Method and system for constructing PID (Proportion Integration Differentiation) controller of magnetic bearing

Abstract

The invention discloses a magnetic suspension bearing PID controller construction method and system, and relates to the technical field of magnetic suspension bearing control, and the method comprises the steps: building a first mathematical model of a target magnetic suspension bearing based on a magnetic circuit Ampere circuital theorem and a Newton's law; based on the first mathematical model, establishing a second mathematical model of a displacement sensor and a power amplifier of the target magnetic suspension bearing; based on the second mathematical model, constructing an incremental PID control module; training control parameters of a PID control module based on an improved PINN neural network, and setting the control parameters of the PID control module; the improved PINN neural network comprises a PINN neural network combined with a genetic algorithm. The technical problem that the stability of an existing magnetic suspension bearing control system is reduced under complex working conditions is solved.

Description

Technical Field

[0001] The invention relates to the technical field of magnetic suspension bearing control, and in particular to a magnetic suspension bearing PID controller construction method and system. Background Art

[0002] As a world industrial power, with the rapid development in aerospace, transportation, machinery manufacturing, medical equipment and other fields, rotating mechanical equipment is developing towards high speed, intelligence, high precision and other directions, and the performance of the support unit is crucial, which directly determines the reliability of the equipment and the stability of long-term operation. Traditional mechanical bearings will inevitably produce mechanical wear, low heat dissipation efficiency, lubricating oil pollution and other problems that deteriorate the performance of mechanical bearings, while high-precision and high-stability magnetic bearing systems are increasingly being used in various high-end mechanical equipment, such as high-speed rotating machinery and precision measuring equipment. The control technology of magnetic bearings is one of the key technologies to ensure the efficient operation of these systems. Although traditional control methods such as PID control perform well in many industrial applications, they have certain limitations in dealing with nonlinear systems and parameter uncertainties.

[0003] In actual industrial production, it is difficult to establish an accurate mathematical model of magnetic bearings due to the small suspension gap, nonlinearity, and severe coupling of magnetic bearing systems. In addition, it is difficult to control open-loop unstable systems. Therefore, although there has been some progress in the research on magnetic suspension control in recent years, it still cannot meet the needs of large-scale engineering applications. Physical information neural network (PINN) is an emerging technology that combines traditional neural networks with physical models. By introducing physical laws as additional constraints during the training process, it can improve the prediction accuracy and generalization ability of the model. However, even for PINN, when faced with extreme working conditions and severe environmental interference, the control performance may still be reduced due to the inability of model parameters to adapt to these changes. In addition, although existing PINN applications have shown potential in some fields, their research on magnetic bearing control is still insufficient, especially in the real-time adjustment of PID control parameters when the system changes rapidly. There is a lack of effective solution strategies, and the high performance and stability of the system are difficult to guarantee under various complex working conditions. Summary of the invention

[0004] In order to solve the above technical problems existing in the prior art, an embodiment of the present invention provides a method and system for constructing a magnetic bearing PID controller. The technical solution is as follows:

[0005] On the one hand, a method for constructing a PID controller for a magnetic bearing is provided, the method comprising: establishing a first mathematical model of a target magnetic bearing based on Ampere's loop theorem and Newton's laws of a magnetic circuit; establishing a second mathematical model of a displacement sensor and a power amplifier of the target magnetic bearing based on the first mathematical model; constructing an incremental PID control module based on the second mathematical model; training control parameters of the PID control module based on an improved PINN neural network, and adjusting the control parameters of the PID control module; the improved PINN neural network comprises a PINN neural network combined with a genetic algorithm.

[0006] Optionally, based on Ampere's loop theorem and Newton's laws of magnetic circuit, a first mathematical model of the target magnetic bearing is established, including: based on Ampere's loop theorem and Newton's laws of magnetic circuit, forming a static mathematical relationship among the stator, rotor, electromagnet and coil winding of the target magnetic bearing; based on the static mathematical relationship, establishing a differential equation between the control current, bias current, rotor balance point air gap length and the displacement of the rotor of the target magnetic bearing through a mechanism modeling method to obtain the first mathematical model.

[0007] Optionally, the first mathematical model includes a nonlinear differential equation; the second mathematical model includes a differential control model of the target magnetic bearing; based on the first mathematical model, a second mathematical model of the displacement sensor and the power amplifier of the target magnetic bearing is established, including: converting the nonlinear differential equation into a Laplace model; expressing the mathematical models of the displacement sensor and the power amplifier as a first-order inertia link and performing a Laplace transform to obtain a transfer function model of the displacement sensor and the power amplifier; coupling the Laplace model and the transfer function model to obtain a differential control model of the target magnetic bearing.

[0008] Optionally, the improved PINN neural network uses a genetic algorithm to perform optimization training on initial parameters of the neural network, and uses a loss function including a data adaptation term and a physical adaptation term.

[0009] Optionally, the control parameters of the PID control module include proportional parameters, integral parameters and differential parameters.

[0010] On the other hand, a magnetic bearing PID controller construction system is also provided, including: a first establishment module, a second establishment module, a building module and a tuning module; wherein the first establishment module is used to establish a first mathematical model of a target magnetic bearing based on the Ampere loop theorem of the magnetic circuit and Newton's law; the second establishment module is used to establish a second mathematical model of a displacement sensor and a power amplifier of the target magnetic bearing based on the first mathematical model; the building module is used to construct an incremental PID control module based on the second mathematical model; the tuning module is used to train the control parameters of the PID control module based on an improved PINN neural network and tune the control parameters of the PID control module; the improved PINN neural network includes a PINN neural network combined with a genetic algorithm.

[0011] Optionally, the first establishment module is also used to: establish the static mathematical relationship between the stator, rotor, electromagnet and coil winding of the target magnetic bearing based on the Ampere loop theorem and Newton's laws of magnetic circuit; and establish the differential equations between the control current, bias current, rotor balance point air gap length and the displacement of the rotor of the target magnetic bearing through mechanism modeling based on the static mathematical relationship to obtain the first mathematical model.

[0012] Optionally, the first mathematical model includes a nonlinear differential equation; the second mathematical model includes a differential control model of the target magnetic levitation bearing; the second establishment module is also used to: convert the nonlinear differential equation into a Laplace model; express the mathematical models of the displacement sensor and the power amplifier as a first-order inertia link and perform Laplace transform to obtain a transfer function model of the displacement sensor and the power amplifier; couple the Laplace model and the transfer function model to obtain the differential control model of the target magnetic levitation bearing.

[0013] On the other hand, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided by the present invention when executing the computer program.

[0014] On the other hand, a computer-readable storage medium is provided, in which a program code is stored. The program code can be called by a processor to execute the method provided by the present invention.

[0015] The embodiment of the present invention provides a method and system for constructing a PID controller for a magnetic bearing, which utilizes a PINN neural network to integrate physical laws to predict and simulate the dynamic behavior of a magnetic bearing, and then dynamically adjusts the parameters of the PID controller to adapt to the performance requirements of the magnetic bearing under various conditions, thereby effectively improving the operating efficiency and reliability of the magnetic bearing, and alleviating the technical problem of reduced stability of existing magnetic bearing control systems under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a flow chart of a method for constructing a magnetic bearing PID controller provided by an embodiment of the present invention;

[0018] Figure 2 is a structural schematic diagram of a magnetic suspension bearing provided by an embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of a PINN neural network structure provided by an embodiment of the present invention;

[0020] Figure 4 is a control flow chart based on a PINN neural network provided by an embodiment of the present invention;

[0021] Figure 5 It is a schematic diagram of a magnetic bearing PID controller construction system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0023] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0024] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0025] Embodiment 1

[0026] Figure 1 1 is a flow chart of a method for constructing a magnetic bearing PID controller according to an embodiment of the present invention. Figure 1 As shown, the method specifically comprises the following steps:

[0027] Step S102: establishing a first mathematical model of the target magnetic bearing based on Ampere's loop theorem and Newton's laws.

[0028] Step S104: establishing a second mathematical model of a displacement sensor and a power amplifier of the target magnetic bearing based on the first mathematical model.

[0029] Step S106: construct an incremental PID control module based on the second mathematical model.

[0030] Step S108, training the control parameters of the PID control module based on the improved PINN neural network, and adjusting the control parameters of the PID control module; the improved PINN neural network includes a PINN neural network combined with a genetic algorithm.

[0031] Specifically, the control parameters of the PID control module include proportional parameters, integral parameters and differential parameters.

[0032] Figure 2 Schematic diagram of a magnetic bearing structure provided according to an embodiment of the present invention. Figure 2 As shown in the figure, the magnetic bearing system achieves contactless suspension of the rotor through precise control of electromagnetic force. First, the controller receives the position information of the rotor and compares it with the preset reference position to calculate the necessary adjustment signal. This signal is sent to the power amplifier to enhance its strength, thereby driving the electromagnet. The electromagnet generates corresponding magnetic forces according to the adjusted current, which accurately adjust the position of the rotor to counteract gravity and other external forces and maintain the stable suspension of the rotor. The adjusted current dynamically adjusts the strength and direction of the magnetic field by increasing or decreasing, ensuring precise control of the rotor at the desired position and the overall stability of the system. Through this closed-loop feedback mechanism, the magnetic bearing can maintain high precision and high stability under various operating conditions, completing a continuous cycle from current regulation to precise suspension.

[0033] Specifically, the first mathematical model includes a nonlinear differential equation; and the second mathematical model includes a differential control model of the target magnetic bearing.

[0034] Specifically, step S102 also includes the following steps:

[0035] Step S1021, based on the Ampere loop theorem and Newton's law of magnetic circuit, establish the static mathematical relationship of the stator, rotor, electromagnet and coil winding of the target magnetic bearing;

[0036] Step S1022, based on the static mathematical relationship, a differential equation between the control current, bias current, rotor balance point air gap length and the displacement of the rotor of the target magnetic bearing is established through a mechanism modeling method to obtain a first mathematical model.

[0037] Specifically, there are three factors that determine the motion of the hybrid magnetic bearing: first, the displacement x of the rotor in the magnetic bearing; second, the control current i that enters the electromagnet through the power amplifier. x , and the third is the external interference force p(t) on the rotor of the magnetic bearing. The relationship between the current-force-displacement of the electromagnet is:

[0038]

[0039] Among them, F is the total attraction between the stator and rotor, μ0 is the magnetic permeability of air, k is the electromagnetic force coefficient, x is the displacement of the rotor in the magnetic bearing, N is the number of turns of the electromagnet coil, A is the cross-sectional area of ​​the stator, rotor and air gap magnetic field path, and i is the coil winding current.

[0040] Correspondingly, the force balance equation of the rotor is:

[0041]

[0042] Among them, p(t) is the external disturbance force, g is the gravitational acceleration, and the sum of the electromagnetic forces generated by the stator in the x-axis direction is F x for:

[0043]

[0044] Where, m is the mass of the rotor (kg), F x1 is the electromagnetic force generated by the upper electromagnet, F x2 is the electromagnetic force generated by the electromagnet below, i0 is the bias current, i x To control the current, x0 is the air gap length at the rotor equilibrium point. Since the disturbance is very small in actual work, the external disturbance force p(t) is assumed to be 0.

[0045] Substituting formula (2) into formula (3) and sorting out, the first mathematical model can be obtained as follows:

[0046]

[0047] It can be seen that the mathematical model of the magnetic bearing is a nonlinear quadratic differential equation.

[0048] Specifically, step S104 also includes the following steps:

[0049] Step S1041, converting the nonlinear differential equation into a Laplace model;

[0050] Step S1042, expressing the mathematical model of the displacement sensor and the power amplifier as a first-order inertia link and performing Laplace transform to obtain a transfer function model of the displacement sensor and the power amplifier;

[0051] Step S1043, coupling the Laplace model and the transfer function model to obtain a differential control model of the target magnetic bearing.

[0052] Specifically, near the working point of the magnetic bearing rotor, that is, x = 0 and i x By performing Taylor expansion of binary functions around =0 and ignoring higher-order infinitesimals, we can obtain:

[0053] F x =k i i x +k x (5)

[0054] where k x is the displacement stiffness of the magnetic bearing, k i is the current stiffness of the magnetic bearing, Although the description accuracy of formula (5) becomes worse as the displacement x moves away from the equilibrium position, practical experience has shown that the controller designed using formula (5) is still suitable in a wide range, and it can be obtained that;

[0055]

[0056] By Laplace transformation of equation (6), we can obtain the x-axis of the magnetic bearing in a single direction: (s) The mathematical model with is the output and I(s) is the input:

[0057]

[0058] Wherein, G(s) is the transfer function of the magnetic bearing in the x-axis direction, X(s) is the output of the magnetic bearing in the x-axis direction, I(s) is the input of the magnetic bearing in the x-axis direction, and s is the complex variable of the magnetic bearing system;

[0059] The displacement sensor converts the change of displacement into the change of output voltage. The input and output can be regarded as a linear relationship in terms of numerical value, but there will be a certain phase delay in this link, so its model can be expressed as a first-order inertia link, and the transfer function can be expressed as:

[0060]

[0061] Among them, G f(s) is the transfer function of the displacement sensor, k s is the gain of the sensor; T s is the sensor lag time constant. Since the actual sensor lag time constant T s It is difficult to measure, and the bandwidth of the sensor is wide, so G in formula (7) f (s) can be approximated as k s .

[0062] The power amplifier converts the voltage signal output by the controller into the excitation current signal in the electromagnet of the magnetic bearing. Because there is a certain phase delay in the power amplifier circuit, the model of this link can also be expressed as a first-order inertia link, and the transfer function can be expressed as:

[0063]

[0064] Among them, G a (s) is the transfer function k of the power amplifier a is the gain of the power amplifier; T a is the hysteresis time constant of the power amplifier. Since the power amplifier has a wide bandwidth, G in equation (9) a (s) can be approximated as k a By combining the above equations, an approximate linear mathematical model between the magnetic bearing, the displacement sensor and the power amplifier can be obtained, which is the second mathematical model.

[0065] The present invention adopts an incremental PID control strategy, and the incremental PID control formula is:

[0066]

[0067] Where, Δe(k) = e(k) - e(k-1). k is the sampling number; u(k) is the computer output value at the kth sampling time, e(k) is the deviation value input at the kth sampling time; e(k-1) is the deviation value input at the k-1th sampling time; K l is the integration coefficient, K l =K p T / T l ; K D is the differential coefficient, K D =K P T D / T,K P is the proportionality coefficient, T D is the differential time, T is the sampling time, T I is the integration time. If the system is required to be stable, the root of the characteristic equation of the closed-loop system with the PID controller must be located on the left half of the s-plane.

[0068] In the embodiment of the present invention, the improved PINN neural network uses a genetic algorithm to perform optimization training of initial parameters of the neural network and uses a loss function including a data adaptation term and a physical adaptation term.

[0069] Specifically, an improved PINN network is constructed. After inputting time and space data, the function is first approximated by a fully connected neural network, and then the automatic differentiation technology is used to solve the partial differential equation residual and initial boundary value residual constraints, and put them into the loss function as regular terms. Finally, the neural network connection weight parameters and the physical parameters of the partial differential equation are obtained by optimization algorithms such as the gradient descent method.

[0070] The physical process is described by using multiple partial differential equations in the magnetic suspension differential controller after adding PID control. (0) (x, y, θ), its two-dimensional partial differential equation g(T) is as follows:

[0071] g(T):f[T (n) (x,y,θ)]+C=0 (11)

[0072] Among them, T (n) (x,y,θ) represents T (0) The nth-order derivative of (x, y, θ), n = {0, 1, 2}, C is a constant, x and y are coordinates, and θ is a parameter.

[0073] Figure 3 is a schematic diagram of a PINN neural network structure provided according to an embodiment of the present invention. Figure 3 As shown, solve T (0) (x, y, θ), first convert the input {x, y, θ} into the form of neurons to generate the initial neural network t0(x, y, θ); use the automatic differentiation algorithm to find the gradient of t0(x, y, θ) to form the partial differential equation g(t0), and use the deviation modulus between g(t0) and g(T) as the loss function; finally, use the gradient descent algorithm to adjust the weight w and bias b of the neural network so that g(t0) approaches g(T), and t(x, y, θ) approaches T (0) (x, y, θ). After multiple iterations of training, t(x, y, θ) is obtained, which is T (0) The PINN proxy model of (x, y, θ). The deep neural network has multiple hidden layers. The input variables are normalized by the input layer and distributed to the neurons in the hidden layer. The calculation results of the output layer are gradually calculated by the activation function. The neurons are connected in a fully connected way. The output of each layer of neurons All serve as neurons in the next layer The input of this forward propagation process is expressed as:

[0074]

[0075] The superscript ι represents the layer number of the neural network, and the subscripts i,j represent the neuron index, for example: and are the weight and bias from the ith neuron in the l-1th layer to the jth neuron in the lth layer; a = σ(z) is the activation function, which can be a hyperbolic tangent function or a sigmoid function:

[0076]

[0077] Among them, z and a are the input value and output value of the neuron respectively.

[0078] The last layer, layer L, is the output layer, which outputs the fitting function t:

[0079] t(x,y,θ)=a L (x,y,θ;w,b) (15)

[0080] Therefore, given any input value based on formula (12), t(x, y, θ) can be quickly calculated, and the output is the three parameters to be set for the PID controller. A genetic algorithm is added on the basis of the PINN neural network, and its basic elements include chromosome encoding method, fitness function, genetic operation and operating parameters.

[0081] Since the number of initial weights of the PINN neural network is relatively large, the present invention adopts a binary encoding method that is easy to implement operations such as crossover and mutation; the fitness function is the sum of the absolute values ​​of errors at all times; the objective function is the inverse of the individual fitness; the selection operation often adopts a proportional selection operator, that is, the probability of each individual being selected is proportional to the value of its fitness function; the crossover operation uses a single-point crossover operator; the mutation operation uses a basic bit mutation operator, which refers to performing a mutation operation on a certain bit or several genes randomly set in the individual code string.

[0082] A hybrid optimization method combining genetic algorithm and PINN algorithm is used to train fuzzy neural network. and Parameters, search globally to find the chromosomes that are better after genetic optimization, that is, to obtain the approximate optimal solution of the weights, and then use the PINN algorithm to quickly and finely adjust them.

[0083] Finally, based on this neural network, it is necessary to make fine adjustments to the parameters according to the influence of key system indicators such as overshoot and stabilization time in the actual simulation curve of the stability of the magnetic levitation bearing, and test the stability, response time and accuracy of the PID controller under different operating conditions and environments to verify its performance.

[0084] Figure 4is a control flow chart based on a PINN neural network provided according to an embodiment of the present invention. Figure 4 As shown, the PINN neural network weights are initialized, and the genetic algorithm optimization part is entered. The network training error is used as the fitness value. The optimal weights are obtained through selection, crossover, mutation and other process optimizations and applied to the PINN neural network. According to the sampled rotor displacement and control current data, the parameter setting values ​​of the incremental PID controller are obtained through adjustment of the PINN neural network.

[0085] It can be seen from the above description that the embodiment of the present invention provides a method for constructing a magnetic bearing PID controller, which has the following technical effects compared with the prior art:

[0086] (1) Using intelligent algorithms and experimental data, a mathematical model of the magnetic bearing control current and bias current, the rotor balance point air gap length, the rotor displacement, the displacement sensor, and the power amplifier is constructed. The improved physical information neural network is integrated into the PID controller to more accurately predict the dynamic behavior of the magnetic bearing, thereby achieving more precise control. The introduction of PINN enables the control system to use physical laws to predict future states, helping the PID controller to adjust parameters in real time to adapt to complex operating conditions and environmental changes, thereby improving the overall stability and response speed of the system.

[0087] (2) Traditional PID controllers may encounter performance bottlenecks when dealing with nonlinear systems, while the use of PINN in the present invention allows the controller to better adapt to those complex nonlinear dynamic changes by learning the physical and dynamic characteristics of the system. This enables the magnetic bearing to maintain stable operation within a wider operating range.

[0088] (3) The established mathematical model and control strategy can be applied to similar magnetic bearing products and different working conditions, which can improve the application range of the equipment and its ability to cope with different working conditions and avoid the high degree of uniformity of the equipment.

[0089] Embodiment 2

[0090] Figure 5 Schematic diagram of a magnetic bearing PID controller construction system provided according to an embodiment of the present invention. Figure 5 As shown, the system includes: a first establishing module 10, a second establishing module 20, a building module 30 and a setting module 40.

[0091] Specifically, the first establishing module 10 is used to establish a first mathematical model of the target magnetic suspension bearing based on the Ampere loop theorem and Newton's law of magnetic circuit;

[0092] A second establishing module 20 is used to establish a second mathematical model of a displacement sensor and a power amplifier of a target magnetic bearing based on the first mathematical model;

[0093] A construction module 30, for constructing an incremental PID control module based on the second mathematical model;

[0094] The tuning module 40 is used to train the control parameters of the PID control module based on the improved PINN neural network and to tune the control parameters of the PID control module; the improved PINN neural network includes a PINN neural network combined with a genetic algorithm.

[0095] Specifically, the first establishing module 10 is further used for:

[0096] Based on the Ampere loop theorem and Newton's laws of magnetic circuit, the static mathematical relationship between the stator, rotor, electromagnet and coil winding of the target magnetic bearing is established;

[0097] Based on the static mathematical relationship, a differential equation between the control current, bias current, air gap length of the rotor balance point and the displacement of the rotor of the target magnetic bearing is established through a mechanism modeling method to obtain a first mathematical model.

[0098] Specifically, the first mathematical model includes a nonlinear differential equation; the second mathematical model includes a differential control model of the target magnetic bearing; the second establishment module 20 is also used for:

[0099] Convert nonlinear differential equations to Laplace models;

[0100] The mathematical models of the displacement sensor and the power amplifier are expressed as first-order inertial links and Laplace transform is performed to obtain the transfer function models of the displacement sensor and the power amplifier;

[0101] The Laplace model and the transfer function model are coupled to obtain the differential control model of the target magnetic bearing.

[0102] The present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0103] The present invention further provides a computer-readable storage medium, in which program codes are stored. The program codes can be called by a processor to execute the method provided in the embodiment of the present invention.

[0104] It should be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0105] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments 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 or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can 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 can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0106] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0107] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0109] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0112] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0113] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for constructing a magnetic bearing PID controller, characterized in that: The method comprises: Based on the Ampere loop theorem and Newton's laws of magnetic circuit, the first mathematical model of the target magnetic bearing is established; Based on the first mathematical model, establishing a second mathematical model of a displacement sensor and a power amplifier of the target magnetic bearing; Based on the second mathematical model, construct an incremental PID control module; The control parameters of the PID control module are trained based on an improved PINN neural network, and the control parameters of the PID control module are adjusted; the improved PINN neural network includes a PINN neural network combined with a genetic algorithm.

2. The method according to claim 1, characterized in that Based on the Ampere loop theorem and Newton's law of magnetic circuit, the first mathematical model of the target magnetic bearing is established, including: Based on the Ampere loop theorem and Newton's laws of magnetic circuit, a static mathematical relationship between the stator, rotor, electromagnet and coil winding of the target magnetic bearing is established; Based on the static mathematical relationship, a differential equation between the control current, bias current, rotor balance point air gap length and the displacement of the rotor of the target magnetic bearing is established through a mechanism modeling method to obtain the first mathematical model.

3. The method according to claim 1, characterized in that The first mathematical model includes a nonlinear differential equation; the second mathematical model includes a differential control model of the target magnetic bearing; based on the first mathematical model, a second mathematical model of a displacement sensor and a power amplifier of the target magnetic bearing is established, including: Converting the nonlinear differential equation into a Laplace model; Representing the mathematical models of the displacement sensor and the power amplifier as a first-order inertia link and performing Laplace transformation to obtain a transfer function model of the displacement sensor and the power amplifier; The Laplace model and the transfer function model are coupled to obtain a differential control model of the target magnetic suspension bearing.

4. The method according to claim 1, characterized in that: The improved PINN neural network uses a genetic algorithm to perform optimization training on initial parameters of the neural network and uses a loss function including a data adaptation term and a physical adaptation term.

5. The method according to claim 1, characterized in that The control parameters of the PID control module include proportional parameters, integral parameters and differential parameters.

6. A magnetic bearing PID controller construction system, characterized in that: include: A first building module, a second building module, a construction module and a setting module; wherein, The first establishing module is used to establish a first mathematical model of the target magnetic suspension bearing based on the Ampere loop theorem and Newton's laws of magnetic circuit; The second establishing module is used to establish a second mathematical model of the displacement sensor and the power amplifier of the target magnetic bearing based on the first mathematical model; The building module is used to build an incremental PID control module based on the second mathematical model; The tuning module is used to train the control parameters of the PID control module based on an improved PINN neural network and to tune the control parameters of the PID control module; the improved PINN neural network includes a PINN neural network combined with a genetic algorithm.

7. The system according to claim 6, characterized in that The first building module is also used to: Based on the Ampere loop theorem and Newton's laws of magnetic circuit, a static mathematical relationship between the stator, rotor, electromagnet and coil winding of the target magnetic bearing is established; Based on the static mathematical relationship, a differential equation between the control current, bias current, rotor balance point air gap length and the displacement of the rotor of the target magnetic bearing is established through a mechanism modeling method to obtain the first mathematical model.

8. The system according to claim 6, characterized in that The first mathematical model includes a nonlinear differential equation; the second mathematical model includes a differential control model of the target magnetic bearing; the second establishment module is further used for: Converting the nonlinear differential equation into a Laplace model; The mathematical models of the displacement sensor and the power amplifier are expressed as a first-order inertia link and Laplace transform is performed to obtain a transfer function model of the displacement sensor and the power amplifier; The Laplace model and the transfer function model are coupled to obtain a differential control model of the target magnetic suspension bearing.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 5.

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