CN110987449B - Electronic throttle opening estimation method and system based on Kalman filtering - Google Patents

Electronic throttle opening estimation method and system based on Kalman filtering Download PDF

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CN110987449B
CN110987449B CN201911279783.0A CN201911279783A CN110987449B CN 110987449 B CN110987449 B CN 110987449B CN 201911279783 A CN201911279783 A CN 201911279783A CN 110987449 B CN110987449 B CN 110987449B
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electronic throttle
kalman
opening
time
throttle valve
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CN110987449A (en
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孙波
刘一鸣
杨君
张承慧
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Shandong University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M15/00—Testing of engines
    • G01M15/04—Testing internal-combustion engines
    • G01M15/042—Testing internal-combustion engines by monitoring a single specific parameter not covered by groups G01M15/06 - G01M15/12
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations

Abstract

The invention provides an electronic throttle opening estimation method and system based on similar Kalman filtering, wherein electronic throttle opening data acquired by an electronic throttle position sensor is acquired, the filtered electronic throttle opening is obtained through the electronic throttle opening data measured by the position sensor through a similar Kalman estimation algorithm, and the filtered electronic throttle opening is fed back to an on-board controller for further control of the electronic throttle opening according to the filtered electronic throttle opening; the output signal of the electronic throttle position sensor is filtered by adopting a Kalman-like estimation algorithm, so that the interference of non-Gaussian white noise on a measurement result is reduced, and the estimation strategy has high estimation precision and strong robustness.

