CN109412488A - A kind of permanent magnet synchronous motor dynamic matrix control method - Google Patents
A kind of permanent magnet synchronous motor dynamic matrix control method Download PDFInfo
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- 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
- H02P21/24—Vector control not involving the use of rotor position or rotor speed sensors
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- 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
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- H—ELECTRICITY
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- 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
- H02P21/141—Flux estimation
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- 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
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- 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
- H02P21/20—Estimation of torque
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- H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation
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Abstract
The invention discloses a kind of permanent magnet synchronous motor dynamic matrix control methods, steps are as follows: one, establishing the dq axis mathematical model of PMSM, using Extended Kalman filter EKF as a kind of random observation device, predictive variable is corrected by the observational variable of expanded Kalman filtration algorithm, to obtain optimal predicted value；Two, vector controlled is constructed with Extended Kalman filter EKF；Three, it realizes that PMSM, DMC algorithm utilize the step response of object with DMC algorithm, realizes that step passes through model prediction, rolling optimization and feedback compensation；Four, the magneto DMC vector controlled structure based on EKF is constituted.Model after variable replacement of the present invention can be considerable controllable, the sensor measurement revolving speed in former permanent magnet synchronous motor dynamic matrix control is replaced by the revolving speed of EKF prediction output, so that entire control process is completed by computer, the controllability of system is improved, convenient in more applications.
Description
Technical Field
The invention relates to a dynamic matrix control method for a permanent magnet synchronous motor, and belongs to the technical field of application of permanent magnet synchronous motors.
Background
In exploration and exploitation of an oil drilling machine system, due to the fact that environmental conditions and geological structures are complex, the oil drilling machine system has the characteristics of nonlinearity, uncertainty and the like in actual work, the drilling machine is required to have good dynamic response in the aspect of driving a motor, the rotating speed is kept stable when load changes, and the DMC (dynamic matrix) in prediction control is applied to a PMSM (permanent magnet synchronous motor) drilling machine system aiming at the problems, so that the oil drilling machine system has a good control effect.
The PMSM is well known for the superiority of high dynamic performance, high efficiency, light weight and the like, and by combining a microelectronic control technology and a power electronic technology, a plurality of integrated electromechanical devices and products with superior performance can be designed and manufactured, and the PMSM has the characteristic of energy conservation, is the first choice when a driving system is designed in the world at present and is increasingly applied to the field of petroleum drilling machines. Based on the superiority of PMSM in structure and performance, especially control, modern control theory and intelligent control strategy are applied more and more in motor control, and model predictive control is applied more and more in PMSM as a novel computer control algorithm.
In order to implement DMC control of a PMSM of an oil rig system, a coaxial mechanical position sensor is used to measure rotor position and speed information, which however causes some disadvantages: 1. the weight and volume of the PMSM increase to increase the cost; 2. the coaxial mounting precision requirement is high, the normal work of the PMSM can be seriously influenced if the coaxial mounting precision requirement is not met, and the disturbance resistance of the PMSM is reduced because components are added; 3. the requirement on the use environment is strict, and the precision of the PMSM is greatly influenced by disturbance caused by external environment change, so that the application of the PMSM in an oil drilling machine system is influenced.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a dynamic matrix control method for a permanent magnet synchronous motor, wherein EKF (extended Kalman Filter) is adopted to design a PMSM (permanent magnet synchronous motor) position-free sensor, and the position and speed information of the motor is observed to carry out closed-loop control on the motor, so that mechanical equipment of a drilling machine is simplified, and a drilling machine system is more flexible and convenient to use and has higher reliability.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a method for controlling a dynamic matrix of a permanent magnet synchronous motor comprises the following steps:
establishing a dq axis mathematical model of PMSM (permanent magnet synchronous Motor), taking an Extended Kalman Filter (EKF) as a random observer, and correcting a prediction variable through an observation variable of an extended Kalman filter algorithm to obtain an optimal prediction value;
constructing vector control by using an Extended Kalman Filter (EKF), setting a rotor flux linkage, a rotating speed and a rotor position angle as state variables, and setting a stator voltage as an input variable, wherein if the sampling frequency of rotating speed closed-loop control is sufficiently larger than a mechanical time constant or rotational inertia of a motor, the rotating speed is constant in a sampling period, so that a rotating speed estimation error is reduced to system noise, and the precision of a motor model is not influenced;
thirdly, PMSM is realized by DMC algorithm, the DMC algorithm utilizes step response of an object, and the steps of model prediction, rolling optimization and feedback correction are realized;
fourthly, forming a permanent magnet motor DMC vector control structure based on EKF.
