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

Abstract

The invention discloses a fault-tolerant control method and device for a complex dynamic network system and a storage medium, and belongs to the technical field of fault-tolerant control research, and the method comprises the steps: obtaining a measurement output signal and a control input signal of each node in the complex dynamic network system; inputting the measurement output signal and the control input signal into a pre-designed adaptive sliding mode observer to generate a system state estimation value, and driving a state estimation error to be converged to zero through the system state estimation value; and based on the system state estimation value, solving a controller gain matrix through a pre-designed adaptive fault-tolerant controller, and driving the tracking error of the system state estimation value and the target node state to be converged to zero through the controller gain matrix. Through fault-tolerant control research of a complex network system, under the condition that the system is stable at the present stage, the system performance is further improved, the system performance is improved, and a correct value can be output under the condition that external attacks exist.

Description

Technical Field

[0001] The present invention relates to a method, device and storage medium for fault-tolerant control of a complex dynamic network system, and belongs to the technical field of fault-tolerant control research. Background Art

[0002] Complex network systems play an increasingly important role in many areas, including society, the economy, and science and technology. They encompass critical infrastructure such as 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, their node structures increasingly diverse, and exhibiting significant dynamic evolution. However, these network systems often exhibit certain vulnerabilities during operation, namely, low tolerance for failures. Failure of key nodes or connections can significantly degrade network performance and even lead to systemic collapse.

[0003] To enhance the robustness of complex networks and mitigate the negative impact of system failures, fault-tolerant control theory has emerged. Fault-tolerant control aims to design and implement control strategies that can maintain normal functionality despite partial system failures or malfunctions, thereby minimizing the risk of system failure and improving network stability and reliability. Research on fault-tolerant technologies for complex networks not only helps improve the system's ability to resist interference but also optimizes resource allocation, improves overall system efficiency, and extends its service life, thereby 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 high-reliability networks and promote the engineering application of related technologies. Systematic fault-tolerant control methods can effectively improve the security and stability of network systems and promote 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 improving network performance, but also has important theoretical value and practical significance in ensuring information security and enhancing system robustness. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method, device and storage medium for fault-tolerant control of complex dynamic network systems. Through the study of fault-tolerant control of complex network systems, the system performance is further improved under the condition of system stability at the current stage, the system performance is improved, and the correct value can be output in the presence of external attacks.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

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

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

[0009] The measured output signal and the 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, a pre-designed adaptive fault-tolerant controller is used to solve the controller gain matrix, and the controller gain matrix is ​​used to drive the tracking error between the system state estimate and the target node state to converge to zero.

[0011] Fault-tolerant control is achieved through the synergy between the adaptive sliding mode observer and the adaptive fault-tolerant controller.

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

[0013] Based on the node dynamics model:

[0014] ,

[0015] Construct an adaptive sliding mode observer, the formula is as follows:

[0016] ;

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

[0018] in, is the system matrix, Indicates time, is a nonlinear function, Node and nodes The estimated value of the system state, yes The derivative of is a node The system status, yes The derivative of is a node The system status, is the control input signal, is the measured output signal, is the design matrix, is the adaptive switching component, It is an actuator bias fault, is a node and nodes The coupling weight, is the observer gain matrix, is the adaptive switching gain, is a sign function, is the total number of network nodes, is an estimate of the measured output signal, is the positive design constant to be selected, yes The estimated value of , is the matrix to be designed, is the upper bound of system interference, yes The derivative of is a positive constant.

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

[0020] .

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

[0022] and ,

[0023] in, is a matrix, , Through inequality Solve to ensure that the state estimation error Converges to zero;

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

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

[0026] Will As a control law;

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

[0028] is the estimated value of actuator bias fault, which is calculated as follows: ;

[0029] Controller gain matrix Through inequality Solution;

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

[0031] To satisfy A bounded matrix.

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

[0033] = ;

[0034] in, yes The derivative of .

[0035] In a second aspect, the present invention provides a complex dynamic network system fault-tolerant control device for implementing any one of the aforementioned complex dynamic network system fault-tolerant control methods, the device comprising:

[0036] An acquisition module is used to obtain the measurement output signals and control input signals of each node in the 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 to generate a system state estimation value, and drive the state estimation error to converge to zero through the system state estimation value;

[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 realize fault-tolerant control through the synergy of the adaptive sliding mode observer and the adaptive fault-tolerant controller.

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

[0041] In a fourth aspect, the present invention provides a computer device, comprising:

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

[0043] A processor is configured to execute the computer program / instructions to implement the steps of any of the aforementioned methods.

