other, j = 1, 2,..., N, j≠h;

[0020] For each wingman g in the UAV formation which does not directly communicate with the long plane, the estimated state quantity of the long plane is obtained through a calculation formula

[0021]

[0022] Derivative of the estimated state quantity of the long aircraft wherein, denotes the position estimate of the long aircraft by the wingman g, denotes the heading angle estimate of the long aircraft by the wingman g, denotes the speed estimate of the long aircraft by the wingman g, denotes the angular velocity estimate of the long aircraft by the wingman g, g∈[1,N), g≠h, g≠j, a gj =1 denotes that the wingman g and the wingman j can communicate with each other, a gj =0 denotes that the wingman g and the wingman j cannot communicate with each other, denotes the estimate of the state quantity of the long aircraft by the wingman j, denotes the estimate of the state quantity of the long aircraft by the wingman g.

[0023] Optionally, the angular velocity of the wingman during the flight is constant;

[0024] Optionally, the expected state quantity of the wingman is calculated according to the estimated state quantity, the expected distance between the wingman and the long aircraft, comprising:

[0025] the expected distance between the wingman and the long aircraft is calculated according to the estimated state quantity obtain the position estimate of the long aircraft by the wingman wherein,

[0026] the expected position of the wingman is obtained by the calculation formula

[0027]

[0028] obtain the expected position of the wingman wherein, denotes the heading angle of the wingman i, denotes the expected distance;

[0029] the expected speed of the wingman is obtained by the calculation formula

[0030]

[0031] obtain the expected speed of the wingman and the expected heading angle wherein, l i denotes the distance from the wingman i to the center of the circle Ω, the center of the circle represents the center of the circular motion of the wingman, and the linear motion and the curved motion of the wingman are both regarded as the circular motion, l R denotes the distance from the long aircraft R to the center of the circle Ω, denotes the positive angle formed by the long aircraft, the center of the circle and the wingman i.

[0032] The expression of the first state error is wherein, represents the first state error, z i represents the actual state quantity of the wingman i, z i = {x i , y i , v i , ψ i , ω i}, (x i , y i ) represents the actual position of the wingman i, ψ i represents the actual heading angle of the wingman i, v i represents the actual speed of the wingman i, ω i represents the actual angular velocity of the wingman i, represents the expected state quantity of the wingman i, represents the expected position of the wingman i, represents the expected heading angle of the wingman i, represents the expected speed of the wingman i, represents the expected angular velocity of the wingman i.

[0033] The expression of the second state error is wherein, represents the second state error, represents the first state error of the wingman i, represents the first state error of the wingman t, i, t = 1, 2,..., N, i≠t.

[0034] The expression of the expected energy function is as follows:

[0035]

[0036] wherein, A represents the adjacency matrix of the UAV formation, M, N represent the weight coefficients of the state quantities, M 11 represents the weight coefficient between the first three components of the first state error, M 22 represents the weight coefficient between the last two components of the first state error, N 11 represents the weight coefficient between the first three components of the second state error, N 22 represents the weight coefficient between the last two components of the second state error, o 3×2 represents a 3x2 zero matrix, o 2×3 represents a 2x3 zero matrix.

[0037] The stable condition is:

[0038]

[0039] where G ⊥ (z) denotes the left annihilator of G(z), z = [z1, z2,..., z i ,...,z N ] T , F(z) denotes the state variable derivative of the UAV formation system without control, F(z) = [f(z1) T ,f(z2) T ,...,f(z i ) T ,...,f(z N ) T ] T , f(z i ) denotes the state variable derivative of a single UAV without control, f(z i ) = h(z i )I 5×1 , h(z i ) denotes an intermediate matrix constructed from the state variables for the convenience of calculating f(z i ), h 12,i denotes the first sub-block of the matrix h(z i ), h 21,i denotes the second sub-block of the matrix h(z i ), h 22,i denotes the third sub-block of the matrix h(z i ), I 5×1 denotes a column vector with all elements being 1, G(Z) denotes the control matrix of the UAV formation system, I N denotes an N-order identity matrix, denotes the Kronecker product, denotes the desired interconnection matrix, denotes the desired damping matrix, denotes the partial derivative symbol.

[0040] Optionally, the expression of the control law is as follows:

[0041]

[0042] where C denotes the coefficient matrix related to the first and second state errors, D represents a coefficient matrix related to the derivative of the state quantity, D = [o 2×3 ,I 2×2 ] and γ represents a coefficient matrix, represents a desired interconnection matrix of a sub-block, k v represents an inertia time constant related to the speed, k ψ represents an inertia time constant related to the heading angle.

[0043] In a second aspect, the present application provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the control method of the wingman in the UAV formation when executing the computer program.

[0044] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the control method of the wingman in the UAV formation.

