CN111555689A - Phase current flow sampling system and method based on Kalman filtering - Google Patents

Phase current flow sampling system and method based on Kalman filtering Download PDF

Info

Publication number
CN111555689A
CN111555689A CN202010443484.2A CN202010443484A CN111555689A CN 111555689 A CN111555689 A CN 111555689A CN 202010443484 A CN202010443484 A CN 202010443484A CN 111555689 A CN111555689 A CN 111555689A
Authority
CN
China
Prior art keywords
phase current
moment
calculation module
data
phase
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010443484.2A
Other languages
Chinese (zh)
Other versions
CN111555689B (en
Inventor
刘清池
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Vmmore Control Technology Co ltd
Original Assignee
Shenzhen Vmmore Control Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Vmmore Control Technology Co ltd filed Critical Shenzhen Vmmore Control Technology Co ltd
Priority to CN202010443484.2A priority Critical patent/CN111555689B/en
Publication of CN111555689A publication Critical patent/CN111555689A/en
Application granted granted Critical
Publication of CN111555689B publication Critical patent/CN111555689B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/22—Current control, e.g. using a current control loop
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/13—Observer control, e.g. using Luenberger observers or Kalman filters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/18—Estimation of position or speed
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/14—Electronic commutators
    • H02P6/16—Circuit arrangements for detecting position
    • H02P6/18—Circuit arrangements for detecting position without separate position detecting elements
    • H02P6/182—Circuit arrangements for detecting position without separate position detecting elements using back-emf in windings
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/28—Arrangements for controlling current
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2205/00—Indexing scheme relating to controlling arrangements characterised by the control loops
    • H02P2205/01—Current loop, i.e. comparison of the motor current with a current reference

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Motors That Do Not Use Commutators (AREA)
  • Control Of Electric Motors In General (AREA)
  • Control Of Ac Motors In General (AREA)

Abstract

The invention discloses a phase current sampling system based on Kalman filtering, which comprises: the encoder communication interface is used for acquiring the original data of the encoder and calculating the rotating speed of the motor; a counter electromotive force calculator for calculating a counter electromotive force; the PWM duty ratio calculation module is used for calculating to obtain the switching time of the three-phase bridge arm and the duty ratio of each PWM control vector; the prediction updating calculation module is used for predicting the phase current data at the moment and updating the variance of the phase current data; and the Gaussian distribution fusion calculation module is used for calculating the optimal estimation value of the phase current at the moment according to the phase current data sampled at the moment and the phase current data at the moment predicted by the prediction updating calculation module. The phase current sampling device can reduce the noise of phase current collected data, improve the data precision, set the oversampling frequency more flexibly, use different oversampling rates according to different working conditions, and further achieve better sampling effect.

