Method and device for optimizing real-time parameters of molecular pump motor and medium
By combining recursive least squares method and Kalman filter, the molecular pump motor parameters are updated in real time, which solves the problems of decreased control accuracy and response lag caused by parameter drift in traditional methods, and improves the control performance and pumping speed of molecular pump.
Patent Information
- Application Number
- CN202511117014.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-11
- Publication Date
- 2025-11-14
AI Technical Summary
Traditional molecular pump motor parameters drift under dynamic conditions, leading to decreased control accuracy and lag in response. Existing technologies cannot provide real-time parameter information and adaptive adjustment.
A method combining recursive least squares with a forgetting factor and Kalman filter is adopted to update motor parameters in real time. A mathematical model is constructed by acquiring real-time parameters, the prediction error is calculated and the parameter matrix is updated, and parameter optimization is performed using Kalman gain.
This technology achieves high real-time performance and strong anti-interference capability of the molecular pump motor under dynamic operating conditions, improves control performance, reduces the number of step loss incidents, and enhances pumping speed and vacuum environment stability.
Smart Images
Figure CN120951587A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of molecular pump technology, and more specifically, to a method, apparatus, and medium for optimizing real-time parameters of a molecular pump motor. Background Technology
[0002] As a high-vacuum generating device, the core driving component of a molecular pump is a high-speed motor (typically rotating at tens of thousands of revolutions per minute). Traditional control methods rely on offline motor parameters (such as resistance, inductance, and moment of inertia). However, in actual operation, motor parameters can drift due to factors such as temperature, wear, and load changes, leading to decreased control accuracy, lag in response, or even loss of synchronization.
[0003] Traditional control methods heavily rely on offline motor parameters such as resistance, inductance, and moment of inertia, which are typically obtained through offline testing under specific conditions. However, in actual operation, motor parameters can drift due to various factors such as temperature, wear, and load changes, leading to deviations between offline and actual values. Parameter drift directly affects the accuracy and stability of the control system, potentially causing problems such as decreased control precision, lag response, or even loss of synchronization.
[0004] In existing technologies, parameter identification often employs offline testing or simple online estimation methods. Offline testing cannot reflect parameter changes in the motor during actual operation, thus failing to provide real-time parameter information. Furthermore, offline parameters cannot adaptively adjust to changes in the motor's parameters during actual operation. This results in the control system failing to maintain optimal control performance under dynamic operating conditions (such as sudden load changes, temperature variations, etc.). While simple online estimation methods can reflect parameter changes to some extent, they typically have poor real-time performance and cannot meet the rapid response requirements of high-speed motors. Summary of the Invention
[0005] The purpose of this invention is to provide a method, device, and medium for optimizing real-time parameters of a molecular pump motor, which solves the problem of decreased control performance caused by parameter mismatch under dynamic operating conditions of molecular pump motors due to the parameter identification methods proposed in the prior art.
[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for optimizing real-time parameters of a molecular pump motor, the method comprising: Obtain real-time parameters of the molecular pump motor; A mathematical model of the molecular pump motor is constructed based on real-time parameters; wherein, the mathematical model includes voltage equations for the d-axis and q-axis; The mathematical model is solved using the recursive least squares method with a forgetting factor, the prediction error of the real-time parameters is calculated, and the parameter matrix is updated based on the prediction error; the parameter matrix is composed of resistance, d-axis inductance, q-axis inductance, and magnetic flux. The updated parameter matrix is input into a pre-built Kalman filter to obtain the Kalman gain; The real-time parameters are updated based on the Kalman gain, and the updated real-time parameters are input into the mathematical model to calculate the predicted voltage values on the d-axis and q-axis. The actual voltage values of the d-axis and q-axis of the molecular pump motor are collected, and the mean square error between the predicted voltage value and the actual voltage value is calculated. If the mean square error meets the threshold, the updated real-time parameters are used as the optimization result of the real-time parameters of the molecular pump motor.
