Synchronous control method and device of memristor chaotic system and electronic equipment

Abstract

The invention relates to a synchronous control method and device of a memristive chaotic system and electronic equipment, and belongs to the technical field of control engineering.The method comprises the steps that the memristive chaotic system is determined according to the type of a memristor; based on a backstepping method and a preset synchronization error range, state feedback design is carried out on the memristor chaotic system, an initial control input function is obtained, and the initial control input function comprises an undetermined nonlinear term; and fitting an undetermined nonlinear term in the initial control input function based on a radial basis function neural network model to obtain a target control input function, and controlling the memristor chaotic system based on the target control input function. According to the method, the undetermined nonlinear term in the initial control input function is fitted by introducing the radial basis function neural network model, so that the operation amount of synchronous control of the memristor chaotic system is remarkably reduced, and the synchronous control performance is improved.

Description

Technical Field

[0001] The present application relates to the field of control engineering technology, and in particular to a synchronization control method, device and electronic equipment for a memristor chaotic system. Background Art

[0002] Memristor chaotic systems are widely used in secure communications, military, confidentiality, image encryption, etc. due to their special chaotic behavior. Many scholars have also been committed to solving the problem of synchronization control of memristor chaotic systems.

[0003] In the prior art, the traditional adaptive synchronization control method needs to continuously identify the model parameters according to the input and output data of the object, which has a large amount of calculation and low calculation performance. Summary of the invention

[0004] In view of this, it is necessary to provide a synchronization control method, device and electronic device for a memristor chaotic system to solve the problems of large amount of calculation and low computing performance in the prior art.

[0005] In order to solve the above problems, the present application provides a synchronization control method of a memristor chaotic system, comprising: Determine the memristor chaotic system based on the memristor type; Based on the backstepping method and a preset synchronization error range, a state feedback design is performed on the memristor chaotic system to obtain an initial control input function, wherein the initial control input function includes a to-be-determined nonlinear term; The undetermined nonlinear term in the initial control input function is fitted based on a radial basis neural network model to obtain a target control input function, and the memristor chaotic system is controlled based on the target control input function.

[0006] In some possible implementations, based on the backstepping method and a preset synchronization error range, a state feedback design is performed on the memristor chaotic system to obtain an initial control input function, wherein the initial control input function includes an undetermined nonlinear term, including: Determining an initial error equation of the memristor chaotic system; Converting the initial error equation into a target error equation with a strict feedback structure, wherein the target error equation includes an undetermined nonlinear term; Based on the backstepping method and a preset synchronization error range, a state feedback design is performed on the target error equation to obtain an initial control input function, wherein the initial control input function includes a to-be-determined nonlinear term.

[0007] In some possible implementations, the memristor type is a cubic passive magnetically controlled memristor, the memristor chaotic system includes a master system and a slave system, the memristor chaotic system is determined by a chaotic circuit, and the chaotic circuit includes a first capacitor, a second capacitor, an inductor, a resistor, and a memristor; The state equation of the main system is:

[0008] In the formula, represents a first voltage across the first capacitor of the main system, represents the derivative of the first voltage of the main system, represents a second voltage across the second capacitor of the main system, represents the derivative of the second voltage of the main system, represents the current through the inductor of the main system, represents the derivative of the current through the inductor of the main system, represents the magnetic flux of the main system through the memristor, represents the derivative of the main system's magnetic flux through the memristor, and Represents a constant, represents the memetic derivative of the main system; The state equation of the slave system is:

[0009] In the formula, represents the first voltage across the first capacitor of the system, represents the derivative of the first voltage from the system, represents the second voltage across the second capacitor of the system, represents the derivative of the second voltage from the system, represents the current from the system through the inductor, represents the derivative of the current through the inductor from the system, represents the magnetic flux from the system through the memristor, represents the derivative of the magnetic flux through the memristor from the system, , , and Represents a constant, represents the memetic derivative of the system, and Indicates external interference, and represents the control input from the system, express The saturation function of express saturation function.