Description

Phase current flow sampling system and method based on Kalman filtering
Technical Field
The invention relates to a motor current sampling processing system, in particular to a phase current sampling system and method based on Kalman filtering.
Background
In the prior art, to realize high-performance control of a servo motor, phase current of the motor needs to be acquired, and the current torque of the motor is acquired as feedback of a current loop by processing the phase current. The phase current sampling ADCs commonly used in the servo motor at present comprise a successive approximation type ADC, an integral type ADC and a sigma-delta type ADC, wherein the sigma-delta type ADC is more and more widely used due to the advantages of higher resolution, high conversion rate, lower cost and the like.
Unlike a typical ADC, the sigma-delta ADC does not perform quantization coding directly according to the size of each sample of the sampled data, but rather according to the difference between a previous magnitude and a subsequent magnitude, the size of a so-called increment. In a sense, it performs quantization coding according to the envelope of the signal waveform. The sigma-delta ADC consists of two parts, the first part being an analog sigma-delta modulator and the second part being a digital decimation filter. The sigma-delta modulator samples the input analog signal at a very high sampling frequency and performs low-order quantization on the difference between the two samples, thereby obtaining a digital signal represented by a low-order number, i.e., a sigma-delta code; the sigma-delta code is then fed to a digital decimation filter of the second part for decimation filtering to obtain a high resolution linear pulse code modulated digital signal. The decimation filter thus effectively behaves as a pattern transformer. The sigma-delta modulator is also called an oversampled ADC converter because it has an extremely high sampling rate, typically many times higher than the nyquist sampling frequency. Sigma-delta ADCs are typically used in conjunction with SINC3 or CIC3 filters for data filtering. Sampling time and data valid bits can be adjusted by adjusting the oversampling rate, and within a certain range, the higher the oversampling rate is, the longer the sampling time is, and the higher the valid bits are.
Kalman filtering is widely used in continuously varying systems, and is a data fusion algorithm that fuses together data from different sensors with the same measurement objective, but in different units, to obtain a more accurate objective measurement. The kalman filter can fit a linear gaussian system, which has the greatest advantage of being computationally inexpensive and capable of using the state at the previous time and possible measurements to obtain an optimal estimate of the state at the current time.
In combination with the above, the following problems mainly exist in the prior art:
firstly, in the field of current motion control, the performance of the MCU is higher and higher, and the PWM carrier frequency is higher and higher, resulting in shorter and shorter sampling time of the sigma-delta ADC within one PWM cycle, and resulting in lower phase current sampling accuracy. For example, 32K carrier frequency, the sigma-delta ADC oversampling rate of 20M external clock input can only be set to 128 bits at most; if a high speed sigma-delta ADC is used, although a higher oversampling ratio can be set, a cost problem is encountered.
In addition, due to the influence of other factors such as an IGBT bridge arm switch, data obtained by sigma-delta ADC sampling may include a part of white noise, which affects the control of the servo current loop.
With the improvement of the PWM carrier frequency, the servo motor also puts forward a higher requirement on the accuracy of phase current sampling in order to pursue higher performance and stability, and the current simple technology of performing phase current sampling by matching the sigma-delta ADC with the filter cannot meet the requirement.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a phase current flow sampling system and method based on Kalman filtering, which can reduce noise of phase current acquisition data, improve data accuracy, set oversampling frequency more flexibly, use different oversampling rates according to different working conditions and further achieve better sampling effect, aiming at the defects of the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme.
A phase current sampling system based on Kalman filtering comprises: the encoder communication interface is communicated with an encoder of the motor and used for acquiring original data of the encoder, acquiring the electrical angle of a motor rotor according to the original data and calculating the rotating speed of the motor; the counter electromotive force calculator is used for calculating counter electromotive force according to the current motor rotating speed and pre-provided counter electromotive force parameters; the PWM duty ratio calculation module is used for acquiring the current torque current, calculating the output torque through a current loop PID algorithm, and then calculating to obtain the switching time of the three-phase bridge arm and the duty ratio of each PWM control vector; the prediction updating calculation module is used for predicting the phase current data at the moment according to a preset motor voltage equation, the duty ratio of a PWM control vector, the switching time of a three-phase bridge arm and the predicted value of the phase current at the previous moment and updating the variance of the phase current data; and the Gaussian distribution fusion calculation module is used for calculating the optimal estimation value of the phase current at the moment according to the phase current data sampled at the moment and in combination with the phase current data at the moment predicted by the prediction updating calculation module.