[0045] The above-mentioned scheme of the present application has the following beneficial effects:

[0046] The control method of the wingman in the UAV formation provided by the present application can obtain the estimated state quantity of the long plane according to the actual state quantity of the wingman and the actual state quantity of the long plane, can solve the problem that part of the wingmen in the UAV formation cannot directly communicate with the long plane, so that the expected state cannot be determined, and can improve the stability of the UAV formation control; the first state error of the wingman and the second state error between each wingman in the UAV formation are calculated according to the expected state quantity and the actual state quantity, and then the expected energy function of the wingman in the UAV formation is constructed according to the first state error and the second state error, so that the interaction between adjacent wingmen is fully considered, the expected energy function is more comprehensive, and the robustness of the UAV formation control is effectively improved; the control law of each wingman is designed according to the expected energy function and the pre-set UAV formation stability condition, the calculation complexity of the control law is reduced, the efficiency of the UAV formation control is improved, and the UAV formation can fly in the expected formation quickly and stably.

[0047] Other beneficial effects of the present application will be described in detail in the subsequent specific embodiment part. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0049] Figure 1 The flow chart of the control method of the wingman in the UAV formation provided by an embodiment of the present application;

[0050] Figure 2 The topological diagram of the UAV formation in an embodiment of the present application;

[0051] Figure 3 The flight trajectory diagram of the UAV formation provided by an embodiment of the present application;

[0052] Figure 4a The distance change diagram of each wingman in the UAV formation in an embodiment of the present application in the horizontal axis direction;

[0053] Figure 4b The distance change diagram of each wingman in the UAV formation in an embodiment of the present application in the vertical axis direction;

[0054] Figure 4c The heading angle change diagram of each wingman in the UAV formation provided by an embodiment of the present application;

[0055] Figure 4d The speed change diagram of each wingman in the UAV formation provided by an embodiment of the present application;

[0056] Figure 4e The angular velocity change diagram of each wingman in the UAV formation provided by an embodiment of the present application;

[0057] Figure 5 The structural diagram of the terminal device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0058] In the following description, for the purpose of explanation and not limitation, specific details are set forth, such as particular system architectures, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can 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.

[0059] It will be understood that the terms “comprises”, “comprising”, “includes”, “including”, “has”, “having” and variants thereof when used in this specification and in the following claims are not to be interpreted in an excluding sense but in an inclusive sense. That is, they will be understood to allow for elements, features, steps, or acts that would otherwise be excluded.

[0060] It will be further understood that the terms “comprises”, “comprising”, “includes”, “including”, “has”, “having” and variants thereof when used in this specification and in the following claims are not to be interpreted in an excluding sense but in an inclusive sense. That is, they will be understood to allow for elements, features, steps, or acts that would otherwise be excluded.

[0061] As used in this specification and claims, the terms “if’ and “when” can be interpreted to mean “upon” or “in response to determining,” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a described condition or event] is detected” can be interpreted to mean “upon determining” or “in response to determining,” or “upon detecting [the described condition or event]” or “in response to detecting [the described condition or event],” depending on the context.

[0062] In addition, the description in the specification of this application and the appended claims, the terms “first”, “second”, “third”, etc. are used merely as labels for convenience, and are not intended to imply or create a relative importance of the elements being described.

[0063] Reference throughout this specification to “one embodiment” or “an embodiment” or “some embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, appearances of the phrases “in one embodiment”, “in some embodiments”, “in other embodiments”, “in additional embodiments”, and so on, in various places throughout this specification are not necessarily all referring to the same embodiment, unless otherwise specified. The terms “comprise”, “comprising”, “has”, “having”, and variants thereof, are meant to be interpreted broadly in an inclusive and not an exclusive sense, unless otherwise specifically indicated.

[0064] In view of the problems that the current control method of the UAV formation has poor robustness and complex calculation process, and the UAV formation cannot fly in the expected formation quickly and stably, the application provides a control method of a wingman in a UAV formation, a terminal device and a medium, wherein the method obtains an estimated state quantity of a leader according to an actual state quantity of the wingman and an actual state quantity of the leader, can solve the problem that part of the wingmen in the UAV formation cannot directly communicate with the leader, so that the expected state of the wingmen cannot be determined, and can improve the stability of the UAV formation control; according to the expected state quantity and the actual state quantity, a first state error of the wingman and a second state error between each wingman in the UAV formation are calculated respectively, and then an expected energy function of the wingman in the UAV formation is constructed according to the first state error and the second state error, the interaction between adjacent wingmen is fully considered, so that the expected energy function is considered more comprehensively, thereby effectively improving the robustness of the UAV formation control; according to the expected energy function and a pre-set UAV formation stability condition, a control law of each wingman is designed, which can reduce the calculation complexity of the control law, thereby improving the efficiency of the UAV formation control, and enabling the UAV formation to fly in the expected formation quickly and stably.

[0065] As shown in Figure 1 The control method of the wingman in the UAV formation provided by the application includes the following steps:

[0066] Step 11, collecting actual state quantities in the UAV formation flight process.