Further, the PMSM adopts a surface-mounted PMSM.
Further, the establishing of the dq axis mathematical model of the PMSM means:
voltage equation (1)
Electromagnetic torque equation (2)
Mechanical equation of motion (3)
In the formula id，iqAnd ud，uqDq components of stator current and voltage, respectively; l isd，LqAnd psid，ψqDq components of stator inductance and flux linkage, respectively; rSIs a stator resistor; omega is the mechanical angular speed of the rotor; n ispIs the number of pole pairs; psifIs a permanent magnet flux linkage; t ise，TLThe electromagnetic torque and the load torque of the motor are respectively; j is moment of inertia; p is the viscous friction coefficient.
Further, the extended kalman filter algorithm is as follows:
x(k+1)＝Ax(k)+Bu(k)+V(k) (4)
y(k)＝Hx(k)+W(k) (5)
in the formula: v (k) is system noise; w (k) is measurement noise.
Further, the step of the extended kalman filter algorithm includes:
wherein, M and N are covariance matrixes of V and W respectively, K (K +1) is a gain matrix, the superscript is a predicted value, and the superscript is an inverted value.
Furthermore, in the second step, the constructing of the vector control by the EKF with the extended Kalman filter means that the dq axis is αβ axis model, an EKF observer is designed under αβ axis, the vector control is constructed by the EKF, and the rotor flux linkage psi is setα、ψβIf the sampling frequency of the rotating speed closed-loop control is large enough compared with the mechanical time constant or the rotational inertia of the motor, the rotating speed is constant in the sampling period, so that the rotating speed estimation error is attributed to the system noise, the precision of the motor model is not influenced,
wherein, the flux linkage equation of PMSM is:
the stator voltage equation is:
substituting the flux linkage equation into the voltage equation can obtain:
setting ω to be constant within each sampling period, results in:
while
Selecting x (t) ═ psiαψβωθ]TAs state variables, a state equation and an output equation are constructed as follows:
wherein,
v is the system noise; and w is measurement noise.
Further, in the third step, the step of implementing PMSM by using DMC algorithm is as follows:
known as PThe MSM has a motion equation of formula (3) using idWhen the control strategy is 0, the electromagnetic torque formula of the motor is as follows:
let the load torque TLWhen the frequency domain model is 0, the frequency domain model obtained by simultaneously taking laplace transform on two sides of the formula (3) is as follows:
wherein, K is 1.5PnψfFurther, from the step response of the controlled object formula (16) of PMSM, the dynamic coefficient a for dynamic characteristics is used1，a2，…，apP is the model time domain length; a ispFor coefficients sufficiently close to the steady state value, at time k, the control action is set to remain constant and there is an initial predicted value for the speed output at a future timeQuadrature axis current i of PMSM in dq coordinate systemqActing Δ i as a control variable in successive control incrementsq(k)，…，ΔiqAnd (k + j-i), the output value of the rotating speed of the motor at each future moment is as follows:
wherein,
the specific control increment of the dynamic matrix algorithm is determined by an optimization criterion formula which is as follows:
wherein q and r are an error weight coefficient and a control weight coefficient respectively, ω (k + j | k) is an expected output, and ω (k + j | k) is set as a reference track given by the rotating speed of the permanent magnet synchronous motor
W＝[ω(k+1|k),ω(k+2|k),L,ω(k+p|k)]T(20)
The optimization criterion equation (19) is expressed as:
in the equation (21), the control vector is differentiated and the result is set to 0, and in the specific rolling implementation, only the instantaneous control increment is taken, and the instantaneous control increment is obtained by the equation (22):
wherein,
q, R denote diagonal matrices of error and control weight coefficients, cT＝[1 0 L 0]D is a control vector, dT＝cT(ATQA+RE)-1ATQ, thus constituting the actual control u (k), i.e.:
after the control action is implemented, the output rotating speed y of the motor at the next moment is collected*(k+1)，