[0010] In some possible implementations, the initial error equation is:

[0011] In the formula, Represents the first voltage error between the master system and the slave system, represents the derivative of the first voltage error, Represents the second voltage error between the master system and the slave system, represents the derivative of the second voltage error, Represents the current error through the inductor of the master system and the slave system, represents the derivative of the current error through the inductor, represents the magnetic flux error through the memristor of the master and slave systems, represents the derivative of the magnetic flux through the memristor, and represents the undetermined nonlinear term.

[0012] In some possible implementations, the target error equation is:

[0013]

[0014] In the formula, and represents the target error equation corresponding to the first subsystem, represents the first control input corresponding to the first subsystem, and represents the target error equation corresponding to the second subsystem, Indicates the second control input corresponding to the second subsystem.

[0015] In some possible implementations, fitting the undetermined nonlinear term in the initial control input function based on the radial basis neural network model to obtain the target control input function includes: Fitting the undetermined nonlinear term in the initial control input function based on a radial basis neural network model to obtain an intermediate control input function, wherein the intermediate control input function includes undetermined weights; Based on the RBFNN approximation law, weights are determined, and the weights are substituted into the intermediate control input function to obtain a target control input function.

[0016] In some possible implementations, the intermediate nonlinear term is:

[0017]

[0018]

[0019] In the formula, represents the pending weight, represents a constant, represents the Gaussian function, represents the approximation error, and represents a dummy intermediate variable.

[0020] In some possible implementations, the weight is:

[0021]

[0022] In the formula, yes The estimate, represents a positive definite matrix, Represents an infinitesimal positive number.

[0023] The present application also provides a synchronization control device for a memristor chaotic system, comprising: A chaotic system acquisition unit, used for determining a memristor chaotic system according to the type of the memristor; A feedback control design unit, used for performing state feedback design on the memristor chaotic system based on a backstepping method and a preset synchronization error range to obtain an initial control input function, wherein the initial control input function includes a to-be-determined nonlinear term; The undetermined parameter fitting unit is used to fit the undetermined nonlinear terms in the initial control input function based on the radial basis neural network model to obtain the target control input function, and control the memristor chaotic system based on the target control input function.

[0024] The present application also provides an electronic device, including a memory and a processor; The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps of the synchronization control method of the memristor chaotic system described above.

[0025] The beneficial effect of the present application is that compared with the existing technology that needs to rely on complex nonlinear term expressions and continuously perform a large number of calculations on nonlinear terms according to the input and output data of the object, the synchronization control method of the memristor chaotic system provided by the present application fits the unknown nonlinear terms in the initial control input function through a radial basis neural network model, determines the target control input function, and thereby controls the synchronization of the memristor chaotic system, which significantly reduces the amount of computational complexity of the synchronization control of the memristor chaotic system and improves the synchronization control performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic flow chart of an embodiment of a synchronous control method for a memristor chaotic system provided in the present application; Figure 2 A schematic diagram of a memristor chaotic circuit provided for this application; Figure 3 For this application Figure 1 A schematic flow chart of an embodiment of step S102; Figure 4 For this application Figure 1 A schematic flow chart of an embodiment of step S103; Figure 5 Provided for this application - Schematic diagram of the phase diagram; Figure 6 Provided for this application - Schematic diagram of the phase diagram; Figure 7 State variables provided for this application Schematic diagram of the change timing diagram; Figure 8 State variables provided for this application Schematic diagram of the change timing diagram; Fig. 9 State variables provided for this application Schematic diagram of the change timing diagram; Fig.10 State variables provided for this application Schematic diagram of the change timing diagram; Fig.11 The error provided for this application Schematic diagram of the change curve; Fig.12 The error provided for this application Schematic diagram of the change curve; Fig.13 The error provided for this application Schematic diagram of the change curve; Fig.14 The error provided for this application Schematic diagram of the change curve; Fig.15 The control input of the first controller of this application Schematic diagram of the curve; Fig.16 The control input of the second controller of this application Schematic diagram of the curve; Fig.17A schematic diagram of the first nonlinear term and its RBFNN estimation provided in this application; Fig.18 A schematic diagram of the second nonlinear term and its RBFNN estimation provided by this application; Fig.19 A schematic structural diagram of an embodiment of a synchronous control device for a memristor chaotic system provided in the present application; Fig. 20 A schematic diagram of the structure of an embodiment of an electronic device provided in this application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0028] It should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts may be implemented out of order, and steps without logical contextual relationships may be reversed in order or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowcharts, or may remove one or more operations from the flowcharts, under the guidance of the content of this application. Some of the block diagrams shown in the accompanying drawings are functional entities, and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor systems and / or microcontroller systems.

