CN115864928A - PMSM model reference self-adaptive rotation speed estimation method based on correction current prediction - Google Patents
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Abstract
The invention discloses a PMSM model reference self-adaptive rotating speed estimation method based on correction current prediction, and belongs to the technical field of motor control. Firstly, deriving a current state equation of the PMSM in a synchronous rotating coordinate system, and discretizing the current state equation by adopting a Taylor formula to obtain a current prediction control equation; secondly, optimizing d-q axis currents of the controller by combining correction factors, and replacing a traditional current prediction controller with a correction current prediction controller; meanwhile, compensation integral terms are respectively introduced into the d axis and the q axis to weaken the current deviation of the d-q axis; and finally, deducing a reference model and an adjustable model containing motor rotating speed information, analyzing the current error model through a Popov ultra-stability theory, obtaining the rotating speed self-adaptive rate of the model reference self-adaptive system, and substituting the improved control voltage into a rotating speed estimation link to estimate the motor rotating speed. The invention improves the rotating speed prediction precision and the dynamic performance of the sensorless speed regulating system.
Description
Technical Field
The invention relates to the technical field of motor control, in particular to a PMSM model reference self-adaptive rotation speed estimation method based on correction current prediction.
Background
The motor is used as a power source of electric appliances and other various machines and is widely applied to various industries. According to statistics, the electric quantity of the motor accounts for about 42-50% of the total electric consumption, and the electric quantity of the motor of 37KW or below accounts for about 50% of the total electric consumption. Among motors, a permanent magnet synchronous motor has recently been favored by researchers in various fields because of its advantages of simple structure, high efficiency, high power density, low noise, fast dynamic response, and the like, and research on a novel control technology of such a motor has been paid much attention by researchers in the related fields. And traditional PMSM control system generally adopts mechanical sensor to acquire rotor position information and rotational speed information in order to reach the purpose of control rotational speed, and wherein mechanical sensor's use can make motor volume increase, cost rising and control stability reduce, so sensorless control technique has appeared.
Since the sensorless control technology uses current and voltage signals to estimate the position of the rotor, which has the advantages of high reliability and low cost, it has been widely noticed by scholars in various fields since the 70 s of the 20 th century. In 1975, an a.abbondati team designs a slip frequency identification strategy based on induction voltage and applies the slip frequency identification strategy to an induction motor for the first time, but the alternating current speed regulation precision and the dynamic performance of the slip frequency identification strategy cannot meet the actual requirements. In 1979, a scholars of m.ishdia et al derived a speed identification algorithm based on tooth harmonic detection by using a relationship between a motor structural feature and a rotation speed signal, but the identification range is limited by the control performance of a digital chip. In 1983, ro.Joetten first transplanted the sensorless technology to the vector control of the AC asynchronous motor, and laid a certain theoretical basis for the development of the sensorless technology. Until 1989, scholars such as jones L.A applied sensorless technology to permanent magnet synchronous motors for the first time. Up to now, the sensorless technology has become the main field of each motor application, and has achieved certain achievements. According to the running speed of the motor, the sensorless control algorithm of a Permanent Magnet Synchronous Motor (PMSM) is divided into two types: the method is suitable for medium and high speed stages (the rotating speed is 10% or more of the rated rotating speed), such as a synovial membrane observer method, a model self-adaptive method, an extended Kalman filter method and the like; the other type is suitable for zero low speed (the rotating speed is less than 10% of the rated rotating speed), such as a high-frequency signal injection method, a zero sequence voltage method, a constant voltage frequency ratio method, a constant current frequency ratio method and the like.
A current loop of a traditional sensorless control system of the permanent magnet synchronous motor usually adopts a proportional-integral control method, and although the method is simple and easy to implement, the problems of phase lag, poor dynamic performance and the like are caused due to the characteristic of low-pass filtering. In some application occasions with high requirements on dynamic performance, such as fields of automatic machine tools, high-performance electric automobiles, aerospace and the like, the dynamic response capability of a current loop in a PMSM dual closed-loop vector control system needs to be improved, and the prediction current control method can well meet the requirements.