Correcting the prediction by utilizing the estimated rotation speed of the PMSM of the oil drilling machine system, carrying out new optimization, and after implementing the instant control function on the PMSM at the moment k, the prediction output at the moment is as follows:
the predicted value at this timeWith the detected speed estimate output y of the PMSM*And (k +1) comparing to obtain an output error as follows:
by aligning the error e*(k +1) weighting to correct the prediction of the PMSM speed at the future time, i.e.:
wherein,
then updating the state and predicting the corrected PMSM rotation speedFinally obtaining the rotation speed forecast of the PMSM at the next moment according to the formula (27) as the initial forecast value at the next momentComprises the following steps:
furthermore, in the fourth step, a permanent magnet motor DMC vector control structure based on EKF is formed, the whole motor system of the control structure is a double closed loop speed regulation system consisting of a rotating speed outer loop and a current inner loop, four inputs of an EKF rotating speed estimation module are stator current and stator voltage under a static coordinate system, the output is estimated rotor rotating speed, the output is used as feedback information of the rotating speed loop, an error obtained after comparison with the predicted output is further corrected through DMC algorithm, and finally sensorless rotating speed control of the whole system is completed.
The beneficial technical effects of the invention are as follows: because EKF needs to carry out matrix operation including addition, multiplication, inversion, transposition and the like in practical application, the whole calculation and control process is very complex, firstly, the high-nonlinearity permanent magnet synchronous motor model is linearly processed through a variable transformation element, the model after variable replacement can be observed and controlled due to the existence of a rotor position angle theta parameter in the variable transformation element process, and on the basis of EKF control, the rotating speed predicted and output through the EKF replaces the rotating speed measured by a sensor in the original permanent magnet synchronous motor dynamic matrix control, so that the whole control process is finished through a computer, the controllability of the system is improved, and the EKF is more convenient to be applied in more occasions.
Drawings
The invention is further elucidated with reference to the drawings and the embodiments.
FIG. 1 is a block diagram of a dynamic matrix calculation process according to the present invention;
FIG. 2 is a block diagram of a motor DMC vector control structure based on EKF.
Detailed Description
Example 1
A method for controlling a dynamic matrix of a permanent magnet synchronous motor comprises the following steps:
establishing a dq axis mathematical model of PMSM (permanent magnet synchronous Motor), taking an Extended Kalman Filter (EKF) as a random observer, and correcting a prediction variable through an observation variable of an extended Kalman filter algorithm to obtain an optimal prediction value;
constructing vector control by using an Extended Kalman Filter (EKF), setting a rotor flux linkage, a rotating speed and a rotor position angle as state variables, and setting a stator voltage as an input variable, wherein if the sampling frequency of rotating speed closed-loop control is sufficiently larger than a mechanical time constant or rotational inertia of a motor, the rotating speed is constant in a sampling period, so that a rotating speed estimation error is reduced to system noise, and the precision of a motor model is not influenced;
thirdly, PMSM is realized by DMC algorithm, the DMC algorithm utilizes step response of an object, and the steps of model prediction, rolling optimization and feedback correction are realized;
fourthly, forming a permanent magnet motor DMC vector control structure based on EKF.
Example 2
Preferably, in embodiment 1, the PMSM is a surface-mounted PMSM. When the PMSM is used as a driving device of an oil drilling machine system, the permanent magnet magnetic pole of the surface-mounted PMSM is convenient for realizing optimal design and is beneficial to improving the control performance of a motor, so that the surface-mounted PMSM is used as a research object.
