Hysteresis switching strategy-based complex network state estimation and synchronization control method

Abstract

The invention relates to the technical field of communication networks, in particular to a complex network state estimation and synchronous control method based on a hysteresis switching strategy, which comprises the following steps: establishing a state space model of a complex network, introducing a Luenberger observer, and constructing a hysteresis switching gain observer as a system observer; setting error parameters for the system observer; establishing a closed-loop system for the state space model, and setting operation conditions for the state space model; updating the state space model to obtain a target state space model; and carrying out positive verification and stability verification on the target state space model. By constructing a state space model and a system observer, the gain of the observer can be dynamically adjusted based on error parameters, operation conditions and the like, so that time lag and uncertainty are processed more effectively, and the synchronization precision and robustness of a communication network are improved; based on the control strategy of hysteresis switching, the control strength can be automatically adjusted according to the change of the network state, so that more accurate and flexible control is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of communication networks, and in particular to a complex network state estimation and synchronization control method based on a hysteresis switching strategy. Background Art

[0002] With the rapid development of science and technology, complex networks have become a bridge connecting multiple disciplines such as biological science, mathematical science, and engineering science. Its theoretical research and application exploration are increasingly becoming the new favorite of the academic community. These complex networks are composed of nodes and connections, where nodes can represent people, objects, or phenomena, and they interact and transmit information through connections. In the signal transmission process of these networks, time delay is a common and inevitable phenomenon. This delay not only increases the instability of the network system, but also increases its complexity. Therefore, in-depth research on the dynamic behavior of time-delay complex networks can more accurately capture and describe the characteristics of the entire network.

[0003] Synchronization is a common dynamic characteristic in complex network systems, describing how nodes in the network evolve over time to achieve coordinated motion. State estimation uses partially observable data in the network to infer the state of the entire network. In complex systems such as drone collaboration, intelligent transportation, wireless sensor tracking, and power systems, synchronous control is essential to solving key problems. State estimation and synchronous control play an important role in ensuring system stability, optimizing resource allocation, and improving efficiency. However, due to the dynamic interactions between nodes and the uncertainty of the external environment, achieving accurate state estimation and effective synchronous control faces major challenges.

[0004] For this reason, traditional complex networks often have time delays and uncertainties, and the synchronization accuracy and robustness of communication networks are poor. Summary of the invention

[0005] Based on this, in order to solve the above technical problems, a complex network state estimation and synchronization control method based on hysteresis switching strategy is provided, which can effectively deal with time lag and uncertainty and improve the synchronization accuracy and robustness of the communication network.

[0006] A complex network state estimation and synchronization control method based on a hysteresis switching strategy, the method comprising:

[0007] Collecting state information and input and output data of each node in the complex network, and establishing a state space model of the complex network according to the state information and input and output data;

[0008] After the Romberg observer is introduced into the state space model, a hysteresis switching gain observer is constructed based on a hybrid modeling estimation method, and the hysteresis switching gain observer is used as a system observer;

[0009] Acquire residual data, and set error parameters for the system observer based on the residual data; establish a closed-loop system for the state-space model, and set operating conditions for the state-space model;

[0010] According to the error parameter, the closed-loop system, and the operating conditions, the state space model is updated to obtain a final target state space model;

[0011] The target state space model is subjected to positivity verification and stability verification, and the verified target state space model is used to realize complex network state estimation and synchronous control.

[0012] In one embodiment, collecting state information and input and output data of each node in a complex network, and establishing a state space model of the complex network according to the state information and input and output data, includes:

[0013] Collecting status information of each node in the complex network, wherein the status information includes position and speed information of the complex network at a target time;

[0014] Collecting observable outputs and interference inputs of each of the nodes in the complex network at the target location;

[0015] Based on the complex network, determine the nonlinear dynamics of data packet loss or recovery, and obtain the internal coupling function between the state variables of each node according to the state information;

[0016] A state space model of the complex network is established according to the state information, observable output, interference input, nonlinear dynamics, and internal coupling functions.

[0017] In one embodiment, the method further comprises:

[0018] The state-space model is simplified using the Kronecker product notation to obtain a simplified form of the state-space model.

[0019] In one embodiment, after the Lumberg observer is introduced into the state space model, a hysteresis switching gain observer is constructed based on a hybrid modeling estimation method, and the hysteresis switching gain observer is used as a system observer, including:

[0020] determining an observer gain of the system, and introducing a Romberg observer into the state-space model according to the observer gain;

[0021] Based on the state space model of the Lumberg observer, the diagonal matrix is ​​determined, and the continuous state and the discrete state are used as switchable states;

[0022] According to the diagonal matrix and the switchable state, a hysteresis switching gain observer is constructed based on a hybrid modeling estimation method.

[0023] In one embodiment, the method further comprises:

[0024] Switching and updating the state information of each node in the complex network according to the hysteresis switching gain observer to obtain an updated node state;

[0025] According to the updated node states, state space model and hysteresis switching gain observer, a complex network hybrid system is obtained.

[0026] In one embodiment, obtaining residual data, and setting error parameters for the system observer based on the residual data, includes:

[0027] constructing an initial error system of the system observer and defining residual data according to the initial error system;

[0028] When the running state of the complex network is stable, the residual data is less than the target threshold;

[0029] An error parameter is set for the system observer according to the residual data.

[0030] In one embodiment, setting operating conditions for the state-space model includes:

[0031] extracting an observer gain matrix in the state-space model;

[0032] The observer gain matrix is ​​adjusted so that the state-space model operates stably under the adjusted observer gain matrix, and data for adjusting the observer gain matrix is ​​used as an operating condition.

[0033] In one embodiment, performing positive verification on the target state space model includes:

[0034] Acquire verification conditions, and introduce the verification conditions into the target state space model,

[0035] According to the verification condition, the Metzler matrix in the target state space model is verified to obtain a matrix verification result;

[0036] The positivity verification of the model is completed based on the matrix verification results.

[0037] In one embodiment, performing stability verification on the target state space model includes:

[0038] The stability of the target state space model is verified using the Lyapunov function.

[0039] The above-mentioned complex network state estimation and synchronization control method based on the hysteresis switching strategy constructs a state space model and a system observer, and can dynamically adjust the gain of the observer based on error parameters, operating conditions, etc., so as to more effectively handle time delays and uncertainties and improve the synchronization accuracy and robustness of the communication network; this control strategy based on hysteresis switching can automatically adjust the control intensity according to changes in the network state, thereby achieving more precise and flexible control. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is an application environment diagram of a complex network state estimation and synchronization control method based on a hysteresis switching strategy in one embodiment;

[0041] Figure 2 It is a flowchart of a complex network state estimation and synchronization control method based on a hysteresis switching strategy in one embodiment;

[0042] Figure 3 It is a structural block diagram of a complex network state estimation and synchronization control system based on a hysteresis switching strategy in one embodiment;

[0043] Figure 4 A schematic diagram of a state estimation and synchronization framework of a complex network based on a hysteresis switching strategy in one embodiment;

[0044] Figure 5 FIG. 4 is a diagram showing the internal structure of a control center in one embodiment. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] Among them, the hysteresis switching strategy of complex networks is an advanced control technology. It reduces the frequent control switching caused by system state changes by setting specific upper and lower thresholds, thereby reducing system instability, improving robustness, and reducing control costs. The core of the hysteresis switching strategy is that the switching of the control input is triggered only when the system state exceeds the preset hysteresis boundary, allowing state fluctuations within a certain range without causing response, thereby reducing oscillation and improving efficiency. When designing a hysteresis switching strategy, the key is to reasonably determine the threshold, design the control law according to the dynamic characteristics of the system, and conduct a comprehensive stability analysis to ensure the stability of the system under different conditions. The hysteresis switching strategy has been widely used in many fields, including frequency control of power systems, signal light control in intelligent transportation systems, multi-robot collaborative task execution, and simulation of gene expression regulation in biological networks, showing its significant advantages in optimizing the dynamic response of complex network systems and improving system stability.

[0047] The traditional observer is a system state estimation method based on fixed gain. It uses a pre-set constant gain to update the state estimate and does not adjust with changes in system state or external conditions. This observer is simple in design and easy to implement, but may not provide optimal performance or sufficient robustness when facing system dynamic changes or parameter uncertainty. The hysteresis switching gain observer is an innovative state estimation technology. Its core advantage is that it can dynamically adjust the observation gain according to the real-time state of the system or key performance indicators. By intelligently switching between different gain values ​​to adapt to system changes, the accuracy and robustness of system state estimation are significantly improved. Under conditions where the system dynamics change rapidly or is subject to external interference, the hysteresis switching gain observer can maintain excellent performance and stability. By dynamically adjusting the gain value to adapt to the dynamic characteristics and external interference of the system, the accuracy of state estimation is effectively improved. The hysteresis switching gain observer can intelligently select the optimal gain according to the real-time changes of the system state and the size of the residual to optimize performance and achieve fast response.

[0048] The complex network state estimation and synchronization control method based on the hysteresis switching strategy provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Figure 1As shown, the application environment is a complex network structure, specifically including a control center, a workstation, a local area network, a bridge, and various network nodes. The control center 110 can collect the state information and input and output data of each node in the complex network, and establish a state space model of the complex network based on the state information and input and output data; the control center 110 can introduce the Lumberg observer into the state space model, construct a hysteresis switching gain observer based on the hybrid modeling estimation method, and use the hysteresis switching gain observer as the system observer; the control center 110 can obtain residual data, and set error parameters for the system observer based on the residual data; establish a closed-loop system for the state space model, and set operating conditions for the state space model; the control center 110 can update the state space model according to the error parameters, the closed-loop system, and the operating conditions to obtain the final target state space model; the control center 110 can perform positivity verification and stability verification on the target state space model, and use the verified target state space model to realize complex network state estimation and synchronous control. Among them, the control center 110 can be, but not limited to, various personal computers, laptops, smart phones, robots, tablet computers and other devices.

[0049] In one embodiment, Figure 2 As shown, a complex network state estimation and synchronization control method based on a hysteresis switching strategy is provided, comprising the following steps:

[0050] Step 202, collect the state information and input and output data of each node in the complex network, and establish a state space model of the complex network according to the state information and input and output data.

[0051] The control center can collect the input and output data of the complex network system and establish a state space model for complex network state estimation and synchronous control.

[0052] Specifically, in one embodiment, a complex network state estimation and synchronization control method based on a hysteresis switching strategy is provided, which may also include a process of establishing a state space model, and the specific process includes: collecting state information of each node in the complex network, the state information including the position and speed information of the complex network at the target time; collecting observable outputs and interference inputs of each node in the complex network at the target position; based on the complex network, determining the nonlinear dynamics of data packet loss or recovery, and obtaining the internal coupling function between the state variables of each node according to the state information; establishing a state space model of the complex network according to the state information, observable output and interference input, nonlinear dynamics, and internal coupling function.

[0053] Among them, the established state space model can be expressed as:

[0054]

[0055] y i (t) = C i x i (t)+E i ω i (t),

[0056] in, It represents the state of the i-th node in the communication network, including the position, speed and other state information of the complex network at time t; represents the observable output of the ith node in the communication network at time t; represents the interference input of the i-th node in the communication network, and represents the external interference acting on the i-th node. z=1,2,...,M represents nonlinear dynamics such as packet loss or recovery in the network. This represents the internal coupling function between the state variables of each node, where W = (w ij ) N×N It represents the coupling configuration matrix of the network, and w ij >0(i≠j), these functions can capture more complex interactions such as congestion control and routing selection in the network, n, m, s, N are positive integers. Assume A 0i and A zi is the Metzler matrix,

[0057] In one embodiment, a complex network state estimation and synchronization control method based on a hysteresis switching strategy may also include a process of simplifying a state space model, and the specific process includes: using a Kronecker product symbol to simplify the state space model to obtain a simplified form of the state space model.

[0058] Among them, the simplified form of the state space model for establishing complex network state estimation and synchronization control is as follows:

[0059]

[0060] y(t)=Cx(t)+Eω(t),

[0061] in, is the Kronecker product symbol, Represents the status of N nodes in the communication network; represents the observable output of N nodes in the communication network at time t; It represents the interference input of N nodes in the communication network and represents the external interference acting on N nodes. Indicates nonlinear dynamics such as signal amplification, packet loss or recovery in the network. A0=diag{A01 ,A 02 ,…,A 0N}, represents a diagonal matrix, whose diagonal elements are the basic dynamic matrices of each subsystem. z =diag{A z1 ,A z2 ,…,A zN} represents the weight matrix associated with the nonlinear function z, which is a diagonal matrix. C=diag{C1,C2,…,C N}D=diag{D1,D2,…,D N} and E=diag{E1,E2,…,E N} respectively represent the system output matrix, the interference weight matrix and the influence matrix of external interference on the output, all of which are diagonal matrices.

[0062] Step 204: After the Lumberg observer is introduced into the state space model, a hysteresis switching gain observer is constructed based on a hybrid modeling estimation method, and the hysteresis switching gain observer is used as a system observer.

[0063] In this embodiment, a complex network state estimation and synchronization control method based on a hysteresis switching strategy is provided, which may also include a process of constructing a hysteresis switching gain observer. The specific process includes: determining the observer gain of the system, and introducing a Lumberg observer in the state space model according to the observer gain; determining a diagonal matrix based on the state space model introducing the Lumberg observer, and using continuous states and discrete states as switchable states; constructing a hysteresis switching gain observer based on the diagonal matrix and the switchable states based on a hybrid modeling estimation method.

[0064] Among them, the introduction of the Lumberg observer can be expressed as: in, is the observer gain of the system.

[0065] In this embodiment, the control center can also construct a hysteresis switching gain observer based on a hybrid modeling estimation method: in, L λ =(L λ1 ,...,L λN ) is a diagonal matrix, in, and Represents two different states, and switches between the two states. Therefore, the introduction of different states of continuous and discrete can be expressed as: Among them, C1 and C2 belong to continuous states, D1 and D2 belong to discrete states, C=C1∪C2, D=D1∪D2, δ1,δ2∈(0,1) and δ1<δ2.

[0066] In one embodiment, a complex network state estimation and synchronization control method based on a hysteresis switching strategy is provided, which may also include a process of deriving a complex network hybrid system. The specific process includes: switching and updating the state information of each node in the complex network according to a hysteresis switching gain observer to obtain an updated node state; and obtaining a complex network hybrid system according to the updated node state, the state space model, and the hysteresis switching gain observer.

[0067] The control center introduces a complex network hybrid system based on the state space model and the hysteresis switching gain observer, which can be expressed as:

[0068]

[0069] in,

[0070] x + represents the updated value of x, After update The value of λ + represents the updated value of λ.

[0071] Step 206, obtaining residual data, setting error parameters for the system observer based on the residual data; establishing a closed-loop system for the state-space model, and setting operating conditions for the state-space model.

[0072] In one embodiment, a complex network state estimation and synchronization control method based on a hysteresis switching strategy is provided, which may also include a process of setting an error for an observer, the specific process including: constructing an initial error system of a system observer, and defining residual data based on the initial error system; when the operating state of the complex network is stable, the residual data is less than a target threshold; and setting error parameters for the system observer based on the residual data.

[0073] In this embodiment, the error system of complex network state estimation and synchronous control system observer is constructed, which can be expressed as: Among them, z = 1, ..., M, according to the error system representation, the residual can be defined as: In a stable state of the complex network system, the value of |r| will be smaller than θ th And θ th ≥0, when the residual is small, λ=1, when the residual is high, λ=2.

[0074] In one embodiment, a complex network state estimation and synchronous control method based on a hysteresis switching strategy may also include a process of establishing a closed-loop system for a state space model. Specifically, the established closed-loop system for complex network state estimation and synchronous control may be expressed as:

[0075]

[0076] in, F(x)=diag{f1(x),f2(x),…,f M (x)}, F(e)=diag{f1(e), f2(e),…,f M (e)}.

[0077] In one embodiment, a complex network state estimation and synchronization control method based on a hysteresis switching strategy is provided, which may also include a process of setting operating conditions for a state space model, the specific process including: extracting an observer gain matrix in the state space model; adjusting the observer gain matrix so that the state space model operates stably under the adjusted observer gain matrix, and using the data of the adjusted observer gain matrix as an operating condition.