At present, the permanent magnet synchronous motor model reference self-adaptive speed regulation system based on the traditional current prediction control has some problems: the performance of the current prediction controller extremely depends on motor parameters, and the mismatch of the motor parameters can influence the stability of a system, so that the current generates oscillation, and the problems of low rotating speed estimation precision, poor dynamic performance and the like are caused.
Disclosure of Invention
1. Technical problem to be solved by the invention
Aiming at the problems of low rotating speed estimation precision, poor dynamic performance and the like caused by current oscillation of a control system in a traditional permanent magnet synchronous motor model reference self-adaptive control speed regulating system based on current prediction control, the invention provides a PMSM model reference self-adaptive rotating speed estimation method based on correction current prediction, and the sensorless vector control of a permanent magnet synchronous motor can be realized. In practical application, the position and the speed of the motor rotor are effectively tracked, the cost of a motor control system is reduced, and the steady-state precision and the dynamic performance of the system are improved.
2. Technical scheme
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
the invention provides a PMSM model reference self-adaptive rotating speed estimation method based on correction current prediction, which comprises the following steps:
establishing a mathematical model of the permanent magnet synchronous motor under a synchronous rotation coordinate system d-q, deducing a current state equation, and discretizing the current state equation by adopting a Taylor formula to obtain a current prediction control equation;
optimizing d-q axis currents of the controller by combining correction factors, and replacing a traditional current prediction controller with a correction current prediction controller to reduce current oscillation and improve the stability of a system;
respectively introducing compensation integral terms into the d axis and the q axis to weaken the current deviation of the d-q axis, improve the current prediction precision and improve the control voltage value of the d-q axis;
and step four, deducing a reference model and an adjustable model containing motor rotating speed information, analyzing a current error model through a Popov ultra-stability theory, obtaining a rotating speed self-adaptive rate of a model reference self-adaptive system, and introducing the improved d-q axis control voltage value into a rotating speed estimation link to finish accurate estimation of the motor rotating speed.
3. Advantageous effects
Compared with the prior art, the technical scheme provided by the invention has the following remarkable effects:
the PMSM model reference self-adaptive rotating speed estimation method based on correction current prediction effectively solves the problems of low rotating speed estimation precision, poor dynamic performance and the like caused by current oscillation in a traditional sensorless permanent magnet synchronous motor control system. The control system has strong robustness, and the position and the speed of the motor rotor can be accurately estimated in a medium-high speed stage. Compared with the traditional current prediction based MRAS, the method overcomes the current oscillation problem, and improves the rotating speed estimation precision and the system dynamic performance.
Drawings
FIG. 1 is a block diagram of a PMSM sensorless vector control based on the proposed method;
fig. 2 (a) is a root trace diagram of the predictive current control system in the Z domain according to the present invention; fig. 2 (b) is a root trace diagram of the conventional predictive current control system in the Z domain;
fig. 3 (a) - (d) are d-q axis current simulation waveform diagrams respectively showing the mismatch of parameters of 0.5 times flux linkage, 1.5 times flux linkage, 0.5 times inductance, and 1.5 times inductance of the control system according to the present invention;
fig. 4 (a) - (d) are respectively d-q axis current simulation waveform diagrams of the conventional control system when parameters of the magnetic flux linkage of 0.5 times, the magnetic flux linkage of 1.5 times, the inductance of 0.5 times and the inductance of 1.5 times are mismatched;
fig. 5 (a) is a simulated comparison waveform diagram of the predicted rotating speed and the actual rotating speed when the loaded rotating speed of the control system of the invention changes suddenly; FIG. 5 (b) is a simulated comparison waveform diagram of the predicted rotation speed and the actual rotation speed when the loaded rotation speed of the conventional control system suddenly changes;
FIG. 6 (a) is a simulated comparison waveform of the error between the predicted rotation speed and the actual rotation speed of the control system of the present invention; FIG. 6 (b) is a simulated comparison waveform of the predicted and actual rotational speed errors of the conventional control system;
fig. 7 (a) is a simulated waveform diagram of a phase a stator current of the control system according to the present invention; fig. 7 (b) is a simulated waveform diagram of a phase a stator current of the conventional control system;
fig. 8 is a simulation comparison waveform of the predicted rotor position angle and the actual rotor position angle of the control system of the present invention.
