阅读说明：本技术 基于卡尔曼滤波器的轨道阀阀位测量系统及方法 (Kalman filter-based rail valve position measuring system and method ) 是由 王悦 倪娜 杨帆 刘伟 甄玉龙 王旭 陈涛 马玉林 张亮 于 2019-08-27 设计创作，主要内容包括：本申请公开了一种基于卡尔曼滤波器的轨道阀阀位测量系统及方法,包括：布置于轨道阀上的陀螺仪和霍尔传感器,还包括控制器,所述控制器通过数据采集器与陀螺仪和霍尔传感器相连接；所述数据采集器获取陀螺仪和霍尔传感器的测量数据,控制器构建卡尔曼滤波器,控制所述数据采集器对陀螺仪测量数据进行量测更新,得到更新后的一步预测第一状态向量与一步预测系统协方差量；根据预测系统协方差量获得更新的卡尔曼滤波器的增益矩阵；根据所述增益矩阵进一步更新当前第一状态向量与当前系统协方差量。本发明通过融合霍尔传感器与陀螺仪数据,解决了单一传感器测量精度较差的问题。(The application discloses track valve position measurement system and method based on Kalman filter, include: the gyroscope and the Hall sensor are arranged on the track valve, and the controller is connected with the gyroscope and the Hall sensor through a data collector; the data acquisition unit acquires measurement data of a gyroscope and a Hall sensor, the controller constructs a Kalman filter, and controls the data acquisition unit to measure and update the measurement data of the gyroscope to obtain updated one-step prediction first state vector and one-step prediction system covariance quantity; obtaining an updated gain matrix of the Kalman filter according to the prediction system covariance quantity; and further updating the current first state vector and the current system covariance amount according to the gain matrix. According to the invention, the problem of poor measurement accuracy of a single sensor is solved by fusing data of the Hall sensor and the gyroscope.)

1. A Kalman filter based rail valve position measurement system, comprising: the gyroscope and the Hall sensor are arranged on the track valve, and the controller is connected with the gyroscope and the Hall sensor through a data collector; wherein the content of the first and second substances,

The data acquisition unit acquires gyroscope measurement data comprising a gyroscope valve corner, a gyroscope angular velocity, a gyroscope constant value deviation and gyroscope measurement noise, and acquires Hall sensor measurement data comprising a Hall sensor valve corner and Hall sensor measurement noise;

the controller determines a next valve corner predicted value of the gyroscope according to the gyroscope measurement data; determining a valve corner measured value of the Hall sensor according to the Hall sensor valve corner and the Hall sensor measuring noise; constructing a Kalman filter by taking a gyroscope valve corner as a first state vector and taking a gyroscope constant deviation estimated by adopting a valve corner measured value of a Hall sensor as a second state vector;

the controller controls the data acquisition unit to measure and update the gyroscope measurement data based on a Kalman filter to obtain updated one-step prediction first state vector and one-step prediction system covariance quantity; obtaining an updated gain matrix of the Kalman filter according to the prediction system covariance quantity; and further updating the current first state vector and the current system covariance amount according to the gain matrix.

2. The Kalman filter based rail valve position measurement system of claim 1,

the method for determining the next gyroscope valve rotation angle estimated value specifically comprises the following steps:

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

the method comprises the following steps that a next gyroscope valve corner predicted value theta (k +1), theta (k) is a current gyroscope valve corner, omega (k) is a current gyroscope angular velocity, b (k) is a current gyroscope constant value deviation, w (k) is current gyroscope measurement noise, dt is a measurement period, k represents the current kth measurement, and k +1 represents the current kth measurement;

The method for determining the valve rotation angle measurement value of the Hall sensor specifically comprises the following steps:

z(k)＝θ(k)+v(k)

and k represents the current k-th measurement, z (k) is the valve rotation angle measurement value of the Hall sensor after error correction, theta (k) is the detected true valve rotation angle of the Hall sensor, and v (k) is the measurement noise of the Hall sensor.