A dq axis mathematical model of PMSM is established as follows:
voltage equation (1)
Electromagnetic torque equation (2)
Mechanical equation of motion (3)
In the formula id，iqAnd ud，uqDq components of stator current and voltage, respectively; l isd，LqAnd psid，ψqDq components of stator inductance and flux linkage, respectively; rSIs a stator resistor; omega is the mechanical angular speed of the rotor; n ispIs the number of pole pairs; psifIs a permanent magnet flux linkage; t ise，TLThe electromagnetic torque and the load torque of the motor are respectively; j is moment of inertia; p is the viscous friction coefficient.
Further, the extended kalman filter algorithm is as follows:
x(k+1)＝Ax(k)+Bu(k)+V(k) (4)
y(k)＝Hx(k)+W(k) (5)
in the formula: v (k) is system noise; w (k) is measurement noise.
Example 3
As a preference to embodiment 2, the step of the extended kalman filter algorithm includes:
wherein, M and N are covariance matrixes of V and W respectively, K (K +1) is a gain matrix, the superscript is a predicted value, and the superscript is an inverted value.
In the second step, the step of constructing the vector control by the EKF with the extended Kalman filter means that the dq axis is αβ axis model, an EKF observer is designed under αβ axis, the EKF is used for constructing the vector control, and the rotor flux linkage psi is setα、ψβIf the sampling frequency of the rotating speed closed-loop control is large enough compared with the mechanical time constant or the rotational inertia of the motor, the rotating speed is constant in the sampling period, so that the rotating speed estimation error is attributed to the system noise, the precision of the motor model is not influenced,
wherein, the flux linkage equation of PMSM is:
the stator voltage equation is:
substituting the flux linkage equation into the voltage equation can obtain:
since the sampling period in a digital system is short, we can assume ω is constant in each sampling period and we can then derive:
while
Selecting x (t) ═ psiαψβωθ]TAs state variables, a state equation and an output equation are constructed as follows:
wherein,
v is the system noise; and w is measurement noise.
Example 4
As a preferred embodiment of embodiment 1, as shown in fig. 1, in the third step, the steps of implementing PMSM by using DMC algorithm are as follows:
the equation of motion of the PMSM is known as equation (3), and i is adopteddWhen the control strategy is 0, the electromagnetic torque formula of the motor is as follows:
let the load torque TLWhen the frequency domain model is 0, the frequency domain model obtained by simultaneously taking laplace transform on two sides of the formula (3) is as follows:
wherein, K is 1.5PnψfFurther, from the step response of the controlled object formula (16) of PMSM, the dynamic coefficient a for dynamic characteristics is used1，a2，…，apP is the model time domain length; a ispFor coefficients sufficiently close to the steady state value, at time k, the control action is set to remain constant and there is an initial predicted value for the speed output at a future timeQuadrature axis current i of PMSM in dq coordinate systemqActing Δ i as a control variable in successive control incrementsq(k)，…，ΔiqAnd (k + j-i), the output value of the rotating speed of the motor at each future moment is as follows:
wherein,
the specific control increment of the dynamic matrix algorithm is determined by an optimization criterion formula which is as follows:
wherein q and r are an error weight coefficient and a control weight coefficient respectively, ω (k + j | k) is an expected output, and ω (k + j | k) is set as a reference track given by the rotating speed of the permanent magnet synchronous motor
W＝[ω(k+1|k),ω(k+2|k),L,ω(k+p|k)]T(20)
The optimization criterion equation (19) is expressed as:
in the equation (21), the control vector is differentiated and the result is set to 0, and in the specific rolling implementation, only the instantaneous control increment is taken, and the instantaneous control increment is obtained by the equation (22):
wherein,
q, R denote diagonal matrices of error and control weight coefficients, cT＝[1 0 L 0]D is a control vector, dT＝cT(ATQA+RE)-1ATQ, thus constituting the actual control u (k), i.e.:
after the control action is implemented, the output rotating speed y of the motor at the next moment is collected*(k+1)，
Correcting the prediction by utilizing the estimated rotation speed of the PMSM of the oil drilling machine system, carrying out new optimization, and after implementing the instant control function on the PMSM at the moment k, the prediction output at the moment is as follows:
the predicted value at this timeWith the detected speed estimate output y of the PMSM*And (k +1) comparing to obtain an output error as follows:
by aligning the error e*(k +1) weighting to correct the prediction of the PMSM speed at the future time, i.e.:
wherein,
then updating the state and predicting the corrected PMSM rotation speedFinally obtaining the rotation speed forecast of the PMSM at the next moment according to the formula (27) as the initial forecast value at the next momentComprises the following steps:
as shown in fig. 2, in the fourth step, an EKF-based DMC vector control structure of the permanent magnet motor is formed, the whole motor system of the control structure is a double closed loop speed regulation system consisting of a rotation speed outer loop and a current inner loop, four inputs of an EKF rotation speed estimation module are stator current and stator voltage in a stationary coordinate system, an estimated rotor rotation speed is output, the output is used as feedback information of the rotation speed loop, an error obtained after comparison with a predicted output is further corrected through a DMC algorithm, and finally sensorless rotation speed control of the whole system is completed.
The use of the traditional position sensor can increase the mechanical load of a drilling machine system, is not beneficial to the installation and the movement of a drilling machine, and the work of the sensor can be interfered under severe environment, which can influence the control effect of the PMSM and the normal work of the drilling machine, so the EKF (extended Kalman Filter) is adopted to carry out the PMSM position-free sensor design, the motor is subjected to closed-loop control by observing the position speed information of the motor, the mechanical equipment of the drilling machine is simplified, the use of the drilling machine system is more flexible, and the reliability is improved.
The rotation speed and the position information of the PMSM are estimated by detecting electric signals such as voltage and current in the PMSM and adding a proper algorithm program, namely, a mechanical sensor is not needed, so that the PMSM position-sensor-free control technology is formed. The Kalman filtering algorithm is developed from the linear system field to the nonlinear field by utilizing the excellent capability of weakening random interference and measuring noise, the designed Kalman filtering gain of the filter can be adaptively adjusted along with the environment, and then the system state can be estimated on line, and the real-time control of the system state is realized. Because of the excellent capability of the Kalman filtering algorithm in weakening random interference and measuring noise and the characteristic of the Kalman filtering algorithm that the Kalman filtering algorithm can be adaptively adjusted along with the environment, the EKF algorithm is selected to design the PMSM position-free sensor. On the basis of the control of a PMSM speed ring DMC of an oil drilling machine system, the EKF sensorless technology is applied, the installation of mechanical equipment is reduced, the cost is reduced, the reliability of the drilling machine system is improved, the working efficiency of a drilling machine can be further improved, and the operation under a complex environment is facilitated.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (7)
1. A dynamic matrix control method for a permanent magnet synchronous motor is characterized by comprising the following steps:
establishing a dq axis mathematical model of PMSM (permanent magnet synchronous Motor), taking an Extended Kalman Filter (EKF) as a random observer, and correcting a prediction variable through an observation variable of an extended Kalman filter algorithm to obtain an optimal prediction value;
constructing vector control by using an Extended Kalman Filter (EKF), setting a rotor flux linkage, a rotating speed and a rotor position angle as state variables, and setting a stator voltage as an input variable, wherein if the sampling frequency of rotating speed closed-loop control is sufficiently larger than a mechanical time constant or rotational inertia of a motor, the rotating speed is constant in a sampling period, so that a rotating speed estimation error is reduced to system noise, and the precision of a motor model is not influenced;
thirdly, PMSM is realized by DMC algorithm, the DMC algorithm utilizes step response of an object, and the steps of model prediction, rolling optimization and feedback correction are realized;
fourthly, forming a permanent magnet motor DMC vector control structure based on EKF.
2. The permanent magnet synchronous motor dynamic matrix control method according to claim 1, characterized in that: the PMSM adopts a surface-mounted PMSM.
3. The permanent magnet synchronous motor dynamic matrix control method according to claim 1, characterized in that: the establishment of the dq axis mathematical model of the PMSM refers to the following steps:
voltage equation (1)
Electromagnetic torque equation (2)
Mechanical equation of motion (3)
In the formula id，iqAnd ud，uqDq components of stator current and voltage, respectively; l isd，LqAnd psid，ψqDq components of stator inductance and flux linkage, respectively; rSIs a stator resistor; omega is the mechanical angular speed of the rotor; n ispIs the number of pole pairs; psifIs a permanent magnet flux linkage; t ise，TLFor electromagnetic torque and negative of the motor respectivelyA load torque; j is moment of inertia; p is the viscous friction coefficient.
4. The permanent magnet synchronous motor dynamic matrix control method according to claim 1, characterized in that: the extended Kalman filtering algorithm is as follows:
x(k+1)＝Ax(k)+Bu(k)+V(k) (4)
y(k)＝Hx(k)+W(k) (5)
in the formula: v (k) is system noise; w (k) is measurement noise.
Further, the step of the extended kalman filter algorithm includes:
wherein, M and N are covariance matrixes of V and W respectively, K (K +1) is a gain matrix, the superscript is a predicted value, and the superscript is an inverted value.
5. The method for controlling the PMSM dynamic matrix according to claim 1, wherein in the second step, the EKF structure vector control using the extended Kalman filter is that the dq axis is αβ axis model, an EKF observer is designed under αβ axis, the EKF structure vector control is used, and the rotor flux linkage psi is setα、ψβIf the sampling frequency of the rotating speed closed-loop control is large enough compared with the mechanical time constant or the rotational inertia of the motor, the rotating speed is constant in the sampling period, so that the rotating speed estimation error is attributed to the system noise, the precision of the motor model is not influenced,
wherein, the flux linkage equation of PMSM is:
the stator voltage equation is:
substituting the flux linkage equation into the voltage equation can obtain:
setting ω to be constant within each sampling period, results in:
while
Selecting x (t) ═ psiαψβω θ]TAs state variables, a state equation and an output equation are constructed as follows:
wherein,
v is the system noise; and w is measurement noise.
6. The permanent magnet synchronous motor dynamic matrix control method according to claim 1, characterized in that: in the third step, the steps of implementing PMSM by DMC algorithm are as follows:
the equation of motion of the PMSM is known as equation (3), and i is adopteddWhen the control strategy is 0, the electromagnetic torque formula of the motor is as follows:
let the load torque TLWhen the frequency domain model is 0, the frequency domain model obtained by simultaneously taking laplace transform on two sides of the formula (3) is as follows:
wherein, K is 1.5PnψfFurther, from the step response of the controlled object formula (16) of PMSM, the dynamic coefficient a for dynamic characteristics is used1，a2，…，apP is the model time domain length; a ispFor coefficients sufficiently close to the steady state value, at time k, the control action is set to remain constant and there is an initial predicted value for the speed output at a future timeQuadrature axis current i of PMSM in dq coordinate systemqActing Δ i as a control variable in successive control incrementsq(k)，…，ΔiqAnd (k + j-i), the output value of the rotating speed of the motor at each future moment is as follows:
wherein,
the specific control increment of the dynamic matrix algorithm is determined by an optimization criterion formula which is as follows:
wherein q and r are an error weight coefficient and a control weight coefficient respectively, ω (k + j | k) is an expected output, and ω (k + j | k) is set as a reference track given by the rotating speed of the permanent magnet synchronous motor
W＝[ω(k+1|k),ω(k+2|k),…,ω(k+p|k)]T(20)
The optimization criterion equation (19) is expressed as:
in the equation (21), the control vector is differentiated and the result is set to 0, and in the specific rolling implementation, only the instantaneous control increment is taken, and the instantaneous control increment is obtained by the equation (22):
wherein,
q, R denote diagonal matrices of error and control weight coefficients, cT＝[1 0 … 0]D is a control vector, dT＝cT(ATQA+RE)-1ATQ, thus constituting the actual control u (k), i.e.:
after the control action is implemented, the output rotating speed y of the motor at the next moment is collected*(k+1)，
Correcting the prediction by utilizing the estimated rotation speed of the PMSM of the oil drilling machine system, carrying out new optimization, and after implementing the instant control function on the PMSM at the moment k, the prediction output at the moment is as follows:
the predicted value at this timeWith the detected speed estimate output y of the PMSM*And (k +1) comparing to obtain an output error as follows:
by aligning the error e*(k +1) weighting to correct the prediction of the PMSM speed at the future time, i.e.:
wherein,
then updating the state and predicting the corrected PMSM rotation speedFinally obtaining the rotation speed forecast of the PMSM at the next moment according to the formula (27) as the initial forecast value at the next momentComprises the following steps:
7. the permanent magnet synchronous motor dynamic matrix control method according to claim 1, characterized in that: in the fourth step, a permanent magnet motor DMC vector control structure based on EKF is formed, the whole motor system of the control structure is a double closed loop speed regulation system consisting of a rotating speed outer loop and a current inner loop, four inputs of an EKF rotating speed estimation module are stator current and stator voltage under a static coordinate system, the output is estimated rotor rotating speed, the output is used as feedback information of the rotating speed loop, an error obtained after comparison with predicted output is further corrected through DMC algorithm, and finally sensorless rotating speed control of the whole system is completed.
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| CN110289749A (en) * | 2019-05-31 | 2019-09-27 | 东南大学 | One kind being based on straight line-motor-driven pumping unit system of rotation double freedom and its control method |
| CN110765717A (en) * | 2019-10-22 | 2020-02-07 | 哈尔滨理工大学 | FPGA-based extended Kalman filter circuit structure design method |
| CN110798107A (en) * | 2019-09-30 | 2020-02-14 | 山东休普动力科技股份有限公司 | Free piston linear generator control system and control method based on DMC algorithm |
| CN111193448A (en) * | 2020-01-20 | 2020-05-22 | 江苏新安电器股份有限公司 | Surface-mounted permanent magnet synchronous motor load torque observation method based on extended Kalman filter |
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| CN115065290A (en) * | 2022-05-16 | 2022-09-16 | 北京理工大学 | Permanent magnet synchronous motor current harmonic suppression method based on data driving |
| CN117725687A (en) * | 2024-02-06 | 2024-03-19 | 华东交通大学 | Electric automobile road surface adhesion coefficient estimation method based on permanent magnet synchronous motor |
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| CN110289749A (en) * | 2019-05-31 | 2019-09-27 | 东南大学 | One kind being based on straight line-motor-driven pumping unit system of rotation double freedom and its control method |
| US11283380B2 (en) | 2019-08-06 | 2022-03-22 | Conti Temic Microelectronic Gmbh | Method and device for determining the rotational speed and the angle of rotation of a motor shaft of a mechanically commutated DC motor |
| CN110798107A (en) * | 2019-09-30 | 2020-02-14 | 山东休普动力科技股份有限公司 | Free piston linear generator control system and control method based on DMC algorithm |
| CN110765717A (en) * | 2019-10-22 | 2020-02-07 | 哈尔滨理工大学 | FPGA-based extended Kalman filter circuit structure design method |
| CN111193448A (en) * | 2020-01-20 | 2020-05-22 | 江苏新安电器股份有限公司 | Surface-mounted permanent magnet synchronous motor load torque observation method based on extended Kalman filter |
| CN111193448B (en) * | 2020-01-20 | 2022-11-04 | 江苏新安电器股份有限公司 | Permanent magnet synchronous motor load torque observation method based on extended Kalman filter |
| CN115065290A (en) * | 2022-05-16 | 2022-09-16 | 北京理工大学 | Permanent magnet synchronous motor current harmonic suppression method based on data driving |
| CN115065290B (en) * | 2022-05-16 | 2024-06-28 | 北京理工大学 | Permanent magnet synchronous motor current harmonic suppression method based on data driving |
| CN117725687A (en) * | 2024-02-06 | 2024-03-19 | 华东交通大学 | Electric automobile road surface adhesion coefficient estimation method based on permanent magnet synchronous motor |
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