Detailed Description
For a further understanding of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawings and examples.
Example 1
Fig. 1 is a block diagram of PMSM sensorless vector control based on the algorithm proposed in this embodiment. As shown in fig. 1, the control system is a masterThe motor comprises a motor body, a three-phase inverter, an SVPWM module, a PI rotating speed outer ring, a correction current prediction module, a flux linkage regulator, an inductance regulator, an MRAS module and the like. Obtaining alpha and beta axis given voltage u after inverse Park conversion α 、u β The SVPWM is used as an input value of voltage space vector modulation, and the PWM switching waveform is adjusted to control the on-off of the inverter thyristor, so that the sensorless vector speed regulation control of the permanent magnet synchronous motor is realized.
Sampling three-phase current i abc Is converted into i by Clark/Park dq And simultaneously calling d-q axis control voltage u optimized by the correction current prediction module provided by the invention d ，u q Substituting the two into a model reference self-adaptive rotating speed estimation link to obtain a reference model and an adjustable model containing motor rotating speed parameters, subtracting an estimated adjustable model current equation from an actual adjustable model current equation to obtain a current error equation, and estimating the speed of the motor by a Popov ultra-stability theory
And rotor position->
The specific steps of this embodiment are:
step one, establishing a mathematical model of the permanent magnet synchronous motor under a synchronous rotation coordinate system d-q, deducing a current state equation, and discretizing the current state equation by adopting a Taylor formula to obtain a current prediction control equation:
in order to simplify the analysis, the PMSM is assumed to be an ideal motor, a mathematical model of the permanent magnet synchronous motor under a two-phase rotating coordinate system is obtained through coordinate transformation as shown in a formula (1),
in formula (1): u. of d 、u q Is stator voltage u s Components at the d-axis and q-axis; i.e. i d 、i q Is stator current i s Components at the d-axis and q-axis; r s Is a stator resistor; psi d 、ψ q The components of the stator flux linkage in the d-axis and the q-axis; l is d 、L q The components of the stator inductance in the d axis and the q axis; w is a e Is the electrical angular velocity; psi f Is a permanent magnet flux linkage.
Will phi d 、ψ q Substituting the value into a stator voltage equation of the permanent magnet synchronous motor under a two-phase rotating coordinate system to deduce a current state equation:
due to the sampling period T s Sufficiently small, so that the Taylor formula is adopted to discretize the current state equation to obtain i d(k) ，i q(k) About i d(k+1) ，i q(k+1) The equation of (a) is:
wherein: i.e. i d(k+1) ，i q(k+1) And i d(k) ，i q(k) Respectively given values of d-q axis current at the time k +1 and the time k.
The discretized permanent magnet synchronous motor current prediction control equation obtained by the joint formula (2) and the formula (3) is as follows:
will i d(k) ，i q(k) As the next time T (k+1) Input amount i of d(k+1) ，i q(k+1) Wherein i d(k) ，i q(k) For d-q axis current set point, use i d Vector control strategy of =0, the voltage equation under the synchronous rotating coordinate system at time k can be written as:
and step two, optimizing d-q axis currents of the controller by combining correction factors, and replacing a traditional current prediction controller with a correction current prediction controller to reduce current oscillation and improve the stability of the system:
analyzing factors influencing the stability of the predictive control system, substituting the real values of the parameters of the motor model of the controller into a PMSM voltage and current equation to obtain:
in the above formula:
in formula (7):
since the motor speed changes relatively slowly with respect to the current, w will be e(k) As a disturbance term, Z transformation processing is carried out on the formula (7), and then the discrete domain closure of the traditional current prediction control system is obtainedLoop transfer function:
with reference to fig. 2 (b), the system is stable when the motor nominal inductance is in the range of 0-2 times the true inductance; if the range is exceeded, the closed loop pole of the system is located in the right half plane of the S plane, so that the system oscillates, and the stability margin of the system is relatively small. In order to improve the stability margin of a system and reduce the current oscillation of the system, a correction factor alpha (0 < alpha < 1) is introduced to process a d-q axis current feedback value:
in the above formula:
for controller current set point, i dq(k) The actual value of the controller current.