A phase current oversampling method based on Kalman filtering is realized based on a system, the system comprises an encoder communication interface, a back electromotive force calculator, a PWM duty ratio calculation module, a prediction update calculation module and a Gaussian distribution fusion calculation module, the encoder communication interface is communicated with an encoder of a motor, and the method comprises the following steps: step S1, the encoder communication interface acquires original data from the encoder, acquires the electric angle of the motor rotor according to the original data, and calculates the rotating speed of the motor; step S2, the counter electromotive force calculator calculates the counter electromotive force according to the current motor speed and the pre-provided counter electromotive force parameter for the calling of step S4; step S3, the PWM duty ratio calculation module obtains the current torque current, calculates the output torque through a current loop PID algorithm, and then calculates the switching time of the three-phase bridge arm and the duty ratio of each PWM control vector for calling in step S4; step S4, the prediction updating calculation module predicts the phase current data of the moment according to a preset motor voltage equation, the duty ratio of a PWM control vector, the switching time of a three-phase bridge arm and the predicted value of the phase current at the previous moment, and updates the variance of the phase current data; step S5, the Gaussian distribution fusion calculation module calculates the best estimation value of the phase current at the moment according to the phase current data sampled at the moment and by combining the phase current data at the moment predicted by the prediction updating calculation module; step S6, the prediction updating calculation module predicts and updates the phase current predicted value at the next moment and judges whether the current PWM cycle is finished, if yes, the step S7 is executed, and if not, the step S4 is returned to; step S7, taking the best estimated value of the phase current at the moment as the feedback value of the current loop PID algorithm and participating in calculation; and step S8, entering the next PWM cycle, and iteratively executing the step S1 to the step S7 to obtain the optimal estimated value of the phase current at each moment.
Preferably, in step S1, the encoder communication interface periodically obtains the original data of the encoder, where the original data includes the position data of the encoder, and in each communication cycle, the encoder communication interface performs a difference operation on the position data of the current cycle and the position data of the previous cycle to obtain the rotation speed data.
According to the phase current sampling system and method based on Kalman filtering, disclosed by the invention, more accurate phase current values can be obtained by performing Kalman filtering processing on current values at various moments in a PWM period for multiple times. In order to achieve the purpose, the invention designs a Kalman filter, the observed quantity is phase current data obtained by an ADC, according to a motor voltage equation, parameters such as the current electrical angle of a motor, the rotor speed, the current PWM cycle duty ratio and the like are used for calculating phase current predicted quantity, phase current data acquisition and prediction are carried out on a plurality of moments in a PWM cycle, and finally phase current data fed back to motion control calculation are obtained. Compared with the prior art, the method and the device have the advantages that the jitter of the phase current is reduced through the processing of the phase current acquisition original data, the accuracy of the phase current is improved, and meanwhile, the servo system can set higher speed and position loop gain, so that better performance is brought.
Drawings
FIG. 1 is a block diagram of a phase current sampling system based on Kalman filtering according to the present invention;
FIG. 2 is a flow chart of a phase current oversampling method based on Kalman filtering according to the present invention.
Detailed Description
The invention is described in more detail below with reference to the figures and examples.
The invention discloses a phase current sampling system based on Kalman filtering, please refer to FIG. 1, which comprises:
the encoder communication interface 1 is communicated with an encoder 2 of the motor and used for acquiring original data of the encoder 2, acquiring an electrical angle of a motor rotor according to the original data and calculating the rotating speed of the motor;
a counter electromotive force calculator 3, which is used for calculating counter electromotive force according to the current motor rotating speed and the pre-provided counter electromotive force parameter;
the PWM duty ratio calculation module 4 is used for acquiring the current torque current, calculating the output torque through a current loop PID algorithm, and then calculating to obtain the switching time of the three-phase bridge arm and the duty ratio of each PWM control vector;