[0029] The descriptions of "first", "second", etc. involved in the embodiments of the present application are only used to describe the implicit purpose, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" and "second" may explicitly or implicitly include at least one of the features. "And / or" describes the association relationship of the associated objects, indicating that three relationships may exist, for example: A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone.

[0030] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0031] The present application provides a synchronization control method for a memristor chaotic system, which is described below.

[0032] Figure 1 A schematic flow chart of an embodiment of a synchronous control method for a memristor chaotic system provided in the present application is shown in FIG. Figure 1 As shown, the synchronization control method of the memristor chaotic system includes: S101, determining a memristor chaotic system according to the type of memristor; S102, based on the backstepping method and the preset synchronization error range, a state feedback design is performed on the memristor chaotic system to obtain an initial control input function, where the initial control input function includes a to-be-determined nonlinear term; S103, fitting the undetermined nonlinear terms in the initial control input function based on the radial basis neural network model to obtain a target control input function, and controlling the memristor chaotic system based on the target control input function.

[0033] Compared with the existing technology that needs to rely on complex nonlinear expressions and continuously perform a large number of calculations on nonlinear terms according to the input and output data of the object, the present application uses a radial basis neural network model to fit the unknown nonlinear terms in the initial control input function and determine the target control input function to control the synchronization of the memristor chaotic system, which significantly reduces the amount of computational complexity of the synchronization control of the memristor chaotic system and improves the synchronization control performance.

[0034] In a specific embodiment, the memristor adopts a cubic passive magnetically controlled memristor, and its memristor function is , where Represents memristance, represents the magnetic flux through the memristor, and represents a constant, and the memristor corresponding to the chaotic circuit is as follows Figure 2 As shown, Figure 2 In the memristor chaotic circuit, the first capacitor , the second capacitor ,inductance , resistor R and memristor According to its circuit relationship, the state equation is:

[0035] In the formula, , , and are four state variables, Represents the first capacitor The first voltage across the two ends, represents the derivative of the first voltage, Represents the second capacitor The second voltage across the two ends, represents the derivative of the second voltage, Indicates that the inductor The current, Indicates that through the inductor The derivative of the current, Represented by memristor The magnetic flux, Represented by memristor The derivative of the magnetic flux, represents the memetic derivative function.

[0036] The memristor chaotic system includes a master system and a slave system. According to the circuit normalization principle, let: , , , , and assign parameters: , , , , the normalized state equation of the main system is:

[0037] In the formula, represents a first voltage across the first capacitor of the main system, represents the derivative of the first voltage of the main system, represents a second voltage across the second capacitor of the main system, represents the derivative of the second voltage of the main system, represents the current through the inductor of the main system, represents the derivative of the current through the inductor of the main system, represents the magnetic flux of the main system through the memristor, represents the derivative of the main system's magnetic flux through the memristor, and Represents a constant, represents the memetic derivative of the main system.

[0038] Correspondingly, the normalized state equation of the slave system is:

[0039]

[0040]

[0041] In the formula, represents the first voltage across the first capacitor of the system, represents the derivative of the first voltage from the system, represents the second voltage across the second capacitor of the system, represents the derivative of the second voltage from the system, represents the current from the system through the inductor, represents the derivative of the current through the inductor from the system, represents the magnetic flux from the system through the memristor, represents the derivative of the magnetic flux through the memristor from the system, , , , and Represents a constant, represents the memetic derivative of the system, and Indicates external interference, and represents the control input from the system, represents a saturation function.

