Method and system for designing network control by utilizing genetic algorithm and adaptive event trigger

Abstract

The invention discloses a method and a system for designing network control by utilizing a genetic algorithm and self-adaptive event triggering. The method comprises the following steps of: establishing a state space model and an observer model of a networked system and a mathematical model of transmission data which is possibly subjected to spoofing attack in a network in the system; designing a Lyapunov function, and designing a controller constraint condition matrix theta capable of realizing robust stability performance of the system; performing decoupling processing on the theta matrix, and converting the theta matrix into an LMI form; the designed controller conditions are solved in computer software, controller parameters meeting the system performance are calculated, and control of the control system under the event trigger control strategy is achieved; a designed controller is combined with a genetic algorithm, genetic algorithm parameters are set according to an obtained initial solution, a gamma minimum value is obtained according to optimized parameters needed by the system, a self-adaptive event trigger control strategy is adopted, more flexible and more reliable safety control can be achieved, and the method is suitable for various different types of control systems and application scenes.

Description

Technical Field

[0001] The present invention relates to the field of communication technology of event triggering mechanism, and in particular to a network control method and system designed by using genetic algorithm and adaptive event triggering. Background Art

[0002] With the development of the Internet, network security issues have become increasingly prominent, including spoofing attacks. Spoofing attacks may cause system communication interruption and seriously affect the operation and stability of the system. Therefore, how to effectively combat spoofing attacks has become one of the important challenges in today's network communication field.

[0003] In actual engineering, this technology can be applied to various networked control systems, especially those facing cyber security threats. For example: intelligent transportation systems, this technology can be used to improve the communication efficiency of intelligent transportation systems and enhance the system's resistance to deception attacks, thereby ensuring the stable operation of transportation systems. Intelligent power systems, in power systems, this technology can be used to improve the system's communication efficiency and ensure the stability of the system, thereby improving the operating efficiency and safety of the power system. Summary of the invention

[0004] The present invention proposes a method and system for designing network control by using genetic algorithm and adaptive event triggering, which can solve at least one of the technical problems in the background technology.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for designing network control using genetic algorithm and adaptive event triggering includes the following steps:

[0007] S100, establishing a state space model and an observer model of the networked system and a mathematical model of transmission data in the system that may be subject to deception attacks in the network;

[0008] S200. Setting event trigger parameters based on the established model, combining the adaptive mechanism, designing the Lyapunov function, and designing the controller constraint matrix Θ that can achieve the robust stability performance of the system.

[0009] S300, decoupling the Θ matrix and converting it into LMI form; solving the designed controller conditions in computer software, calculating the controller parameters that meet the system performance, and realizing the control system control under the event-triggered control strategy;

[0010] S400, combining the designed controller with the genetic algorithm, setting the genetic algorithm parameters according to the preliminary solution obtained in steps S100-S300, and obtaining the minimum value of γ for the optimization parameters required by the system, where γ is the reaction H ∞A performance indicator that measures the system's sensitivity to external disturbances. In control theory, the smaller this index is, the stronger the system's ability to suppress disturbances is, that is, the better the system's robust performance is.

[0011] On the other hand, the present invention also discloses a network control system designed by using genetic algorithm and adaptive event triggering, which is used to implement the above-mentioned network control method designed by using genetic algorithm and adaptive event triggering, and includes the following modules:

[0012] An event trigger module based on adaptive threshold determination combines the adaptive mechanism to set the event trigger mechanism threshold to determine whether the data packet is transmitted through the network, thereby determining whether the system status data is updated;

[0013] The system computing unit is used to calculate the networked system under dual-channel deception attack, based on the adaptive event trigger mechanism, so that the system can meet H ∞ Controller gain of performance. Based on the networked system state space equations, observer design and adaptive event triggering mechanism, the controller gain and observer gain of the system are calculated;

[0014] The networked control system is used to finally design the controller input to achieve safe control based on adaptive event-triggered control strategy under deception attacks.

[0015] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0016] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0017] It can be seen from the above technical solutions that the network control method and system designed by using genetic algorithm and adaptive event triggering of the present invention, and the communication scheme based on the event triggering mechanism, are intended to better handle the dual-channel deception attack of the network control system. The scheme improves the communication efficiency through the event triggering mechanism and ensures the security and stability of the system under the dual-channel deception attack. Through the adaptive event triggering mechanism, the system can respond more quickly and accurately, reduce the communication pressure, improve the efficiency, and protect the normal operation of the control system.