the prediction updating calculation module 5 is used for predicting the phase current data at the moment according to a preset motor voltage equation, the duty ratio of a PWM control vector, the switching time of a three-phase bridge arm and the predicted value of the phase current at the previous moment, and updating the variance of the phase current data;
and the Gaussian distribution fusion calculation module 6 is used for calculating the optimal estimation value of the phase current at the moment according to the phase current data sampled at the moment and by combining the phase current data at the moment predicted by the prediction updating calculation module 5.
In the system, an encoder communication interface is communicated with an encoder to periodically acquire original data of the encoder, the original data of the encoder generally only contain position data, the position data of the period and the position data of the previous period are differentiated in each communication period to acquire rotating speed data, and the encoder data and the rotating speed acquired in each communication period of the interface are used for calculating counter electromotive force. After the counter electromotive force calculation module obtains the electrical angle and the rotating speed, the counter electromotive force calculation module obtains the counter electromotive force parameter K provided by a motor manufacturerEAnd calculating the respective back electromotive forces of the three phases. The servo motor is a continuously variable system where the electrical angle and speed of rotation can be applied to one or both of the servo three-ring configurationsWithin a few PWM periods, the calculation result is not influenced. The PWM duty ratio calculation module is a servo current loop module, a servo speed loop gives a target torque, the current loop obtains a current torque current (namely the torque current obtained by the conversion of a Kalman filtering result of the last PWM period through clark and park), the output torque is calculated through PID, and then the on-off state and the duty ratio of the three-phase bridge arm are calculated. Each PWM cycle can be equally divided into N time intervals by utilizing the prediction updating calculation module, and Kalman filtering calculation is performed once every time interval passes. And determining a voltage equation formula by judging the switching state and the duration in the next period of time according to the current Kalman filtering optimal estimation value at the current moment, and calculating the phase current predicted value at the next moment. Here, the calculation modes are changed according to the difference of the switch states and the duration and the difference of the Kalman filtering times in one PWM period, but differential equations can be determined according to kirchhoff current law KCL and voltage law KVL, and are first-order constant coefficient non-homogeneous differential equations with the following formats:
Figure BDA0002504784340000061
where τ is the time constant and γ is the sum of the back-emf divided by the inductance.
The Gaussian distribution fusion calculation module is used for predicting at the current moment according to the previous moment
Figure BDA0002504784340000062
And the phase current Z sampled by the ADC at the current momentkAnd calculating to obtain a current moment Kalman filtering result, namely the current moment phase current optimal estimation. And repeating the process, and iterating to obtain the optimal estimated value of the phase current at each moment.
Based on the process, the method can effectively reduce the noise of phase current acquired data, so as to improve the data precision, and meanwhile, compared with the traditional sigma-delta type ADC matched filter method, the oversampling frequency setting of the method is more free and flexible, different oversampling rates can be used according to different working conditions, so that a better sampling effect is achieved, and the sampling requirement is better met.
On this basis, the invention also relates to a phase current oversampling method based on kalman filtering, which is realized based on a system comprising an encoder communication interface 1, a back electromotive force calculator 3, a PWM duty ratio calculation module 4, a prediction update calculation module 5 and a gaussian distribution fusion calculation module 6, and is combined with fig. 1 and fig. 2, wherein the encoder communication interface 1 establishes communication with an encoder 2 of a motor, and the method comprises the following steps:
step S1, the encoder communication interface 1 acquires original data from the encoder 2, acquires the electric angle of the motor rotor according to the original data, and calculates the rotating speed of the motor;
step S2, the back electromotive force calculator 3 calculates the back electromotive force according to the current motor speed and the back electromotive force parameter provided in advance, for the step S4 to call;
step S3, the PWM duty ratio calculation module 4 obtains the current torque current, calculates the output torque through a current loop PID algorithm, and then calculates the switching time of the three-phase bridge arm and the duty ratio of each PWM control vector for calling in step S4;