Obtaining a PMSM voltage equation after introducing a correction factor:
similarly, the above formula is subjected to Z conversion to obtain a discrete domain closed-loop transfer function of the system after the correction factor is introduced:
from the equation (11), when the mismatch coefficient of the inductance parameter is in the range of [0,2/(1- α) ], the system is stable, and compared with the equation (8), the stability margin of the system is improved by the method provided by the invention. When α =0.6 is taken in conjunction with (a) and (b) in fig. 2, the range in which the inductance parameter can be stabilized after mismatch is increased from 0-2 to 0-5, thereby improving the stability margin of the system.
Respectively introducing compensation integral terms into the d axis and the q axis to weaken the current deviation of the d-q axis, improve the current prediction precision and improve the control voltage value of the d-q axis:
in order to analyze the cause of the deviation between the d-q axis command current and the actual current, a model for the deviation of the d-q axis current is obtained. Because the sampling period is very short, the current change of d-q axes can be ignored in two adjacent current loop control periods, and the d-q axis current deviation of the PMSM control system with the introduced correction factor is analyzed according to the relation between the real current and the given current in the step two, and the deviation analysis model is as follows:
in the formula (12), the factors influencing the d-axis current deviation are mainly d-axis inductance parameter errors and correction factor parameters; the factors influencing the q-axis current deviation are mainly q-axis inductance parameter errors, flux linkage errors and correction factor parameters.
The generation of the current deviation can reduce the prediction accuracy of the current, and in order to solve the influence caused by the problem, the invention provides a compensation strategy to weaken the current deviation. First, based on i d On the basis of a vector control strategy of =0, in order to weaken the d-axis current deviation, a compensation integral term is introduced on the d-axis control voltage, namely:
wherein, u' d (k) For compensated d-axis control voltage, k d Is the d-axis current offset compensation factor.
On the basis of the completion formula (13), the d-axis current deviation is greatly weakened, and at the moment, the factor influencing the q-axis current deviation is mainly flux linkage, so flux linkage parameters are adjusted by adjusting the dynamic response of the q-axis current, and flux linkage errors are reduced, so that the q-axis current deviation is weakened. For this purpose, an integral term is introduced to make the flux linkage parameter of the controller motor model converge to a true value:
wherein: psi' f (k) To compensate for flux linkage parameters, k, of the post-controller motor model q The flux linkage error compensation coefficient.
As can be seen from fig. 3 and 4, the simulation result is consistent with the theoretical analysis result, and the control system with the introduced compensation integral term can greatly reduce the current deviation, thereby improving the current prediction accuracy and further improving the rotational speed estimation accuracy.
Step four, deducing a reference model and an adjustable model containing motor rotating speed information, analyzing a current error model through a Popov ultra-stability theory, obtaining a rotating speed self-adaptive rate of a model reference self-adaptive system, and introducing the improved d-q axis control voltage value into a rotating speed estimation link to finish accurate estimation of the motor rotating speed:
in the fourth step, in order to obtain an adjustable model containing the motor rotation speed parameters, the current state equation of the permanent magnet synchronous motor is rewritten to obtain:
herein is defined:
i′ q ＝i q ，/>
u′ q ＝u q 。
an adjustable model of the PMSM reference adaptive system can be obtained:
wherein: w is a e Is an adjustable parameter to be identified.
The PMSM is used as a reference model, and a proper adaptive rate is needed to complete the establishment of MRAS. The current value and the motor speed value of the above formula are replaced by estimated values:
variables with 'Lambda' represent corresponding estimated values, and an adjustable model based on motor estimated parameters can be obtained:
and subtracting the estimated adjustable model current equation from the actual adjustable model current equation to obtain a current error equation:
herein is defined:
rewriting formula (18) to the form:
from the Popov hyperstability theory, the conditions for stabilizing the system are as follows:
(1) Transfer function H (s) ＝(sI-A) -1 Is a strict positive definite matrix;
(2)
γ 0 is an arbitrary finite positive number. At this time, then there are
I.e. MRAS is asymptotically stable.