[0007] In one implementation, the real-time parameters include resistance, d-axis inductance, q-axis inductance, angular velocity, and flux linkage.
[0008] In one implementation, the voltage equation along the d-axis is: ;in, , These represent the voltage and current along the d-axis, respectively. w The angular velocity of the molecular pump motor is... It is the d-axis inductance; The voltage equation along the q-axis is: ,in, For magnetic linkage, It is the q-axis inductance. , These represent the voltage and current along the q-axis, respectively.
[0009] In one implementation, the expression for the recursive least squares method with a forgetting factor includes: ; ; ; ; in, Let cost function be Let be the parameter matrix at time k. Let be the parameter matrix at time k-1. The inverse of the covariance matrix. Here is the gain matrix. These are actual measured values. Forgetting factor, The transformation matrix of the parameter vector. Forgetting factor, It is the identity matrix. Let be the previous value of the inverse of the covariance matrix. Let be a parameter vector consisting of resistance, d-axis inductance, q-axis inductance, and flux linkage, where k is the total time. Let i be the input vector at time i. Let be the input vector at time i. Let T be the forgetting factor, and T be the transpose of the matrix.
[0010] In one implementation, the updated parameter matrix is input to a pre-constructed Kalman filter to obtain the Kalman gain, including: The state prediction and covariance prediction are performed using a Kalman filter to obtain the predicted covariance matrix. Define the covariance matrix of the observation matrix and the measurement noise, and calculate the Kalman gain based on the predicted covariance matrix, the observation matrix, and the measurement noise covariance matrix.
[0011] In one implementation, the Kalman gain is calculated as follows: ,in, Let H be the Kalman gain, H be the observation matrix, and R be the covariance matrix of the measurement noise. This is the predicted covariance matrix.
[0012] In one implementation, the predicted covariance matrix is calculated as follows: Where Q is the covariance of the system process noise, and A is the state transition matrix. Let T be the covariance matrix of the previous time step, and T be the transpose of the matrix.
[0013] In one implementation, the method further includes: if the mean square error does not meet the threshold, adjusting the value of the forgetting factor in the recursive least squares method with a forgetting factor.
[0014] A second aspect of the present invention provides an electronic device, including a memory and a processor; A memory for storing computer programs, the computer programs including program instructions; A processor is configured to execute the program instructions to cause the electronic device to perform the steps of a method for optimizing real-time parameters of a molecular pump motor as provided in the first aspect of the invention.
[0015] A third aspect of the present invention provides a computer-readable storage medium comprising a computer program that, when executed by one or more processors, implements a method for optimizing real-time parameters of a molecular pump motor as provided in the first aspect of the present invention.
[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention employs a recursive least squares (RLS) algorithm with a forgetting factor, combined with Kalman filtering for noise reduction, to achieve parameter optimization with high real-time performance and strong anti-interference capabilities. This solves the problem of reduced control performance caused by parameter mismatch under dynamic operating conditions of molecular pump motors in existing parameter identification methods. Attached Figure Description
[0017] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart illustrating a method for optimizing real-time parameters of a molecular pump motor according to an embodiment of the present invention; Figure 2 This is a flowchart for optimizing real-time parameters of a molecular pump motor, provided as an embodiment of the present invention. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.
[0019] It should be noted that the terms "comprising" or "may include" used in the various embodiments of this application indicate the presence of the claimed function, operation, or element, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in the various embodiments of this application, the terms "comprising," "having," and their cognates are intended only to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or adding one or more combinations of the foregoing.
[0020] It should be understood that terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.
[0021] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for optimizing real-time parameters of a molecular pump motor according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes: S101, obtain the real-time parameters of the molecular pump motor.
[0022] In this embodiment, the measurement of basic real-time parameters of the molecular pump motor is common knowledge in the technical field. Therefore, this embodiment provides a detailed explanation of how to obtain the parameters.
[0023] It should be noted that the real-time parameters include resistance, d-axis inductance, q-axis inductance, angular velocity, and flux linkage.
