A method for designing a compensation control protocol of a multi-node fuzzy communication network

Abstract

The application discloses a kind of compensation control protocol design methods of multi-node fuzzy communication network, belong to automation technology and modern control field, utilize fuzzy positive multi-agent system to establish the state space model of communication network system, with the aid of fuzzy rule, nonlinear system is approximated to linear system, using matrix decomposition technique, linear programming and Lyapunov function design a kind of consistency control method based on compensation controller, so that communication network system can normally operate whether it is disturbed by outside situation.The application provides a kind of compensation control protocol design methods of multi-node fuzzy communication network, fully considers the positive and nonlinear problems in actual communication network system when modeling, realizes the relative stable motion state between each subnet transmission data packet in communication network system.

Description

Technical Field

[0001] This invention belongs to the field of automation technology and modern control, and in particular relates to a design method for a compensation control protocol for a multi-node fuzzy communication network. Background Technology

[0002] With the rapid development of technology, communication networks play a vital role in modern life. They are the infrastructure for people to communicate, transmit information, and share data, encompassing various forms such as telephone communication, the internet, mobile communication, and satellite communication. The development of communication networks has not only changed people's lifestyles but also promoted socio-economic progress and technological innovation breakthroughs. The importance of communication networks is reflected in many aspects. First, it greatly improves the efficiency of communication between people. Traditional letter mail takes a long time to reach its destination, while through communication networks, information can be transmitted almost in real time, greatly shortening the time and spatial distance of communication. Second, communication networks promote the globalization of business activities. Through the internet, enterprises can conduct business globally, realize e-commerce and cross-border transactions, greatly expanding markets and business opportunities. Furthermore, communication networks also support the informatization development of education, healthcare, and government management, providing society with greater convenience and efficiency. However, in real life, communication networks also face various external interferences, which may lead to signal quality degradation or even communication interruptions. Sources of interference can be natural factors, such as electromagnetic interference, poor weather conditions, or geographical limitations; or they can be human factors, such as electromagnetic radiation, power supply interference, equipment failure, or network attacks. The presence of interference has a serious impact on the normal operation of communication networks and information transmission. Therefore, reducing or avoiding interference has become an important task to ensure the stability and reliability of communication networks.

[0003] In practical applications, communication network systems are often high-order nonlinear systems, and the study of nonlinear systems relies on sound theoretical analysis methods. Unlike linear systems, nonlinear systems possess more complex characteristics; they do not satisfy properties such as homogeneity and superposition, and the input-output relationship cannot be described by a simple transfer function. For linear systems, when the input and initial conditions are constant, the system is always stable, meaning the stability of a linear system is global. However, the stability of nonlinear systems is affected by many factors, including not only the system's structural parameters but also its initial state. When analyzing the stability of communication networks, it is often necessary to consider both global and local stability. Therefore, there is an urgent need for a compensation control protocol design method for multi-node fuzzy communication networks to address the problems of signal quality degradation and communication interruption caused by external interference in practical applications. Summary of the Invention

[0004] The purpose of this invention is to provide a design method for a compensation control protocol for a multi-node fuzzy communication network, which solves the problem that the communication network is subject to external interference in practical applications, resulting in signal quality degradation and communication interruption.

[0005] To achieve the above objectives, this invention provides a method for designing a compensation control protocol for a multi-node fuzzy communication network, comprising the following steps:

[0006] Step 1: Establish a state-space model of the number of data packets during the data transmission process in the communication network;

[0007] Step 2: Establish a communication network with m data packets;

[0008] Step 3: Design a compensation control protocol for a multi-node fuzzy communication network;

[0009] Step 4: Determine the gain under disturbance factors and define the function:

[0010]

[0011] Where ξ>0, ξ represents the weighted L1 gain performance index, ω(t) represents the perturbation, and ρ represents the exponential convergence parameter. Indicates the output of the augmented system;

[0012] Step 5: Combining Step 2 and Step 3, construct the closed-loop matrix system for the entire communication network system;

[0013] Step 6: Design the gain matrix of the distributed consensus controller for the communication network system;

[0014] Step 7: Establish conditions for the stable operation of the communication network system;

[0015] Step 8: Design Lyapunov functions according to whether there is an external disturbance.

