Inductive sensor linearity improving method and system based on Kalman filtering

Abstract

The invention discloses an inductive sensor linearity improving method and system based on Kalman filtering, belongs to the field of inductive sensors, and solves the problems that a traditional inductive sensor filtering method is limited in processing effect on complex conditions such as environmental noise and device drifting and is difficult to meet application requirements of high precision and high reliability. The method comprises the steps of collecting a state quantity and a measurement quantity of an inductive sensor, and determining an observation equation and a measurement equation of the inductive sensor; adopting a Kalman filtering method to calculate and obtain a Kalman gain; and according to the observation equation, the measurement equation and Kalman gain calculation, an optimal estimation value is obtained, and the linearity of the inductive sensor is improved. The method is suitable for an inductive sensor optimization design and manufacturing scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of inductance sensors, and in particular to technologies related to inductance sensor data filtering and linearity improvement. BACKGROUND

[0002] Inductance sensors work based on the variable-reluctance electromagnetic principle. When the coil is driven by an alternating signal, an alternating magnetic field is generated inside the coil. When the magnetic core moves, the magnetic field distribution changes accordingly, thereby generating a voltage amplitude in the circuit that is approximately proportional to the displacement of the magnetic core. The common measurement circuit uses a transformer AC bridge, and the output voltage reflects the displacement change of the magnetic core. In theory, the output voltage and displacement should maintain a strict linear relationship, but in actual applications, the output curve of the inductance sensor will show obvious nonlinear deviation due to factors such as environmental noise, electromagnetic interference, and device drift. The increase in this nonlinearity leads to a decrease in the measurement accuracy and long-term stability of the sensor.

[0003] Existing filtering techniques (such as moving average, median filtering, etc.) mainly perform smoothing processing on random noise, but cannot effectively deal with complex nonlinear error sources, especially in dynamic environments or complex electromagnetic field conditions, and the processing effect is limited. At the same time, some research has attempted to use Kalman filtering to optimize sensor data, but these methods are mostly used in multi-sensor information fusion or fault detection scenarios, such as the existing "Fault detection method for Kalman filter sensor information fusion". However, this type of method does not address the problem of inductance sensor linearity improvement.

[0004] It should be emphasized that the effect of Kalman filtering in different application scenarios depends on the accurate modeling of the system state equation and the observation equation. For inductance sensors, the output voltage as an observation reflects the displacement effect of the magnetic core. Therefore, the system state variable should be defined as the position and velocity of the magnetic core, and the observation as the position of the magnetic core. How to construct an adaptive state space model on this basis and derive the Kalman gain and optimal estimate is a key difficulty in effectively applying Kalman filtering to inductance sensor linearity improvement. SUMMARY

[0005] The present application provides a Kalman filter-based inductance sensor linearity improvement method and system, which aims to solve the problem that traditional inductance sensor filtering methods have limited processing effect on complex situations such as environmental noise and device drift, making it difficult to meet the application requirements of high precision and high reliability.

[0006] The Kalman filter-based inductance sensor linearity improvement method proposed by the present application includes: Collecting the state variables and measurement variables of the inductance sensor, determining the observation equation and measurement equation of the inductance sensor; The Kalman gain is calculated by using Kalman filtering method; The optimal estimation value is calculated according to the observation equation, the measurement equation and the Kalman gain, and the linearity of the inductance sensor is improved.

[0007] Further, the preferred scheme is provided: the state quantity of the inductance sensor is the position and speed of the magnetic core; and the measurement quantity of the inductance sensor is the position of the magnetic core.

[0008] Further, the preferred scheme is provided: the position of the magnetic core is determined by the output voltage, and is expressed as: , wherein, represents the displacement change amount of the magnetic core, represents the output voltage, represents the alternating voltage amplitude of the driving coil, represents the initial length of the magnetic core, represents the total length of the coil, represents the radius of the coil, represents the radius of the magnetic core, represents the relative permeability of the magnetic core.

[0009] Further, the preferred scheme is provided: the observation equation is expressed as: , wherein, represents the state of the system at time, represents the state transition matrix, represents the state of the system at k-1 time, represents the control matrix, represents the control input quantity at k-1 time, represents the process noise.

[0010] Further, the preferred scheme is provided: the measurement equation is expressed as: , wherein, represents the observation value at k time, represents the measurement matrix, represents the measurement noise.

[0011] Further, the preferred scheme is provided: the Kalman gain is expressed as: , wherein, represents the prior error covariance matrix, represents the transpose of the measurement matrix H, represents the covariance matrix of the measurement noise.

[0012] Further, the preferred scheme is provided: the optimal estimate value is represented as: .