3. the kalman filter-based orbit valve position measurement system according to claim 1, wherein the gyro constant deviation b (k) is estimated by the following formula when using the valve angle measurement z (k) of the hall sensor to θ (k + 1):

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

z(k)＝θ(k)+v(k)。

4. the kalman filter-based orbit valve position measurement system of claim 1 or 2, wherein the kalman filter is:

Wherein the system state matrix is:

The system measurement vector is: h ═ 10

the state vector matrix is:

k denotes the kth measurement, k-1 denotes the kth measurement,

U (k-1) is the angular velocity of the gyroscope at the previous time, X (k-1) is the rotation angle of the gyroscope at the previous time, Z (k) is the rotation angle measured by the Hall sensor, W (k) is the noise measured by the gyroscope, V (k) is the noise measured by the Hall sensor, and T is the system sampling period.

5. a method for measuring a valve position of a track valve based on a Kalman filter is characterized by comprising the following steps:

obtaining gyroscope measurement data comprising a gyroscope valve corner, a gyroscope angular velocity, a gyroscope constant deviation and gyroscope measurement noise according to detection calculation, and determining a next valve corner estimated value of the gyroscope; determining a valve corner measured value of the Hall sensor according to the Hall sensor valve corner obtained by detection and calculation and the Hall sensor measurement noise; constructing a Kalman filter by taking a gyroscope valve corner as a first state vector and taking a gyroscope constant deviation estimated by adopting a valve corner measured value of a Hall sensor as a second state vector;

Measuring and updating gyroscope measurement data based on a Kalman filter to obtain an updated one-step prediction first state vector and one-step prediction system covariance quantity; obtaining an updated gain matrix of the Kalman filter according to the prediction system covariance quantity; and further updating the current first state vector and the current system covariance amount according to the gain matrix.

6. the Kalman filter-based rail valve position measurement method according to claim 5, characterized in that the way of determining the next gyroscope valve corner estimated value is specifically:

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

The method comprises the steps of obtaining a current gyroscope valve rotation angle through a gyroscope, wherein the next gyroscope valve rotation angle estimated value of theta (k +1), theta (k) is the current gyroscope valve rotation angle, omega (k) is the current gyroscope angular speed, b (k) is the current gyroscope constant value deviation, w (k) is the current gyroscope measurement noise, dt is the measurement period, k represents the current kth measurement, and k +1 represents the current kth measurement.

7. the Kalman filter-based rail valve position measurement method according to claim 5, characterized in that the manner of determining the valve rotation angle measurement value of the Hall sensor is specifically as follows:

z(k)＝θ(k)+v(k)

and k represents the current k-th measurement, z (k) is the valve rotation angle measurement value of the Hall sensor after error correction, theta (k) is the detected true valve rotation angle of the Hall sensor, and v (k) is the measurement noise of the Hall sensor.

8. the Kalman filter based rail valve position measurement method of claim 5,

The method for determining the next gyroscope valve rotation angle estimated value specifically comprises the following steps:

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

The method comprises the following steps that a next gyroscope valve corner predicted value theta (k +1), theta (k) is a current gyroscope valve corner, omega (k) is a current gyroscope angular velocity, b (k) is a current gyroscope constant value deviation, w (k) is current gyroscope measurement noise, dt is a measurement period, k represents the current kth measurement, and k +1 represents the current kth measurement;

the method for determining the valve rotation angle measurement value of the Hall sensor specifically comprises the following steps:

z(k)＝θ(k)+v(k)

and k represents the current k-th measurement, z (k) is the valve rotation angle measurement value of the Hall sensor after error correction, theta (k) is the detected true valve rotation angle of the Hall sensor, and v (k) is the measurement noise of the Hall sensor.