step S4, the prediction update calculation module 5 predicts the phase current data of the present moment according to a preset motor voltage equation, the duty ratio of the PWM control vector, the switching time of the three-phase bridge arm, and the predicted value of the phase current at the previous moment, and updates the variance of the phase current data at the same time;
step S5, the gaussian distribution fusion calculation module 6 calculates the best estimation value of the phase current at this time according to the phase current data sampled at this time and in combination with the phase current data at this time predicted by the prediction update calculation module 5;
step S6, the prediction update calculation module 5 predicts and updates the predicted phase current value at the next time, and determines whether the current PWM cycle is finished, if yes, step S7 is executed, otherwise, step S4 is returned to;
step S7, taking the best estimated value of the phase current at the moment as the feedback value of the current loop PID algorithm and participating in calculation;
and step S8, entering the next PWM cycle, and iteratively executing the step S1 to the step S7 to obtain the optimal estimated value of the phase current at each moment.
Further, in step S1, the encoder communication interface 1 periodically obtains the original data of the encoder 2, where the original data includes the position data of the encoder 2, and in each communication cycle, the encoder communication interface 1 performs a difference operation on the position data of the current cycle and the position data of the previous cycle to obtain the rotation speed data.
In step S2, the back electromotive force calculator 3 preferably calculates three back electromotive forces according to the following formula:
Figure BDA0002504784340000081
Figure BDA0002504784340000082
Figure BDA0002504784340000083
wherein:
KEis a pre-provided back electromotive force parameter; in particular, KEThe back electromotive force parameter is provided for a motor manufacturer in a unit of V/KRPM;
Vrthe motor rotating speed;
θEis an electrical angle.
In step S4 of this embodiment, the prediction update formula adopted by the prediction update calculation module 5 is as follows:
Figure BDA0002504784340000092
in the formula, Xk-1～N(μ1，σ1 2) And then, it follows:
Figure BDA0002504784340000093
wherein:
Figure BDA0002504784340000094
is the predicted state at time k;
Xk-1is the k-1 moment kalman filtering result;
μ1is Xk-1Optimum phase current estimate of σ1Is its standard deviation;
K1、K2is a parameter obtained by the operation of a motor voltage equation.
With respect to K1、K2In the embodiment, X is used ask-1The phase current value at each moment in the PWM period can be predicted by calculating the current change of each switching state.
K1、K2The operation process comprises the following steps:
when the three-phase upper bridge arm is completely closed:
Figure BDA0002504784340000091
ia+ib+ic＝0；
wherein:
l is a single-phase inductive reactance;
r is single-phase impedance;
ia、ib、icthree-phase current values respectively;
EA、EB、ECthree-phase back electromotive force;
the replacement variables may yield:
Figure BDA0002504784340000101
the above differential equation can be solved if iaIs initialized to μ1(initial time is set to T)0) Then, any time i in the time period of the complete closing of the three-phase upper bridge arm in the PWM cycle can be obtainedaThe value of (c):
Figure BDA0002504784340000102
at this time:
Figure BDA0002504784340000103
Figure BDA0002504784340000104
the above operation results can be generalized to other switch states within the PWM period.
Further, the phase current value acquired by the ADC at time k is:
Zk～N(μ2，σ2 2)；
wherein, mu2Is the phase current value, σ, acquired at time k2Is its standard deviation;
in step S5, the formula for the gaussian distribution fusion calculation module 6 to perform the gaussian distribution fusion calculation is:
Figure BDA0002504784340000107
multiplication of two gaussian distributions is still gaussian, then:
Figure BDA0002504784340000105
Figure BDA0002504784340000106
wherein:
Xkis the result of Kalman filtering at the moment k;
μ is XkThe best phase current estimate, σ, is its standard deviation.
According to the phase current sampling system and method based on Kalman filtering, disclosed by the invention, more accurate phase current values can be obtained by performing Kalman filtering processing on current values at various moments in a PWM period for multiple times. In order to achieve the purpose, the invention designs a Kalman filter, the observed quantity is phase current data obtained by an ADC, according to a motor voltage equation, parameters such as the current electrical angle of a motor, the rotor speed, the current PWM cycle duty ratio and the like are used for calculating phase current predicted quantity, phase current data acquisition and prediction are carried out on a plurality of moments in a PWM cycle, and finally phase current data fed back to motion control calculation are obtained. Compared with the prior art, the method and the device have the advantages that the jitter of the phase current is reduced through the processing of the phase current acquisition original data, the accuracy of the phase current is improved, and meanwhile, the servo system can set higher speed and position loop gain, so that better performance is brought.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents or improvements made within the technical scope of the present invention should be included in the scope of the present invention.

