A complex dynamic network system fault-tolerant control method and device and storage medium

Abstract

The application discloses a kind of complex dynamic network system fault-tolerant control method, device and storage medium, belong to fault-tolerant control research technical field, its method includes obtaining the measurement output signal and control input signal of each node in complex dynamic network system;The measurement output signal and control input signal are input into the adaptive sliding mode observer designed in advance, generate system state estimation value, and through system state estimation value, drive state estimation error to converge to zero;Based on system state estimation value, through the adaptive fault-tolerant controller designed in advance, solve controller gain matrix, and through controller gain matrix drive system state estimation value and the tracking error of target node state converges to zero.The application is improved further to system performance under the condition that system is stable at present stage, improves the performance of system, and can output correct value under the condition that there is external attack.

Description

Technical Field

[0001] This invention relates to a fault-tolerant control method, device, and storage medium for complex dynamic network systems, belonging to the field of fault-tolerant control research technology. Background Technology

[0002] Complex network systems play an increasingly important role in many fields such as society, economy, and technology, encompassing multiple critical infrastructures including social networks, power systems, transportation networks, and financial systems. With the rapid development of information technology, communication networks, and artificial intelligence, complex networks are becoming increasingly large in scale, with more diverse node structures and significant dynamic evolutionary characteristics. However, these network systems often exhibit certain vulnerabilities during operation, meaning they have a low tolerance for failures. If a critical node or connection fails, it can lead to a significant decline in network performance or even trigger a systemic collapse.

[0003] To enhance the robustness of complex networks and mitigate the negative impacts of system failures, fault-tolerant control theory has emerged. Fault-tolerant control aims to design and implement control strategies that maintain normal function even in the event of partial system failure or malfunction, thereby minimizing the risk of system collapse and improving network stability and reliability. Research on fault-tolerant techniques for complex networks not only helps improve the system's anti-interference capabilities but also optimizes resource allocation, increases overall system efficiency, and extends its lifespan, thus reducing maintenance costs.

[0004] In key areas such as smart grids, the Internet of Things, and financial networks, research on fault-tolerant control can provide theoretical support for the construction of highly reliable networks and promote the engineering application of related technologies. Through systematic fault-tolerant control methods, the security and stability of network systems can be effectively improved, promoting the sustainable development of information infrastructure. Therefore, in-depth research on fault-tolerant control of complex network systems is not only one of the core technical paths to improve network performance, but also has significant theoretical value and practical implications for ensuring information security and enhancing system robustness. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a fault-tolerant control method, device and storage medium for complex dynamic network systems. Through research on fault-tolerant control of complex network systems, the system performance can be further improved under the current stable system conditions, thereby enhancing the system's performance and enabling it to output correct values ​​in the event of external attacks.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] In a first aspect, the present invention provides a fault-tolerant control method for complex dynamic network systems, comprising:

[0008] Acquire the measurement output signals and control input signals of each node in a complex dynamic network system;

[0009] The measured output signal and control input signal are input into a pre-designed adaptive sliding mode observer to generate a system state estimate, and the system state estimate is used to drive the state estimation error to converge to zero.

[0010] Based on the system state estimate, the controller gain matrix is ​​solved by a pre-designed adaptive fault-tolerant controller, and the tracking error between the system state estimate and the target node state is driven to converge to zero by the controller gain matrix.

[0011] Fault-tolerant control is achieved through the synergistic effect of an adaptive sliding mode observer and an adaptive fault-tolerant controller.

[0012] Furthermore, the design method of the adaptive sliding mode observer includes:

[0013] Based on node dynamics model:

[0014] ,

[0015] The adaptive sliding mode observer is constructed using the following formula:

[0016] ;

[0017] Design adaptive switching components , ,in, , And through the adaptive update law Dynamically adjust adaptive switching gain ;

[0018] in, It is a system matrix. Indicates time, It is a non-linear function. These are nodes and nodes The system state estimate, yes The derivative, It is a node The system status, yes The derivative of It is a node The system status, It is a control input signal. It measures the output signal. It is a design matrix. It is an adaptive switching component. It is an actuator bias fault. It is a node and nodes Coupling weights, It is the observer gain matrix. For adaptive gain switching, It is a symbolic function. The total number of network nodes. It is an estimated value of the measured output signal. These are the candidate positive design constants. yes The estimated value, , It is the matrix to be designed. This is the upper bound of system interference. yes The derivative of It is a normal number.

[0019] Furthermore, the actuator bias fault is a bounded bias fault, satisfying:

[0020] .

[0021] Furthermore, the parameter configuration of the adaptive sliding mode observer satisfies:

[0022] and ,

[0023] in, It is a matrix. , Using inequalities Solve to ensure the state estimation error. Converging to zero;

[0024] In the inequality, To meet A bounded matrix; It is a scalar and ; It is the identity matrix. The function represented is , express The transpose of the matrix, It is the transpose symbol.

[0025] Furthermore, the adaptive fault-tolerant controller satisfies:

[0026] Will As a control law;

[0027] in, The tracking error between the system state estimate and the target node state is expressed as: = , The state of the target node;

[0028] The estimated value for actuator bias fault is calculated using the following formula: ;

[0029] Controller gain matrix Using inequalities Solve this problem;

[0030] in, It is a positive definite matrix. , It is a scalar and ;

[0031] To meet A bounded matrix.

[0032] Furthermore, the calculation formula for the target node state is as follows:

[0033] = ;

[0034] in, yes The derivative of .

[0035] In a second aspect, the present invention provides a fault-tolerant control device for complex dynamic network systems, used to implement the fault-tolerant control method for complex dynamic network systems described in any one of the preceding claims, the device comprising:

[0036] The acquisition module is used to acquire the measurement output signals and control input signals of each node in a complex dynamic network system;

[0037] The first processing module is used to input the measurement output signal and the control input signal into a pre-designed adaptive sliding mode observer, generate a system state estimate, and drive the state estimation error to converge to zero through the system state estimate.

[0038] The second processing module is used to solve the controller gain matrix based on the system state estimate through a pre-designed adaptive fault-tolerant controller, and drive the tracking error between the system state estimate and the target node state to converge to zero through the controller gain matrix.

[0039] The fault-tolerant control module is used to achieve fault-tolerant control through the synergistic effect of an adaptive sliding mode observer and an adaptive fault-tolerant controller.

[0040] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0041] Fourthly, the present invention provides a computer device, comprising:

[0042] Memory, used to store computer programs / instructions;

[0043] A processor for executing the computer program / instructions to implement the steps of any of the methods described above.

[0044] Fifthly, the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0045] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0046] This invention provides a fault-tolerant control method, device, and storage medium for complex dynamic network systems. By designing an adaptive sliding mode observer and an adaptive fault-tolerant controller, this invention can further improve system performance and ensure accurate observation of the system state by the observer compared to existing research on fault-tolerant control. Attached Figure Description

[0047] Figure 1 This is a flowchart of a fault-tolerant control method for a complex dynamic network system provided in an embodiment of the present invention;

[0048] Figure 2 This is the system state provided in the embodiments of the present invention. Compared with system state estimates The running curve;

[0049] Figure 3 This is the system state provided in the embodiments of the present invention. Compared with system state estimates The running curve;

[0050] Figure 4 This is the system state provided in the embodiments of the present invention. Compared with system state estimates The running curve;

[0051] Figure 5 This is the system state provided in the embodiments of the present invention. Compared with system state estimates The running curve;

[0052] Figure 6 This is the system state provided in the embodiments of the present invention. Compared with system state estimates The running curve;

[0053] Figure 7 This is the system state estimate provided in the embodiments of the present invention. The running curve of the target node state s;

[0054] Figure 8 This is the system state estimate provided in the embodiments of the present invention. The running curve of the target node state s;

[0055] Figure 9 This is the system state estimate provided in the embodiments of the present invention. The running curve of the target node state s;

[0056] Figure 10 This is the system state estimate provided in the embodiments of the present invention. The running curve of the target node state s;

[0057] Figure 11 This is the system state estimate provided in the embodiments of the present invention. The running curve of the target node state s. Detailed Implementation

[0058] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0059] Example 1: This example introduces a fault-tolerant control method for complex dynamic network systems, including:

[0060] Acquire the measurement output signals and control input signals of each node in a complex dynamic network system;

[0061] The measured output signal and control input signal are input into a pre-designed adaptive sliding mode observer to generate a system state estimate, and the system state estimate is used to drive the state estimation error to converge to zero.

[0062] Based on the system state estimate, the controller gain matrix is ​​solved by a pre-designed adaptive fault-tolerant controller, and the tracking error between the system state estimate and the target node state is driven to converge to zero by the controller gain matrix.

[0063] Fault-tolerant control is achieved through the synergistic effect of an adaptive sliding mode observer and an adaptive fault-tolerant controller.

[0064] like Figure 1 As shown in this embodiment, the fault-tolerant control method for complex dynamic network systems involves the following steps in its application process:

[0065] Consider the dynamic model parameters and attack signals of a complex dynamic network system as follows:

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] in It is a system matrix. It is the controller gain matrix. It is a positive definite matrix;

[0073] The initial conditions and parameters involved are defined as follows:

[0074] Initial conditions for the system and its estimates:

[0075] The initial conditions for system state 1. ;

[0076] The initial conditions for system state 2. ;

[0077] The initial conditions for system state 3. ;

[0078] The initial conditions for system state 4. ;

[0079] The initial conditions for system state 5. ;

[0080] The estimation conditions for system state 1 ;

[0081] The estimation conditions for system state 2. ;

[0082] The estimation conditions for system state 3. ;

[0083] The estimation conditions for system state 4 ;

[0084] The estimation conditions for system state 5. ;

[0085] Target node status ;

[0086] A gain designed ;

[0087] First, for a complex network system, each node satisfies the following dynamic model:

[0088] ;

[0089] ;

[0090] In this embodiment, , Represents a node The system status, Indicates the measured output signal. express The domain of definition is dimensional vector, Indicates time, yes The derivative of It is a node The system status, It is a non-linear function. express From the domain to the range, express The domain and range are, for dimensional vector Indicates the control input signal. express The domain of definition is dimensional vector, It is an actuator bias fault, and it is bounded, that is... , This indicates the maximum value of the actuator bias fault. Represents a node and nodes Coupling weights, yes The derivative of .

[0091] Actuator bias fault is represented as:

[0092] .

[0093] nonlinear functions satisfy:

[0094] ;

[0095] ;

[0096] ;

[0097] in express The first value, express The second value, express The third value, , It is a known rational number;

[0098] in , .

[0099] The target node state of a complex dynamic network system is defined as follows:

[0100] ;

[0101] in , It is the state of the target node. yes The derivative of express The domain of definition is Dimensional vector.

[0102] Then, for complex network systems, an adaptive sliding mode observer is designed to generate system state estimates, and the system state estimates are used to drive the state estimation error to converge to zero.

[0103] To estimate the unknown system state in a complex dynamic network system , for nodes Design the following distributed state observer:

[0104] ;

[0105] = ;

[0106] in, These are the nodes output by the observer. and nodes The system state estimate.

[0107] design ,in , , It is the matrix to be designed. The observer gain matrix is ​​to be determined. It is a matrix. and Determined by solving the following formula:

[0108] ;

[0109] ;

[0110] Where the matrix , It is a scalar and satisfies , It is a bounded matrix. It is the identity matrix. The function represented is , express The transpose of the matrix, It is the transpose symbol.

[0111] The state estimation error is expressed as: .

[0112] To achieve accurate observation, a discontinuous adaptive switching component needs to be designed. The design is as follows:

[0113] ;

[0114] in It is the output estimation error, defined as ,in It is a scalar, designed for adaptive switching gain, and is as follows:

[0115] ;

[0116] in yes The estimated value, These are the candidate positive design constants. The definition is , It is an unknown constant. yes The maximum value of this value is called the upper bound of the system disturbance.

[0117] Design an adaptive update law to dynamically adjust the adaptive switching gain. :

[0118] ;

[0119] in, yes The derivative of It is a normal number.

[0120] By designing an adaptive sliding mode observer, the state estimation error can be improved. The stability of the system tends towards zero, thus completing the system state. System state estimate The following. The corresponding simulation results are as follows: Figures 2-6 As shown, Figure 2 System status Compared with system state estimates The running curve; Figure 3 System status Compared with system state estimates The running curve; Figure 4 System status Compared with system state estimates The running curve; Figure 5 System status Compared with system state estimates The running curve; Figure 6 System status Compared with system state estimates The running curve. In the figure, express The first value, express The second value, express The third value, yes The system state estimate, yes The system state estimate, yes The system state estimate, .from Figures 2-6 It can be seen that after a period of time, the estimated system state values ​​all coincide with the system state, and the two remain consistent.

[0121] Based on the system state estimate, an adaptive fault-tolerant controller is designed, and the controller gain matrix is ​​solved. The tracking error between the system state estimate and the target node state is driven to converge to zero through the controller gain matrix.

[0122] Consider the following adaptive fault-tolerant control law:

[0123] ;

[0124] = ;

[0125] ;

[0126] in, The tracking error between the system state estimate and the target node state is... This is the estimated value for actuator bias fault. The state of the target node.

[0127] The controller gain matrix is ​​obtained by solving the following inequality:

[0128] ;

[0129] in It is a positive definite matrix. , It is a scalar and satisfies ;

[0130] It is a bounded matrix that satisfies ;

[0131] The calculation formula is: .

[0132] Achieve this by designing an adaptive fault-tolerant controller. The system state estimate is completed when the stability approaches zero. State of the target node The following. The corresponding simulation results are as follows: Figures 7-11 As shown, Figure 7 It is the system state estimate. The running curve of the target node state s; Figure 8 It is the system state estimate. The running curve of the target node state s; Figure 9 It is a state estimate The running curve of the target node state s; Figure 10 It is the system state estimate. The running curve of the target node state s; Figure 11 It is the system state estimate. The running curve of the target node state s. In the figure, These represent the three components of the target node's state s. From... Figures 7-11 It can be seen that after a period of time, the state of the target node coincides with the estimated state of the system, and the two remain consistent.

[0133] Fault-tolerant control is achieved through the synergistic effect of an adaptive sliding mode observer and an adaptive fault-tolerant controller.

[0134] Example 2: This Example 2 provides a fault-tolerant control device for a complex dynamic network system, comprising:

[0135] The acquisition module is used to acquire the measurement output signals and control input signals of each node in a complex dynamic network system;

[0136] The first processing module is used to input the measurement output signal and the control input signal into a pre-designed adaptive sliding mode observer, generate a system state estimate, and drive the state estimation error to converge to zero through the system state estimate.

[0137] The second processing module is used to solve the controller gain matrix based on the system state estimate through a pre-designed adaptive fault-tolerant controller, and drive the tracking error between the system state estimate and the target node state to converge to zero through the controller gain matrix.

[0138] The fault-tolerant control module is used to achieve fault-tolerant control through the synergistic effect of an adaptive sliding mode observer and an adaptive fault-tolerant controller.

[0139] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0140] Example 3: This Example 3 provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of Examples 1.

[0141] Example 4: This example 4 provides a computer device, including:

[0142] Memory, used to store computer programs / instructions;

[0143] A processor for executing the computer program / instructions to implement the steps of any of the methods described in Embodiment 1.

[0144] Example 5: This Example 5 provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the method described in any of Examples 1.

[0145] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0146] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0147] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0148] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0149] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.

Claims

1. A fault-tolerant control method for complex dynamic network systems, characterized in that, include: Acquire the measurement output signals and control input signals of each node in a complex dynamic network system; The measured output signal and control input signal are input into a pre-designed adaptive sliding mode observer to generate a system state estimate, and the system state estimate is used to drive the state estimation error to converge to zero. Based on the system state estimate, the controller gain matrix is ​​solved by a pre-designed adaptive fault-tolerant controller, and the tracking error between the system state estimate and the target node state is driven to converge to zero by the controller gain matrix. Fault-tolerant control is achieved through the synergistic effect of an adaptive sliding mode observer and an adaptive fault-tolerant controller. The design method of the adaptive sliding mode observer includes: Based on node dynamics model: ， The adaptive sliding mode observer is constructed using the following formula: ； Design adaptive switching components , ,in, , And through the adaptive update law Dynamically adjust adaptive switching gain ; in, It is a system matrix. Indicates time, It is a non-linear function. These are nodes and nodes The system state estimate, yes The derivative of It is a node The system status, yes The derivative of It is a node The system status, It is a control input signal. It measures the output signal. It is a design matrix. It is an adaptive switching component. It is an actuator bias fault. It is a node and nodes Coupling weights, It is the observer gain matrix. For adaptive gain switching, It is a symbolic function. The total number of network nodes. It is an estimated value of the measured output signal. These are the candidate positive design constants. yes The estimated value, , It is the matrix to be designed. This is the upper bound of system interference. yes The derivative of It is a positive number; The parameter configuration of the adaptive sliding mode observer satisfies: and , in, It is a matrix. , Through inequalities Solve to ensure the state estimation error. Converging to zero; In the inequality, To meet A bounded matrix, It is a scalar and , It is the identity matrix. The function represented is , express The transpose of the matrix, It is the transpose symbol; The adaptive fault-tolerant controller satisfies: Will As a control law; in, The tracking error between the system state estimate and the target node state is expressed as: = , The state of the target node; The estimated value for actuator bias fault is calculated using the following formula: ; Controller gain matrix Through inequalities Solve this problem; in, It is a positive definite matrix. , It is a scalar and ; To meet A bounded matrix.

2. The fault-tolerant control method for complex dynamic network systems according to claim 1, characterized in that, The actuator bias fault is a bounded bias fault, satisfying the following: 。 3. The fault-tolerant control method for complex dynamic network systems according to claim 1, characterized in that, The formula for calculating the state of the target node is as follows: = ； in, yes The derivative of .

4. A fault-tolerant control device for a complex dynamic network system, characterized in that, The apparatus for implementing the fault-tolerant control method for complex dynamic network systems according to any one of claims 1-3, the apparatus comprising: The acquisition module is used to acquire the measurement output signals and control input signals of each node in a complex dynamic network system; The first processing module is used to input the measurement output signal and the control input signal into a pre-designed adaptive sliding mode observer, generate a system state estimate, and drive the state estimation error to converge to zero through the system state estimate. The second processing module is used to solve the controller gain matrix based on the system state estimate through a pre-designed adaptive fault-tolerant controller, and drive the tracking error between the system state estimate and the target node state to converge to zero through the controller gain matrix. The fault-tolerant control module is used to achieve fault-tolerant control through the synergistic effect of an adaptive sliding mode observer and an adaptive fault-tolerant controller.

5. An electronic device, characterized in that, include: Memory, used to store computer programs / instructions; A processor for executing the computer program / instructions to implement the steps of the method according to any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-3.

7. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1-3.

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