Description

Electronic throttle opening estimation method and system based on Kalman filtering
Technical Field
The disclosure relates to the technical field of electronic throttle opening estimation, in particular to an electronic throttle opening estimation method and system based on Kalman filtering.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The electronic throttle valve is an important control component of an automobile engine and is composed of a direct current motor, a position sensor, a return spring and the like. The electronic throttle valve is a single-input single-output system, when voltage is input, the electronic throttle valve executes instructions of an Electronic Control Unit (ECU), a throttle valve sheet generates rotary motion, and the ECU controls the air inflow of an engine by adjusting the opening of the electronic throttle valve.
Electronic throttles play a fundamental role in the regulation of engine torque and the emission of pollutants, and their performance determines the accuracy of the engine torque response and fuel/air (F/a) ratio. Accurate estimation of the position of the electronic throttle valve is important for smooth operation of the engine and protection of the ecological environment. Due to the nonlinearity of the electronic throttle valve, the electrical noise under the operation condition and other problems, the accuracy of the position sensor for measuring the throttle opening degree is influenced.
The inventor of the disclosure finds that the position of the electronic throttle valve plate is estimated by adopting a Kalman filtering method in most existing documents, and the real position of the electronic throttle valve plate is estimated by utilizing input voltage values of a motor and an accelerator position sensor; researchers have also estimated the position of a real-time automotive electronic throttle using a polyhedral approximation algorithm with a set of values estimated for a switched linear system. However, the interference noise is assumed to be gaussian noise in the currently proposed estimation method of the electronic throttle, but the noise in the sensor circuit is the superposition of multiple random noises, which belongs to non-gaussian white noise, and under the influence of the non-gaussian white noise, the existing method cannot accurately estimate the position of the electronic throttle valve plate.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides an electronic throttle opening estimation method and system based on similar Kalman filtering.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
the first aspect of the disclosure provides an electronic throttle opening estimation method based on Kalman filtering.
An electronic throttle opening estimation method based on Kalman filtering comprises the following steps:
acquiring electronic throttle opening data acquired by an electronic throttle position sensor;
obtaining the opening data of the electronic throttle valve measured by the position sensor through a Kalman estimation-like algorithm to obtain the filtered opening of the electronic throttle valve;
and feeding back the filtered opening value of the electronic throttle valve to a vehicle-mounted controller for further controlling the opening of the electronic throttle valve according to the filtered opening value.
As possible implementation manners, the Kalman estimation algorithm at least comprises five steps of state prediction, state updating, gain matrix calculation, covariance prediction and covariance updating, the sensor measurement opening of the throttle valve is selected as a state vector of the Kalman-like current moment, and the measurement noise is selected as non-Gaussian white noise to obtain an observation vector of the current moment for state updating.
As a further limitation, the state prediction specifically includes: the state vector predictor at the next time is equal to the product of the state transition matrix at the current time and the state matrix at the current time.
As a further limitation, the status update specifically includes:
Figure BDA0002316408220000031
wherein the content of the first and second substances,
Figure BDA0002316408220000032
is the state vector at time k +1,
Figure BDA0002316408220000033
state vector predictor at time k +1 for prediction at time k, Gk+1Kalman gain-like matrix for the time k +1, yk+1Is the observation vector at time k +1, yk+1/kAnd predicting the observation vector prediction value at the k +1 moment predicted for the k moment.
As a further limitation, the gain matrix is specifically:
Figure BDA0002316408220000034
wherein G isk+1Kalman gain-like matrix for the time k +1, Pk+1/kIs a covariance matrix of prediction errors at time k +1 predicted at time k,
Figure BDA0002316408220000035
is the transpose of the observation matrix at time k, HkIs the observation matrix at the time k,
Figure BDA0002316408220000036
the covariance matrix of the noise is measured for time k.
As a further limitation, the covariance prediction specifically includes:
Figure BDA0002316408220000037
wherein phi iskIs the state transition matrix at time k, is the covariance matrix of the prediction error at time k, QkIs the covariance matrix of the system noise at time k.
As a further limitation, the covariance update specifically includes:
Figure BDA0002316408220000038
a second aspect of the present disclosure provides an electronic throttle opening estimation system based on kalman-like filtering.
An electronic throttle opening estimation system based on Kalman filtering comprises;
a data acquisition module configured to: acquiring electronic throttle opening data acquired by an electronic throttle position sensor;
a data filtering module configured to: obtaining the opening data of the electronic throttle valve measured by the position sensor through a Kalman estimation-like algorithm to obtain the filtered opening of the electronic throttle valve;
a feedback control module configured to: and feeding back the filtered opening value of the electronic throttle valve to a vehicle-mounted controller, and further controlling the opening of the electronic throttle valve by the vehicle-mounted controller according to the filtered opening value.
A third aspect of the present disclosure provides a medium having stored thereon a program that, when executed by a processor, implements the steps in the electronic throttle opening estimation method based on kalman-like filtering according to the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, where the processor implements the steps in the method for estimating an opening degree of an electronic throttle valve based on kalman-like filtering according to the first aspect of the present disclosure when executing the program.
Compared with the prior art, the beneficial effect of this disclosure is:
1. according to the method, the output signal of the electronic throttle position sensor is filtered by adopting a Kalman-like estimation algorithm, so that the interference of non-Gaussian white noise on a measurement result is reduced, and the estimation strategy has high estimation precision and strong robustness.
2. According to the Kalman-like estimation algorithm, through updating of a calculation method for predicting error covariance and updating the error covariance, compared with a standard Kalman estimation algorithm, higher-precision estimation and higher robustness of the Kalman-like estimation algorithm are realized, and the control requirement of an electronic throttle valve can be met.
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Fig. 1 is a schematic flow chart of an electronic throttle opening estimation method based on kalman-like filtering according to embodiment 1 of the present disclosure.
Fig. 2 is a schematic view of a kalman-like filtering structure provided in embodiment 1 of the present disclosure.
Fig. 3 is a schematic flowchart of kalman-like filtering provided in embodiment 1 of the present disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
the embodiment 1 of the disclosure provides an electronic throttle opening estimation method based on Kalman filtering, as shown in FIG. 1, in which θ is0Is a given angle, yinIs the input of the controller, u is the input voltage of the electronic throttle system, d is the disturbance of the system, theta is the true opening of the throttle, v is the measurement noise,
Figure BDA0002316408220000051
the method is an estimation value of the true opening degree of the throttle valve and comprises the following steps:
step 1: an electronic throttle position sensor collects electronic throttle opening data.
Step 2: and obtaining the filtered opening degree of the electronic throttle valve by the opening degree of the electronic throttle valve measured by the position sensor through a Kalman estimator.
The kalman filtering structure of step 2 is shown in fig. 2, and the kalman filtering algorithm flow diagram is shown in fig. 3, and specifically includes the following steps:
(2-1) State prediction:
Figure BDA0002316408220000052
(2-2) status update:
Figure BDA0002316408220000053
Figure BDA0002316408220000061
(2-3) gain matrix:
Figure BDA0002316408220000062
(2-4) covariance prediction:
Figure BDA0002316408220000063
(2-6) covariance update:
Figure BDA0002316408220000064
wherein the content of the first and second substances,
Figure BDA0002316408220000065
is a state vector, phikIs a state transition matrix, GkIs a Kalman gain-like matrix, ykIs an observation vector, HkIs an observation matrix, PkIs a covariance matrix of the prediction error,
Figure BDA0002316408220000066
is a covariance matrix, Q, of the measured noisekIs the covariance matrix of the system noise.
And step 3: and feeding back the filtered opening value of the electronic throttle valve to the controller, and accurately controlling the opening of the electronic throttle valve by the controller to realize high-performance control of the electronic throttle valve.
In this embodiment, an electronic throttle model is built in Simulink according to the estimation strategy of fig. 1, specific parameters are shown in table (1), a PID controller is selected, and a kalman-like estimator is used to estimate the true position of the electronic throttle.
Table 1: and (4) electronic throttle model parameters.
Parameter(s) Numerical value Unit of Parameter(s) Numerical value Unit of
J 0.0021 kg·m2 kpre 0.396 Nm
kt 0.0185 Nm/A Fs 0.284 Nm
Ra 1.15 Ω ksp 0.087 Nm/rad
kb 0.0285 V·s/rad n 17.68 /
B 0.0088 Nm·s/rad θ0 0.0349 deg
In the embodiment, a standard Kalman estimator and a quasi-Kalman estimator are used for estimating the output opening of the system respectively, three random noises with variances of 0.005, 0.01 and 0.02 are introduced, a sine signal and a ramp signal are used as given inputs respectively, and the estimation effects of the quasi-Kalman estimator and the standard Kalman estimator are compared. And subtracting the absolute value of the estimated value from the true value to obtain an estimated error, and calculating the variance mean value of the estimated error to respectively obtain two kinds of estimator performance comparison (table 2) under sine input and two kinds of estimator performance comparison (table 3) under slope input.
Table 2: the performance of the two estimators was compared at sinusoidal input.
Figure BDA0002316408220000071
Table 3: the performance of the two estimators at the ramp input is compared.
Figure BDA0002316408220000072
In the embodiment, a sine signal and a ramp signal are respectively used as the input of the controller, and a standard Kalman estimator and a Kalman-like estimator are adopted to estimate the position of the electronic throttle valve. Simulation results show that under the influence of three different noises, the mean value of errors of the Kalman estimator is always below 0.2 degrees, and the mean value of variances of estimation errors is always stable below 0.3 along with the increase of the noises. Under different noises and different input signals, the accuracy and stability of the Kalman estimator are better than those of the standard Kalman estimator. Under the condition of sinusoidal signal input, the estimation effect of a Kalman estimator is more obvious, and the mean error value and the mean variance value are smaller than those of slope input under the same noise influence.
In the embodiment, an electronic throttle valve is taken as a research object, a mathematical model containing nonlinear factors is established, a quasi-Kalman estimator is designed for the interference of non-Gaussian white noise on the position measurement of the electronic throttle valve, the quasi-Kalman estimator is compared with a standard Kalman filter, and the difference between the quasi-Kalman estimator and the standard Kalman filter in the aspects of the estimation accuracy and the stability of the throttle valve position is analyzed by taking the mean error value and the mean variance value as indexes.
Under the interference of random noise with different variances, the accuracy and stability of the Kalman-like estimator are obviously superior to those of a standard Kalman filter. Simulation experiments show that the estimation strategy based on the quasi-Kalman estimator designed by the method is high in estimation precision and strong in robustness, and can realize high-performance control of the electronic throttle system.
Example 2:
the embodiment 2 of the present disclosure provides an electronic throttle opening estimation system based on kalman-like filtering, including;
a data acquisition module configured to: acquiring electronic throttle opening data acquired by an electronic throttle position sensor;
a data filtering module configured to: obtaining the opening data of the electronic throttle valve measured by the position sensor through a Kalman estimation-like algorithm to obtain the filtered opening of the electronic throttle valve;
a feedback control module configured to: and feeding back the filtered opening value of the electronic throttle valve to a vehicle-mounted controller, and further controlling the opening of the electronic throttle valve by the vehicle-mounted controller according to the filtered opening value.
For a specific kalman estimation algorithm, refer to embodiment 1, which is not described herein again.
Example 3:
embodiment 3 of the present disclosure provides a medium on which a program is stored, which when executed by a processor, implements the steps in the electronic throttle opening estimation method based on kalman-like filtering according to embodiment 1 of the present disclosure.
Example 4:
the embodiment 4 of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and operable on the processor, where the processor implements the steps in the electronic throttle opening estimation method based on kalman filter according to embodiment 1 of the present disclosure when executing the program.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims (9)

1. An electronic throttle opening estimation method based on Kalman filtering is characterized by comprising the following steps:
acquiring electronic throttle opening data acquired by an electronic throttle position sensor;
obtaining the opening data of the electronic throttle valve measured by the position sensor through a Kalman estimation-like algorithm to obtain the filtered opening of the electronic throttle valve;
feeding back the filtered opening value of the electronic throttle valve to a vehicle-mounted controller for further controlling the opening of the electronic throttle valve according to the filtered opening value;
the Kalman estimation algorithm at least comprises five steps of state prediction, state updating, gain matrix calculation, covariance prediction and covariance updating, wherein the measurement opening of a sensor of a throttle valve is selected as a state vector of the Kalman-like current moment, and measurement noise is selected as non-Gaussian white noise to obtain an observation vector of the current moment for state updating.
2. The electronic throttle opening estimation method based on kalman-like filtering according to claim 1, characterized in that the state prediction specifically is: the state vector predictor at the next time is equal to the product of the state transition matrix at the current time and the state matrix at the current time.
3. The electronic throttle opening estimation method based on kalman-like filtering according to claim 1, characterized in that the state updating specifically comprises:
Figure FDA0002927401780000011
wherein the content of the first and second substances,
Figure FDA0002927401780000012
is the state vector at time k +1,
Figure FDA0002927401780000013
state vector predictor at time k +1 for prediction at time k, Gk+1Kalman gain-like matrix for the time k +1, yk+1Is the observation vector at time k +1, yk+1/kAnd predicting the observation vector prediction value at the k +1 moment predicted for the k moment.
4. The method for estimating the opening degree of the electronic throttle valve based on the kalman filter according to claim 3, wherein the gain matrix is specifically:
Figure FDA0002927401780000021
wherein G isk+1Kalman gain-like matrix for the time k +1, Pk+1/kIs a covariance matrix of prediction errors at time k +1 predicted at time k,
Figure FDA0002927401780000022
is the transpose of the observation matrix at time k, HkIs the observation matrix at the time k,
Figure FDA0002927401780000023
the covariance matrix of the noise is measured for time k.
5. The method for estimating the opening of the electronic throttle valve based on the kalman filter as claimed in claim 4, wherein the covariance prediction specifically comprises:
Figure FDA0002927401780000024
wherein phi iskFor the state transition matrix at time k, Pk/kCovariance matrix, Q, of prediction error at time kkIs the covariance matrix of the system noise at time k.
6. The electronic throttle opening estimation method based on kalman-like filtering according to claim 5, wherein the covariance update specifically includes:
Figure FDA0002927401780000025
7. an electronic throttle opening estimation system based on Kalman filtering is characterized by comprising;
a data acquisition module configured to: acquiring electronic throttle opening data acquired by an electronic throttle position sensor;
a data filtering module configured to: obtaining the opening data of the electronic throttle valve measured by the position sensor through a Kalman estimation-like algorithm to obtain the filtered opening of the electronic throttle valve;
a feedback control module configured to: feeding back the opening value of the filtered electronic throttle valve to a vehicle-mounted controller, and further controlling the opening of the electronic throttle valve by the vehicle-mounted controller according to the filtered opening value;
a Kalman estimation like algorithm module configured to: the method at least comprises five steps of state prediction, state updating, gain matrix calculation, covariance prediction and covariance updating, wherein the sensor measurement opening of the throttle valve is selected as a state vector of a similar Kalman current moment, and the measurement noise is selected as non-Gaussian white noise to obtain an observation vector of the current moment for state updating.
8. A medium having a program stored thereon, wherein the program, when executed by a processor, implements the steps in the kalman filter-like based electronic throttle opening estimation method according to any one of claims 1 to 6.
9. An electronic device comprising a memory, a processor and a program stored on the memory and operable on the processor, wherein the processor implements the steps of the method for estimating an opening of an electronic throttle valve based on kalman-like filtering according to any one of claims 1 to 6 when executing the program.
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