[0024] S102, construct a mathematical model of the molecular pump motor based on real-time parameters; the mathematical model includes voltage equations for the d-axis and q-axis.
[0025] In this embodiment, the voltage equation along the d-axis is: ;in, , These represent the voltage and current along the d-axis, respectively. w The angular velocity of the molecular pump motor is... It is the d-axis inductance; The voltage equation along the q-axis is: ,in, For magnetic linkage, It is the q-axis inductance. , These represent the voltage and current along the q-axis, respectively.
[0026] S103 uses the recursive least squares method with a forgetting factor to solve the mathematical model, calculates the prediction error of the real-time parameters, and updates the parameter matrix based on the prediction error; the parameter matrix is composed of resistance, d-axis inductance, q-axis inductance and flux linkage.
[0027] In this embodiment, the expression for the recursive least squares method with a forgetting factor includes: ; ; ; ; in, Let cost function be Let be the parameter matrix at time k. Let be the parameter matrix at time k-1. The inverse of the covariance matrix. Here is the gain matrix. These are actual measured values. Forgetting factor, Let be the transformation matrix of the parameter vector. Forgetting factor, It is the identity matrix. Let be the previous value of the inverse of the covariance matrix. Let be a parameter vector consisting of resistance, d-axis inductance, q-axis inductance, and flux linkage, where k is the total time. Let i be the input vector at time i. Let be the input vector at time i. Let T be the forgetting factor, and T be the transpose of the matrix.
[0028] It should be noted that, Typically between 0 and 1, with values closer to 0 suitable for rapidly changing systems. Measured values include resistance, inductance, and flux linkage.
[0029] By analyzing the cost function Differentiate, introduce the recurrence relation, and update the parameter matrix. and gain matrix The parameter matrix, gain matrix, and covariance matrix are updated by calculating how much the predicted voltage error decreases.
[0030] S104. The updated parameter matrix is input into the pre-built Kalman filter to obtain the Kalman gain.
[0031] In this embodiment, the updated parameter matrix, covariance matrix, and observation matrix are input into the Kalman filter. First, state prediction and covariance matrix prediction are performed, and then the Kalman gain is calculated.
[0032] The process of state prediction is as follows: ; Here, A is the prediction parameter, B is the state transition matrix, and A is the control matrix. To control the input.
[0033] The process of predicting the covariance matrix is as follows: Q is the covariance of the system process noise, and A is the state transition matrix. Let T be the covariance matrix of the previous time step, and T be the transpose of the matrix.
[0034] The Kalman gain is calculated as follows: Define the observation matrix and the covariance matrix of the measurement noise, and calculate the Kalman gain based on the predicted covariance matrix, the observation matrix, and the covariance matrix of the measurement noise.
[0035] Specifically, the formula for calculating the Kalman gain is: ,in, Let H be the Kalman gain, H be the observation matrix, and R be the covariance matrix of the measurement noise. This is the predicted covariance matrix.
[0036] S105 updates the real-time parameters based on the Kalman gain, inputs the updated real-time parameters into the mathematical model, and calculates the predicted voltage values for the d-axis and q-axis.
[0037] In this embodiment, the real-time parameters are updated based on the Kalman gain, and the specific calculation method is as follows: ;in, H is the Kalman gain, and H is the observation matrix. These are measured values.
[0038] Then update the covariance matrix as follows: .
[0039] S106: Collect the actual voltage values of the d-axis and q-axis of the molecular pump motor, calculate the mean square error between the predicted voltage value and the actual voltage value, and if the mean square error meets the threshold, use the updated real-time parameters as the optimization result of the real-time parameters of the molecular pump motor.
[0040] In this embodiment, the calculation of the mean squared error (MSE) is common knowledge in the technical field, and will not be described in detail here. The threshold provided in this embodiment is 1× If the mean square error is less than 1× It records the parameters and forgetting factor of the Kalman filter at the current moment and outputs the updated real-time parameters for vector control of the molecular pump motor.