And (3) reversely solving a Popov integral inequality to obtain the MRAS self-adaption rate:
in the above formula: k p And K i Respectively, the proportional coefficient and the integral coefficient of the MRAS.
With reference to fig. 1, the d-q axis control voltage value optimized in step three needs to be substituted into the rotation speed estimation model, where the rotation speed estimation equation is:
integrating the above equation to obtain the rotor position estimation value as follows:
the design process of the method of the embodiment is simulated and tested by MATLAB/Simulink simulation and a Pocket bench semi-physical platform. A speed regulating system based on the PMSM rotating speed estimation method provided by the invention is compared with a PMSM model reference self-adaptive speed regulating system based on traditional current prediction through simulation. The parameters of the permanent magnet synchronous motor are as follows: rated speed of rotation
Stator resistance R s =0.4578 Ω, quadrature axis inductance L d ＝L q =3.34mH, rotor flux linkage
Pole pair number P =4, moment of inertia J =14.69kg · cm 2 . Motor load (T) N =10N · m) is started, the system initially sets a given rotational speed ÷ in>
When the motor control system operates to 0.5s, the rotating speed is suddenly changed from 1000rad/min to 2000rad/min, the given torque of the system is unchanged, and the simulation operation time is 1s.
As can be seen from the analysis of fig. 3 and 4, the PMSM was started in a loaded state, and the deviation of the d-q axis current within 0.5s was examined. In fig. 4, when the inductance parameter is smaller, the feedback value of the q-axis response current is smaller than the given value, and the feedback value of the d-axis response current is larger than the given value; when the inductance parameter is larger, the feedback values of the d-q axis response current are smaller than the given value, but the influence on the q axis is relatively small. When the flux linkage parameter is smaller, the feedback value of the q-axis response current is smaller than a given value, and the d-axis current basically has no deviation; when the flux linkage parameter is larger, the feedback value of the q-axis response current is larger than a given value, and the feedback value of the d-axis response current is slightly smaller than the given value. Similarly, in fig. 3, the current condition after the deviation processing algorithm is introduced is analyzed, and compared with fig. 4, the d-q axis current deviation is greatly weakened, so that the effectiveness of the algorithm is proved.
As can be seen from the analysis of FIG. 5, the system is started in the loaded state, and the given rotating speed is
The sudden change of the rotating speed is 2000rad/min at 0.5s, both control methods can reach the given value quickly, and the fluctuation ranges of the rotating speed estimated value of the PMSM control system based on the method provided by the invention are 998 rad/min-1002 rad/min and 1998 rad/min-2002 rad/min respectively through a local amplification oscillogram in the stabilizing process, and the range of the stable rotating speed amplitude jump is +/-2 rad/min; the fluctuation ranges of the PMSM rotating speed estimated value controlled based on the traditional algorithm are 990 rad/min-1010 rad/min and 1990 rad/min-2020 rad/min respectively, the range of the stable rotating speed amplitude jump is +/-11 rad/min, and however, the rotating speed waveform buffeting is large when the rotating speed is started and suddenly changed. As can be seen from FIG. 5, the control system based on the method for estimating the increased rotational speed according to the present invention has pulsation estimated from the rotational speed of the motor, compared to the control system based on the conventional methodThe amplitude is lower, the buffeting is smaller when the rotating speed jumps, and the dynamic and steady performance is better.
As can be seen from the analysis of FIG. 6, after the motor is stabilized at a given rotation speed, the variation range of the PMSM rotation speed estimation error controlled by the method provided by the invention is within the range of + -3 rad/min, while the variation range of the PMSM rotation speed estimation error controlled by the traditional method is within the range of + -15 rad/min. Therefore, the PMSM control system based on the method provided by the invention has higher estimation precision and better stability.