[0067] It should be understood that the UAV formation is usually composed of multiple wingmen and one leader, and the division of the leader and the wingmen is pre-set. The leader is used to control the formation of the UAV formation. In the application, the control of the UAV formation adopts distributed control, that is, adjacent UAVs in the UAV formation can interact information, which can avoid the problem of information jamming of the UAV formation caused by the failure of part of the UAVs in the centralized control. The above information interaction can be carried out through common information interaction modes, including radio signal transmission, network transmission, etc.

[0068] The actual state quantities in the UAV formation flight process include the actual state quantities of each UAV (including the wingmen and the leader) in the UAV formation. Exemplarily, the relevant data can be collected through the sensors pre-installed on the UAV. In the application, the state quantity of the UAV includes the position, speed, heading angle and acceleration of the UAV. The expected position of the wingman is determined by the leader. A common position determination method is to establish a reference coordinate system with the leader as the reference point, and determine the expected position of each wingman according to the pre-set distance between the wingman and the leader and the pre-set formation of the UAV formation.

[0069] The motion model of the UAV formation in the flight process and its construction process in the application will be described exemplarily as follows.

[0070] In the embodiments of the present application, the flight process of the UAV is regarded as a two-dimensional plane motion, and its motion model is as follows:

[0071]

[0072] wherein (x, y) represents the coordinates of the UAV in the inertial coordinate system (north-east coordinate system), ψ represents the heading angle of the UAV, v represents the speed of the UAV, and ω represents the angular velocity of the UAV.

[0073] The autopilot model is used to control the speed and heading angle of the UAV respectively, and the specific process is as follows:

[0074]

[0075] wherein v c represents the speed control instruction of the UAV, ψ c represents the heading angle control instruction of the UAV, k v represents the inertia time constant related to the speed, and its unit is S -1 , S -1 represents per second, k ψ represents the inertia time constant related to the heading angle, and its unit is S -2 , S -2 represents per second squared, and k ω represents the inertia time constant related to the angular velocity, and its unit is S -1 , S -1 represents per second.

[0076] The state constraints in the UAV formation flight process are as follows:

[0077]

[0078] wherein v min represents the minimum speed of the UAV, v max represents the maximum flight speed of the UAV, v(t) represents the speed of the UAV at any time t, a max represents the maximum acceleration, ω max represents the maximum angular velocity, and α max represents the maximum angular acceleration. Each of the above quantities is a suitable quantity, and its absolute value represents the numerical value, and the positive and negative signs represent the direction. The positive direction and the negative direction are divided in advance according to the actual flight requirements.

[0079] The UAV motion model is converted into the form of a general nonlinear system, which is as follows:

[0080]

[0081] wherein Z represents the derivative of the state variables of N wingmen, where Z = [z1, z2, ..., zn]. i ,...,z N ] T , z i Let z represent the state variable of the i-th wingman among N wingmen. i ={x i ,y i ,ψ i ,v i ,ω i} T Let i = 1, 2, ..., N, where N represents the total number of wingmen in the drone formation, and x i Let y represent the x-coordinate of the i-th wingman in the inertial coordinate system. i Let ψ represent the ordinate of the i-th wingman in the inertial coordinate system. i Let v represent the heading angle of the i-th wingman. i Let ω represent the speed of the i-th wingman. i Let U represent the angular velocity of the i-th wingman, and U represent the control input quantities of the N wingmen, where U = [u1, u2, ..., u...]. i ,...,u N ] T u i This represents the control quantity of the i-th wingman. This represents the heading angle control value of the i-th wingman. Let F(Z) represent the speed control variable of the i-th wingman, and let F(Z) represent the state derivative of the UAV formation system without control variables. F(Z) = [f(z1)]. T ,f(z2) T ,...,f(z i ) T ,...,f(z N ) T ] T ,f(z i f(z) represents the derivative of the state variables of a single UAV without control variables. i )=h(z i )I 5×1 ,h(z i ) is used to simplify the calculation of f(z) i Construct an intermediate matrix from the state variables. h 12,i Represents matrix h(z) i The first sub-block of ) h 21,i Represents matrix h(z) i The second sub-block of ) h 22,i Represents matrix h(z) i The third sub-block of ) I 5×1 Let G(Z) represent a column vector with all elements equal to 1, and let G(Z) represent the control matrix of the UAV formation system. I N Represents an N-order identity matrix. G(z) represents the Kronecker product. i ) represents the control matrix of a single UAV. g2 represents a sub-block of the control matrix.

[0082] Step 12: For each of the N wingmen, obtain the estimated state of the wingman relative to the leader based on the actual state of the wingman and the actual state of the leader.

[0083] It should be understood that in drone swarms, some wingmen may be unable to communicate directly with the lead drone. In such cases, traditional centralized control is used. If a wingman malfunctions and cannot transmit information to the next node (wingman or lead drone), information congestion will occur. Figure 2 As shown, in one embodiment of this application, the drone formation consists of a lead drone (UAV0) and six wingmen (UAV1, UAV2, UAV3, UAV4, UAV5, UAV6). The formation of the drone formation is pre-set as a regular hexagon centered on the lead drone. Figure 2 Wingmen who do not communicate directly with the lead aircraft include UAV4, UAV3, and UAV2.

[0084] In the above scenario, the state estimates of each wingman relative to the lead drone can be obtained using a drone formation topology consistency algorithm. Specifically:

[0085] For each wingman h in a drone formation that communicates directly with the lead drone, the calculation formula is used.

[0086]

[0087] The derivatives of the estimated state variables of the lead aircraft are obtained.

[0088] in, The derivative of the actual state quantity of the lead aircraft, z R This represents the actual state parameters of the lead aircraft that were collected in advance. This represents the wingman's estimate of the lead aircraft's actual condition. The initial value is given by z h And the pre-set desired distance between the wingman and the lead aircraft is determined, z hdenotes the actual state quantity of the wingman h which is pre-collected and directly communicated with the leader, denotes the position estimation of the leader by the wingman h, denotes the heading angle estimation of the leader by the wingman h, denotes the speed estimation of the leader by the wingman h, denotes the angular velocity estimation of the leader by the wingman h, γ denotes the degree of dependence between the wingmen, γ > 0, a hj denotes the coefficient in the adjacency matrix, the adjacency matrix denotes the matrix composed of the communication relationship between the unmanned aerial vehicles, a hj = (0, 1), a hj = 1 denotes that the wingman h and the wingman j can communicate with each other, a hj = 0 denotes that the wingman h and the wingman j cannot communicate with each other, j = 1, 2,..., N, j ≠ j.

[0089] For each wingman g which does not directly communicate with the leader in the unmanned aerial vehicle formation, the estimated state quantity of the leader is obtained by the calculation formula

[0090]

[0091] derivative of the estimated state quantity of the leader is obtained

[0092] wherein, denotes the position estimation of the leader by the wingman g, denotes the heading angle estimation of the leader by the wingman g, denotes the speed estimation of the leader by the wingman g, denotes the angular velocity estimation of the leader by the wingman g, g ∈ [1, N], g ≠ h, g ≠ j, a gj = 1 denotes that the wingman g and the wingman j can communicate with each other, a gj = 0 denotes that the wingman g and the wingman j cannot communicate with each other, denotes the estimation of the leader state quantity by the wingman j, denotes the estimation of the leader state quantity by the wingman g.

[0093] Step 13, according to the estimated state quantity, the expected distance between the wingman and the leader which is pre-set, the expected state quantity of the wingman is calculated.

[0094] It should be noted that for the straight line movement of the UAV formation, it can be regarded as the circular movement along the infinite radius, and for the general curve movement of the UAV formation, it can be regarded as the circular movement with the changeable radius, therefore, the above two movement modes of the UAV formation (straight line movement and general curve movement) are regarded as the circular movement. Based on this, in the flight process of the UAV formation, in order to make the UAV formation keep the fixed formation, the angular velocity of the wingman will not change.

[0095] Specifically, step 13 includes the following steps:

[0096] Step 13.1, according to the estimated state quantity Obtain the position estimation of the wingman to the leader

[0097] Wherein,

[0098] Step 13.2, through the calculation formula

[0099]

[0100] Get the expected position of the wingman Wherein, represents the heading angle of the wingman i, represents the expected distance.

[0101] Step 13.3, through the calculation formula

[0102]

[0103] Get the expected speed of the wingman and the expected heading angle

[0104] Wherein, l i represents the distance from the wingman i to the center of the circle Ω, the center of the circle represents the center of the circle in which the wingman moves in a circle, and the straight line movement and the curve movement of the wingman are regarded as the circular movement, l R represents the distance from the leader R to the center of the circle Ω, represents the positive angle formed by the leader, the center of the circle and the wingman i.

[0105] Step 14, according to the expected state quantity and the actual state quantity, respectively calculating the first state error of the wingman and the second state error between each wingman in the UAV formation.

[0106] Wherein, the first state error represents the state error of the wingman relative to the leader.

[0107] Specifically, the expression of the first state error is Wherein, represents the first state error, zi actual state quantity of the wingman i, z i = {x i , y i , v i , ψ i , ω i}, (x i , y i ) represents the actual position of the wingman i, ψ i represents the actual heading angle of the wingman i, v i represents the actual speed of the wingman i, and ω i represents the actual angular velocity of the wingman i, desired state quantity of the wingman i, desired position of the wingman i, desired heading angle of the wingman i, desired speed of the wingman i, desired angular velocity of the wingman i.

[0108] The expression of the second state error is wherein, represents the second state error, represents the first state error of the wingman i, represents the first state error of the wingman t, i, t = 1, 2,..., N, i ≠ t.

[0109] It should be noted that the control target of the unmanned aerial vehicle formation flight is that the first state error is equal to zero, i.e. the second state error is equal to zero, i.e. At this time, the entire unmanned aerial vehicle formation is in a balanced state, which is one of the conditions for maintaining the fixed formation of the unmanned aerial vehicle formation.

[0110] Step 15, according to the first state error and the second state error, the desired energy function of the wingman in the unmanned aerial vehicle formation is constructed.

[0111] As can be seen from the previous step, when the unmanned aerial vehicle formation is in a balanced state , the actual state quantity of each wingman should be equal to the desired state quantity, and the actual state quantity between each wingman should be the same. Therefore, the expression of the constructed desired energy function is as follows:

[0112]

[0113] wherein A represents the adjacency matrix of the unmanned aerial vehicle formation, M, N represent the weight coefficients of the state quantity, M 11 represents the weight coefficient between the first three components of the first state error, M 22denote the weight coefficient between the last two components of the first state error, N 11 denote the weight coefficient between the first three components of the second state error, N 22 denote the weight coefficient between the last two components of the second state error, o 3×2 denote a 3-by-2 zero matrix, o 2×3 denote a 2-by-3 zero matrix.

[0114] The derivative of the above equation with respect to is

[0115]

[0116] where, denote the state error of the wingman i, L denotes the Laplace matrix, and the matrix elements thereof satisfy the following conditions:

[0117]

[0118] Step 16, according to the expected energy function and the pre-set unmanned aerial vehicle formation stability condition, design the control law of each wingman in the N wingmen.

[0119] The above stability condition is

[0120] wherein G ⊥ (z) denotes the left annihilator of G(z), z = [z1, z2,..., z i ,...,z N ] T , F(z) denotes the state quantity derivative of the unmanned aerial vehicle formation system without control, F(z) = [f(z1) T ,f(z2) T ,...,f(z i ) T ,...,f(z N ) T ] T , f(z i ) denotes the state quantity derivative of a single unmanned aerial vehicle without control, f(z i ) = h(z i )I 5×1 , h(z i ) denotes an intermediate matrix constructed from the state quantity for the convenience of calculating f(z i ), j 12,i denotes the first sub-block of the matrix h(z i ), j 21,i denotes the first sub-block of the matrix h(z iThe second sub-block of ) h 22,i Represents matrix h(z) i The third sub-block of ) I 5×1 Let G(Z) represent a column vector with all elements equal to 1, and let G(Z) represent the control matrix of the UAV formation system. I N Represents an N-order identity matrix. Indicates the Kronecker product. Represents the desired interconnection matrix. Represents the desired damping matrix. The symbol represents the partial derivative.

[0121] The derivation process is illustrated below:

[0122] Based on Proposition 1 (2.1. General Nonlinear Systems--Proposition 1) of Reference 1, "Interconnection and Damping Assignment Passivity-Based Control: A Survey [J]. European Journal of Control, 2004, 10(5): 432-450", and Section 3 (III. STATE ERROR PCH TRACKING CONTROL LAW) of Reference 2, "State Error PCH Trajectory Tracking Control of an Unmanned Surface Vehicle [C] / / 2018 Chinese Automation Congress, 2018, 3908-3912", the design of the controller for the state error interconnection and damping passive control method of general nonlinear systems satisfies the following inference:

[0123] Corollary: For general nonlinear systems

[0124] If a matrix g exists ⊥ (X), sum function H d (X): Satisfy the following partial differential equations:

[0125]

[0126] where g ⊥ (X) is a left annihilator of g(X) satisfying g ⊥ (X)g(X) = 0, there exists a minimum point

[0127] If the general nonlinear system can be transformed into the form of the desired port Hamiltonian system under the control law u = β(X), where

[0128]

[0129] If the maximal invariant set of the closed loop system is equal to {x *}, and is contained in

[0130] then the system is asymptotically stable.

[0131] The above inference is proved as follows.

[0132] The specific proof is as follows:

[0133] Suppose that under the action of u = β(X), the right side (f(X) + g(X)u) of the equation is equal to the right side of the equation An equality relationship is established, and a matching equation is obtained:

[0134]

[0135] The left side of the above equation is multiplied by the matrix g ⊥ (X) on the left, and the partial differential equation The left side of the above equation is multiplied by the pseudo-inverse of g(X) on the left, and the expression of the control quantity is obtained: Thus, based on the equation the following can be obtained:

[0136]

[0137] It can be seen that satisfies the condition of Lyapunov function, and by the Russell invariance principle (the derivative is semi-negative, i.e., the derivative is equal to 0 only at the equilibrium point, and the derivative is always less than 0 at other times) and it can be known that the system is asymptotically stable.

[0138] ​Based on the above inference and the expected energy function obtained If the UAV formation system is transformed into a desired port Hamilton system as

[0139]

[0140] The above stability conditions must be met

[0141] Since the UAV formation system has the same subsystem, the above formula can be converted into the following form:

[0142]

[0143] Wherein, the left zero factor of the control matrix is designed To meet g ⊥ (z)g(z)=o 2×2 The desired interconnection matrix is designed The desired damping matrix is designed Substituting the above formula can be obtained

[0144]

[0145] In the above formula, p i =[0,ω] T , P and are arbitrary vectors, M 11 =N 11 =I 3×3 , M 22 =I 2×2 , N 22 =o 2×2 , J 11 =R 11 =o 3×3 , J 22 =o 2×2 , k1,k2 are variable coefficients, r v is the damping coefficient corresponding to the speed, and r ω is the damping coefficient corresponding to the angular velocity.

[0146] Step 17, according to the control law, the wingman in the UAV formation is controlled.

[0147] Illustratively, according to the control law, the control amount of each UAV in the UAV formation is obtained, and the control amount of each UAV is input into the motion model of the UAV to realize the control of the wingman in the UAV formation.

[0148] The control law designed in step 16 is exemplarily illustrated as follows.

[0149] Specifically, the expression of the control law is as follows:

[0150]

[0151] wherein C represents a coefficient matrix related to the first and second state errors, D represents a coefficient matrix related to the derivative of the state quantity, D = [0 2×3 ,I 2×2 ], γ represents a coefficient matrix, represents a sub-block of the expected interconnection matrix k v represents an inertia time constant related to the speed, ψ k represents an inertia time constant related to the heading angle.

[0152] In order to verify the effectiveness of the control method of the wingman in the UAV formation provided in the present application, in an embodiment of the present application, the following simulation experiment is performed:

[0153] The initial conditions of the UAV formation are shown in the following table:

[0154]

[0155] For the selection of simulation parameters, the inertia time constants in the UAV autopilot are respectively set as: k v = 0.5 s -1 , k ψ = 0.5 s -2 , k ω = 0.3 s -1 , the flight state constraints of the UAV are v min = 10 m / s, v max = 50 m / s, a max = 7 m / s 2 , ω max = 0.1 rad / s, α max = 0.1 rad / s 2 , the coefficients in the interconnection matrix and the damping matrix in the controller are set as k1 = 8 x 10 -2 , k2 = 5 x 10 -3 , r v = 1, r ω = 2, the simulation time and the simulation step are 70 s and 0.1 s respectively.

[0156] The desired angular velocity of the UAV formation circumferential motion is set to 0.03 rad / s, and the flight trajectory is as shown in Figure 3 Figure 3 The dashed line in the figure represents the flight trajectory of the leader, Figure 3 The solid line in the figure represents the flight trajectory of the wingman, the horizontal axis represents the eastward distance, and the vertical axis represents the northward distance, and it can be seen from the figure that the UAV formation is finally stabilized to the desired formation under the control of the wingman control method of the UAV formation provided in the application. Figure 3

[0157] During the simulation, the distance change of each wingman in the UAV formation in the horizontal axis direction is as shown in Figure 4a The distance change in the vertical axis direction is as shown in Figure 4b The heading angle change is as shown in Figure 4c The speed change is as shown in Figure 4d The angular velocity change is as shown in Figure 4e It can be seen that each state quantity (position (distance in the horizontal axis direction, distance in the vertical axis direction), heading angle, speed, angular velocity) of the wingman can reach a stable state, which shows that the wingman control method of the UAV formation provided in the application has good stability, robustness and computational efficiency.

[0158] As shown in Figure 5 An embodiment of the application provides a terminal device, as shown in Figure 5 The terminal device D10 of the embodiment includes at least one processor D100 (only one processor is shown in the figure), a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 implements the steps in any of the method embodiments described above when executing the computer program D102. Figure 5

[0159] ​​​Specifically, the processor D100 executes the computer program D102 to acquire actual state quantities in the process of formation flight of the unmanned aerial vehicle, and then for each of the N wingmen, the processor D100 acquires an estimated state quantity of the leader according to the actual state quantity of the wingman and the actual state quantity of the leader, and then calculates an expected state quantity of the wingman according to the estimated state quantity and an expected distance between the wingman and the leader set in advance, and then calculates a first state error of the wingman and a second state error between each of the wingmen in the unmanned aerial vehicle formation according to the expected state quantity and the actual state quantity, and then constructs an expected energy function of the wingmen in the unmanned aerial vehicle formation according to the first state error and the second state error, and then designs a control law of each of the N wingmen according to the expected energy function and a preset stable condition of the unmanned aerial vehicle formation, and finally controls the wingmen in the unmanned aerial vehicle formation according to the control law. The acquisition of the estimated state quantity of the leader according to the actual state quantity of the wingman and the actual state quantity of the leader can solve the problem that some wingmen in the unmanned aerial vehicle formation cannot directly communicate with the leader, so that the expected state of the wingmen cannot be determined, and can improve the stability of the unmanned aerial vehicle formation control. The calculation of the first state error of the wingman and the second state error between each of the wingmen in the unmanned aerial vehicle formation according to the expected state quantity and the actual state quantity, and then the construction of the expected energy function of the wingmen in the unmanned aerial vehicle formation according to the first state error and the second state error, fully consider the interaction between adjacent wingmen, so that the expected energy function is more comprehensive, thereby effectively improving the robustness of the unmanned aerial vehicle formation control. The design of the control law of each wingman according to the expected energy function and the preset stable condition of the unmanned aerial vehicle formation can reduce the calculation complexity of the control law, thereby improving the efficiency of the unmanned aerial vehicle formation control, and enabling the unmanned aerial vehicle formation to fly in the expected formation quickly and stably.

[0160] The processor D100 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or can also be any conventional processor.

[0161] The storage D101 can be an internal storage unit of the terminal device D10, such as a hard disk or a memory of the terminal device D10, in some embodiments. The storage D101 can also be an external storage device of the terminal device D10, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device D10, in other embodiments. Further, the storage D101 can include both an internal storage unit and an external storage device of the terminal device D10. The storage D101 is used to store an operating system, an application program, a boot loader, data, and other programs, such as program codes of the computer program, etc. The storage D101 can also be used to temporarily store data that has been output or is to be output.

[0162] The embodiments of the present application provide a computer program product, when the computer program product runs on a terminal device, causes the terminal device to execute the steps in the above-mentioned various method embodiments.

[0163] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application can implement all or part of the processes in the above-mentioned embodiments by a computer program to instruct related hardware to complete, and the computer program can be stored in a computer readable storage medium, and the computer program can implement the steps in the above-mentioned various method embodiments when executed by a processor. The computer program includes computer program codes, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program codes to the terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium can not be an electrical carrier signal and a telecommunications signal.

[0164] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0165] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0166] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / network device and method can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely schematic. The division of the modules or units is merely a logical function division. There can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the display or discussion about the coupling or direct coupling or communication connection between the units can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0167] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0168] As can be seen from the above, the control method of the wingman in the UAV formation provided by the present application has the following advantages:

[0169] (1) Based on the port Hamilton system theory and the passive control of interconnection-damping configuration, a state error interconnection-damping configuration passive control method suitable for general nonlinear systems is derived.

[0170] (2) Based on the state error model of the UAV formation, the expected Hamilton function of the system is defined, and a distributed control law of the UAV formation is designed from the perspective of energy, so that the control process of the UAV formation has a clear physical meaning.

[0171] The above describes the preferred embodiments of the present application. It should be noted that for those skilled in the art, without departing from the principles described in the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method for controlling wingmen in a drone formation, characterized in that, The application relates to a control method for a drone formation, and belongs to the field of unmanned aerial vehicle (UAV) control. The method comprises the following steps: collecting actual state quantities of the drone formation in flight; the drone formation comprises N wing drones and a leader drone; for each of the N wing drones, acquiring an estimated state quantity of the leader drone according to the actual state quantity of the wing drone and the actual state quantity of the leader drone; calculating an expected state quantity of the wing drone according to the estimated state quantity and a pre-set expected distance between the wing drone and the leader drone; calculating a first state error of the wing drone and a second state error between the wing drones in the drone formation according to the expected state quantity and the actual state quantity; the first state error represents a state error of the wing drone relative to the leader drone; constructing an expected energy function of the wing drones in the drone formation according to the first state error and the second state error; designing a control law of each of the N wing drones according to the expected energy function and a pre-set drone formation stability condition; controlling the wing drones in the drone formation according to the control law; Wherein, A represents the adjacency matrix of the UAV formation, M, N represents the weight coefficient matrix of the state quantity, M 11 represents the weight coefficient between the first three components of the state error, M 22 represents the weight coefficient between the last two components of the first state error, N 11 represents the weight coefficient between the first three components of the second state error, N 22 represents the weight coefficient between the last two components of the second state error, o 3×2 represents a 3x2 zero matrix, o 2×3 represents a 2x3 zero matrix, represents the second state error, represents the first state error of the wingman i.