[0018] It can be seen from the above technical solution that the present invention uses genetic algorithm and adaptive event triggering to design a network control system controller to achieve H under dual-channel deception attack ∞ Control has the following advantages over existing technologies:

[0019] First, the present invention establishes a detailed state space model and observer model for the networked control system, and takes into account the possibility that the network module in the system may be subjected to dual-channel deception attacks, so that these models can more accurately reflect the dynamic characteristics and state changes of the actual networked control system.

[0020] Furthermore, the present invention adopts an adaptive event-triggered control strategy, which can achieve more flexible and reliable safety control and is applicable to various types of control systems and application scenarios, such as industrial control, robot control and intelligent transportation systems.

[0021] In addition, in order to solve the problem that the introduction of the decoupling method leads to additional restrictions on the solution parameters, a genetic algorithm is introduced to find the optimal solution around the known solution through genetic operations to make the parameter γ optimal.

[0022] Finally, the algorithm in the present invention is simple and easy to operate and implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of the present invention;

[0024] Figure 2 It is the event trigger timing diagram;

[0025] Figure 3 It is the flow chart of genetic algorithm;

[0026] Figure 4 This is a schematic diagram of the ITTS system configuration of this embodiment;

[0027] Figure 5 This is a schematic diagram of the time when a deception attack occurs in this embodiment;

[0028] Figure 6 The data transmission time point under the event triggering mechanism of this embodiment;

[0029] Figure 7 This is the system status diagram of this embodiment;

[0030] Figure 8 Graph 1 shows a comparison of the γ values ​​obtained by this embodiment and the existing control method. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0032] like Figure 1 As shown, the method for designing network control by using genetic algorithm and adaptive event triggering described in this embodiment includes the following steps:

[0033] S100, establishing a state space model and an observer model of the networked system and a mathematical model of transmission data in the system that may be subject to deception attacks in the network;

[0034] S200. Setting event trigger parameters based on the established model, combining the adaptive mechanism, designing the Lyapunov function, and designing the controller constraint matrix Θ that can achieve the robust stability performance of the system.

[0035] S300, decoupling the Θ matrix and converting it into LMI form; solving the designed controller conditions in computer software, calculating the controller parameters that meet the system performance, and realizing the control system control under the event-triggered control strategy;

[0036] S400, combining the designed controller with the genetic algorithm, setting the genetic algorithm parameters according to the preliminary solution obtained in steps S100-S300, and obtaining the minimum value of γ for the optimization parameters required by the system, where γ is the reaction H ∞ Performance index, measuring the system's sensitivity to external disturbances. In control theory, the smaller this index is, the stronger the system's ability to suppress disturbances is, that is, the better the system's robust performance is.

[0037] Specifically, the S100 establishes a networked control system state space model, an observer model, and a mathematical model of a deception attack, specifically including:

[0038] First, define the state space model of the networked control system as follows:

[0039]

[0040] Among them, x k is the system state vector; y k is the measurement output vector; z k is the controlled output vector; u k is the control input; v k is a perturbation vector that obeys l2[0,∞); A,B,B v ,,is a coefficient parameter matrix of known suitable dimension;

[0041] According to S100, there are two network channels in the network control system, which are vulnerable to deception attacks. Channel 1 refers to the network module between the sensor and the controller. The mathematical model of the attack is established as

[0042]

[0043] Here r k is satisfied For any bounded signal, is a given positive scalar.

[0044] In the case where such a spoofing attack is possible, the data actually transmitted through channel 1 is

[0045]

[0046] where α k It represents a deception attack, and its value follows the Bernoulli distribution and is 1 or 0. The probability of occurrence is express.

[0047]

[0048] In combination with the deception attack mentioned in claim 3, the observer model is designed as follows:

[0049]

[0050] in, represents the state estimate of the system; is the observer gain; is the controller gain; considering the deception attack The possibility of existence, and u k satisfy

[0051]

[0052] in is the control input without spoofing attack, is a deception attack, which can be modeled as

[0053]

[0054] Here's ω k is satisfied Any bounded signal of is a given positive scalar. The random variable β k With α k Independent of each other, all follow the Bernoulli distribution, and take the value of 1 or 0 as follows

[0055]

[0056] where β k ∈[0,1) is a known constant.