[0044] In a fifth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of any of the aforementioned methods when executed by a processor.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention provides a method, device and storage medium for fault-tolerant control of a complex dynamic network system. By designing an adaptive sliding mode observer and an adaptive fault-tolerant controller, the present invention can further improve system performance and ensure the observer's accurate observation of the system state compared to existing observer research in fault-tolerant control. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0048] Figure 2 The system status provided by the embodiment of the present invention and system state estimate The operating curve of

[0049] Figure 3 The system status provided by the embodiment of the present invention and system state estimate The operating curve of

[0050] Figure 4 The system status provided by the embodiment of the present invention and system state estimate The operating curve of

[0051] Figure 5 The system status provided by the embodiment of the present invention and system state estimate The operating curve of

[0052] Figure 6 The system status provided by the embodiment of the present invention and system state estimate The operating curve of

[0053] Figure 7 is the system state estimation value provided by the embodiment of the present invention The running curve of the target node state s;

[0054] Figure 8 is the system state estimation value provided by the embodiment of the present invention The running curve of the target node state s;

[0055] Figure 9 is the system state estimation value provided by the embodiment of the present invention The running curve of the target node state s;

[0056] Figure 10 is the system state estimation value provided by the embodiment of the present invention The running curve of the target node state s;

[0057] Figure 11 is the system state estimation value provided by the embodiment of the present invention The running curve of the target node state s. DETAILED DESCRIPTION

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

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

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

[0061] The measured output signal and the 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, a pre-designed adaptive fault-tolerant controller is used to solve the controller gain matrix, and the controller gain matrix is ​​used to drive the tracking error between the system state estimate and the target node state to converge to zero.

[0063] Fault-tolerant control is achieved through the synergy between the adaptive sliding mode observer and the adaptive fault-tolerant controller.

[0064] like Figure 1 As shown, the application process of the complex dynamic network system fault-tolerant control method provided by this embodiment specifically involves the following steps:

[0065] Considering the dynamic model parameters and attack signals of complex dynamic network systems, we can see that:

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] in is the system matrix, is the controller gain matrix, 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] : Initial conditions of system state 1, ;

[0076] : Initial conditions of system state 2, ;

[0077] : Initial conditions of system state 3, ;

[0078] : Initial conditions of system state 4, ;

[0079] : Initial conditions of system state 5, ;

[0080] : estimated condition of system state 1, ;

[0081] : Estimated conditions for system state 2, ;

[0082] : Estimated conditions for system state 3, ;

[0083] : Estimated conditions for system state 4, ;

[0084] : Estimated conditions for system state 5, ;

[0085] Target node status ;

[0086] A gain of design ;

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

[0088] ;

[0089] ;

[0090] In this embodiment, , Representation node The system status, Indicates the measurement output signal, express The domain of dimensional vector, Indicates time, yes The derivative of is a node The system status, is a nonlinear function, express From the domain to the range, express The domain and range of dimensional vector, Indicates the control input signal, express The domain of dimensional vector, is the actuator bias fault, which is bounded, i.e. , Indicates the maximum value of the actuator bias fault, Representation node and nodes The coupling weight, yes The derivative of .

[0091] An actuator bias fault is represented by:

[0092] .

[0093] Nonlinear functions satisfy:

[0094] ;

[0095] ;

[0096] ;

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

[0098] in , .

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

[0100] ;

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

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

[0103] In order to estimate the unknown system state in complex dynamic network systems , for nodes Design the following distributed state observer:

[0104] ;

[0105] = ;

[0106] in, They are the nodes output by the observer and nodes The estimated value of the system state.

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

[0108] ;

[0109] ;

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

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

[0112] In order to achieve the goal of accurate observation, a discontinuous adaptive switching component needs to be designed , designed as:

[0113] ;

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

[0115] ;

[0116] in yes The estimated value of is the positive design constant to be selected, The definition of , is an unknown constant, yes The maximum value of is called the upper bound of system interference.

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

[0118] ;

[0119] in, yes The derivative of is a positive constant.

[0120] By designing an adaptive sliding mode observer, the state estimation error is achieved The stability tends to zero, thus completing the system state Estimate of system state The corresponding simulation results are as follows: Figure 2-Figure 6 As shown, Figure 2 Is the system status and system state estimate The operating curve of Figure 3 Is the system status and system state estimate The operating curve of Figure 4 Is the system status and system state estimate The operating curve of Figure 5 Is the system status and system state estimate The operating curve of Figure 6 Is the system status and system state estimate In the figure, express The first value of express The second value of express The third value of yes The estimated value of the system state, yes The estimated value of the system state, yes The estimated value of the system state, .from Figure 2-6 It can be seen that after a period of time, the estimated value of the system state coincides with the system state, and the two remain consistent.

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

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

[0123] ;

[0124] = ;

[0125] ;

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

[0127] is the controller gain matrix, which is obtained by solving the following inequality:

[0128] ;

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

[0130] is a bounded matrix that satisfies ;

[0131] The calculation formula is: .