Claims (7)

1. A phase current sampling system based on Kalman filtering is characterized by comprising:
the encoder communication interface (1) is communicated with an encoder (2) of the motor and used for acquiring original data of the encoder (2), acquiring an electrical angle of a motor rotor according to the original data and calculating the rotating speed of the motor;
a counter electromotive force calculator (3) for calculating counter electromotive force according to the current motor speed and pre-provided counter electromotive force parameters;
the PWM duty ratio calculation module (4) is used for acquiring the current torque current, calculating the output torque through a current loop PID algorithm, and then calculating to obtain the switching time of the three-phase bridge arm and the duty ratio of each PWM control vector;
the prediction updating calculation module (5) is used for predicting the phase current data at the moment and updating the phase current data variance at the same time according to a preset motor voltage equation, the duty ratio of a PWM control vector, the switching time of a three-phase bridge arm and the phase current predicted value at the previous moment;
and the Gaussian distribution fusion calculation module (6) is used for calculating the optimal estimation value of the phase current at the moment according to the phase current data sampled at the moment and by combining the phase current data at the moment predicted by the prediction updating calculation module (5).
2. A phase current oversampling method based on Kalman filtering is characterized in that the method is realized based on a system, the system comprises an encoder communication interface (1), a counter electromotive force calculator (3), a PWM duty ratio calculation module (4), a prediction updating calculation module (5) and a Gaussian distribution fusion calculation module (6), the encoder communication interface (1) is communicated with an encoder (2) of a motor, and the method comprises the following steps:
step S1, the encoder communication interface (1) acquires original data from the encoder (2), acquires the electric angle of the motor rotor according to the original data, and calculates the rotating speed of the motor;
step S2, the counter electromotive force calculator (3) calculates the counter electromotive force according to the current motor speed and the pre-provided counter electromotive force parameter for calling in the step S4;
step S3, the PWM duty ratio calculation module (4) acquires the current torque current, calculates the output torque through a current loop PID algorithm, and then calculates the switching time of the three-phase bridge arm and the duty ratio of each PWM control vector for calling in the step S4;
step S4, the prediction updating calculation module (5) predicts the phase current data of the moment according to a preset motor voltage equation, the duty ratio of a PWM control vector, the switching time of a three-phase bridge arm and the predicted value of the phase current at the previous moment, and updates the variance of the phase current data at the same time;
step S5, the Gaussian distribution fusion calculation module (6) calculates the best estimation value of the phase current at the moment according to the phase current data sampled at the moment and by combining the phase current data at the moment predicted by the prediction updating calculation module (5);
step S6, the prediction updating calculation module (5) predicts and updates the phase current predicted value at the next moment, and judges whether the current PWM cycle is finished, if yes, the step S7 is executed, and if not, the step S4 is returned to;
step S7, taking the best estimated value of the phase current at the moment as the feedback value of the current loop PID algorithm and participating in calculation;
and step S8, entering the next PWM cycle, and iteratively executing the step S1 to the step S7 to obtain the optimal estimated value of the phase current at each moment.
3. The phase current oversampling method based on kalman filtering according to claim 2, wherein in step S1, the encoder communication interface (1) periodically obtains raw data of the encoder (2), the raw data includes position data of the encoder (2), and in each communication period, the encoder communication interface (1) performs a difference operation on the position data of the period and the position data of the previous period to obtain rotation speed data.
4. The phase current oversampling method based on kalman filter according to claim 2, wherein in the step S2, the back electromotive force calculator (3) calculates three back electromotive forces according to the following formula:
Figure FDA0002504784330000031
Figure FDA0002504784330000032
Figure FDA0002504784330000033
wherein:
KEis a pre-provided back electromotive force parameter;
Vrthe motor rotating speed;
θEis an electrical angle.
5. The phase current oversampling method based on kalman filtering according to claim 2, wherein in step S4, the prediction update calculation module (5) adopts a prediction update formula as follows:
Figure FDA0002504784330000034
in the formula, Xk-1～N(μ1，σ1 2) And then, it follows:
Figure FDA0002504784330000035
wherein:
Figure FDA0002504784330000036
is the predicted state at time k;
Xk-1is the k-1 moment kalman filtering result;
μ1is Xk-1Optimum phase current estimate of σ1Is its standard deviation;
K1、K2is a parameter obtained by the operation of a motor voltage equation.
6. Phase current oversampling method based on kalman filtering, according to claim 5, characterized in that K1、K2The operation process comprises the following steps:
when the three-phase upper bridge arm is completely closed:
Figure FDA0002504784330000041
ia+ib+ic＝0；
wherein:
l is a single-phase inductive reactance;
r is single-phase impedance;
ia、ib、icthree-phase current values respectively;
EA、EB、ECthree-phase back electromotive force;
the replacement variables may yield:
Figure FDA0002504784330000042
the above differential equation can be solved if iaIs initialized to μ1Then, any time i in the time period of the complete closing of the three-phase upper bridge arm in the PWM cycle can be obtainedaThe value of (c):
Figure FDA0002504784330000043
at this time:
Figure FDA0002504784330000044
Figure FDA0002504784330000045
7. the phase current oversampling method based on kalman filtering according to claim 6, wherein the phase current value acquired by the ADC at time k is:
Zk～N(μ2，σ2 2)；
wherein, mu2Is the phase current value, σ, acquired at time k2Is its standard deviation;
in step S5, the gaussian distribution fusion calculation module (6) performs a gaussian distribution fusion calculation according to the following formula:
Figure FDA0002504784330000051
multiplication of two gaussian distributions is still gaussian, then:
Figure FDA0002504784330000052
Figure FDA0002504784330000053
wherein:
Xkis the result of Kalman filtering at the moment k;
μ is XkThe best phase current estimate, σ, is its standard deviation.
CN202010443484.2A 2020-05-22 2020-05-22 Phase current flow sampling system and method based on Kalman filtering Active CN111555689B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010443484.2A CN111555689B (en) 2020-05-22 2020-05-22 Phase current flow sampling system and method based on Kalman filtering