9. the kalman filter-based orbit valve position measurement method according to claim 8, wherein the gyro constant deviation b (k) is estimated by the following formula when using the valve angle measurement value z (k) of the hall sensor to θ (k + 1):

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

z(k)＝θ(k)+v(k)。

10. The Kalman filter based rail valve position measurement method according to claim 5 or 8, characterized in that the Kalman filter is:

wherein the system state matrix is:

the system measurement vector is: h ═ 10

the state vector matrix is:

k denotes the kth measurement, k-1 denotes the kth measurement,

U (k-1) is the angular velocity of the gyroscope at the previous time, X (k-1) is the rotation angle of the gyroscope at the previous time, Z (k) is the rotation angle measured by the Hall sensor, W (k) is the noise measured by the gyroscope, V (k) is the noise measured by the Hall sensor, and T is the system sampling period.

Technical Field

the application relates to a valve position detection technology in the field of valves, in particular to a system and a method for measuring a valve position of a track valve based on a Kalman filter.

background

the track valve is used as a ball valve with a single valve seat and bidirectional sealing, integrates the advantages of a gate valve, a ball valve, a stop valve and a plug valve, and is widely applied to gas pipelines. Orbit valves are typically configured with a feedback valve position feedback. The feedback device can realize remote feedback of the valve opening and closing state by matching with a field PLC (programmable logic circuit) system, and provides corresponding valve position signal output for a monitoring system for managing the valve. However, the integrated feedback device is limited by the structure of the existing track valve, is mainly used for observation of field operators, is limited in sampling precision when being directly combined with a PLC system, and causes adverse effects on the assembly and disassembly of auxiliary facilities on a pipeline due to the fact that cable laying between the field PLC system and a sensor is interfered by operation of a control hand wheel.

the non-contact sensor is adopted to collect the rotation amount of the valve, and the current universal sensors comprise a gyroscope, a Hall sensor and the like. The bidirectional Hall sensor can count ferromagnetic objects placed at intervals and can also distinguish increase and decrease directions. And a rack-shaped disc is arranged on a hand wheel of the track valve, so that the rotation amount of the valve can be acquired. However, during the rotation of the valve, it is difficult to avoid the vibration interference perpendicular to the rotation surface, which causes the counting error of the hall sensor. The gyroscope has various errors such as temperature drift, time drift, random interference and the like, so that the gyroscope is difficult to be independently applied to a rotation measurement system. Therefore, how to realize efficient and accurate valve position measurement of the orbit valve becomes an important technical problem facing currently.

disclosure of Invention

the embodiment of the application provides a system and a method for measuring the valve position of a track valve based on a Kalman filter, and solves the technical problem of efficient and accurate measurement of the valve position of the track valve.

the technical scheme of this application provides a track valve position measurement system based on kalman filter, includes: the gyroscope and the Hall sensor are arranged on the track valve, and the controller is connected with the gyroscope and the Hall sensor through a data collector; wherein the content of the first and second substances,

the data acquisition unit acquires gyroscope measurement data comprising a gyroscope valve corner, a gyroscope angular velocity, a gyroscope constant value deviation and gyroscope measurement noise, and acquires Hall sensor measurement data comprising a Hall sensor valve corner and Hall sensor measurement noise; the controller determines the next valve corner estimated value of the gyroscope according to the gyroscope measurement data, and the specific mode is as follows:

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

The method comprises the following steps that a next gyroscope valve corner predicted value theta (k +1), theta (k) is a current gyroscope valve corner, omega (k) is a current gyroscope angular velocity, b (k) is a current gyroscope constant value deviation, w (k) is current gyroscope measurement noise, dt is a measurement period, k represents the current kth measurement, and k +1 represents the current kth measurement;

According to the valve corner of the Hall sensor and the measurement noise of the Hall sensor, the valve corner measurement value of the Hall sensor is determined, and the specific mode is as follows:

z(k)＝θ(k)+v(k)

wherein k represents the current k-th measurement, z (k) is a valve corner measurement value of the Hall sensor after error correction, theta (k) is a detected true valve corner of the Hall sensor, and v (k) is measurement noise of the Hall sensor;

The method comprises the following steps of taking a gyroscope valve corner as a first state vector, taking a gyroscope constant value deviation obtained by estimation of a valve corner measured value of a Hall sensor as a second state vector, and constructing a Kalman filter, wherein the Kalman filter is as follows:

