A communication congestion control method for multiple unmanned aerial vehicle TCP networks in power inspection

Abstract

This invention discloses a communication congestion control method for a multi-UAV TCP network in power line inspection, relating to the field of power line inspection. First, an error transformation mechanism with fixed-time convergence characteristics is constructed to convert the initial tracking error of the multi-UAV queue information transmission into a virtual error, eliminating the dependence of traditional control methods on initial conditions. Second, an adaptive funnel boundary function is designed, which dynamically adjusts the performance boundary by introducing a correction signal associated with the virtual error and the internal monitoring boundary, effectively addressing the reliability degradation problem caused by sudden strong electromagnetic interference or high fluctuations in information transmission between UAVs during collaborative power line inspection by multiple UAVs. Finally, the above mechanism is integrated into a backstepping control framework with command filtering to ensure the boundedness of preset information transmission tracking performance indicators and closed-loop signals, maintaining the stability of information transmission between UAVs under complex operating conditions, and ensuring that multiple UAVs can collaboratively complete the inspection work.

Description

Technical Field

[0001] This invention relates to the field of power line inspection, and specifically to a method for communication congestion control in a multi-UAV TCP network during power line inspection. Background Technology

[0002] With the continuous expansion of power system scale and the improvement of its intelligence level, the use of multiple drones for collaborative inspection of power equipment has gradually become an important means to ensure the safe and stable operation of the power grid. In the process of multiple drones performing various collaborative inspection tasks, in order to ensure the efficient and accurate progress of the collaborative inspection work, the drones need to transmit various types of information in real time and reliably. This information specifically includes inspection data collected during the inspection process, the operating status information of each drone, and control commands used to control the actions of each drone. The TCP communication network is the key carrier for realizing this information transmission and plays a crucial role in information transmission. However, power inspection operations are usually carried out in environments with strong electromagnetic interference. There are numerous high-voltage transmission lines, substation equipment, and other strong electromagnetic sources on site. These factors can easily cause sudden and high-intensity electromagnetic interference to the wireless communication links on which the drones rely. Such interference can cause abnormal phenomena such as drastic fluctuations in network bandwidth, a significant increase in transmission latency, and frequent loss of data packets, which severely weaken the reliability and stability of the TCP communication link. The decline in TCP communication network transmission performance will further disrupt the collaborative control effect between multiple drones. In severe cases, it may even directly lead to the interruption or failure of the entire power equipment inspection mission.

[0003] Traditional TCP network congestion control methods were initially developed for the needs of general internet environments. These methods are relatively slow in response speed and have weak anti-interference capabilities, making them unsuitable for the high demands of transient performance and transmission stability in the specific scenario of power line inspection. Furthermore, existing TCP network congestion control methods have a significant drawback: they are typically sensitive to the initial state of multi-UAV TCP communication network systems, exhibiting considerably different performance under different initial conditions. Moreover, these methods lack strict constraints and effective guarantees regarding the transient performance of tracking errors during information transmission, failing to ensure that tracking errors remain within a reasonable range during the transient phase, further limiting their applicability in power line inspection scenarios. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the present invention provides a communication congestion control method for a multi-UAV TCP network in power inspection. It aims to solve the problems of reduced control reliability and difficulty in guaranteeing preset transient performance in complex power inspection environments with sudden strong interference, where the TCP communication network between multiple UAVs is susceptible to sudden electromagnetic interference. The method of the present invention can significantly improve the transient performance, convergence speed and resilience to sudden disturbances in information transmission between UAVs, thereby ensuring the stable and efficient execution of collaborative inspection tasks.

[0005] The technical solution of this invention is:

[0006] A method for communication congestion control in a multi-UAV TCP network during power line inspection, comprising the following steps:

[0007] Establish a mathematical model that can describe the dynamic characteristics of a multi-UAV TCP communication network affected by external interference;

[0008] Define the tracking error of the communication network queue and introduce a fixed-time tracking performance function to construct a virtual tracking error with an initial value of zero. ;

[0009] based on With a fixed-time tracking performance function, preset performance control targets for multi-UAV TCP communication networks;

[0010] Design adaptive funnel boundary function It dynamically generates and outputs self-adjusting funnel boundaries, providing a basic constraint range for the performance of multi-UAV TCP communication networks;

[0011] Constructing funnel error variables , to indicate In the present The relative position of the elements is used to define performance constraints. Equivalent land conversion Boundedness;

[0012] based on Construct an adaptive state feedback controller The system outputs control signals online and applies them to the UAV TCP communication network, adjusting the rate of change of its congestion window to achieve the performance control target of the multi-UAV TCP communication network.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] (1) This invention constructs a zero-initial-value virtual tracking error for a fixed-time tracking performance function, which mathematically eliminates the dependence on the initial state, thereby significantly improving the universality and robustness of the control method under different initial conditions.