[0132] By designing an adaptive fault-tolerant controller, we can achieve The stability tends to zero, thus completing the system state estimation Target node status The corresponding simulation results are as follows: Figure 7-11 As shown, Figure 7 is the estimated value of the system state The running curve of the target node state s; Figure 8 is the estimated value of the system state The running curve of the target node state s; Figure 9 is the estimated value of the system state The running curve of the target node state s; Figure 10 is the estimated value of the system state The running curve of the target node state s; Figure 11 is the estimated value of the system state The running curve of the target node state s. Represents the three components of the target node state s. Figure 7-11 It can be seen that after a period of time, the target node state coincides with the system state estimate, and the two remain consistent.

[0133] Fault-tolerant control is achieved through the synergy between the adaptive sliding mode observer and the adaptive fault-tolerant controller.

[0134] Embodiment 2: This embodiment 2 provides a complex dynamic network system fault-tolerant control device, including:

[0135] An acquisition module is used to obtain the measurement output signals and control input signals of each node in the 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 to generate a system state estimation value, and drive the state estimation error to converge to zero through the system state estimation value;

[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 realize fault-tolerant control through the synergy of the adaptive sliding mode observer and the adaptive fault-tolerant controller.

[0139] The specific functional implementation of each of the above modules can be found in the relevant content of the method in Example 1 and will not be elaborated on here.

[0140] Embodiment 3. This embodiment 3 provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of any one of the methods described in embodiment 1 are implemented.

[0141] Embodiment 4: This embodiment 4 provides a computer device, including:

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

[0143] A processor, configured to execute the computer program / instructions to implement the steps of any one of the methods described in Example 1.

[0144] Example 5. This example 5 provides a computer program product, including a computer program / instruction, which implements the steps of any one of the methods described in Example 1 when executed by a processor.

[0145] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

[0146] Those skilled in the art will appreciate that embodiments of the present disclosure may be provided as methods, systems, or computer program products. Thus, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0147] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

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

Claims

1. A fault-tolerant control method for a complex dynamic network system, characterized in that: include: Obtain measurement output signals and control input signals of each node in a complex dynamic network system; The measured output signal and the 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, a pre-designed adaptive fault-tolerant controller is used to solve the controller gain matrix, and the controller gain matrix is ​​used to drive the tracking error between the system state estimate and the target node state to converge to zero. Fault-tolerant control is achieved through the synergy between the adaptive sliding mode observer and the adaptive fault-tolerant controller.

2. The fault-tolerant control method for complex dynamic network systems according to claim 1, characterized in that: The design method of the adaptive sliding mode observer includes: Based on the node dynamics model: ， Construct an adaptive sliding mode observer, the formula is as follows: ； Designing adaptive switching components , ,in, , , and through the adaptive update law Dynamically adjust adaptive switching gain ; in, is the system matrix, Indicates time, is a nonlinear function, Node and nodes The estimated value of the system state, yes The derivative of is a node The system status, yes The derivative of is a node The system status, is the control input signal, is the measured output signal, is the design matrix, is the adaptive switching component, It is an actuator bias fault, is a node and nodes The coupling weight, is the observer gain matrix, is the adaptive switching gain, is a sign function, is the total number of network nodes, is an estimate of the measured output signal, is the positive design constant to be selected, yes The estimated value of , is the matrix to be designed, is the upper bound of system interference, yes The derivative of is a positive constant.

3. The fault-tolerant control method for complex dynamic network systems according to claim 2, characterized in that: The actuator bias fault is a bounded bias fault that satisfies: 。 4. The fault-tolerant control method for complex dynamic network systems according to claim 2, characterized in that: The parameter configuration of the adaptive sliding mode observer satisfies: and , in, is a matrix, , Through inequality Solve to ensure that the state estimation error Converges to zero; In the inequality, To satisfy The bounded matrix of is a scalar and , is the identity matrix, The function represented is , express The transposed matrix of is the transpose symbol.

5. The complex dynamic network system fault-tolerant control method according to claim 4, characterized in that: The adaptive fault-tolerant controller satisfies: Will As a control law; in, is the tracking error between the estimated value of the system state and the target node state, expressed as: = , is the target node status; is the estimated value of actuator bias fault, which is calculated as follows: ; Controller gain matrix Through inequality Solution; in, is a positive definite matrix, , is a scalar and ; To satisfy A bounded matrix.

6. The fault-tolerant control method for complex dynamic network systems according to claim 5, characterized in that: The calculation formula of the target node status is as follows: = ； in, yes The derivative of .

7. A fault-tolerant control device for a complex dynamic network system, characterized in that: The device is used to implement the complex dynamic network system fault-tolerant control method according to any one of claims 1 to 6, comprising: An acquisition module is used to obtain the measurement output signals and control input signals of each node in the 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 to generate a system state estimation value, and drive the state estimation error to converge to zero through the system state estimation value; 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 realize fault-tolerant control through the synergy of the adaptive sliding mode observer and the adaptive fault-tolerant controller.

8. An electronic device, characterized in that: include: Memory, used to store computer programs / instructions; A processor, configured to execute the computer program / instructions to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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