[0042] Obviously, the above-mentioned memristor chaotic system does not have a strict feedback structure, that is, there are some memristor chaotic systems that do not have a strict feedback structure, and the premise of applying the backstepping method is that the system has a strict feedback structure. Therefore, in some embodiments, in order to better apply the backstepping method to solve, such as Figure 3 As shown, step S102 includes: S301, determining an initial error equation of a memristor chaotic system; It should be noted that the difference between the master system and the slave system is defined as the error signal of the memristor chaotic system. From the state equations of the master system and the slave system, the initial error equation can be obtained as follows:

[0043]

[0044] In the formula, Represents the first voltage error between the master system and the slave system, represents the derivative of the first voltage error, Represents the second voltage error between the master system and the slave system, represents the derivative of the second voltage error, Represents the current error through the inductor of the master system and the slave system, represents the derivative of the current error through the inductor, represents the magnetic flux error through the memristor of the master and slave systems, represents the derivative of the magnetic flux through the memristor, and represents the undetermined nonlinear term.

[0045] It should be noted that the formula:

[0046] That is, the nonlinear term calculation method adopted by the traditional adaptive control method has the problems of complex formulas and large amount of calculation.

[0047] S302, converting the initial error equation into a target error equation with a strict feedback structure, wherein the target error equation includes an undetermined nonlinear term; It should be noted that the system corresponding to the initial error equation is divided into two coupled subsystems, the first subsystem and the second subsystem. Each subsystem corresponds to an independent controller, and the target error equation is obtained as follows:

[0048]

[0049] In the formula, and represents the target error equation corresponding to the first subsystem, represents the first control input of the first controller corresponding to the first subsystem, and represents the target error equation corresponding to the second subsystem, Represents the second control input of the second controller corresponding to the second subsystem.

[0050] Therefore, the output of each of the above subsystems is directly fed back to the input. This is called a strict feedback structure. Since each subsystem has a strict feedback structure, the backstepping method can be applied to each subsystem individually. Next, the state feedback design is performed based on the backstepping method.

[0051] S303: Based on the backstepping method and the preset synchronization error range, a state feedback design is performed on the target error equation to obtain an initial control input function, wherein the initial control input function includes a to-be-determined nonlinear term.

[0052] It should be noted that the backstepping design process is: In order to achieve synchronization and stability of the system, the purpose of this application is to make the tracking error of the system within a specified range, specifically:

[0053]

[0054]

[0055] In the formula, express time, , , , , and are all positive numbers, and , , the initial condition of the output synchronization tracking error is: , .

[0056] First design the control input function of the first controller: Defining Position Error and for:

[0057] In the formula, represents a dummy intermediate variable.

[0058] The derivative of the position error is:

[0059] make:

[0060]

[0061] In the formula, is a positive design parameter, Indicates the positive minimum value.

[0062] Introduce asymmetric barrier Lyapunov function (BLF) to construct Lyapunov function:

[0063]

[0064] The derivative of is:

[0065] Will Substituting into the above formula, we get:

[0066]

[0067] Will Substituting into the above formula, we get .

[0068] Then, design another Lyapunov function: , The derivative of is:

[0069]

[0070]

[0071] Therefore, the initial control input function of the first controller is designed as:

[0072] Assumptions is a positive parameter, which will design Substitution ,have to:

[0073] It can be seen that the system The region is gradually stable.

[0074] After designing the initial control input function corresponding to the first controller, design the initial control input function corresponding to the second controller: Defining Position Error and for:

[0075] In the formula, represents a dummy intermediate variable.

[0076] The derivative of the position error is:

[0077] make:

[0078]

[0079] In the formula, is a positive design parameter, represents a positive minimum.

[0080] Introduce asymmetric barrier Lyapunov function (BLF) to construct Lyapunov function:

[0081]

[0082] The derivative of is:

[0083]

[0084] Will Substituting into the above formula, we get:

[0085]

[0086] Will Substituting into the above formula, we get .