[0041] In some embodiments, the method further includes: if the mean square error does not meet the threshold, adjusting the value of the forgetting factor in the recursive least squares method with a forgetting factor.
[0042] In this embodiment, the mean square error (MSE) residual analysis threshold (1×) is used. The strategy of combining the dynamic forgetting factor λ adjustment can improve the convergence time of the parameters by 40% compared with the traditional RLS algorithm. Stator resistance (R) error <2%; d-axis and q-axis inductance error <3%; permanent magnet flux linkage (λm) error <1.5%.
[0043] like Figure 2 As shown, the process of optimizing parameters is given. This process can be referred to steps S101-S106 described in the above embodiment. This embodiment will not describe each process in detail.
[0044] A certain production line requires a vacuum environment with extremely high cleanliness requirements (ultimate pressure < 1×10⁻⁶). -9Under the conditions of Pa), traditional molecular pumps, due to their fixed parameters, experience large fluctuations in pumping speed. The optimization method provided in this embodiment optimizes the parameters by recursively updating the parameter matrix in real time with a forgetting factor (error < 2%), ensuring the control model always matches the actual motor state and achieving dynamic parameter adjustment. Secondly, during the acceleration phase with repeated start-stop cycles, the number of step-outs is reduced from 0.8 times / hour using traditional methods to 0.1 times / hour, improving stability by 87.5% and reducing the step-out rate. Finally, under the same nozzle diameter, the pumping speed increases from 450 L / s to 490 L / s (a 9% increase), and the time to reach the background vacuum is shortened by 13.3% (from 15 minutes to 13 minutes), further improving the pumping speed.
[0045] This invention also provides an electronic device. The electronic device includes a processor, a memory, a communication interface, and at least one communication bus for connecting the processor, the memory, and the communication interface. The memory includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (PROM), or portable read-only memory (CD-ROM), and is used for related instructions and data.
[0046] The communication interface is used to receive and send data. The processor can be one or more CPUs; if the processor is a single CPU, it can be a single-core CPU or a multi-core CPU. The processor in the electronic device reads one or more programs stored in the memory and performs the following operations: acquiring real-time parameters of the molecular pump motor; constructing a mathematical model of the molecular pump motor based on the real-time parameters; wherein the mathematical model includes voltage equations for the d-axis and q-axis; solving the mathematical model using a recursive least squares method with a forgetting factor, calculating the prediction error of the real-time parameters, and updating the parameter matrix based on the prediction error; wherein the parameter matrix consists of resistance, d-axis inductance, q-axis inductance, and flux linkage; inputting the updated parameter matrix into a pre-constructed Kalman filter to obtain the Kalman gain; updating the real-time parameters based on the Kalman gain, inputting the updated real-time parameters into the mathematical model, and calculating the predicted voltage values for the d-axis and q-axis; acquiring the actual voltage values of the molecular pump motor for the d-axis and q-axis, calculating the mean square error between the predicted voltage values and the actual voltage values, and if the mean square error meets a threshold, using the updated real-time parameters as the optimized result of the real-time parameters of the molecular pump motor.
[0047] It should be noted that the specific implementation of each operation can be described above. Figure 1 The corresponding description of the method embodiments shown indicates that the electronic device can be used to execute a method for optimizing real-time parameters of a molecular pump motor according to the above method embodiments of this application, which will not be described in detail here.
[0048] This invention also provides a computer-readable storage medium, which is a memory device in a computer device for storing programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for optimizing real-time parameters of a molecular pump motor in the above embodiments. Those skilled in the art should understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0049] This invention also provides a computer program product containing program instructions. The computer program product can be software or program products containing program instructions, capable of running on a computing device or stored on any available medium. When the computer program product runs on at least one electronic device, it causes the at least one electronic device to execute a method for optimizing real-time parameters of a molecular pump motor.
[0050] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for optimizing real-time parameters of a molecular pump motor, characterized in that, The methods include: Obtain real-time parameters of the molecular pump motor; A mathematical model of the molecular pump motor is constructed based on real-time parameters; wherein, the mathematical model includes voltage equations for the d-axis and q-axis; The mathematical model is solved using the recursive least squares method with a forgetting factor, the prediction error of the real-time parameters is calculated, and the parameter matrix is updated based on the prediction error; the parameter matrix is composed of resistance, d-axis inductance, q-axis inductance, and magnetic flux. The updated parameter matrix is input into a pre-built Kalman filter to obtain the Kalman gain; The real-time parameters are updated based on the Kalman gain, and the updated real-time parameters are input into the mathematical model to calculate the predicted voltage values on the d-axis and q-axis. The actual voltage values of the d-axis and q-axis of the molecular pump motor are collected, and the mean square error between the predicted voltage value and the actual voltage value is calculated. If the mean square error meets the threshold, the updated real-time parameters are used as the optimization result of the real-time parameters of the molecular pump motor.
2. The method for optimizing real-time parameters of a molecular pump motor according to claim 1, characterized in that, The real-time parameters include resistance, d-axis inductance, q-axis inductance, angular velocity, and flux linkage.
3. The method for optimizing real-time parameters of a molecular pump motor according to claim 1, characterized in that, The voltage equation along the d-axis is: ;in, , These represent the voltage and current along the d-axis, respectively. w The angular velocity of the molecular pump motor is... R is the d-axis inductance, and R is the resistance. The voltage equation along the q-axis is: ,in, For magnetic linkage, It is the q-axis inductance. , These represent the voltage and current along the q-axis, respectively.
4. The method for optimizing real-time parameters of a molecular pump motor according to claim 1, characterized in that, The expression for the recursive least squares method with a forgetting factor includes: ； ； ； ； in, Let cost function be Let be the parameter matrix at time k. Let be the parameter matrix at time k-1. It is the inverse of the covariance matrix. Here is the gain matrix. These are actual measured values. Forgetting factor, The transformation matrix of the parameter vector. Forgetting factor, It is the identity matrix. Let be the previous value of the inverse of the covariance matrix. Let be a parameter vector consisting of resistance, d-axis inductance, q-axis inductance, and flux linkage, where k is the total time. Let i be the input vector at time i. Let be the input vector at time i. Let T be the forgetting factor, and T be the transpose of the matrix.
5. The method for optimizing real-time parameters of a molecular pump motor according to claim 1, characterized in that, The updated parameter matrix is input into a pre-built Kalman filter to obtain the Kalman gain, including: The state prediction and covariance prediction are performed using a Kalman filter to obtain the predicted covariance matrix. Define the covariance matrix of the observation matrix and the measurement noise, and calculate the Kalman gain based on the predicted covariance matrix, the observation matrix, and the measurement noise covariance matrix.
6. The method for optimizing real-time parameters of a molecular pump motor according to claim 5, characterized in that, The formula for calculating Kalman gain is: ,in, Let H be the Kalman gain, H be the observation matrix, and R be the covariance matrix of the measurement noise. This is the predicted covariance matrix.
7. The method for optimizing real-time parameters of a molecular pump motor according to claim 6, characterized in that, The formula for calculating the predicted covariance matrix is: Where Q is the covariance of the system process noise, and A is the state transition matrix. Let T be the covariance matrix of the previous time step, and T be the transpose of the matrix.
8. The method for optimizing real-time parameters of a molecular pump motor according to claim 1, characterized in that, The method further includes: if the mean square error does not meet the threshold, adjusting the value of the forgetting factor in the recursive least squares method with forgetting factor.
9. An electronic device, characterized in that, Including memory and processor; A memory for storing computer programs, the computer programs including program instructions; A processor is configured to execute the program instructions to cause the electronic device to perform the steps of a method for optimizing real-time parameters of a molecular pump motor as described in any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program that, when executed by one or more processors, implements a method for optimizing real-time parameters of a molecular pump motor as described in any one of claims 1 to 8.