[0078] Among them, the conditions for the smooth operation of the complex network state estimation and synchronization control system are as follows:

[0079] Design constants μ>0,γ>0,0<α1≤α2, vector Make

[0080]

[0081] where _{1,…,N} holds for any i∈{1,…,N}, ι=1,…,m and κ=1,…,n. The complex network state estimation synchronous control system is positive and stable under the observer gain matrix, which is:

[0082]

[0083] Among them, 1 r represents an r-dimensional column vector whose elements are all 1, It represents an n-dimensional column vector whose rth element is 1 and the rest are 0, and T represents the transpose of a vector or matrix. Symbol

[0084] Step 208, updating the state space model according to the error parameters, the closed loop system, and the operating conditions to obtain a final target state space model.

[0085] Step 210 , performing positivity verification and stability verification on the target state space model, and using the verified target state space model to implement complex network state estimation and synchronous control.

[0086] In one embodiment, a complex network state estimation and synchronization control method based on a hysteresis switching strategy is provided, which may also include a process for positively verifying a target state space model. The specific process includes: obtaining verification conditions, and introducing the verification conditions into the target state space model; according to the verification conditions, verifying the Metzler matrix in the target state space model to obtain a matrix verification result; and completing the positive verification of the model based on the matrix verification result.

[0087] Since the nonlinear function in the target state space model is continuous, the following conditions are introduced: Among them, ρ j and is a scalar, j=1,2,…,n.

[0088] From the initial value have to

[0089]

[0090] When λ=1, the above conditions (1), (3), and (5) give:

[0091]

[0092] Then we can get and is the Metzler matrix. When λ = 2, the above conditions (2), (3), and (6) give:

[0093]

[0094] Then we can deduce and is a Metzler matrix. It follows that the matrix is a Metzler matrix and the matrix Therefore, the closed-loop system and the observer are positive.

[0095] In one embodiment, a complex network state estimation and synchronization control method based on a hysteresis switching strategy may further include a stability verification process, and the specific process includes: using a Lyapunov function to verify the stability of a target state space model.

[0096] Among them, the stability verification process of complex network state estimation and synchronous control system is as follows:

[0097] Select the Lyapunov function in According to the known conditions, we can get

[0098]

[0099]

[0100] Considering the case where ω=0 and λ=1, the above conditions (7), (9), (15), and (16) can be used to obtain:

[0101]

[0102] When λ=2, the above conditions (8), (10), (15), and (16) can be used to deduce:

[0103]

[0104] It is then known that the closed-loop matrix of the system is asymptotically stable.

[0105] Consider the case where ω≠0 and It can be concluded that:

[0106]

[0107] When λ=1, the above conditions (7), (11), (13), (15), and (16) give:

[0108]

[0109] When λ=2, the above conditions (8), (12), (14), (15), and (16) can be used to deduce:

[0110]

[0111] From the above derivation, we can conclude that: Then we can conclude

[0112] Then

[0113] From the above steps, we can deduce It can be concluded that the system is stable with L1 gain performance.

[0114] A complex network state estimation and synchronization control method based on a hysteresis switching strategy in the present application can more effectively handle time lag and uncertainty by dynamically adjusting the gain of the observer and the controller, and improve the synchronization accuracy and robustness of the communication network. This control strategy based on hysteresis switching can automatically adjust the control intensity according to the change of the network state, thereby achieving more accurate and flexible control. Specifically, firstly, the dynamic characteristics of the complex network are captured by precise modeling, and the state space model is established by using the complex network theory. With the help of the hysteresis switching strategy and the observer design technology, the control signal switching caused by frequent state changes is reduced, thereby reducing the system energy consumption and response delay; the network control device is designed with the state estimation and synchronization controller, so that the communication network effectively avoids problems such as state changes and synchronization errors; through the theory of Yu Zheng Lyapunov function, combined with the linear programming method, the positivity and synchronization of the system are ensured, and the stability and robustness of the network in the face of parameter changes and external disturbances are enhanced; based on the dynamic switching mechanism, according to the changes in the network state and the dynamics of the system, the control strategy is actively adjusted at the switching time, which effectively alleviates the phenomenon of network delay and reduces the uncertainty and external interference of the network.

[0115] It should be understood that, although the various steps in the above-mentioned flow charts are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above-mentioned various flow charts may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0116] In one embodiment, Figure 3 As shown, a complex network state estimation and synchronization control system based on a hysteresis switching strategy is provided, including: a state space model building module 310, a hysteresis switching gain observer building module 320, a parameter and condition setting module 330, a model adjustment module 340 and a verification module 350, wherein:

[0117] The state space model building module 310 is used to collect the state information and input and output data of each node in the complex network, and build the state space model of the complex network according to the state information and input and output data;

[0118] A hysteresis switching gain observer construction module 320 is used to construct a hysteresis switching gain observer based on a hybrid modeling estimation method after introducing a Lumberg observer into a state space model, and use the hysteresis switching gain observer as a system observer;

[0119] The parameter and condition setting module 330 is used to obtain residual data, set error parameters for the system observer based on the residual data, establish a closed-loop system for the state space model, and set operating conditions for the state space model;

[0120] The model adjustment module 340 is used to update the state space model according to the error parameters, the closed-loop system, and the operating conditions to obtain the final target state space model;

[0121] The verification module 350 is used to perform positivity verification and stability verification on the target state space model, and use the verified target state space model to implement complex network state estimation and synchronous control.

[0122] In one embodiment, the state-space model building module 310 is also used to build a state-space model of a complex network based on state information and input-output data, including: collecting state information of each node in the complex network, the state information including the position and speed information of the complex network at the target time; collecting observable outputs and interference inputs of each node in the complex network at the target position; based on the complex network, determining the nonlinear dynamics of data packet loss or recovery, and obtaining the internal coupling function between the state variables of each node based on the state information; building a state-space model of the complex network based on the state information, observable outputs, interference inputs, nonlinear dynamics, and internal coupling functions.

[0123] In one embodiment, the state-space model building module 310 is further configured to simplify the state-space model using the Kronecker product symbol to obtain a simplified form of the state-space model.

[0124] In one embodiment, the hysteresis switching gain observer construction module 320 is also used to determine the observer gain of the system, introduce a Lumberg observer in the state space model according to the observer gain; determine the diagonal matrix based on the state space model introducing the Lumberg observer, and use the continuous state and the discrete state as switchable states; and construct the hysteresis switching gain observer based on the diagonal matrix and the switchable state based on the hybrid modeling estimation method.

[0125] In one embodiment, the hysteresis switching gain observer construction module 320 is also used to switch and update the state information of each node in the complex network according to the hysteresis switching gain observer to obtain an updated node state; based on the updated node state, the state space model, and the hysteresis switching gain observer, a complex network hybrid system is obtained.

[0126] In one embodiment, the parameter and condition setting module 330 is also used to construct an initial error system of the system observer and define residual data according to the initial error system; when the operation state of the complex network is stable, the residual data is less than the target threshold; and the error parameters of the system observer are set according to the residual data.

[0127] In one embodiment, the parameter and condition setting module 330 is also used to extract the observer gain matrix in the state-space model; adjust the observer gain matrix so that the state-space model runs stably under the adjusted observer gain matrix, and use the data of the adjusted observer gain matrix as the operating condition.

[0128] In one embodiment, the verification module 350 is also used to obtain verification conditions and introduce the verification conditions into the target state space model. According to the verification conditions, the Metzler matrix in the target state space model is verified to obtain a matrix verification result; and the positivity verification of the model is completed based on the matrix verification result.

[0129] In one embodiment, the verification module 350 is further configured to use a Lyapunov function to perform stability verification on the target state space model.

[0130] Specifically, in one embodiment, a framework of a complex network state estimation and synchronization control system based on a hysteresis switching strategy is provided as follows: Figure 4 As shown in the figure, by collecting the input and output data of the complex network system, a state space model is established, and a hysteresis switching gain observer and a synchronization controller are introduced to cope with the rapid changes and nonlinear characteristics of the network state, while dealing with the uncertainty and external interference in the network. The specific steps include establishing a state space model, constructing an observer, designing system stable operation conditions, positivity verification, and stability verification. By dynamically adjusting the gains of the observer and the controller, time lag and uncertainty can be effectively handled, and the synchronization accuracy and robustness of the communication network can be improved.

[0131] In one embodiment, a control center is provided. The control center may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown. The control center includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the control center is used to provide computing and control capabilities. The memory of the control center includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the control center is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a complex network state estimation and synchronous control method based on a hysteresis switching strategy is implemented. The display screen of the control center can be a liquid crystal display screen or an electronic ink display screen, and the input device of the control center can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the shell of the control center, or an external keyboard, touchpad or mouse, etc.

[0132] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the control center to which the scheme of the present application is applied. The specific control center may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0133] In one embodiment, a control center is provided, including a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of a complex network state estimation and synchronization control method based on a hysteresis switching strategy are implemented.

[0134] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of a complex network state estimation and synchronization control method based on a hysteresis switching strategy are implemented.

[0135] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0136] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A complex network state estimation and synchronization control method based on hysteresis switching strategy, characterized in that: The method comprises: Collecting state information and input and output data of each node in the complex network, and establishing a state space model of the complex network according to the state information and input and output data; After the Romberg observer is introduced into the state space model, a hysteresis switching gain observer is constructed based on a hybrid modeling estimation method, and the hysteresis switching gain observer is used as a system observer; Acquire residual data, and set error parameters for the system observer based on the residual data; establish a closed-loop system for the state-space model, and set operating conditions for the state-space model; According to the error parameter, the closed-loop system, and the operating conditions, the state space model is updated to obtain a final target state space model; The target state space model is subjected to positivity verification and stability verification, and the verified target state space model is used to realize complex network state estimation and synchronous control.

2. The complex network state estimation and synchronization control method based on hysteresis switching strategy according to claim 1 is characterized in that: Collecting state information and input and output data of each node in the complex network, and establishing a state space model of the complex network according to the state information and input and output data, including: Collecting status information of each node in the complex network, wherein the status information includes position and speed information of the complex network at a target time; Collecting observable outputs and interference inputs of each of the nodes in the complex network at the target location; Based on the complex network, determining the nonlinear dynamics of packet loss or recovery, and obtaining the internal coupling function between the state variables of each node according to the state information; A state space model of the complex network is established according to the state information, observable output, interference input, nonlinear dynamics, and internal coupling functions.

3. The complex network state estimation and synchronization control method based on hysteresis switching strategy according to claim 2 is characterized in that: The method further comprises: The state-space model is simplified using the Kronecker product notation to obtain a simplified form of the state-space model.

4. The complex network state estimation and synchronization control method based on hysteresis switching strategy according to claim 1 is characterized in that: After the Lumberg observer is introduced into the state space model, a hysteresis switching gain observer is constructed based on a hybrid modeling estimation method, and the hysteresis switching gain observer is used as a system observer, including: determining an observer gain of the system, and introducing a Romberg observer into the state-space model according to the observer gain; Based on the state space model of the Lumberg observer, the diagonal matrix is ​​determined, and the continuous state and the discrete state are used as switchable states; According to the diagonal matrix and the switchable state, a hysteresis switching gain observer is constructed based on a hybrid modeling estimation method.

5. The complex network state estimation and synchronization control method based on hysteresis switching strategy according to claim 4 is characterized in that: The method further comprises: Switching and updating the state information of each node in the complex network according to the hysteresis switching gain observer to obtain an updated node state; According to the updated node states, state space model and hysteresis switching gain observer, a complex network hybrid system is obtained.

6. The complex network state estimation and synchronization control method based on hysteresis switching strategy according to claim 1 is characterized in that: Acquiring residual data, and setting error parameters for the system observer based on the residual data, comprising: constructing an initial error system of the system observer and defining residual data according to the initial error system; When the running state of the complex network is stable, the residual data is less than the target threshold; An error parameter is set for the system observer according to the residual data.

7. The complex network state estimation and synchronization control method based on hysteresis switching strategy according to claim 1 is characterized in that: Setting operating conditions for the state-space model includes: extracting an observer gain matrix in the state-space model; The observer gain matrix is ​​adjusted so that the state-space model operates stably under the adjusted observer gain matrix, and data for adjusting the observer gain matrix is ​​used as an operating condition.

8. The complex network state estimation and synchronization control method based on hysteresis switching strategy according to claim 1 is characterized in that: The target state space model is positively verified, including: Acquire verification conditions, and introduce the verification conditions into the target state space model, According to the verification condition, the Metzler matrix in the target state space model is verified to obtain a matrix verification result; The positivity verification of the model is completed based on the matrix verification results.

9. The complex network state estimation and synchronization control method based on hysteresis switching strategy according to claim 1 is characterized in that: The target state space model is subjected to stability verification, including: The stability of the target state space model is verified using the Lyapunov function.

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