[0087] Then, design another Lyapunov function: , The derivative of is:

[0088]

[0089]

[0090] Therefore, the initial control input function of the second controller is designed as:

[0091] Assumptions is a positive parameter, which will design Substitution ,have to:

[0092] It can be seen that the system The region is gradually stable.

[0093] because and is non-positive within the preset range, which ensures , , and When time approaches infinity, it is zero, that is, , that is, the two subsystems achieve global stability, thus making the memristor chaotic system achieve global synchronization.

[0094] The initial control input functions corresponding to the final first controller and the second controller are:

[0095]

[0096] After that, we need to solve the undetermined nonlinear terms and .

[0097] In order to better determine the undetermined nonlinear term, in some embodiments, as Figure 4 As shown, step S103 includes: S401, fitting the undetermined nonlinear terms in the initial control input function based on the radial basis neural network model to obtain an intermediate control input function, where the intermediate control input function includes undetermined weights; It should be noted that, in order to realize the design of a controller with unknown parameters, the present application adopts a radial basis neural network model (RBFNN) to approximate the uncertain nonlinear terms, and obtains that the intermediate nonlinear term is:

[0098]

[0099]

[0100] In the formula, represents the pending weight, represents a constant, represents the Gaussian function, represents the approximation error.

[0101] Substituting the intermediate nonlinear term into the initial control input function, the intermediate control input function is obtained as follows:

[0102]

[0103] S402 , based on the RBFNN approximation law, determine the weights, substitute the weights into the intermediate control input function, and obtain the target control input function.

[0104] It should be noted that yes Therefore, the RBFNN approximation law will be redefined with the weights as:

[0105]

[0106] In the formula, represents a positive definite matrix, Represents an infinitesimal positive number.

[0107] Based on the above-mentioned controller, the present application adopts a neuro-adaptive learning synchronization method for a predictive memristor chaotic system, which significantly improves the accuracy and stability of the synchronization of the chaotic system compared to traditional technologies; through the combination of predictive control and neuro-adaptive learning, the system can more intelligently adjust the synchronization strategy, effectively suppress environmental noise and interference, and improve the reliability and efficiency of the synchronization process; the memristor effect is used to optimize the synchronization algorithm, further enhancing the robustness of the system, so that it has higher application potential and practical value in key areas such as communication and secure transmission.

[0108] Finally, in order to verify the synchronization effect of the above controller, the following experiment was designed: The four-dimensional differential equation adopts the Ode45 (Dormand-Prince) method, the step size is set to 0.001, and the typical initial values ​​of the master and slave systems of the memristor chaotic system are set to (0, 0, 0.1, 0) and (-0.6, -0.2, 1.2, 0.6), and we get - Phase diagram Figure 5 As shown, - Phase diagram Figure 6 As shown; the maximum allowable values ​​of external interference, control signals, and performance limits are set to: , , , , ; In RBFNN, select the nodes of the hidden layer of the neural network , the center points are evenly distributed between [-6,6], and the width of the Gaussian basis function is , the initial value of the weight , the design parameters of the first controller and the second are: , , , , 1.

[0109] According to the above conditions, we can get Figure 7-18 The simulation results shown; Figure 7-10 The time series diagram of each state variable changing over time in the master-slave system, where: Figure 7 is a state variable Change timing diagram, Figure 8 is a state variable Change timing diagram, Fig. 9 is a state variable Change timing diagram, Fig.10 is a state variable Change timing diagram; Figure 11-14 is the curve of the error signal of the master-slave system changing with time, where: Fig.11 Error Change curve, Fig.12 Error Change curve, Fig.13 Error Change curve, Fig.14 Error The change curve from Figure 11-14 It can be seen that the four state errors all tend to 0 quickly, indicating that the synchronous controller designed in this application is effective; Figure 15-16 is the curve of the control input signal of the memristor chaotic system controller increasing with time, where: Fig.15 The control input of the first controller curve, Fig.16 The control input of the second controller curve; Figure 17-18 is the nonlinear term and its RBFNN estimate, where Fig.17 is the first nonlinear term and its RBFNN estimate, Fig.18 The second nonlinear term and its RBFNN estimate, the nonlinear term is the uncertainty term.

[0110] In order to better implement a synchronization control method of a memristor chaotic system in an embodiment of the present application, based on a synchronization control method of a memristor chaotic system, correspondingly, as Fig.19 As shown, the embodiment of the present application further provides a synchronization control device 1900 for a memristor chaotic system, comprising: A chaotic system acquisition unit 1901 is used to determine a memristor chaotic system according to the type of memristor; A feedback control design unit 1902 is used to perform state feedback design on the memristor chaotic system based on the backstepping method and a preset synchronization error range to obtain an initial control input function, wherein the initial control input function includes a to-be-determined nonlinear term; The undetermined parameter fitting unit 1903 is used to fit the undetermined nonlinear terms in the initial control input function based on the radial basis neural network model to obtain the target control input function, and control the memristor chaotic system based on the target control input function.

[0111] The synchronization control device 1900 for a memristor chaotic system provided in the above embodiment can implement the technical solution described in the above embodiment of the synchronization control method for a memristor chaotic system. The specific implementation principles of the above units can refer to the corresponding contents in the above embodiment of the synchronization control method for a memristor chaotic system, which will not be repeated here.

[0112] like Fig. 20 As shown, the present application also provides an electronic device 2000. The electronic device 2000 includes a processor 2001, a memory 2002 and a display 2003. Fig. 20Only some components of the electronic device 2000 are shown, but it should be understood that it is not required to implement all of the shown components, and more or fewer components may be implemented instead.

[0113] In some embodiments, the memory 2002 may be an internal storage unit of the electronic device 2000, such as a hard disk or memory of the electronic device 2000. In other embodiments, the memory 2002 may also be an external storage device of the electronic device 2000, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 2000.

[0114] Furthermore, the memory 2002 may include both an internal storage unit of the electronic device 2000 and an external storage device. The memory 2002 is used to store application software installed in the electronic device 2000 and various data.

[0115] In some embodiments, the processor 2001 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 2002, such as the synchronization control method of the memristor chaotic system in the present application.

[0116] In some embodiments, the display 2003 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 2003 is used to display information on the electronic device 2000 and to display a visual user interface. The components 2001-2003 of the electronic device 2000 communicate with each other via a system bus.

[0117] In some embodiments of the present application, when the processor 2001 executes the synchronization control program of the memristor chaotic system in the memory 2002, the following steps may be implemented: Determine the memristor chaotic system based on the memristor type; Based on the backstepping method and the preset synchronization error range, the state feedback design of the memristor chaotic system is carried out to obtain the initial control input function, which includes the undetermined nonlinear term. The undetermined nonlinear terms in the initial control input function are fitted based on the radial basis function neural network model to obtain the target control input function, and the memristor chaotic system is controlled based on the target control input function.

[0118] It should be understood that: when the processor 2001 executes the synchronization control program of the memristor chaotic system in the memory 2002, in addition to the above functions, other functions can also be realized, and the details can be referred to the description of the corresponding method embodiment above.

[0119] Furthermore, the embodiment of the present application does not specifically limit the type of the electronic device 2000 mentioned, and the electronic device 2000 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present application, the electronic device 2000 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0120] The above is a detailed introduction to the synchronization control method of a memristor chaotic system provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, based on the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0121] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. A synchronous control method for a memristor chaotic system, characterized in that: include: Determine the memristor chaotic system based on the memristor type; Based on the backstepping method and a preset synchronization error range, a state feedback design is performed on the memristor chaotic system to obtain an initial control input function, wherein the initial control input function includes a to-be-determined nonlinear term; The undetermined nonlinear term in the initial control input function is fitted based on a radial basis neural network model to obtain a target control input function, and the memristor chaotic system is controlled based on the target control input function.

2. The synchronous control method of the memristor chaotic system according to claim 1, characterized in that: Based on the backstepping method and the preset synchronization error range, the state feedback design is performed on the memristor chaotic system to obtain an initial control input function, wherein the initial control input function includes undetermined nonlinear terms, including: Determining an initial error equation of the memristor chaotic system; Converting the initial error equation into a target error equation with a strict feedback structure, wherein the target error equation includes an undetermined nonlinear term; Based on the backstepping method and a preset synchronization error range, a state feedback design is performed on the target error equation to obtain an initial control input function, wherein the initial control input function includes a to-be-determined nonlinear term.