[0057] According to S200, event trigger parameters are set based on the established model, and Lyapunov functions are designed in combination with the adaptive mechanism to design the controller constraint matrix Θ that can achieve the robust stability performance of the system. The specific steps are S201, S202, and S203.

[0058] S201, design an adaptive event triggering mechanism.

[0059] Based on the existing information, the event trigger mechanism selectively triggers actions according to predefined conditions instead of constantly updating data. Its advantages are to optimize calculations, reduce unnecessary operations, and reduce system response delays.

[0060] To explain Figure 1 The event triggering process described in this article is divided into the following three parts:

[0061] In the interval A, k∈[k s-1 ,k s ). When the event trigger condition meets k s-1 , the system will use the status value Until the next event trigger condition is met.

[0062] In the interval B, k∈[k s ,k s+1 ). When the event trigger condition meets k s , the system will use the status value Until the next event trigger condition is met.

[0063] In the interval C, k∈[k s+1 ,k s+2 ). When the event trigger condition meets k s+2 , the system will use the status value Until the next event trigger condition is met. The event trigger timing diagram is as follows Figure 2 As shown;

[0064] S202, design the following controller based on the event trigger mechanism

[0065]

[0066] It can be summarized as

[0067]

[0068] in,

[0069] In addition, the weight matrix W>0 and σ k ∈(0,1) is a threshold variable, and the adaptive trigger condition is determined by the following rule

[0070]

[0071] Where σ0 and σ1 are known constants and 0<0≤σ1≤1.

[0072] S203, based on the event trigger mechanism, deception attack model, controller and observer design designed in S201-S202, a closed-loop control system is obtained:

[0073]

[0074] It can be further summarized as:

[0075]

[0076] in

[0077]

[0078] S204, based on S201-S203, select the Lyapunov equation as

[0079]

[0080] The constraint matrix Θ is designed to achieve the robust stability performance of the system.

[0081]

[0082] in

[0083]

[0084] The step S300 solves the designed controller conditions in computer software, calculates the controller parameters that meet the system performance, i.e., the final designed controller input, and implements the safety control of the networked control system under deception attack based on the adaptive event trigger mechanism based on the conditions of steps S100-S200;

[0085] S301, according to the conditional matrix Θ given in S200, by using the decoupling method, it satisfies the following relationship:

[0086]

[0087] in

[0088]

[0089] S302, according to the required performance indicators, based on steps S100-S200, the target parameters required by the system Substitute the decoupled conditional matrix given in S301 into Solving, we can get R1,2. Then we can deduce the controller K=(Υ T ) -1 Ω -1 M -1 Ωγ TR1 and observer gain

[0090] The S400 combines the designed controller with a genetic algorithm, such as Figure 3 , according to the preliminary solution obtained in steps S100-S300, the initial value of the genetic algorithm is set, and the system parameters are determined, including the genetic termination generation, population size, chromosome length, mutation probability, fitness function selection, etc., and the value obtained in S300 is optimized to find a better solution that can make the minimum value of γ better.

[0091] S401 initializes the population. First, we need to determine the encoding method to represent the solution space of the problem. Existence u * v variables. For the purpose of parameter encoding, the phenotype is represented as a row vector in the search space:

[0092]

[0093] S402, according to S200, the encoding result value of K is represented as K0. Each individual in the initial population of size Np is randomly generated between 0.6K0 and 2K0, and each individual ζ i (i=1,2,…,N p ) is represented as a chromosome.

[0094] S403 calculates the fitness, and calculates each individual in the population according to the fitness function to obtain the fitness value of each individual. These fitness values ​​will serve as the basis for subsequent selection operations.

[0095] S404 continues the genetic operations of selection, crossover, and mutation. The selection is to select individuals from the current population for breeding the next generation using a suitable selection method. After the crossover operation determines the crossover point and crossover method, the selected individuals are crossover operated to generate a new individual combination. In the mutation operation, the mutation position and mutation method are first determined, and then the individuals after the crossover are mutated to introduce a new gene combination.