Wherein the system state matrix is:

The system measurement vector is: h ═ 10

the state vector matrix is:

k denotes the kth measurement, k-1 denotes the kth measurement,

U (k-1) is angular velocity of the gyroscope at the previous time, and X (k-1) is the rotation angle of the gyroscope at the previous time

Z (k) is a rotation angle measured by the Hall sensor, W (k) is noise measured by the gyroscope, V (k) is noise measured by the Hall sensor, and T is a system sampling period.

the gyro constant deviation b (k) can be estimated by the following formula when the valve angle measurement value z (k) of the hall sensor is θ (k + 1):

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

z(k)＝θ(k)+v(k)。

The controller controls the data acquisition unit to measure and update the gyroscope measurement data based on a Kalman filter to obtain updated one-step prediction first state vector and one-step prediction system covariance quantity; obtaining an updated gain matrix of the Kalman filter according to the prediction system covariance quantity; and further updating the current first state vector and the current system covariance amount according to the gain matrix.

based on the above-mentioned orbit valve position measurement system, the technical scheme of this application still provides an orbit valve position measurement method based on kalman filter, includes:

obtaining gyroscope measurement data comprising a gyroscope valve corner, a gyroscope angular velocity, a gyroscope constant deviation and gyroscope measurement noise according to detection calculation, and determining a next valve corner estimated value of the gyroscope; determining a valve corner measured value of the Hall sensor according to the Hall sensor valve corner obtained by detection and calculation and the Hall sensor measurement noise; constructing a Kalman filter by taking a gyroscope valve corner as a first state vector and taking a gyroscope constant deviation estimated by adopting a valve corner measured value of a Hall sensor as a second state vector;

measuring and updating gyroscope measurement data based on a Kalman filter to obtain an updated one-step prediction first state vector and one-step prediction system covariance quantity; obtaining an updated gain matrix of the Kalman filter according to the prediction system covariance quantity; and further updating the current first state vector and the current system covariance amount according to the gain matrix.

the method for determining the next gyroscope valve rotation angle estimated value specifically comprises the following steps:

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

the method comprises the steps of obtaining a current gyroscope valve rotation angle through a gyroscope, wherein the next gyroscope valve rotation angle estimated value of theta (k +1), theta (k) is the current gyroscope valve rotation angle, omega (k) is the current gyroscope angular speed, b (k) is the current gyroscope constant value deviation, w (k) is the current gyroscope measurement noise, dt is the measurement period, k represents the current kth measurement, and k +1 represents the current kth measurement.

The method for determining the valve rotation angle measurement value of the Hall sensor specifically comprises the following steps:

z(k)＝θ(k)+v(k)

And k represents the current k-th measurement, z (k) is the valve rotation angle measurement value of the Hall sensor after error correction, theta (k) is the detected true valve rotation angle of the Hall sensor, and v (k) is the measurement noise of the Hall sensor.

The Kalman filter is as follows:

Wherein the system state matrix is:

the system measurement vector is: h ═ 10

The state vector matrix is:

k denotes the kth measurement, k-1 denotes the kth measurement,

u (k-1) is angular velocity of the gyroscope at the previous time, and X (k-1) is the rotation angle of the gyroscope at the previous time

Z (k) is a rotation angle measured by the Hall sensor, W (k) is noise measured by the gyroscope, V (k) is noise measured by the Hall sensor, and T is a system sampling period.