[0016] Preferably, the specific process of step 1 is as follows:

[0017] S11. Analyze the dynamic process of data transmission in the communication network and collect model data to simulate the actual operation process of the system.

[0018] S12. Construct the state-space model of the communication network system, in the following form:

[0019]

[0020]

[0021] in, For data packets transmitted in a communication network, n i This represents the number of data packets in the i-th communication network; g represents the disturbance experienced by a communication network system during data transmission. i This represents the number of disturbances in the i-th communication network; r represents the number of data packets received by the i-th data terminal. i This represents the number of measurement output sensors in the i-th communication network; l represents the control input at time t for the subsequent operating state of the i-th subnet. i Indicates the number of measurement input sensors; h p (z(t)) represents the network usage in the communication system. R is the system matrix of the i-th subnet. n R r R l R g R n×n R n ×l R r×n Let these represent an n-dimensional vector, an r-dimensional vector, an l-dimensional vector, a g-dimensional vector, an n×n-dimensional matrix, an n×l-dimensional matrix, and an r×n-dimensional matrix, respectively. The derivative represents the system state.

[0022] Preferably, in step 2, the communication network topology is a connected directed graph, in the following form:

[0023] Ω=(M,Θ,A),

[0024] Where M = {1, 2, ..., m}, m ∈ N + A set of nodes that represents the abstract representation of data packets in a communication network; An edge set representing communication between data packets; This represents an adjacency matrix of data packets in a communication network. When node i receives information from node j, a ij =1, otherwise a ij =0;

[0025] Introducing the Laplacian matrix Describe the communication topology between data packets in a communication network, and define... for:

[0026]

[0027] Where Σ represents the summation symbol, a ij This represents the value of the adjacency matrix in the i-th row and j-th column.

[0028] Preferably, the specific form of the compensation control protocol for the multi-node fuzzy communication network in step 3 is as follows:

[0029]

[0030]

[0031] in, All of these are gain matrices that need to be designed. It is a matrix related to the communication topology between agents. If the i-th agent communicates with the j-th agent, then a ij >0, otherwise, a ij =0, the dimension of the matrix is ​​related to the number of agents in a multi-agent system. l represents the control input at time t for the subsequent operating state of the i-th subnet. i Indicates the number of measurement input sensors; The derivative of the compensator state, h q (z(t)) represents the membership function. This represents the number of data packets received by the i-th data terminal. This represents the number of data packets received by the j-th data terminal.

[0032] Preferably, the closed-loop matrix system of the entire communication network system in step 5 is as follows:

[0033]

[0034]

[0035]

[0036] in,

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] h p (z(t)) represents the network usage in the communication system, and ω(t) represents the disturbance. S represents the state of the augmented system. p Let h represent the perturbation coefficient matrix. q(z(t)) represents the membership function. The state derivative of the augmented system is represented by the following: This represents the output of the augmented system. This represents the derivative of the perturbation.

[0045] Preferably, the gain matrix of the consensus controller designed in step 6 is as follows:

[0046]

[0047]

[0048] Where i represents the i-th agent, q is the q-th subsystem in the fuzzy system, and l, r, and n represent l-dimensional, r-dimensional, and n-dimensional column vectors, respectively. l represents the i-th element being 1 and all other elements being 0. i Dimensional column vector.

[0049] Preferably, the conditions for the stable operation of the communication network system constructed in step 7 are as follows:

[0050] Design constants μ0>0, σ>0, γ>0, δ>1, ε>1, ξ>0, ρ>0, vector Make

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] If this holds true, then the control protocol designed in step 3 satisfies the controller gain matrix designed in step 6, and the closed-loop matrix system of the communication network system obtained in step 5 satisfies positivity and consistency, where I represents the identity matrix. m represents an element that is 1 i 3D column vector, v ip Let represent the Lyapunov function of the p-th fuzzy rule in the i-th subsystem. This represents the transpose of the system matrix of the p-th fuzzy rule in the i-th subsystem. ψ represents the transpose of the system matrix of the p-th fuzzy rule in the i-th subsystem. rb Let a represent an rb-dimensional vector. ji This represents the element in the j-th row and i-th column of the adjacency matrix, 1 r B represents an r-dimensional vector whose elements are all 1s. ip This represents the transpose of the system matrix of the p-th fuzzy rule in the i-th subsystem. Let l represent the transpose of an rb-dimensional column vector whose ι-th element is 1 and all other elements are 0. max This represents the maximum value of vector l. l represents the i-th element being 1 and all other elements being 0. i Dimensional column vector.