[0013] The inductance sensor linearity improvement system based on Kalman filtering provided by the application is realized based on the Kalman filtering-based inductance sensor linearity improvement method according to any one or more of the above-mentioned scheme combinations, and the system comprises: A parameter acquisition module is configured to acquire state variables and measurement variables of the inductance sensor, and determine an observation equation and a measurement equation of the inductance sensor. A gain calculation module is configured to calculate a Kalman gain by using the Kalman filtering method. An optimal calculation module is configured to calculate an optimal estimate value according to the Kalman gain, so as to realize inductance sensor linearity improvement.

[0014] The application further provides a computer device, which comprises a memory and a processor, and the memory stores a computer program; when the processor runs the computer program stored in the memory, the processor executes the Kalman filtering-based inductance sensor linearity improvement method according to any one or more of the above-mentioned scheme combinations.

[0015] The application further provides a computer readable storage medium, which is used for storing a computer program, and the computer program executes the Kalman filtering-based inductance sensor linearity improvement method according to any one or more of the above-mentioned scheme combinations.

[0016] Compared with the prior art, the application has the following advantages: The application provides a Kalman filtering-based inductance sensor linearity improvement method. By introducing the Kalman filtering algorithm and combining the actual characteristics of the inductance sensor, the data collected by the sensor can be filtered and optimized in real time, so that the linearity of the inductance sensor is significantly improved, the influence of noise and drift is reduced, and the data output by the sensor is more stable and accurate.

[0017] Based on the output voltage of the inductance sensor measurement circuit, the application innovatively establishes a system state space equation taking the position and speed of the magnetic core as state variables and the position as an observation variable, and combines the recursive estimation process of the Kalman filter to compensate the influence of noise and drift on the output signal in real time. Through this method, the nonlinearity of the inductance sensor is significantly reduced, the accuracy and long-term stability of the measurement result are obviously improved, and excellent results are achieved in complex electromagnetic environments and high real-time requirement scenarios. This technical scheme not only breaks through the modeling difficulty limitation of Kalman filtering in different application scenarios, but also provides a new solution for high-precision applications of inductance sensors.

[0018] Specifically, the present application adopts a Kalman filter-based method to effectively reduce the nonlinearity of inductance sensor data. Compared with traditional filtering methods, the linearity of the present application is significantly improved, thereby improving the accuracy and reliability of the measurement results. The present application performs well in handling environmental noise. By adjusting the filtering parameters in real time, the present application can effectively suppress the interference of noise on inductance sensor data, making the system more resistant to noise, especially in complex electromagnetic environments. The drift of inductance sensors often affects the long-term stability of measurement results. The present application uses the Kalman filter algorithm to compensate and correct the drift of the sensor in real time, significantly reducing the influence of drift on data and improving the long-term stability of the sensor. The present application has superior real-time and adaptability, and can quickly respond and adjust the filtering parameters in a dynamic environment, ensuring that the sensor system can maintain efficient and stable working state under different working conditions.

[0019] The present application is suitable for the optimization design and manufacturing scene of inductance sensors. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The flowchart of the Kalman filter-based inductance sensor linearity improvement method according to the specific embodiment I of the present application; Figure 2 The comparison chart before and after filtering according to the specific embodiment I of the present application, wherein the horizontal coordinate is the actual displacement measured by the standard device, and the vertical coordinate is the bridge output voltage value. DETAILED DESCRIPTION

[0021] In the following description, specific details are set forth such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art should understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary details.

[0022] The technical solutions in the embodiments of the present application are described in detail below with reference to the drawings in the present application specification. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor fall within the scope of protection of the present application.

[0023] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details. In other instances, well-known methods have not been described in detail in order not to unnecessarily obscure aspects of the present application.

[0024] Embodiment one: The Kalman filter-based inductance sensor linearity improvement method comprises: Collecting state variables and measurement variables of the inductance sensor, determining an observation equation and a measurement equation of the inductance sensor; Calculating the Kalman gain by using the Kalman filter method; Calculating the optimal estimation value according to the observation equation, the measurement equation and the Kalman gain to realize the inductance sensor linearity improvement.

[0025] Specifically: The inductance sensor works according to the variable reluctance electromagnetic principle. When the coil is driven by an alternating signal, a magnetic field is generated inside the coil. When the magnetic core inside the coil moves, the magnetic field distribution inside the coil changes to generate a voltage amplitude proportional to the displacement of the magnetic core. The measurement circuit uses a transformer AC bridge, and the output voltage of the bridge is represented as: , (1) Wherein, represents the displacement change of the magnetic core, represents the output voltage, represents the amplitude of the alternating voltage driving the coil, represents the initial length of the magnetic core, represents the total length of the coil, represents the radius of the coil, represents the radius of the magnetic core, represents the relative permeability of the magnetic core.

[0026] The output voltage is proportional to the displacement change of the magnetic core , and the output voltage value reflects the displacement effect of the magnetic core.