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010443484.2A CN111555689B (en) 2020-05-22 2020-05-22 Phase current flow sampling system and method based on Kalman filtering

Publications (2)

Publication Number Publication Date
CN111555689A true CN111555689A (en) 2020-08-18
CN111555689B CN111555689B (en) 2022-06-10

Family

ID=72006639

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010443484.2A Active CN111555689B (en) 2020-05-22 2020-05-22 Phase current flow sampling system and method based on Kalman filtering

Country Status (1)

Country Link
CN (1) CN111555689B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113364291A (en) * 2021-05-06 2021-09-07 深圳第三代半导体研究院 Two-mode control method and system for bidirectional reversible direct current converter
CN114123894A (en) * 2021-11-19 2022-03-01 九江精密测试技术研究所 Indexing mechanism three-loop control method based on Kalman

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011234581A (en) * 2010-04-30 2011-11-17 Denso Corp Control unit of rotary machine
US20130246006A1 (en) * 2012-03-13 2013-09-19 King Fahd University Of Petroleum And Minerals Method for kalman filter state estimation in bilinear systems
CN104730511A (en) * 2015-04-10 2015-06-24 西安电子科技大学 Tracking method for multiple potential probability hypothesis density expansion targets under star convex model
CN108521246A (en) * 2018-04-23 2018-09-11 湖南科力尔电机股份有限公司 The method and device of permanent magnet synchronous motor single current sensor predictive current control
CN109194229A (en) * 2018-09-27 2019-01-11 北京理工大学 A kind of permanent magnet synchronous motor MTPA control system and method based on torque closed loop
US20190207543A1 (en) * 2016-08-31 2019-07-04 SZ DJI Technology Co., Ltd. Methods and systems for brushless motor control