The gyro constant deviation b (k) can be estimated by the following formula when the valve angle measurement value z (k) of the hall sensor is θ (k + 1):

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

z(k)＝θ(k)+v(k)。

The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:

the invention uses a multi-sensor fusion filtering method to fuse data of a Hall sensor and a gyroscope, inhibits noise interference, solves the problem of poor measurement accuracy of a single sensor, and particularly reduces the influence of vibration interference perpendicular to a rotating surface on rotating angle calculation by establishing a sensor error model to compensate random drift error and using an adaptive measurement noise matrix. According to the technical scheme, the valve position measuring system with low cost and high precision is realized, and a high-efficiency and accurate valve position measuring method is developed.

drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1 is a schematic diagram of a Kalman filter based rail valve position measurement system according to the present application;

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

The kalman filter algorithm is an "optimized autoregressive data processing algorithm (optimal regression algorithm)" which has been widely used for over 30 years, and the application fields include robot navigation, control, sensor data fusion, radar systems, missile tracking, and the like. More recently, computer image processing has become more useful, such as for example, head and face recognition, image segmentation, image edge detection, and so forth. The Kalman Filter (The Kalman Filter) introduces a system of discrete control processes. The system can be described by a Linear Stochastic differential equation (Linear Stochastic differential) plus the system's measurements:

the linear equation: x (k) ═ A X (k-1) + B U (k) + W (k)

measurement values: z (k) ═ H X (k) + V (k)

in the above two formulas, the kalman filter assumes that the true state at time k is evolved from the state at time (k-1), X (k) is the system state at time k, and U (k) is the control quantity of the system at time k. A and B are system parameters, and for multi-model systems, they are matrices. Z (k) is the measured value at time k, H is a parameter of the measurement system, and H is a matrix for a multi-measurement system. W (k) and V (k) represent process and measurement noise, respectively. The operation of the kalman filter comprises two phases: and (4) predicting and updating. In the prediction phase, the filter uses the estimate of the last state to make an estimate of the current state. In the update phase, the filter optimizes the predicted value obtained in the prediction phase using the observed value for the current state to obtain a more accurate new estimated value.

Aiming at the problem that the valve position of the rail valve cannot be efficiently and accurately measured due to the fact that a single sensor is poor in measurement accuracy in the prior art, the technical scheme of the application acquires detection data of a gyroscope and a Hall sensor, a Kalman filter is constructed, and data fusion is performed based on the Kalman filter, so that efficient and accurate valve position measurement of the rail valve is achieved.

The technical scheme of this application provides a track valve position measurement system based on kalman filter, as shown in fig. 1, includes: the gyroscope and the Hall sensor are arranged on the track valve, and the controller is connected with the gyroscope and the Hall sensor through a data collector; wherein the content of the first and second substances,

The data acquisition unit acquires gyroscope measurement data comprising a gyroscope valve corner, a gyroscope angular velocity, a gyroscope constant value deviation and gyroscope measurement noise, and acquires Hall sensor measurement data comprising a Hall sensor valve corner and Hall sensor measurement noise; the controller determines the next valve corner estimated value of the gyroscope according to the gyroscope measurement data, and the specific mode is as follows:

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

the method comprises the following steps that a next gyroscope valve corner predicted value theta (k +1), theta (k) is a current gyroscope valve corner, omega (k) is a current gyroscope angular velocity, b (k) is a current gyroscope constant value deviation, w (k) is current gyroscope measurement noise, dt is a measurement period, k represents the current kth measurement, and k +1 represents the current kth measurement;

According to the valve corner of the Hall sensor and the measurement noise of the Hall sensor, the valve corner measurement value of the Hall sensor is determined, and the specific mode is as follows:

z(k)＝θ(k)+v(k)

wherein k represents the current k-th measurement, z (k) is a valve corner measurement value of the Hall sensor after error correction, theta (k) is a detected true valve corner of the Hall sensor, and v (k) is measurement noise of the Hall sensor;

the method comprises the following steps of taking a gyroscope valve corner as a first state vector, taking a gyroscope constant value deviation obtained by estimation of a valve corner measured value of a Hall sensor as a second state vector, and constructing a Kalman filter, wherein the Kalman filter is as follows:

Wherein the system state matrix is:

The system measurement vector is: h ═ 10

The state vector matrix is:

k denotes the kth measurement, k-1 denotes the kth measurement,

U (k-1) is angular velocity of the gyroscope at the previous time, and X (k-1) is the rotation angle of the gyroscope at the previous time

z (k) is a rotation angle measured by the Hall sensor, W (k) is noise measured by the gyroscope, V (k) is noise measured by the Hall sensor, and T is a system sampling period.

The gyro constant deviation b (k) can be estimated by the following formula when the valve angle measurement value z (k) of the hall sensor is θ (k + 1):

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

z(k)＝θ(k)+v(k)。

The controller controls the data acquisition unit to measure and update the gyroscope measurement data based on a Kalman filter to obtain updated one-step prediction first state vector and one-step prediction system covariance quantity; obtaining an updated gain matrix of the Kalman filter according to the prediction system covariance quantity; and further updating the current first state vector and the current system covariance amount according to the gain matrix.

Based on the above system for measuring the valve position of the rail valve, a method for measuring the valve position of the rail valve based on a kalman filter can be provided, which comprises:

Obtaining gyroscope measurement data comprising a gyroscope valve corner, a gyroscope angular velocity, a gyroscope constant deviation and gyroscope measurement noise according to detection calculation, and determining a next valve corner estimated value of the gyroscope; determining a valve corner measured value of the Hall sensor according to the Hall sensor valve corner obtained by detection and calculation and the Hall sensor measurement noise; constructing a Kalman filter by taking a gyroscope valve corner as a first state vector and taking a gyroscope constant deviation estimated by adopting a valve corner measured value of a Hall sensor as a second state vector;

Measuring and updating gyroscope measurement data based on a Kalman filter to obtain an updated one-step prediction first state vector and one-step prediction system covariance quantity; obtaining an updated gain matrix of the Kalman filter according to the prediction system covariance quantity; and further updating the current first state vector and the current system covariance amount according to the gain matrix.

The method for determining the next gyroscope valve rotation angle estimated value specifically comprises the following steps:

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

The method comprises the steps of obtaining a current gyroscope valve rotation angle through a gyroscope, wherein the next gyroscope valve rotation angle estimated value of theta (k +1), theta (k) is the current gyroscope valve rotation angle, omega (k) is the current gyroscope angular speed, b (k) is the current gyroscope constant value deviation, w (k) is the current gyroscope measurement noise, dt is the measurement period, k represents the current kth measurement, and k +1 represents the current kth measurement.

the method for determining the valve rotation angle measurement value of the Hall sensor specifically comprises the following steps:

z(k)＝θ(k)+v(k)

and k represents the current k-th measurement, z (k) is the valve rotation angle measurement value of the Hall sensor after error correction, theta (k) is the detected true valve rotation angle of the Hall sensor, and v (k) is the measurement noise of the Hall sensor.

the Kalman filter is as follows:

wherein the system state matrix is:

The system measurement vector is: h ═ 10

the state vector matrix is:

k denotes the kth measurement, k-1 denotes the kth measurement,

u (k-1) is angular velocity of the gyroscope at the previous time, and X (k-1) is the rotation angle of the gyroscope at the previous time

z (k) is a rotation angle measured by the Hall sensor, W (k) is noise measured by the gyroscope, V (k) is noise measured by the Hall sensor, and T is a system sampling period.

The gyro constant deviation b (k) can be estimated by the following formula when the valve angle measurement value z (k) of the hall sensor is θ (k + 1):

θ(k+1)＝θ(k)+(ω(k)-b(k)+w(k))dt

z(k)＝θ(k)+v(k)。

the valve position measuring system and the valve position measuring method can be suitable for resolving valve positions of various manual and electric valves and are also suitable for valves rotating for multiple circles. The track valve is a manual and parallel multi-circle rotary valve, a rack-shaped disc is fixedly arranged on a hand wheel of the track valve, and a Hall type rotary sensing sensor, an MEMS inertial sensing sensor and an MEMS gyroscope are adopted. Different sensors have different interference and error characteristics, so that firstly, an error model of the sensor and a resolving model of related physical quantity are determined, and data fusion is carried out on the sampled measured data of the gyroscope and the Hall sensor by utilizing Kalman filtering to obtain rotation angle information (valve position) of the orbit valve.