[0063] Preferably, the positive verification of the closed-loop matrix system of the communication network system is as follows:

[0064] Based on condition (2) in step 7 and the gain matrix of the consensus controller designed in step 6, we obtain

[0065] A ip -B ip M iq C ip +l max B ip H iq C ip +μ0I≥0

[0066] Combined with H iq <0, further obtaining B ip H iq C jp <0, therefore, It is the Metzler matrix, derived from the conditions in step 7, F iq It is also a Metzler matrix and B ip K iq >0, G iq C ip >0; finally, we can conclude that A cIt is a Metzler matrix; therefore, the closed-loop matrix system of the entire communication network system is positive, where H iq Let represent the gain matrix of the i-th agent and the q-th subsystem of the fuzzy system.

[0067] Preferably, when there are no external disturbances, the Lyapunov function is designed. in Furthermore, taking the derivative with respect to V(t), we obtain

[0068]

[0069] in,

[0070]

[0071] Based on condition (3) in step 7, we can obtain:

[0072]

[0073] From condition (9) in step 7, we can deduce that:

[0074]

[0075]

[0076]

[0077] Using conditions (4) and (10) in step 7 and the gain matrix of the consensus controller designed in step 6, we obtain:

[0078]

[0079] Combining conditions (9) and (11) in steps 6 and 7, we obtain

[0080]

[0081] roll out As explained in step 7, the communication network eventually reaches a consistent state under the distributed consensus control protocol designed in step 3, where τ is a parameter that the membership function needs to satisfy. This represents the transpose of the output matrix of the p-th subsystem of the N-th fuzzy agent. This represents the transpose of the gain matrix of the Nth fuzzy agent and the qth subsystem. Let a represent the transpose of the control matrix of the p-th subsystem of the N-th fuzzy agent. Nj Represents the elements of the adjacency matrix. This represents the gain matrix of the Nth fuzzy agent and the qth subsystem. Represents the gain matrix The positive component, Represents the gain matrix The negative component, Let represent the gain matrix of the j-th fuzzy agent and the q-th subsystem. Represents the gain matrix The positive component, Represents the gain matrix The negative component, v represents the transpose of the control matrix of the j-th fuzzy agent and the p-th subsystem. jp Let v be the positive vector representing the Lyapunov function of the j-th fuzzy agent and the p-th subsystem. Np η Np Both represent the positive vectors of the Lyapunov function of the p-th subsystem of the N-th fuzzy agent. This represents the gain matrix of the Nth fuzzy agent and the qth subsystem. Let Γ represent the coefficient matrix of the p-th subsystem of the N-th fuzzy agent, and let Γ represent the scaled vector in the stability proof.

[0082] Preferably, when external disturbances are present, a Lyapunov function is designed.

[0083]

[0084] definition get:

[0085]

[0086] in,

[0087]

[0088]

[0089] Based on the conditions in step 7, we obtain therefore, Thus obtain The communication network system eventually reaches a consensus on its state and satisfies L1 gain stability. Regardless of external disturbances, each subnet in the communication network system can operate normally with its surrounding subnets, where ω(t) represents the disturbance. Let represent the output of the augmented system, Π represent the scaled vector of the state coefficients in the stability proof, and Ξ represent the scaled vector of the perturbation coefficients in the stability proof.

[0090] Therefore, the present invention adopts the above-mentioned design method for a compensation control protocol of a multi-node fuzzy communication network, which has the following beneficial effects: First, a state space model of the communication network system is established using a fuzzy positive multi-agent system. The communication topology graph and graph theory knowledge are used to describe the information exchange of data packets transmitted between subnets. Matrix factorization technology is used to design the gain matrix of the compensation controller. The positiveness and consistency of the communication network system are analyzed using linear copositive Lyapunov functions and linear programming methods, thereby realizing that the data packets transmitted by each subnet in the communication network system maintain a relatively stable motion state.

[0091] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0092] Figure 1 This is a schematic diagram of the communication network process;

[0093] Figure 2 This is a schematic diagram of the communication network system based on the compensation controller of the present invention. Detailed Implementation

[0094] Example

[0095] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0096] Please see Figure 1-2 A method for designing a compensation control protocol for a multi-node fuzzy communication network includes the following steps:

[0097] Step 1: Establish a state-space model of the number of data packets during the data transmission process in the communication network. The specific process is as follows:

[0098] S11. Analyze the dynamic process of data transmission in the communication network and collect model data to simulate the actual operation process of the system.

[0099] S12. Construct the state-space model of the communication network system, in the following form:

[0100]

[0101]

[0102] in, For data packets transmitted in a communication network, n i This represents the number of data packets in the i-th communication network; g represents the disturbance experienced by a communication network system during data transmission. i This represents the number of disturbances in the i-th communication network; r represents the number of data packets received by the i-th data terminal. i This represents the number of measurement output sensors in the i-th communication network; l represents the control input at time t for the subsequent operating state of the i-th subnet. i Indicates the number of measurement input sensors; h p (z(t)) represents the network usage in the communication system. R is the system matrix of the i-th subnet. n R r R l R g R n×n R n ×l R r×n Let these represent an n-dimensional vector, an r-dimensional vector, an l-dimensional vector, a g-dimensional vector, an n×n-dimensional matrix, an n×l-dimensional matrix, and an r×n-dimensional matrix, respectively. The derivative represents the system state.

[0103] Step 2: Establish a communication network with m data packets. The topology of the communication network is a connected directed graph, as follows:

[0104] Ω=(M,Θ,A),

[0105] Where M = {1, 2, ..., m}, m ∈ N + A set of nodes that represents the abstract representation of data packets in a communication network; An edge set representing communication between data packets; This represents an adjacency matrix of data packets in a communication network. When node i receives information from node j, a ij =1, otherwise a ij =0;

[0106] Introducing the Laplacian matrix Describe the communication topology between data packets in a communication network, and define... for:

[0107]

[0108] Where Σ represents the summation symbol, a ij This represents the value of the adjacency matrix in the i-th row and j-th column.

[0109] Step 3: Design a compensation control protocol for a multi-node fuzzy communication network, in the following form:

[0110]

[0111]

[0112] in, All of these are gain matrices that need to be designed. It is a matrix related to the communication topology between agents. If the i-th agent communicates with the j-th agent, then a ij >0, otherwise, a ij =0, the dimension of the matrix is ​​related to the number of agents in a multi-agent system. l represents the control input at time t for the subsequent operating state of the i-th subnet. i Indicates the number of measurement input sensors; The derivative of the compensator state, h q (z(t)) represents the membership function. This represents the number of data packets received by the i-th data terminal. This represents the number of data packets received by the j-th data terminal.

[0113] Step 4: Determine the gain under disturbance factors and define the function:

[0114]

[0115] Where ξ>0, ξ represents the weighted L1 gain performance index, ω(t) represents the perturbation, and ρ represents the exponential convergence parameter. Indicates the output of the augmented system;

[0116] Step 5: Combining Steps 2 and 3, construct the closed-loop matrix system for the entire communication network system, as follows:

[0117]

[0118]

[0119]

[0120] in,

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] h p (z(t)) represents the network usage in the communication system, and ω(t) represents the disturbance. S represents the state of the augmented system. p Let h represent the perturbation coefficient matrix. q (z(t)) represents the membership function. The state derivative of the augmented system is represented by the following: This represents the output of the augmented system. This represents the derivative of the perturbation.

[0129] Step 6: Design the gain matrix of the distributed consensus controller for the communication network system. The designed gain matrix of the consensus controller is as follows:

[0130]

[0131]

[0132] Where i represents the i-th agent, q is the q-th subsystem in the fuzzy system, and l, r, and n represent l-dimensional, r-dimensional, and n-dimensional column vectors, respectively. l represents the i-th element being 1 and all other elements being 0. i Dimensional column vector.

[0133] Step 7: Establish the conditions for the stable operation of the communication network system, as follows:

[0134] Design constants μ0>0, σ>0, γ>0, δ>1, ε>1, ξ>0, ρ>0, vector Make

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146] If this holds true, then the control protocol designed in step 3 satisfies the controller gain matrix designed in step 6, and the closed-loop matrix system of the communication network system obtained in step 5 satisfies positivity and consistency, where I represents the identity matrix. m represents an element that is 1 i 3D column vector, v ip Let represent the Lyapunov function of the p-th fuzzy rule in the i-th subsystem. This represents the transpose of the system matrix of the p-th fuzzy rule in the i-th subsystem. ψ represents the transpose of the system matrix of the p-th fuzzy rule in the i-th subsystem. rb Let a represent an rb-dimensional vector. ji This represents the element in the j-th row and i-th column of the adjacency matrix, 1 r B represents an r-dimensional vector whose elements are all 1s. ip This represents the transpose of the system matrix of the p-th fuzzy rule in the i-th subsystem. Let l represent the transpose of an rb-dimensional column vector whose ι-th element is 1 and all other elements are 0. max This represents the maximum value of vector l. l represents the i-th element being 1 and all other elements being 0. i Dimensional column vector.

[0147] Step 8: Design Lyapunov functions based on whether external disturbances exist; design Lyapunov functions when there are no external disturbances. in Furthermore, taking the derivative with respect to V(t), we obtain

[0148]

[0149] in,

[0150]

[0151] Based on condition (3) in step 7, we can obtain:

[0152]

[0153] From condition (9) in step 7, we can deduce that:

[0154]

[0155]

[0156]

[0157] Using conditions (4) and (10) in step 7 and the gain matrix of the consensus controller designed in step 6, we obtain:

[0158]

[0159] Combining conditions (9) and (11) in steps 6 and 7, we obtain

[0160]

[0161] roll out As explained in step 7, the communication network eventually reaches a consistent state under the distributed consensus control protocol designed in step 3, where τ is a parameter that the membership function needs to satisfy. This represents the transpose of the output matrix of the p-th subsystem of the N-th fuzzy agent. This represents the transpose of the gain matrix of the Nth fuzzy agent and the qth subsystem. Let a represent the transpose of the control matrix of the p-th subsystem of the N-th fuzzy agent. Nj Represents the elements of the adjacency matrix. This represents the gain matrix of the Nth fuzzy agent and the qth subsystem. Represents the gain matrix The positive component, Represents the gain matrix The negative component, Let represent the gain matrix of the j-th fuzzy agent and the q-th subsystem. Represents the gain matrix The positive component, Represents the gain matrix The negative component, v represents the transpose of the control matrix of the j-th fuzzy agent and the p-th subsystem. jp Let v be the positive vector representing the Lyapunov function of the j-th fuzzy agent and the p-th subsystem. Np η Np Both represent the positive vectors of the Lyapunov function of the p-th subsystem of the N-th fuzzy agent. This represents the gain matrix of the Nth fuzzy agent and the qth subsystem. Let Γ represent the coefficient matrix of the p-th subsystem of the N-th fuzzy agent, and let Γ represent the scaled vector in the stability proof.

[0162] Design Lyapunov functions when external disturbances are present.

[0163]

[0164] definition get:

[0165]

[0166] in,

[0167]

[0168]

[0169] Based on the conditions in step 7, we obtain therefore, Thus obtain The communication network system eventually reaches a consensus on its state and satisfies L1 gain stability. Regardless of external disturbances, each subnet in the communication network system can operate normally with its surrounding subnets, where ω(t) represents the disturbance. Let represent the output of the augmented system, Π represent the scaled vector of the state coefficients in the stability proof, and Ξ represent the scaled vector of the perturbation coefficients in the stability proof.

[0170] The positive verification of the closed-loop matrix system of the communication network system is as follows:

[0171] Based on condition (2) in step 7 and the gain matrix of the consensus controller designed in step 6, we obtain

[0172] A ip -B ip M iq C ip +l max B ip H iq C ip +μ0I≥0

[0173] Combined with H iq <0, further obtaining B ip H iq C jp <0, therefore, It is the Metzler matrix, derived from the conditions in step 7, F iq It is also a Metzler matrix and B ip K iq >0, G iq C ip >0; finally, we can conclude that A cIt is a Metzler matrix; therefore, the closed-loop matrix system of the entire communication network system is positive, where H iq Let represent the gain matrix of the i-th agent and the q-th subsystem of the fuzzy system.

[0174] Therefore, this invention adopts the above-mentioned design method for a compensation control protocol of a multi-node fuzzy communication network. It uses a fuzzy positive multi-agent system to establish a state-space model of the communication network system, uses fuzzy rules to approximate the nonlinear system as a linear system, and uses matrix decomposition technology, linear programming and Lyapunov functions to design a consistency control method based on a compensation controller. This enables the communication network system to operate normally regardless of whether it is affected by external disturbances, and solves the problem of signal quality degradation and communication interruption caused by external interference in the practical application of existing communication networks.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for designing a compensation control protocol for a multi-node fuzzy communication network, characterized in that, Includes the following steps: Step 1: Establish a state-space model of the number of data packets during the data transmission process in the communication network; Step 2, Establish A communication network of data packets; Step 3: Design a compensation control protocol for a multi-node fuzzy communication network; Step 4: Determine the gain under disturbance factors and define the function: in, , Indicates weighted Gain performance metrics Indicates a disturbance. Represents the exponential convergence parameter. Indicates the output of the augmented system; Step 5: Combining Step 2 and Step 3, construct the closed-loop matrix system for the entire communication network system; Step 6: Design the gain matrix of the distributed consensus controller for the communication network system; Step 7: Establish conditions for the stable operation of the communication network system; Step 8: Design Lyapunov functions according to whether there is an external disturbance; The specific form of the compensation control protocol for the multi-node fuzzy communication network in step 3 is as follows: in, All of these are gain matrices that need to be designed. It is a matrix related to the communication topology between agents, if the... The first agent and the second Communication between intelligent agents, then ,otherwise, The dimension of the matrix is ​​related to the number of agents in a multi-agent system. express Time for the first The control input for the next running state of the subnet Indicates the number of measurement input sensors; The derivative representing the state of the compensator. Represents the membership function. Indicates the first The number of data packets received by each data terminal was measured. Indicates the first The number of data packets received by each data terminal was measured.

2. The design method for a compensation control protocol in a multi-node fuzzy communication network according to claim 1, characterized in that, The specific process of step 1 is as follows: S11. Analyze the dynamic process of data transmission in the communication network and collect model data to simulate the actual operation process of the system. S12. Construct the state-space model of the communication network system, in the following form: in, Data packets for data transmission in a communication network. Representing the The number of data packets in a communication network; This indicates the disturbance experienced by the communication network system during data transmission. Indicates the first The number of disturbances in a communication network; Indicates the first The number of data packets received by each data terminal was measured. Indicates the first The number of measurement output sensors in a communication network; express Time for the first The control input for the next running state of the subnet Indicates the number of measurement input sensors; This indicates the network usage status in the communication system. , , , It is the first The system matrix of each subnet, , , , , , , They represent dimensional vector, dimensional vector, dimensional vector, dimensional vector, 3D matrix 3D matrix 3D matrix The derivative represents the system state.

3. The design method for a compensation control protocol in a multi-node fuzzy communication network according to claim 1, characterized in that, In step 2, the communication network topology is a connected directed graph, in the following form: in, This represents a set of nodes that are abstracted from data packets in a communication network. An edge set representing communication between data packets; A matrix representing the adjacency of data packets in a communication network, where nodes... Receive from node When receiving information, ,otherwise ; Introducing the Laplacian matrix Describe the communication topology between data packets in a communication network, and define... for: in, The summation symbol is used to represent the summation symbol. Indicates the first OK The value of the adjacency matrix of the column.

4. The design method for a compensation control protocol in a multi-node fuzzy communication network according to claim 1, characterized in that, The closed-loop matrix system of the entire communication network system in step 5 is as follows: in, ， ， This indicates the network usage status in the communication system. Indicates a disturbance. Indicates the state of the augmented system. Represents the perturbation coefficient matrix. Represents the membership function. This represents the state derivative of the augmented system. This represents the output of the augmented system. This represents the derivative of the perturbation.

5. The design method for a compensation control protocol in a multi-node fuzzy communication network according to claim 4, characterized in that, The gain matrix of the consensus controller designed in step 6 is as follows: in, Indicates the first An intelligent agent. It is the first in the fuzzy system Subsystems , , They represent , , 3D column vector, Indicates the first One element is 1 and all other elements are 0. Dimensional column vector.

6. The design method for a compensation control protocol in a multi-node fuzzy communication network according to claim 5, characterized in that, The specific conditions for the stable operation of the communication network system constructed in step 7 are as follows: Design constants , , , , , , , , ,vector , , , , , , , , , , , , , , making If this holds true, then the control protocol designed in step 3 satisfies the controller gain matrix designed in step 6, and the closed-loop matrix system of the communication network system obtained in step 5 satisfies positivity and consistency. Represents the identity matrix. , Indicates an element that is 1 3D column vector, Indicates the first Subsystem Lyapunov functions for fuzzy rules, Indicates the first Subsystem The transpose of the system matrix of a fuzzy rule Indicates the first Subsystem The transpose of the system matrix of a fuzzy rule express dimensional vector, Represents the adjacency matrix of the th Line 1 Column elements, Indicates that all elements are 1 dimensional vector, Indicates the first Subsystem The transpose of the system matrix of a fuzzy rule Indicates the first One element is 1 and all other elements are 0. Transpose of a dimensional column vector Representing vectors The maximum value, Indicates the first One element is 1 and all other elements are 0. Dimensional column vector.

7. The design method for a compensation control protocol in a multi-node fuzzy communication network according to claim 6, characterized in that, The positive verification of the closed-loop matrix system of the communication network system is as follows: Based on condition (2) in step 7 and the gain matrix of the consensus controller designed in step 6, we obtain Combined Further obtained ,therefore, It is the Metzler matrix, derived from the conditions in step 7. It is also a Metzler matrix and , Finally, we can conclude that It is a Metzler matrix; therefore, the closed-loop matrix system of the entire communication network system is positive, where, Indicates the first The first intelligent agent, the fuzzy system Gain matrices of each subsystem.

8. The design method for a compensation control protocol in a multi-node fuzzy communication network according to claim 7, characterized in that: Design Lyapunov functions when there are no external disturbances. in Furthermore, regarding Taking the derivative, we get in, Based on condition (3) in step 7, we can obtain: From condition (9) in step 7, we can deduce that: Using conditions (4) and (10) in step 7 and the gain matrix of the consensus controller designed in step 6, we obtain: Combining conditions (9) and (11) in steps 6 and 7, we obtain roll out As explained in step 7, the communication network eventually reaches a consistent state under the distributed consensus control protocol designed in step 3. These are the parameters that the membership function needs to satisfy. This represents the transpose of the output matrix of the p-th subsystem of the N-th fuzzy agent. This represents the transpose of the gain matrix of the Nth fuzzy agent and the qth subsystem. This represents the transpose of the control matrix of the p-th subsystem of the N-th fuzzy agent. Represents the elements of the adjacency matrix. This represents the gain matrix of the Nth fuzzy agent and the qth subsystem. Represents the gain matrix The positive component, Represents the gain matrix The negative component, Let represent the gain matrix of the j-th fuzzy agent and the q-th subsystem. Represents the gain matrix The positive component, Represents the gain matrix The negative component, This represents the transpose of the control matrix of the j-th fuzzy agent and the p-th subsystem. Let represent the positive vector of the Lyapunov function of the j-th fuzzy agent and the p-th subsystem. , Both represent the positive vectors of the Lyapunov function of the p-th subsystem of the N-th fuzzy agent. This represents the gain matrix of the Nth fuzzy agent and the qth subsystem. This represents the coefficient matrix of the p-th subsystem of the Nth fuzzy agent. This represents the scaled vector used in the stability proof.

9. The design method for a compensation control protocol in a multi-node fuzzy communication network according to claim 7, characterized in that: Design Lyapunov functions when external disturbances are present. definition ,get: in, Based on the conditions in step 7, we obtain ,therefore, , Thus obtain The state of the communication network system eventually reaches a consensus and satisfies The gain is stable, and regardless of external disturbances, each subnet in the communication network system can operate normally with its surrounding subnets. Indicates a disturbance. This represents the output of the augmented system. Let represent the scaled vector of the state coefficients in the stability proof. This represents the scaled vector of the perturbation coefficients in the stability proof.

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