[0015] (2) By defining a predetermined performance constraint target and combining a fixed-time tracking performance function with an adaptive funnel boundary function, the present invention actively constrains the dynamic process of tracking error, thereby planning the transient and steady-state operation of the system to a certain extent, and realizing strict constraints and reliable guarantees on the transient and steady-state performance of information transmission tracking error.

[0016] (3) In response to sudden strong interference, in order to ensure the feasibility of the preset performance constraint target under interference, this invention designs an adaptive funnel boundary function with a dynamic adjustment mechanism. This mechanism can intelligently relax the performance boundary when interference occurs to prevent control failure; and automatically restore the boundary after the interference weakens, thereby significantly enhancing the anti-interference capability and stability of the multi-UAV TCP communication network system in complex electromagnetic environments.

[0017] (4) By defining a fixed-time convergence target and combining it with a high-gain funnel error feedback mechanism, the present invention enables the multi-UAV TCP communication network system to obtain a stronger correction effect when the error is large, thereby achieving a faster response speed and convergence speed, and meeting the high requirements of power inspection for real-time synchronization.

[0018] (5) In order to achieve the stability target of the closed-loop system, by integrating adaptive law and error feedback mechanism in the controller, the present invention can estimate and compensate for network uncertainty and external disturbances online, reduce the dependence on accurate model, and thus improve the control accuracy and robust stability of multi-UAV TCP communication network system while ensuring closed-loop stability. Attached Figure Description

[0019] Figure 1 This is a flowchart of the communication congestion control method for a multi-UAV TCP network in power line inspection according to this embodiment;

[0020] Figure 2 This is a schematic diagram of the communication congestion control system of a multi-UAV TCP network in power line inspection according to this embodiment. Detailed Implementation

[0021] To facilitate understanding of this application, a more comprehensive description of this application will be provided below with reference to the accompanying drawings.

[0022] Figure 1 This is a flowchart of the communication congestion control method for a multi-UAV TCP network in power line inspection according to this embodiment. Figure 1 As shown, the communication congestion control method for a multi-UAV TCP network in power line inspection includes the following steps:

[0023] Step 1: Construct a nonlinear dynamic model of a multi-UAV TCP communication network affected by external interference;

[0024] In this step, the first step is to establish a mathematical model that can accurately describe the dynamic characteristics of the TCP communication network. This model must fully consider complex factors such as sudden electromagnetic interference in the power inspection environment.

[0025] This implementation method is based on the topology and transmission protocol of the UAV TCP communication network, such as the TCP Reno protocol, and establishes a congestion window size. and communication network queue length The nonlinear dynamic model shown in equation (1) is a state variable. The model is established in strict accordance with network protocol specifications, while taking into account the special characteristics of the actual application environment.

[0026] (1)

[0027] in It represents time and is a continuous variable; This refers to the available link capacity; This refers to the number of TCP sessions. It is a delay in propagation; It is the controller to be designed; External interference; It is to satisfy The round-trip time, of which This indicates the maximum capacity of the communication network queue. The introduced... This is used to characterize external interference caused by unresponsive streams (such as UDP streams);

[0028] In the model construction process, special consideration was given to the sudden and severe interference caused by strong electromagnetic equipment in the power inspection environment to the wireless communication link. This type of interference can lead to problems such as drastic fluctuations in network bandwidth, increased transmission latency, and packet loss. Therefore, bounded external disturbances were introduced into the model. This is specifically designed to characterize external interference caused by unresponsive streams (such as UDP streams). Such interference is unavoidable in real-world network environments, and is particularly prominent in scenarios with complex electromagnetic environments such as power systems.

[0029] Due to network window size This is a relatively abstract concept. To describe network status more intuitively, this implementation uses transmission rate. As one of the state variables, it facilitates subsequent controller design and performance analysis. Based on the determination of the state variables, we obtain... and The dynamic relationship between the states is established, and the complete state-space expression is as follows:

[0030] (2)

[0031] To facilitate controller design, stability analysis, and presentation, and By precise variable substitution, equation (2) is transformed into equation (3), which represents a second-order strictly feedback nonlinear system, i.e., a nonlinear dynamic model of a multi-UAV TCP communication network affected by external disturbances:

[0032] (3)

[0033] in, This represents the number of TCP sessions, reflecting the network load. This indicates the available link capacity, reflecting the network's transmission capability; It represents external disturbances and describes the impact of environmental interference; , , , For the controller to be designed, and This is the equivalent external disturbance.