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011234581A (en) * 2010-04-30 2011-11-17 Denso Corp Control unit of rotary machine
US20130246006A1 (en) * 2012-03-13 2013-09-19 King Fahd University Of Petroleum And Minerals Method for kalman filter state estimation in bilinear systems
CN104730511A (en) * 2015-04-10 2015-06-24 西安电子科技大学 Tracking method for multiple potential probability hypothesis density expansion targets under star convex model
US20190207543A1 (en) * 2016-08-31 2019-07-04 SZ DJI Technology Co., Ltd. Methods and systems for brushless motor control
CN108521246A (en) * 2018-04-23 2018-09-11 湖南科力尔电机股份有限公司 The method and device of permanent magnet synchronous motor single current sensor predictive current control
CN109194229A (en) * 2018-09-27 2019-01-11 北京理工大学 A kind of permanent magnet synchronous motor MTPA control system and method based on torque closed loop

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
燕必希等: "卡尔曼滤波单目相机运动目标定位研究", 《仪器仪表学报》 *
王荣志: "四旋翼无人机飞行速度估计方法研究与实现", 《中国优秀硕士学位论文全文数据库（工程科技II辑）》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113364291A (en) * 2021-05-06 2021-09-07 深圳第三代半导体研究院 Two-mode control method and system for bidirectional reversible direct current converter
CN114123894A (en) * 2021-11-19 2022-03-01 九江精密测试技术研究所 Indexing mechanism three-loop control method based on Kalman

Also Published As

Publication number Publication date
CN111555689B (en) 2022-06-10

Similar Documents

Publication Publication Date Title
CN111555689B (en) Phase current flow sampling system and method based on Kalman filtering
KR101329400B1 (en) Methods and systems for adaptive control
Gawthrop Hybrid self-tuning control
US7856464B2 (en) Decimation filter
JP6599943B2 (en) System and method for analog / digital conversion
JP2010528385A (en) Closed loop control method and closed loop control device with multi-channel feedback
JPH10311741A (en) Output signal processing device of encoder
CN114944801A (en) PMSM (permanent magnet synchronous motor) position sensorless control method based on innovation self-adaptive extended Kalman
CN110601630B (en) Flux linkage angle amplitude limiting processing and velocity sensorless vector control method and system
US10895866B1 (en) Position error correction for electric motors
CN111337153A (en) Temperature sensor and temperature analog signal digitization method
Schmidt et al. High-performance control architecture for automation drives based on a low-cost microcontroller in combination with a low-cost FPGA
CN114726285A (en) Current loop control method for permanent magnet synchronous motor
CN109444529A (en) A kind of current sample method and servo-driver based on Σ Δ type ADC
CN113075446B (en) Current acquisition method and device
RU2428784C1 (en) Method of sensor-free evaluation of angular position of rotor of multi-phase electric motor
RU2716129C1 (en) Method for control of switched reluctance motor
CN110708039B (en) Coefficient fitting method of FARROW filter
CN113037150A (en) Method for realizing multi-axis control of industrial robot based on DSP + FPGA servo
CN109217759B (en) Servo system current loop regulator optimization method
CN112486065A (en) Alternating current signal sampling method and device and handheld code scanning printer
JP3513633B2 (en) Method and apparatus for identifying unknown continuous-time system, and control system using the same
Jin et al. A novel accelaration estimation algorithm based on Kalman filter and adaptive windowing using low-resolution optical encoder
Gayathiridevi et al. Discrete controller for high frequency buck converter
CN113411013B (en) Brushless direct current motor control system and method for back electromotive force function integral prediction

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant