Method for controlling minimum energy formation of roiser swarms under random topology switching condition

Abstract

The invention relates to a flight rover bee colony minimum energy formation control method under a random topology switching condition, and the method comprises the steps: carrying out the modeling of all possible switching topologies into a random switching topology set according to the link connection and disconnection conditions of all flight rovers in a flight rover bee colony system; determining a minimum non-zero characteristic value and a maximum characteristic value of Laplacian matrixes of all switching topologies in the random switching topology set; based on the minimum non-zero characteristic value, the maximum characteristic value, the system matrix, the control matrix and the global control energy consumption index function weight matrix, constructing a constraint condition of a minimum energy parameter optimization value for ensuring formation realization; solving a constraint condition by utilizing a generalized eigenvalue method to obtain a matrix variable and a minimum energy constraint parameter; and obtaining a control gain matrix based on the matrix variable. According to the method, the minimum energy formation optimization energy control of the unmanned aerial vehicle swarm system can be realized under the condition of random switching topology, the random'on-off 'requirement of a communication link between unmanned aerial vehicles can be met, and the method has wide applicability.

Description

Technical Field

[0001] The present application relates to the field of formation optimization control of unmanned aerial vehicle swarm systems, and specifically to a minimum energy formation control method for an unmanned aerial vehicle swarm under random topology switching conditions. Background Art

[0002] As unmanned aerial vehicles (UAVs) capable of combining reconnaissance, surveillance, and precision guidance, they can carry communications equipment, reconnaissance equipment, and strike force, quickly reaching target areas and performing diverse missions such as reconnaissance, surveillance, damage assessment, and airstrike strikes. They possess advantages such as high cost-effectiveness and a high degree of intelligence. Certain missions require multiple UAVs to form a desired formation, facilitating more accurate multi-angle and multi-directional reconnaissance and surveillance or executing specialized missions, thereby enhancing the diversity and complexity of mission execution capabilities. However, during formation formation, to reduce communication resource consumption, bandwidth limitations, data packet loss, or intentional or unintentional interference from other parties can lead to partial link disconnection and reconnection between UAVs. This disconnection and reconnection between multiple UAVs can be mathematically modeled as a switching of different action topologies. Furthermore, in situations such as long target distances, frequent speed changes, and complex missions, energy conservation and minimization of power and fuel consumption are also crucial. Existing technologies rarely involve technologies for minimizing global control energy consumption of unmanned aerial vehicle swarm systems and realizing formation control under random switching topology conditions. Summary of the Invention

[0003] In order to overcome at least one deficiency in the prior art, the present application provides a minimum energy formation control method for a patrol vehicle swarm under random topology switching conditions.

[0004] In a first aspect, a minimum energy formation control method for a patrol vehicle swarm under random topology switching conditions is provided, comprising: Step 1: Determine the system matrix and control matrix based on the control performance of a single patrol vehicle, and set the weight matrix of the global control energy consumption index function; Step 2: Set the expected time-varying formation vector. If the formation feasibility requirements are met, proceed to step 3. Otherwise, return to step 1 and redetermine the system matrix, control matrix, and expected time-varying formation vector. Step 3: Based on the on-off status of each patroller link in the patroller swarm system, all possible switching topologies are modeled as a random switching topology set. Step 4, determine the minimum non-zero eigenvalue and the maximum eigenvalue of the Laplacian matrix of all switching topologies in the random switching topology set; Step 5: Based on the minimum non-zero eigenvalue, the maximum eigenvalue, the system matrix, the control matrix, and the weight matrix of the global control energy consumption index function, a constraint condition is constructed to ensure the minimum energy parameter optimization value achieved by the formation; the constraint condition includes matrix variables; Step 6: Use the generalized eigenvalue method to solve the constraints and obtain matrix variables and minimum energy parameters; obtain the control gain matrix based on the matrix variables.

[0005] In one embodiment, the formation control protocol of the patrol swarm system is:

[0006] in, For patrol aircraft The control input, is the control gain matrix, for Time Patrol The neighbor set of for Time Patrol To the patrol vehicle The weight of the effect, For patrol aircraft The state vector of For patrol aircraft The state vector of For patrol aircraft The expected time-varying formation vector, For patrol aircraft The expected time-varying formation vector, Indicates the time interval, is the lower limit of the time interval, is the upper limit of the time interval, is the label of the time interval, is the global control energy consumption index function, N is the number of patrol vehicles, is the weight matrix of the global control energy consumption index function, T Indicates transpose.

[0007] In one embodiment, the constraints are:

[0008]

[0009]

[0010] in, is the minimum energy parameter, H is a matrix variable, A is the system matrix, Bis the control matrix, is the smallest non-zero eigenvalue, is the maximum eigenvalue, is the weight matrix of the global control energy consumption index function, is the identity matrix, represents the conjugate transpose, T Indicates transpose.

[0011] In one embodiment, the control gain matrix is obtained based on the matrix variables using the following formula:

[0012] in, is the control gain matrix, is the smallest non-zero eigenvalue, B is the control matrix, H is a matrix variable.

[0013] In a second aspect, a minimum energy formation control device for a patrol aircraft swarm under random topology switching conditions is provided, comprising: The parameter setting module is used to determine the system matrix and control matrix according to the control performance of a single patrol vehicle, and to set the weight matrix of the global control energy consumption index function; The formation feasibility judgment module is used to set the expected time-varying formation vector. If the formation feasibility requirements are met, the random switching topology set construction module is executed. Otherwise, the module returns to the parameter setting module to redetermine the system matrix, control matrix, and expected time-varying formation vector. The random switching topology set construction module is used to model all possible switching topologies as random switching topology sets according to the on-off status of the links of each patroller in the patroller swarm system; An eigenvalue determination module is used to determine the minimum non-zero eigenvalue and the maximum eigenvalue of the Laplacian matrix of all switching topologies in the random switching topology set; A constraint condition construction module is used to construct constraint conditions that ensure the minimum energy parameter optimization value achieved by the formation based on the minimum non-zero eigenvalue, the maximum eigenvalue, the system matrix, the control matrix, and the weight matrix of the global control energy consumption index function; the constraint conditions include matrix variables; The solution module is used to solve the constraint conditions using the generalized eigenvalue method to obtain matrix variables and minimum energy constraint parameters; and obtain the control gain matrix based on the matrix variables.

[0014] Compared with the prior art, the present application has the following beneficial effects: the minimum energy formation control method for patrol swarms under random topology switching conditions of the present application, while solving the formation control problem under random communication interruption conditions, also considers the problem of minimizing global control energy consumption during the formation control process. The present application can realize the minimum energy formation optimization control of the unmanned patrol swarm system under random switching topology conditions, can adapt to the random "on-off" requirements of the communication links between unmanned patrol vehicles, and has a wide range of applicability. According to the set formation constraints, it is first required that the distributed unmanned patrol swarm system can realize formation control under random switching topology conditions. In addition, the formation of the unmanned patrol swarm system can be optimized and controlled to ensure that the global control energy consumption in the sense of generalized eigenvalues is minimized during the formation realization process, and the upper bound of the global control energy consumption is constrained by minimum energy, ensuring that the expected time-varying formation can be realized while minimizing the global control consumption in the sense of generalized eigenvalues. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present application may be better understood by referring to the following description in conjunction with the accompanying drawings, which together with the following detailed description are incorporated into and form a part of this specification. In the drawings: Figure 1 The flowchart of the minimum energy formation control method of patrol aircraft swarm under random topology switching conditions is shown; Figure 2 Shows a schematic diagram of the non-periodic action topology and intermittent communication time sub-interval; Figure 3 The state formation topology set is shown; Figure 4 The simulation curve of the state formation tracking error is shown, where (a) is The simulation curve of (b) is The simulation curve of Simulation curve of Figure 5 The figure shows the instantaneous spatial position simulation diagram of each patrol aircraft at different times, where (a) is the instantaneous spatial position simulation diagram of each patrol aircraft at 0s, (b) is the instantaneous spatial position simulation diagram of each patrol aircraft at 2s, (c) is the instantaneous spatial position simulation diagram of each patrol aircraft at 8s, and (d) is the instantaneous spatial position simulation diagram of each patrol aircraft at 10s. Figure 6 A simulation diagram comparing global control energy consumption with and without minimum energy constraints is shown. DETAILED DESCRIPTION

[0016] Exemplary embodiments of the present application are described below with reference to the accompanying drawings. For the sake of clarity and conciseness, not all features of actual embodiments are described in this specification. However, it should be understood that in the process of developing any such actual embodiment, many implementation-specific decisions may be made to achieve the developer's specific goals, and these decisions may vary from one implementation to another.

[0017] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, the accompanying drawings only show the device structure closely related to the solution according to the present application, while other details that are not closely related to the present application are omitted.

[0018] It should be understood that the present application is not limited to the described embodiments due to the following description with reference to the accompanying drawings. In this document, where feasible, the embodiments may be combined with each other, features between different embodiments may be replaced or borrowed, and one or more features may be omitted in one embodiment.

[0019] The embodiment of the present application provides a method for controlling a patrol aircraft swarm with minimum energy formation under random topology switching conditions. Figure 1 The flow chart of the minimum energy formation control method of patrol aircraft swarm under random topology switching conditions is shown in Figure 1 , methods include: Step 1: Determine the system matrix based on the control performance of a single patrol vehicle A and control matrix B , and set the weight matrix of the global control energy consumption index function .

[0020] The unmanned patrol vehicle swarm system consists of N patrol vehicles. The state differential equation of a single patrol vehicle is described as follows: ,

[0021] in, For patrol aircraft The state vector of For patrol aircraft The control input, A is the system matrix, B is the control matrix, for The derivative of . N is the number of patrol vehicles.

[0022] The formation control protocol of the patrol vehicle swarm system is:

[0023] in, For patrol aircraft The control input, is the control gain matrix, for Time Patrol The neighbor set of for Time Patrol To the patrol vehicle The weight of the effect, For patrol aircraft The state vector of For patrol aircraft The state vector of For patrol aircraft The expected time-varying formation vector, For patrol aircraft The expected time-varying formation vector, Indicates the time interval, is the lower limit of the time interval, is the upper limit of the time interval, is the label of the time interval, is the global control energy consumption index function, N is the number of patrol vehicles, is the weight matrix of the global control energy consumption index function, T Indicates transpose.

[0024] Step 2: Set the expected time-varying formation vector. If the formation feasibility requirements are met, proceed to step 3. Otherwise, return to step 1 and redetermine the system matrix, control matrix, and expected time-varying formation vector.

[0025] Expected time-varying formation vector , For patrol aircraft The expected time-varying formation vector of , it is considered that the formation feasibility requirements are met.

[0026] Step 3: Based on the on-off status of each patroller link in the patroller swarm system, all possible switching topologies are modeled as a random switching topology set.

[0027] The entire time process is divided into time intervals , The union of the two groups is represented by the maximum time interval defined as , the minimum time interval is , N represents an integer.

[0028] Topological Set Indicates that Indicates the total number of switching topologies, 、 They all represent the switching topology in the topology set. Indicates a random switching signal. One-to-one topological set The corresponding switching topology within. Figure 2 A schematic diagram of the non-periodic action topology and intermittent communication time sub-intervals is shown. Figure 3 A state formation topology set is shown.

[0029] Step 4: Determine the minimum non-zero eigenvalue and maximum eigenvalue of the Laplacian matrix of all switching topologies in the random switching topology set. This can be calculated using the Gerschgorin disk theorem.

[0030] Step 5: Based on the minimum non-zero eigenvalue, the maximum eigenvalue, the system matrix, the control matrix, and the weight matrix of the global control energy consumption index function, the constraint conditions are constructed to ensure the minimum energy parameter optimization value achieved by the formation; the constraint conditions include matrix variables.

[0031] min

[0032] The constraints are:

[0033]

[0034]

[0035] in, is the minimum energy parameter, H is a matrix variable, A is the system matrix, B is the control matrix, is the smallest non-zero eigenvalue, is the maximum eigenvalue, is the weight matrix of the global control energy consumption index function, is a 6×6 identity matrix, represents the conjugate transpose, T Indicates transpose.

[0036] Based on the above constraints, it is possible to ensure the desired time-varying formation vector At the same time, the global control energy consumption is minimized in the sense of generalized eigenvalue.

[0037] Step 6: Use the generalized eigenvalue method to solve the constraints and obtain matrix variables and minimum energy parameters; obtain the control gain matrix based on the matrix variables.

[0038] Specifically, the control gain matrix is obtained based on the matrix variables using the following formula:

[0039] in, is the control gain matrix, is the smallest non-zero eigenvalue, B is the control matrix, H is a matrix variable.

[0040] Furthermore, the control gain Substitute the proposed formation control protocol into the model to verify the feasibility of the formation and the global control energy consumption. Then, without the minimum energy constraint, solve the control gain matrix and the global control energy consumption. Finally, compare the two to verify the superiority of the proposed method.

[0041] Figure 4 The simulation curve of the state formation tracking error is shown, where (a) is The simulation curve of (b) is The simulation curve of The simulation curve of .

[0042] Figure 5 The figure shows the instantaneous spatial position simulation diagram of each patrol aircraft at different times, where (a) is the instantaneous spatial position simulation diagram of each patrol aircraft at 0s, (b) is the instantaneous spatial position simulation diagram of each patrol aircraft at 2s, (c) is the instantaneous spatial position simulation diagram of each patrol aircraft at 8s, and (d) is the instantaneous spatial position simulation diagram of each patrol aircraft at 10s.

[0043] Figure 6 A simulation diagram comparing global control energy consumption with and without minimum energy constraints is shown.

[0044] Based on the same inventive concept as the method for controlling a swarm of patrol aircraft with minimum energy under random topology switching conditions, this embodiment further provides a corresponding apparatus for controlling a swarm of patrol aircraft with minimum energy under random topology switching conditions, including: The parameter setting module is used to determine the system matrix and control matrix according to the control performance of a single patrol vehicle, and to set the weight matrix of the global control energy consumption index function; The formation feasibility judgment module is used to set the expected time-varying formation vector. If the formation feasibility requirements are met, the random switching topology set construction module is executed. Otherwise, the module returns to the parameter setting module to redetermine the system matrix, control matrix, and expected time-varying formation vector. The random switching topology set construction module is used to model all possible switching topologies as random switching topology sets according to the on-off status of the links of each patroller in the patroller swarm system; An eigenvalue determination module is used to determine the minimum non-zero eigenvalue and the maximum eigenvalue of the Laplacian matrix of all switching topologies in the random switching topology set; A constraint condition construction module is used to construct constraint conditions that ensure the minimum energy parameter optimization value achieved by the formation based on the minimum non-zero eigenvalue, the maximum eigenvalue, the system matrix, the control matrix, and the weight matrix of the global control energy consumption index function; the constraint conditions include matrix variables; The solution module is used to solve the constraint conditions using the generalized eigenvalue method to obtain matrix variables and minimum energy constraint parameters; and obtain the control gain matrix based on the matrix variables.

[0045] The device for controlling a swarm of patrol aircraft with minimum energy under random topology switching conditions in this embodiment has the same inventive concept as the method for controlling a swarm of patrol aircraft with minimum energy under random topology switching conditions mentioned above. Therefore, the specific implementation method of the device can be seen in the embodiment part of the method for controlling a swarm of patrol aircraft with minimum energy under random topology switching conditions mentioned above, and its technical effects correspond to those of the above-mentioned method, which will not be repeated here.

[0046] In summary, this application has the following technical effects: The present application can realize the minimum energy formation optimization energy control of the unmanned aerial vehicle swarm system under the condition of random switching topology, can adapt to the random "on-off" requirements of the communication link between the unmanned aerial vehicles, and has a wide range of applicability. According to the set formation constraints, it is first required that the distributed unmanned aerial vehicle swarm system can realize formation control under the condition of random switching topology. In addition, it can optimize the control of the formation of the unmanned aerial vehicle swarm system to ensure that the global control energy consumption in the sense of generalized eigenvalues is minimized during the formation realization process, and the upper bound of the global control energy consumption is constrained by the minimum energy, ensuring that the expected time-varying formation can be realized while minimizing the global control consumption in the sense of generalized eigenvalues.

[0047] The above descriptions are merely examples of various embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any modifications or substitutions that can be readily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A minimum energy formation control method for patrol aircraft swarms under random topology switching conditions, characterized by: include: Step 1: Determine the system matrix and control matrix based on the control performance of a single patrol vehicle, and set the weight matrix of the global control energy consumption index function; Step 2: Set the expected time-varying formation vector. If the formation feasibility requirements are met, proceed to step 3. Otherwise, return to step 1 and redetermine the system matrix, control matrix, and expected time-varying formation vector. Step 3: Based on the on-off status of each patroller link in the patroller swarm system, all possible switching topologies are modeled as a random switching topology set. Step 4, determine the minimum non-zero eigenvalue and the maximum eigenvalue of the Laplacian matrix of all switching topologies in the random switching topology set; Step 5: Based on the minimum non-zero eigenvalue, the maximum eigenvalue, the system matrix, the control matrix, and the weight matrix of the global control energy consumption index function, construct a constraint condition to ensure the minimum energy parameter optimization value achieved by the formation; the constraint condition includes matrix variables; Step 6, solving the constraint conditions using a generalized eigenvalue method to obtain matrix variables and minimum energy parameters; A control gain matrix is obtained based on the matrix variables.

2. The method according to claim 1, wherein The formation control protocol of the patrol vehicle swarm system is: in, For patrol aircraft The control input, is the control gain matrix, for Time Patrol The neighbor set of for Time Patrol To the patrol vehicle The weight of the effect, For patrol aircraft The state vector of For patrol aircraft The state vector of For patrol aircraft The expected time-varying formation vector, For patrol aircraft The expected time-varying formation vector, Indicates the time interval, is the lower limit of the time interval, is the upper limit of the time interval, is the label of the time interval, is the global control energy consumption index function, N is the number of patrol vehicles, is the weight matrix of the global control energy consumption index function, T Indicates transpose.

3. The method according to claim 1, wherein The constraints are: in, is the minimum energy parameter, H is a matrix variable, A is the system matrix, B is the control matrix, is the smallest non-zero eigenvalue, is the maximum eigenvalue, is the weight matrix of the global control energy consumption index function, is the identity matrix, represents the conjugate transpose, T Indicates transpose.

4. The method according to claim 1, wherein Based on the matrix variables, the control gain matrix is obtained using the following formula: in, is the control gain matrix, is the smallest non-zero eigenvalue, B is the control matrix, H is a matrix variable, T Indicates transpose.

5. A minimum energy formation control device for patrol aircraft swarms under random topology switching conditions, characterized in that: include: The parameter setting module is used to determine the system matrix and control matrix according to the control performance of a single patrol vehicle, and to set the weight matrix of the global control energy consumption index function; The formation feasibility judgment module is used to set the expected time-varying formation vector. If the formation feasibility requirements are met, the random switching topology set construction module is executed. Otherwise, the module returns to the parameter setting module to redetermine the system matrix, control matrix, and expected time-varying formation vector. The random switching topology set construction module is used to model all possible switching topologies as random switching topology sets according to the on-off status of the links of each patroller in the patroller swarm system; An eigenvalue determination module is used to determine the minimum non-zero eigenvalue and the maximum eigenvalue of the Laplacian matrix of all switching topologies in the random switching topology set; a constraint condition construction module, configured to construct a constraint condition for ensuring the minimum energy parameter optimization value achieved by the formation based on the minimum non-zero eigenvalue, the maximum eigenvalue, the system matrix, the control matrix, and the global control energy consumption index function weight matrix; the constraint condition includes a matrix variable; A solution module, configured to solve the constraint conditions using a generalized eigenvalue method to obtain matrix variables and minimum energy constraint parameters; A control gain matrix is obtained based on the matrix variables.

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