[0027] In this embodiment, the state variable is the position and speed of the magnetic core, which is represented as: , (2) The measurement variable is the position of the magnetic core, which is represented as: , (3) Therefore, the observation equation is: , (4) Wherein, represents the system in The state at any given moment, Represents the state transition matrix. This represents the state of the system at time k-1. Represents the control matrix. This represents the control input at time k-1. This indicates process noise.

[0028] The measurement equation is: (5) in, Represents the observation value at time k. Represents the measurement matrix. This indicates measurement noise.

[0029] Obtained from the bridge output voltage Middle position: (6) Then calculate the Kalman gain: (7) in, Denotes the prior error covariance matrix. This represents the transpose of the measurement matrix H. This represents the covariance matrix of the measurement noise.

[0030] The optimal estimate is obtained based on the Kalman gain calculation, and is expressed as: (8) The method proposed in this embodiment achieves an optimal balance between noise suppression and state tracking by adjusting the Kalman gain in real time. Figure 2 As shown, Figure 2 In this diagram, `disp` represents the original data line, and `filter disp` represents the data line processed using the method described in this embodiment. It is evident that the original data line demonstrates the severe defects of traditional inductive sensors under noise interference. However, the data line processed by Kalman filtering experiences a sharp reduction in noise peak-to-peak value and a significant improvement in signal-to-noise ratio. Furthermore, the linearity of the inductive sensor processed using the method described in this embodiment is significantly improved.

[0031] Implementation Method Two: A linearity improvement system for inductor sensors based on Kalman filtering is provided. The system is implemented using the linearity improvement method for inductor sensors based on Kalman filtering as described in Embodiment 1. The system includes: Parameter acquisition module: used to acquire the state and measured quantities of the inductive sensor, and to determine the observation equation and measurement equation of the inductive sensor; The gain calculation module is configured to calculate the Kalman gain by using the Kalman filtering method. The optimal calculation module is configured to calculate the optimal estimation value according to the observation equation, the measurement equation and the Kalman gain, and realize the inductance sensor linearity improvement.

Claims

1. A Kalman filter based inductance sensor linearity improvement method, characterized in that, The method comprises: collecting state quantity and measurement quantity of the inductance sensor, determining observation equation and measurement equation of the inductance sensor; calculating Kalman gain by using Kalman filtering method; calculating optimal estimation value according to the observation equation, the measurement equation and the Kalman gain, and realizing inductance sensor linearity improvement.

2. The Kalman filter based inductance sensor linearity improvement method according to claim 1, characterized in that, The state quantity of the inductance sensor is the position and speed of the magnetic core; the measurement quantity of the inductance sensor is the position of the magnetic core.

3. The Kalman filter based inductance sensor linearity improvement method according to claim 2, characterized in that, The position of the magnetic core is determined by the output voltage and is expressed as: ， wherein, represents the magnetic core displacement change amount, represents the output voltage, represents the alternating voltage amplitude of the drive coil, represents the initial length of the magnetic core, represents the total length of the coil, represents the radius of the coil, represents the radius of the magnetic core, represents the relative permeability of the magnetic core.

4. The Kalman filter based inductance sensor linearity improvement method of claim 1, wherein, The observation equation is expressed as: , wherein represents the state of the system at time instant, represents the state transition matrix, represents the state of the system at k -1 time instant, represents the control matrix, represents k the control input at -1 time instant, represents the process noise.

5. The Kalman filter based inductance sensor linearity improvement method of claim 1, wherein, The measurement equation is expressed as: ， wherein denotes k an observation at time instant denotes a measurement matrix, denotes measurement noise.

6. The Kalman filter based inductance sensor linearity improvement method of claim 1, wherein, The Kalman gain is expressed as: ， wherein represents a priori error covariance matrix, represents a measurement matrix H the transpose of, represents a covariance matrix of measurement noise.

7. The Kalman filter based inductance sensor linearity improvement method of claim 1, wherein, The optimal estimation value is expressed as: 。 8. A Kalman filter based inductance sensor linearity enhancement system, characterized in that, The system is realized based on the Kalman filtering-based inductance sensor linearity improvement method according to any one of claims 1-7, and the system comprises: a parameter collection module for collecting state quantity and measurement quantity of the inductance sensor, determining observation equation and measurement equation of the inductance sensor; a gain calculation module for calculating Kalman gain by using Kalman filtering method; an optimal calculation module for calculating optimal estimation value according to the observation equation, the measurement equation and the Kalman gain, and realizing inductance sensor linearity improvement.

9. A computer device, comprising: The computer device comprises a memory and a processor, the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the Kalman filtering-based inductance sensor linearity improvement method according to any one of claims 1-7.

10. A computer readable storage medium characterized by, The computer readable storage medium is used for storing a computer program, and the computer program executes the Kalman filtering-based inductance sensor linearity improvement method according to any one of claims 1-7.

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