3. The synchronous control method of the memristor chaotic system according to claim 2, characterized in that: The memristor type is a cubic passive magnetically controlled memristor, the memristor chaotic system includes a master system and a slave system, the memristor chaotic system is determined by a chaotic circuit, and the chaotic circuit includes a first capacitor, a second capacitor, an inductor, a resistor and a memristor; The state equation of the main system is: In the formula, represents a first voltage across the first capacitor of the main system, represents the derivative of the first voltage of the main system, represents a second voltage across the second capacitor of the main system, represents the derivative of the second voltage of the main system, represents the current through the inductor of the main system, represents the derivative of the current through the inductor of the main system, represents the magnetic flux of the main system through the memristor, represents the derivative of the main system's magnetic flux through the memristor, and Represents a constant, represents the memetic derivative of the main system; The state equation of the slave system is: In the formula, represents the first voltage across the first capacitor of the system, represents the derivative of the first voltage from the system, represents the second voltage across the second capacitor of the system, represents the derivative of the second voltage from the system, represents the current from the system through the inductor, represents the derivative of the current through the inductor from the system, represents the magnetic flux from the system through the memristor, represents the derivative of the magnetic flux through the memristor from the system, , , and Represents a constant, represents the memetic derivative of the system, and Indicates external interference, and represents the control input from the system, express The saturation function of express saturation function.

4. The synchronous control method of the memristor chaotic system according to claim 3, characterized in that: The initial error equation is: In the formula, Represents the first voltage error between the master system and the slave system, represents the derivative of the first voltage error, Represents the second voltage error between the master system and the slave system, represents the derivative of the second voltage error, Represents the current error through the inductor of the master system and the slave system, represents the derivative of the current error through the inductor, represents the magnetic flux error through the memristor of the master and slave systems, represents the derivative of the magnetic flux through the memristor, and represents the undetermined nonlinear term.

5. The synchronous control method of the memristor chaotic system according to claim 4, characterized in that: The target error equation is: In the formula, and represents the target error equation corresponding to the first subsystem, represents the first control input corresponding to the first subsystem, and represents the target error equation corresponding to the second subsystem, Indicates the second control input corresponding to the second subsystem.

6. The synchronous control method of the memristor chaotic system according to claim 1, characterized in that: The undetermined nonlinear term in the initial control input function is fitted based on the radial basis neural network model to obtain the target control input function, including: Fitting the undetermined nonlinear term in the initial control input function based on a radial basis neural network model to obtain an intermediate control input function, wherein the intermediate control input function includes undetermined weights; Based on the RBFNN approximation law, weights are determined, and the weights are substituted into the intermediate control input function to obtain a target control input function.

7. The synchronous control method of the memristor chaotic system according to claims 5 and 6, characterized in that: The intermediate nonlinear term is: In the formula, represents the pending weight, represents a constant, represents the Gaussian function, represents the approximation error, and represents a dummy intermediate variable.

8. The synchronous control method of the memristor chaotic system according to claim 7, characterized in that: The weights are: In the formula, yes The estimate, represents a positive definite matrix, Represents an infinitesimal positive number.

9. A synchronous control device for a memristor chaotic system, characterized in that: include: A chaotic system acquisition unit, used for determining a memristor chaotic system according to the type of the memristor; A feedback control design unit, used for performing state feedback design on the memristor chaotic system based on a backstepping method and a preset synchronization error range to obtain an initial control input function, wherein the initial control input function includes a to-be-determined nonlinear term; The undetermined parameter fitting unit is used to fit the undetermined nonlinear terms in the initial control input function based on the radial basis neural network model to obtain the target control input function, and control the memristor chaotic system based on the target control input function.

10. An electronic device, characterized in that: including memory and processor; The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps of the synchronization control method of the memristor chaotic system as described in any one of claims 1 to 8.

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