[0034] Step 2: Based on the Fixed-Time Tracking Performance Function (FTPF), construct a virtual tracking error expression with an initial value of zero. ;

[0035] Traditional control methods are often sensitive to the initial state of multi-UAV TCP communication network systems and lack strict constraints and guarantees on the transient performance of tracking errors. By introducing a fixed-time tracking performance function, a virtual tracking error with an initial value of zero is constructed. This effectively solved the problem.

[0036] Step 2 specifically includes:

[0037] Define the expression for the communication network queue tracking error. as follows:

[0038] (4)

[0039] in The expected queue length can be time-varying or constant. This error variable directly reflects the deviation between the actual operating state and the expected state of the multi-UAV TCP communication network system, and is a key indicator for measuring the performance of the multi-UAV TCP communication network system.

[0040] Choose a fixed-time performance function The function must satisfy at least the following two conditions:

[0041] (1) Initial value This ensures the consistency of the transformation;

[0042] (2) Within the user-preset boundary convergence time The internal decay reaches 0, ensuring that the multi-UAV TCP communication network system reaches the performance requirements within a predetermined time, i.e., there exists a user-preset time constant. , making when hour, 0;

[0043] Using the fixed-time performance function and the initial value of tracking error For communication network queue tracking error Perform mathematical transformations to generate virtual tracking error. for:

[0044] (5)

[0045] This transformation ensures virtual tracking error initial value Among them, the fixed-time performance function Adopt the following form:

[0046] (6)

[0047] in These are design parameters used to adjust the decay pattern of the function. This function form has smooth decay characteristics, which can avoid the impact of abrupt changes on the stability of the multi-UAV TCP communication network system, while ensuring that performance requirements are met within a preset time.

[0048] The core value of this transformation lies in ensuring virtual tracking error... initial value This completely eliminates the dependence of traditional control methods on initial conditions. This approach frees controller design from the constraints of specific initial states, significantly enhancing the adaptability and robustness of multi-UAV TCP communication network systems. In practical applications, this means that regardless of the initial state from which the multi-UAV TCP communication network system starts, it can guarantee achieving the desired performance indicators within a preset time.

[0049] Step 3: Determine the preset performance control targets for the multi-UAV TCP communication network:

[0050] Based on the virtual tracking error defined in step 2 and fixed-time performance function The control objectives of this method are thus clearly defined, specifically including the following three aspects:

[0051] (1) Fixed-time convergence target: within a fixed time preset by the user Internally, to reduce virtual tracking error It converges to an arbitrarily small neighborhood of the origin, i.e. ,in Let be an arbitrarily small positive number. Since... Communication network queue tracking error Through fixed-time performance function This is equivalent to requiring the communication network queue to track errors. In time The internal drive is adjusted to the desired accuracy range.

[0052] (2) Preset performance constraint target: throughout the entire control process, the virtual tracking error The amplitude is strictly constrained within a time-varying, dynamically adjustable boundary, that is, it requires... Always true, among which This is the adaptive funnel boundary function to be designed in the next step. This objective directly plans the transient process and steady-state accuracy of the system state.

[0053] (3) Stability target of closed-loop system: Design an adaptive state feedback controller and an adaptive law to achieve the above performance target while ensuring that all signals of the closed-loop system composed of the adaptive state feedback controller, the adaptive law and the nonlinear dynamic model of the multi-UAV TCP communication network are consistent and bounded; and through the feedback mechanism based on funnel error and the online adjustment of the adaptive law, compensate for the uncertainty and external bounded disturbance in the nonlinear dynamic model of the multi-UAV TCP communication network in real time, so as to ensure the stability of the closed-loop system under the conditions of inaccurate parameters and interference.

[0054] The aforementioned control objectives collectively constitute the preset performance control requirements for queue tracking performance in multi-UAV TCP communication networks. All designs in steps 4 through 6 will revolve around achieving these three core objectives.

[0055] Step 4: Construct the self-adjustable funnel boundary function (SFBF).

[0056] Communication networks may face various unpredictable and sudden interferences, and traditional fixed performance boundaries are difficult to adapt to such dynamically changing environments. Therefore, this implementation design presents an adaptive funnel boundary function, which can effectively address the decrease in control reliability caused by sudden and strong interferences by dynamically adjusting the performance boundaries. The design of the adaptive funnel boundary function comprises three key components: a fixed funnel boundary function, a dynamically adjusted signal, and a monitoring mechanism. The fixed funnel boundary function forms the basic framework of the performance boundary.

[0057] This implementation assumes an initial value of a finite positive number that converges to a positive number over time. Fixed funnel boundary function , Adopt the following form;

[0058] (7)

[0059] in These are the initial and final values ​​of the performance boundary of the multi-UAV TCP communication network, respectively. The preset boundary convergence time, To adjust the design parameters of the function decay shape, The function form defined in Equation 6; this fixed funnel boundary function provides a basic constraint range for the performance of the multi-UAV TCP communication network system, ensuring that the operating state of the multi-UAV TCP communication network system is within a controllable range under normal conditions, and comprehensively improving the transient and steady-state operation of the communication network transmission network.

[0060] Design a dynamically adjusted signal The signal is composed of a non-negative variable. It is generated through a strictly increasing continuous function, specifically as follows: ,in These are design parameters used to control the maximum magnitude of boundary adjustments;

[0061] The nonnegative variable It is generated by the following first-order dynamic equation:

[0062] (8)

[0063] in, These are design parameters used to control the boundary recovery speed and adjust the sensitivity, respectively. and The monitoring signals are defined as shown in equations (9) and (10), and they monitor the virtual tracking error in real time. The relationship with the security zone boundary is used to promptly trigger the boundary adjustment mechanism when a multi-UAV TCP communication network system faces sudden interference.

[0064] (9)

[0065] (10)

[0066] in, These are design parameters used to define the safe region inside the fixed funnel boundary function. When virtual tracking error When within the safe zone, the multi-UAV TCP communication network system operates according to the original performance boundaries; once the error approaches or exceeds the safe zone, the monitoring signal is immediately activated, triggering the boundary adjustment mechanism.

[0067] The fixed funnel boundary function is added to the dynamic adjustment signal to form an adaptive funnel boundary performance function with an error feedback mechanism. :

[0068] (11)

[0069] This mechanism enables multi-UAV TCP communication network systems to intelligently adjust control requirements when faced with sudden and strong interference, avoiding control failures caused by overly strict boundaries, and significantly improving the adaptability and reliability of multi-UAV TCP communication network systems in complex environments.

[0070] Step 5, based on the virtual tracking error Based on the adaptive funnel boundary function, construct the funnel error variable;

[0071] To effectively incorporate performance constraints into controller design, a special funnel error variable is defined. This variable can transform time-varying performance constraints into mathematically bounded requirements, facilitating controller design and stability analysis.

[0072] This implementation method is based on the virtual tracking error. and the adaptive funnel boundary function with error feedback mechanism The funnel error variable is defined by equation (12). .

[0073] (12)

[0074] This definition imposes performance constraints. Equivalent land conversion The boundedness of. This indicates the virtual tracking error. In the current adaptive funnel boundary function The relative position of the [something]. When near When the denominator approaches zero, The gain will increase dramatically. This characteristic naturally introduces a high-gain mechanism in the communication congestion control system of multi-UAV TCP communication network, which can effectively "push back" the state of multi-UAV TCP communication network system to within the performance boundary.

[0075] Step 6, combine the funnel error variable from Step 5. Construct adaptive laws and adaptive state feedback controllers The system outputs control signals online and applies them to the UAV TCP communication network, adjusting the rate of change of its congestion window to achieve the performance control target of the multi-UAV TCP communication network.

[0076] This step utilizes the established funnel error variables to design a complete adaptive state feedback controller. This controller not only ensures the preset tracking performance indicators but also guarantees the boundedness of all closed-loop signals, maintaining the stability of information transmission between UAVs under complex operating conditions.

[0077] The construction of the adaptive state feedback controller includes the following steps:

[0078] The coordinate transformations shown in equations (13) and (14) establish a new state space:

[0079] (13)

[0080] (14)

[0081] in, The virtual control law shown in equation (15) is simplified by using neural network approximation technology in the subsequent controller design to obtain the derivative of the virtual control law. The design of the virtual control law fully considers the nonlinear characteristics and performance constraints of the multi-UAV TCP communication network system.

[0082] (15)

[0083] Among them, the gain coefficient of the funnel error transformation , For positive controller design parameters, terms related to the funnel boundary derivative. ; Positive design parameters; The first adaptive law is obtained from equation (16).

[0084] (16)

[0085] in, For the positive design parameters of the adaptive law, Positive design parameters have the same effect as . Positive design parameters are used to ensure the first adaptive law. The boundedness of.

[0086] Design a practical controller Actual controller The design continues the design concept of virtual control laws:

[0087] (17)

[0088] in, For positive controller design parameters, The second adaptive law is obtained from the following formula:

[0089] (18)

[0090] in, , This is an adjustable parameter.

[0091] The design of the first and second adaptive laws is the intelligent core of the entire controller. By updating the two adaptive laws in real time, the controller can automatically adapt to different working environments and disturbance levels. This adaptive mechanism enables the controller to maintain good control performance when facing unknown disturbances.

[0092] This embodiment also provides a communication congestion control system for a multi-UAV TCP network in power line inspection, such as... Figure 2 As shown, the system includes:

[0093] TCP communication network status monitoring module: used to obtain the communication network queue length in real time. Window size and expected queue

[0094] Error transformation module: It is connected to the TCP communication network status monitoring module and is used to calculate the tracking error. And output virtual tracking error

[0095] Adaptive funnel boundary generation module: It is connected to the error transformation module and is used to dynamically generate and output self-adjusting funnel boundaries.

[0096] Funnel error calculation module: It is connected to the error transformation module and the self-adjusting funnel boundary generation module respectively, and is used to calculate the funnel error variable.

[0097] Adaptive backstepping controller: It is connected to the funnel error calculation module and the TCP communication network status monitoring module, and is used to receive the status of the multi-UAV TCP communication network system, funnel error and compensation signals, calculate and output control signals online.

[0098] Execution module: used to process the control signals By applying a control method to the TCP communication network of unmanned aerial vehicles (UAVs) and adjusting the rate of change of its congestion window, the performance control target of the multi-UAV TCP communication network can be achieved.

[0099] It should be understood that, inspired by the technical concept of this invention, those skilled in the art can make various improvements or modifications based on the above content without departing from the scope of this invention, and these modifications still fall within the protection scope of this invention.

Claims

1. A method for communication congestion control in a multi-UAV TCP network during power line inspection, characterized in that, The method includes the following steps: Establish a mathematical model that can describe the dynamic characteristics of a multi-UAV TCP communication network affected by external interference; Define the communication network queue tracking error and introduce a fixed-time tracking performance function to construct a virtual tracking error with an initial value of zero. ; based on With a fixed-time tracking performance function, preset performance control targets for multi-UAV TCP communication networks; Design adaptive funnel boundary function It dynamically generates and outputs self-adjusting funnel boundaries, providing a basic constraint range for the performance of multi-UAV TCP communication networks; Constructing funnel error variables , to indicate In the present The relative position of the elements is used to define performance constraints. Equivalent land conversion Boundedness; based on Construct an adaptive state feedback controller The system outputs control signals online and applies them to the UAV TCP communication network, adjusting the rate of change of its congestion window to achieve the performance control target of the multi-UAV TCP communication network.

2. The communication congestion control method for a multi-UAV TCP network in power line inspection according to claim 1, characterized in that, The mathematical model that describes the dynamic characteristics of a multi-UAV TCP communication network affected by external interference is a nonlinear dynamic model of the multi-UAV TCP communication network, and its establishment process is as follows: First, based on the topology and transmission protocol of the UAV TCP communication network, a congestion window size is established. and communication network queue length The nonlinear dynamic model with state variables is as follows: （1） in It represents time and is a continuous variable; This refers to the available link capacity; This refers to the number of TCP sessions. It is a delay in propagation; It is the controller to be designed; It is to satisfy The round-trip time, of which Indicates the maximum capacity of the communication network queue; It is a bounded external disturbance, used to characterize external disturbances caused by non-response flow; Then, using the transmission rate As a state variable, we get and The dynamic relationship between the states is established, and the complete state-space expression is as follows: （2） Finally, let and Equation (2) is transformed into a second-order strictly feedback nonlinear system as shown in Equation (3), resulting in the final nonlinear dynamic model of the multi-UAV TCP communication network affected by external disturbances: （3） in, This represents the number of TCP sessions, used to reflect network load. This indicates the available link capacity, which reflects the network's transmission capability. Indicates external disturbance; , , , and Let be the equivalent external disturbance, and be the controller to be designed.

3. The communication congestion control method for a multi-UAV TCP network in power line inspection according to claim 2, characterized in that, Define the communication network queue tracking error and introduce a fixed-time tracking performance function to construct a virtual tracking error with an initial value of zero. ,include: Define communication network queue tracking error : （4） in The desired communication network queue length is either time-varying or constant. Choose a fixed-time performance function The function must satisfy at least the following two conditions: (1) Initial value ; (2) For the user-preset time constant , making when hour, 0; Using the fixed-time function and the initial value of tracking error For communication network queue tracking error Perform mathematical transformations to generate virtual tracking error. : （5） This transformation ensures virtual tracking error initial value Among them, the fixed-time performance function Adopt the following form: （6） in These are design parameters used to adjust the decay pattern of the function.

4. The communication congestion control method for a multi-UAV TCP network in power line inspection according to claim 3, characterized in that, The performance control objectives of the multi-UAV TCP communication network specifically include: (1) Within a fixed time period preset by the user, the virtual tracking error is reduced. It converges to an arbitrarily small neighborhood of the origin. (2) Virtual tracking error during the entire control process The amplitude is strictly constrained within a time-varying, dynamically adjustable adaptive funnel boundary function; (3) While achieving objectives (1) and (2), ensure that all signals of the closed-loop system consisting of the adaptive state feedback controller and the nonlinear dynamic model of the multi-UAV TCP communication network are consistent and bounded; and compensate for uncertainties and external bounded disturbances in the nonlinear dynamic model of the multi-UAV TCP communication network in real time through the feedback mechanism based on funnel error and the online adjustment of the adaptive law.

5. A communication congestion control method for a multi-UAV TCP network in power line inspection according to claim 4, characterized in that, The adaptive funnel boundary function for design includes: First, we assume an initial value, as shown in equation (7), that is a finite positive number and converges to a positive number over time. Fixed funnel boundary function ; （7） in These are the initial and final values ​​of the performance boundary of the multi-UAV TCP communication network, respectively. The preset boundary convergence time, Design parameters for adjusting the decay pattern of the function; Then, design a dynamic adjustment signal. ,in These are design parameters used to control the maximum range of adjustment for the performance boundaries of multi-UAV TCP communication networks; A nonnegative variable is generated by the following first-order dynamic equation: （8） in, These are design parameters, used to control the boundary recovery speed and adjust the sensitivity, respectively. and The monitoring signals are defined as shown in equations (9) and (10), and they monitor the virtual tracking error in real time. The relationship with the security zone boundary is such that a boundary adjustment mechanism is triggered promptly when a multi-UAV TCP communication network faces sudden interference: （9） （10） in, These are design parameters used to define the safe region inside the fixed funnel boundary function. When virtual tracking error When within a safe zone, the multi-drone TCP communication network operates according to its original performance boundaries; once... When approaching or exceeding the safe zone, the monitoring signal is immediately activated, triggering the boundary adjustment mechanism. Finally, the fixed funnel boundary function is added to the dynamic adjustment signal to form an adaptive funnel boundary function with an error feedback mechanism. : （11）。 6. A communication congestion control method for a multi-UAV TCP network in power line inspection according to claim 5, characterized in that, The funnel error variable as follows: （12） in, Indicates virtual tracking error In the current adaptive funnel boundary function The relative position of the middle; when near When the denominator approaches zero, The congestion rate will increase dramatically. This characteristic can effectively push the state of the multi-UAV TCP communication network back within the performance boundary in the communication congestion control of multi-UAV TCP networks.

7. A communication congestion control method for a multi-UAV TCP network in power line inspection according to claim 6, characterized in that, The basis Construct an adaptive state feedback controller The method is as follows: The coordinate transformations shown in equations (13) and (14) establish a new state space: （13） （14） in, The virtual control law is as follows: （15） Among them, the gain coefficient of the funnel error transformation ; Positive controller design parameters; terms related to the funnel boundary derivative. ; Positive design parameters For the first adaptive law: （16） in, These are the positive design parameters for the adaptive law; Positive design parameters have the same effect as ; Positive design parameters are used to ensure the first adaptive law. Boundedness; Designing an actual controller by continuing the design concept of virtual control laws as follows: （17） in, Positive controller design parameters; The second adaptive law is obtained from the following formula: （18） in, , These are adjustable parameters; the design of the first and second adaptive laws is the intelligent core of the entire controller. By updating the two adaptive laws in real time, the controller can automatically adapt to different working environments and interference levels.

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