[0096] S405 generates a new population until the termination condition is reached. After the selection, crossover and mutation operations, the new individuals obtained constitute a new population. The new population will continue to perform fitness calculation, selection, crossover and mutation operations until the set genetic generation number I is reached. ter After the process is completed, the optimized γ value and the corresponding optimal values ​​of the controller and observer are obtained.

[0097] For example, the proposed control system solution is applied to the interconnected three-tank system (ITTS). The system consists of Figure 4Each of the three water tanks has an independent valve to control the liquid connection to the water reservoir. There are additional valves connecting adjacent water tanks, and an additional flow valve is installed on the right side to connect to the water reservoir. Pump 1 and pump 2 supply liquid from the water reservoir to the water tanks respectively. This system is a circulation system in which the liquid flows from the water tank to the water reservoir and then flows back to the water tank through the pump.

[0098] When the control system is applied to ITTS, the deception attack of the two channels occurs at the following times: Figure 5 As shown;

[0099] The time point of data transmission under the event trigger mechanism is as follows Figure 6 The system status diagram is shown in Figure 7 As shown; after the genetic algorithm is introduced into the control system, Figure 8 As shown, the γ value achieved by the existing control algorithm is significantly optimized.

[0100] In summary, the present invention has a clear and reasonable structure and is easy to implement in a networked control system with dual-channel deception attacks. ∞ It is suitable for modeling systems where both network channels are vulnerable to network deception attacks, and an event trigger mechanism is used to reduce network communication pressure.

[0101] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

[0102] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0103] The communication interface is used for communication between the above electronic device and other devices.

[0104] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0105] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components.

[0106] In another embodiment provided in the present application, a computer-readable storage medium is also provided, in which a computer program is stored. When the computer program is executed by a processor, the steps of the safe decentralized control method under the above-mentioned switching event triggered control strategy are implemented.

[0107] In another embodiment provided in the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute the safe decentralized control method under any switching event triggered control strategy in the above embodiments.

[0108] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0109] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0110] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0111] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for designing network control using genetic algorithm and adaptive event triggering, characterized in that: The following steps are involved: S100, establishing a state space model and an observer model of the networked system and a mathematical model of transmission data in the system that may be subject to deception attacks in the network; S200, setting event trigger parameters based on the established model, combining the adaptive mechanism, designing the Lyapunov function, and designing the controller constraint matrix Θ that can achieve the robust stability performance of the system; S300, decoupling the Θ matrix and converting it into LMI form; solving the designed controller conditions in computer software, calculating the controller parameters that meet the system performance, and realizing the control system control under the event-triggered control strategy; S400, combining the designed controller with the genetic algorithm, setting the genetic algorithm parameters according to the preliminary solution obtained in steps S100-S300, and obtaining the minimum value of γ for the optimization parameters required by the system, where γ is the reaction H ∞ A performance indicator that measures the system's sensitivity to external disturbances. In control theory, the smaller this index is, the stronger the system's ability to suppress disturbances is, that is, the better the system's robust performance is.

2. The method for designing network control using genetic algorithm and adaptive event triggering according to claim 1, characterized in that: The establishment of the state space model of the networked control system in S100 specifically includes: First, define the state space model of the networked control system as follows: Among them, x k is the system state vector; y k is the measurement output vector; z k is the controlled output vector; u k is the control input; v k is a perturbation vector that obeys l2[0,∞); A, B, B v , C, D are coefficient parameter matrices of known appropriate dimensions; There are two network channels in the network control system, so it is vulnerable to deception attacks at these two points; channel one refers to the network module between the sensor and the controller. The mathematical model of the attack is established as Here r k is satisfied For any bounded signal, is a given positive scalar; In the case where such a spoofing attack is possible, the data actually transmitted through channel 1 is where α k It represents a deception attack, and its value follows the Bernoulli distribution and is 1 or 0. The probability of occurrence is express; The observer model for deception attack design is as follows: in, Represents the state estimate of the system; is the observer gain; is the controller gain; considering the deception attack The possibility of existence, and u k satisfy, in is the control input without spoofing attack, is a deception attack, modeled as: Here's ω k is satisfied Any bounded signal of is a given positive scalar; the random variable β k With α k Independent of each other, all follow the Bernoulli distribution and take the value of 1 or 0 as follows: where β k ∈[0, 1) is a known constant.

3. The method for designing network control using genetic algorithm and adaptive event triggering according to claim 2, characterized in that: The specific steps of step S200 are as follows: S201, Design an adaptive event triggering mechanism Based on the existing information, the event trigger mechanism selectively triggers actions according to predefined conditions instead of constantly updating data; The event triggering process is explained as follows: When k∈[k s-1 , k s ), when the event trigger condition meets k s-1 , the system will use the status value Until the next event trigger condition is met; When k∈[k s , k s+1 ), when the event trigger condition meets k s , the system will use the status value Until the next event trigger condition is met; When k∈[k s+1 , k s+2 ), when the event trigger condition meets k s+2 , the system will use the status value Until the next event trigger condition is met; S202, design the following controller based on the event trigger mechanism To sum up in, In addition, the weight matrix W>0 and σ k ∈(0, 1) is a threshold variable, and the adaptive trigger condition is determined by the following rules: Where σ0 and σ1 are known constants and 0<σ0≤σ1≤1; S203, based on the event trigger mechanism, deception attack model, controller and observer design designed in S201-S202, a closed-loop control system is obtained: To further summarize: in S204, based on S201-S203, select the Lyapunov equation as The constraint matrix Θ is designed to achieve the robust stability performance of the system. in 4. According to claim 3, the method for designing network control using genetic algorithm and adaptive event triggering is characterized in that S300 specifically includes: S301, according to the conditional matrix Θ given in S200, decouple it so that it satisfies the following relationship: in S302, according to the required performance indicators, based on steps S100-S200, the target parameters required by the system Substitute the decoupled conditional matrix given in S301 into Solving the problem, we can get R1, R2, and then deduce the system controller K = (γ T ) -1 Ω -1 M -1 Ωγ T R1 and observer gain 5. The method for designing network control using genetic algorithm and adaptive event triggering according to claim 4 is characterized in that: S400 specifically includes: S401, initialize the population, first determine the encoding method to represent the solution space of the problem; since the gain matrix Existence u *n v variables, in order to achieve the purpose of parameter encoding, the phenotype is represented as a row vector in the search space: S402 According to S200, the encoding result value of K is represented as K0, and each individual in the initial population of size Np is randomly generated between 0.6K0 and 2K0. i (i=1,2,...,N p ) is represented as a chromosome; S403 calculates the fitness, and calculates each individual in the population according to the fitness function to obtain the fitness value of each individual, which will serve as the basis for subsequent selection operations; S404 continues the genetic operations of selection, crossover, and mutation; the selection is to select individuals from the current population for breeding the next generation by using a suitable selection method; the crossover operation is to perform a crossover operation on the selected individuals after determining the crossover point and the crossover method to generate a new individual combination; the mutation operation is to first determine the mutation position and the mutation method, and then perform a mutation operation on the individuals after the crossover to introduce a new gene combination; S405 generates a new population until the termination condition is reached. After selection, crossover and mutation operations, the new individuals obtained constitute a new population; the new population will continue to perform fitness calculation, selection, crossover and mutation operations until the set genetic generation number I is reached. ter After the process is completed, the optimized γ value and the corresponding optimal values ​​of the controller and observer are obtained.

6. A method for designing a network control system using a genetic algorithm and adaptive event triggering, used to implement the method for designing a network control using a genetic algorithm and adaptive event triggering as claimed in any one of claims 1 to 4, characterized in that: Includes the following modules: An event trigger module based on adaptive threshold determination combines the adaptive mechanism to set the event trigger mechanism threshold to determine whether the data packet is transmitted through the network, thereby determining whether the system status data is updated; The system computing unit is used to calculate the networked system under dual-channel deception attack, based on the adaptive event trigger mechanism, so that the system can meet H ∞ Controller gain of performance; Calculate the controller gain and observer gain of the system based on the networked system state space equations, observer design and adaptive event triggering mechanism; The networked control system is used to finally design the controller input to achieve safe control based on adaptive event-triggered control strategy under deception attacks.

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