2. The control method according to claim 1, characterized by, wherein the expression of the expected energy function is as follows: the step of acquiring the estimated state quantity of the leader drone according to the actual state quantity of the wing drone and the actual state quantity of the leader drone for each of the N wing drones comprises the following steps: derivative of the estimated state quantity of the long machine wherein, derivative of the actual state quantity of the long machine, z R actual state quantity of the long machine, z estimate of the long machine actual state by the wingman h, the initial value of z h and the desired distance between the wingman and the long machine, h actual state quantity of the wingman h in direct communication with the long machine, z position estimate of the long machine by the wingman h, heading angle estimate of the long machine by the wingman h, speed estimate of the long machine by the wingman h, angular velocity estimate of the long machine by the wingman h, γ represents the degree of dependence between wingmen, γ > 0, a hj coefficient in the adjacency matrix, the adjacency matrix representing a matrix composed of the communication relationship between unmanned aerial vehicles, a hj = (0, 1), a hj = 1 indicates that the wingman h and the wingman j can communicate with each other, a hj = 0 indicates that the wingman h and the wingman j cannot communicate with each other, j = 1, 2,..., N, j ≠ h; for each wing drone h in the drone formation which directly communicates with the leader drone, calculating the estimated state quantity of the leader drone through a calculation formula derivative of the estimated state quantity of the long aircraft wherein, denotes the position estimate of the long aircraft by the wingman g, denotes the heading angle estimate of the long aircraft by the wingman g, denotes the velocity estimate of the long aircraft by the wingman g, denotes the angular velocity estimate of the long aircraft by the wingman g, g e [1, N], g ≠ h, g ≠ j, a gj = 1 means that wingman g and wingman j are able to communicate with each other, gj = 0 means that wingman g and wingman j are not able to communicate with each other, denotes the estimate of the long aircraft state quantity by the wingman j, denotes the estimate of the long aircraft state quantity by the wingman g.

3. The control method according to claim 2, characterized by, for each wing drone g in the drone formation which does not directly communicate with the leader drone, calculating the estimated state quantity of the leader drone through a calculation formula the angular velocity of the wing drone in flight is constant; According to the estimated state quantity Obtaining the position estimate of the wingman to the lead aircraft wherein the step of calculating the expected state quantity of the wing drone according to the estimated state quantity and the pre-set expected distance between the wing drone and the leader drone comprises the following steps: obtaining a desired position of the wingman wherein, denotes a heading angle of the wingman i, denotes the desired distance; calculating the expected state quantity of the wing drone through a calculation formula the desired speed of the wingman and the desired heading angle where l i denotes the distance of the wingman i to the circle center Ω, which denotes the circle center of the circular motion of the wingman, the linear motion and the curvilinear motion of the wingman being both circular motions, l R denotes the distance of the lead aircraft R to the circle center Ω, denotes the positive angle formed by the lead aircraft, the circle center and the wingman i.

4. The control method according to claim 3, characterized by, The expression of the first state error is wherein denotes the first state error, z i denotes the actual state quantity of the wingman i, z i = {x i , y i , v i , ψ i , ω i}, (x i , y i ) denotes the actual position of the wingman i, ψ i denotes the actual heading angle of the wingman i, v i denotes the actual speed of the wingman i, ω i denotes the actual angular speed of the wingman i, denotes the desired state quantity of the wingman i, denotes the desired position of the wingman i, denotes the desired heading angle of the wingman i, denotes the desired speed of the wingman i, denotes the desired angular speed of the wingman i.

5. The control method according to claim 4, characterized by An expression of the second state error is wherein, denotes the second state error, denotes the first state error of the wingman i, denotes the first state error of the wingman t, i, t = 1, 2,..., N, i ≠ t.

6. The control method according to claim 1, characterized by calculating the expected state quantity of the wing drone through a calculation formula where G ⊥ (z) represents the left annihilator of G(z), z = [z1, z2,..., z i ,...,z N ] T , F(z) represents the state variable derivative of the UAV formation system without control, F(z) = [f(z1) T ,f(z2) T ,...,f(z i ) T ,...,f(z N ) T ] T , f(z i ) represents the state variable derivative of a single UAV without control, f(z i ) = h(z i )I 5×1 , h(z i ) represents an intermediate matrix constructed from the state variables for the convenience of calculating f(z i ), h 12,i represents the first sub-block of the matrix h(z i ), h 21,i represents the second sub-block of the matrix h(z i ), h 22,i represents the third sub-block of the matrix h(z i ), I 5×1 represents a column vector with all elements being 1, G(Z) represents the control matrix of the UAV formation system, I N represents an N-order identity matrix, represents the Kronecker product, represents the desired interconnection matrix, represents the desired damping matrix, represents the partial derivative symbol.

7. The control method according to claim 6, characterized by the stability condition is as follows: where C denotes a coefficient matrix related to the first and second state errors, D denotes a coefficient matrix related to the state quantity derivatives, D = [o 2×3 , 2×2 ], γ denotes a coefficient matrix, denotes a desired interconnection matrix of sub-blocks, k v denotes an inertia time constant related to the velocity, k ψ denotes an inertia time constant related to the heading angle.

8. 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 expression of the control law is as follows:

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. the processor executes the computer program to realize the control method for the wing drones in the drone formation according to any one of claims 1 to 7. the computer program is executed by the processor to realize the control method for the wing drones in the drone formation according to any one of claims 1 to 7.

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