Analyzing fig. 7, it can be known that, when the motor is stabilized at a given rotation speed, in the PMSM control speed regulating system based on the conventional method, the a-phase stator current waveform generates local oscillation, and the current amplitude fluctuates; in the PMSM speed regulation control system based on the method provided by the invention, the A-phase stator current waveform basically has no oscillation after the rotating speed is stable, the current amplitude is stable, and the current waveform is smoother.
The PMSM semi-physical platform based on the Pocket Bench is used for carrying out experiments, and mainly comprises a Pocket Bench ultra-compact power converter hardware-in-loop real-time simulator, a TMS320F28335 control board, a YXDSP-XDS100V3 simulator, upper computer CCS software and the like, wherein the Pocket Bench simulator is directly powered by a computer USB interface. Experiments show that the given rotating speed of the motor is 1500rad/min, the permanent magnet synchronous motor is started under the condition of rated load of 10 N.m, the actual waveforms of the current of the motor and the position of a rotor are stable, the actual rotating speed of the motor is finally stabilized at 1500rad/min, and the correctness of the method provided by the invention is verified.
The present invention and its embodiments have been described above schematically, without limitation, and what is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, without departing from the spirit of the invention, the person skilled in the art shall not inventively design the similar structural modes and embodiments to the technical solution, but shall fall within the scope of the invention.
Claims (5)
1. A PMSM model reference self-adaptive rotation speed estimation method based on correction current prediction is characterized by comprising the following steps:
establishing a mathematical model of the permanent magnet synchronous motor under a synchronous rotation coordinate system d-q, deducing a current state equation, and discretizing the current state equation by adopting a Taylor formula to obtain a discretized current prediction control equation;
step two, optimizing d-q axis currents of the controller by combining correction factors, and replacing a traditional current prediction controller with a correction current prediction controller;
respectively introducing compensation integral terms into the d axis and the q axis to weaken the current deviation of the d-q axis, improve the current prediction precision and improve the control voltage value of the d-q axis;
and step four, deducing a reference model and an adjustable model containing motor rotating speed information, analyzing a current error model through a Popov ultra-stability theory, obtaining a rotating speed self-adaptive rate of a model reference self-adaptive system, and introducing the improved d-q axis control voltage value into a rotating speed estimation link to finish accurate estimation of the motor rotating speed.
2. The PMSM model reference adaptive speed estimation method based on corrected current prediction as claimed in claim 1, wherein: in the first step, a mathematical model of the permanent magnet synchronous motor under a two-phase rotating coordinate system is obtained through coordinate transformation, as shown in formula (1),
in formula (1): u. of d 、u q Is stator voltage u s Components at the d-axis and q-axis; i all right angle d 、i q Is stator current i s Components at the d-axis and q-axis; r s Is a stator resistor; psi d 、ψ q The components of the stator flux linkage in the d-axis and the q-axis; l is d 、L q The components of the stator inductance in the d axis and the q axis; w is a e Is the electrical angular velocity; psi f Is a permanent magnet flux linkage;
will phi d 、ψ q Substituting the value of (A) into permanent magnet synchronizationThe stator voltage equation of the motor under the two-phase rotating coordinate system obtains a current state equation as follows:
discretizing the formula (2) by adopting a Taylor formula to obtain i d(k) ，i q(k) About i d(k+1) ，i q(k+1) The equation of (a) is:
wherein: i.e. i d(k+1) ，i q(k+1) And i d(k) ，i q(k) Respectively setting values of d-q axis current at the moment k +1 and the moment k;
the discretized permanent magnet synchronous motor current prediction control equation obtained by the joint vertical type (2) and the formula (3) is as follows:
will i d(k) ，i q(k) As the next time T (k+1) Input amount i of d(k+1) ，i q(k+1) Wherein i is d(k) ，i q(k) For d-q axis current set point, use i d Vector control strategy of =0, the voltage equation under the synchronous rotating coordinate system at time k can be written as:
3. the PMSM model reference adaptive speed estimation method based on corrected current prediction as claimed in claim 2, wherein: in the second step, the factors influencing the stability of the predictive control system are analyzed, and the real values of the parameters of the motor model of the controller are substituted into a PMSM voltage and current equation to obtain:
in the above formula:
in formula (7):
since the motor speed changes relatively slowly with respect to the current, w will be e(k) As a disturbance term, performing Z transformation processing on the formula (7) to obtain a discrete domain closed-loop transfer function of the traditional current prediction control system:
introducing a correction factor alpha, 0< alpha <1, and processing a d-q axis current feedback value:
in the above formula:
for controller current set point, i dq(k) The actual value of the current of the controller;
obtaining a PMSM voltage equation after introducing a correction factor:
similarly, the above formula is subjected to Z transformation to obtain a discrete domain closed-loop transfer function of the system after the correction factor is introduced:
4. the PMSM model reference adaptive speed estimation method based on corrected current prediction as claimed in claim 3, wherein: in the third step, in two adjacent current loop control periods, current changes of d-q axes are ignored, and the d-q axis current deviation of the PMSM control system with the introduced correction factors is analyzed according to the relation between the real current and the given current in the second step, wherein a d-q axis current deviation model is as follows:
in the formula (12), the factors influencing the d-axis current deviation are mainly d-axis inductance parameter error and correction factor parameters; factors influencing the q-axis current deviation mainly include q-axis inductance parameter errors, flux linkage errors and correction factor parameters;
based on i d On the basis of a vector control strategy of =0, in order to weaken the d-axis current deviation, a compensation integral term is introduced on the d-axis control voltage, namely:
wherein u' d (k) For compensated d-axis control voltage, k d D-axis current deviation compensation coefficients;
and (3) introducing an integral term to make the flux linkage parameter of the controller motor model converge to a true value:
wherein: psi f ' (k) is the flux linkage parameter of the compensated controller motor model, k q The flux linkage error compensation coefficient.
5. The PMSM model reference adaptive speed estimation method based on corrected current prediction as claimed in claim 4, wherein: in the fourth step, in order to obtain an adjustable model containing a motor rotation speed parameter, the current state equation of the permanent magnet synchronous motor is rewritten to obtain:
herein is defined:
i′ q ＝i q ，/>
u′ q ＝u q ；
the adjustable model of the PMSM model reference adaptive system can be obtained as follows:
wherein: w is a e Is an adjustable parameter to be identified;
taking the PMSM as a reference model, if a model reference adaptive system is to be established, a proper adaptive rate is needed; the current value and the motor speed value of the above formula are replaced by estimated values:
and expressing corresponding estimated values by using variables with the values, and obtaining an adjustable model based on the estimated parameters of the motor:
subtracting the estimated adjustable model current equation from the actual adjustable model current equation to obtain a current error equation:
herein is defined:
rewriting formula (18) to the form:
according to a Popov hyperstability theory, reversely solving a Popov integral inequality to obtain the MRAS self-adaption rate:
in the above formula: k p And K i Respectively are a proportional coefficient and an integral coefficient of the MRAS;
substituting the improved d-q axis control voltage value into a rotating speed estimation model, wherein a rotating speed estimation equation is as follows:
the rotor position estimate can be found by integrating the above equation:
therefore, the sensorless PMSM model reference self-adaptive rotation speed estimation is realized.
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Cited By (3)
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| CN116896303A (en) * | 2023-05-15 | 2023-10-17 | 苏州海格电控股份有限公司 | MRAS-based permanent magnet synchronous motor speed-free sensor control method and system |
| CN117040339A (en) * | 2023-10-09 | 2023-11-10 | 潍柴动力股份有限公司 | Vector decoupling method, device and equipment for permanent magnet synchronous motor and storage medium |
| CN117767829A (en) * | 2023-12-25 | 2024-03-26 | 赛力斯汽车有限公司 | Closed-loop control method and device for motor rotation speed, electronic equipment and storage medium |
-
2022
- 2022-12-14 CN CN202211605161.4A patent/CN115864928A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116896303A (en) * | 2023-05-15 | 2023-10-17 | 苏州海格电控股份有限公司 | MRAS-based permanent magnet synchronous motor speed-free sensor control method and system |
| CN116896303B (en) * | 2023-05-15 | 2024-04-02 | 苏州海格电控股份有限公司 | MRAS-based permanent magnet synchronous motor speed-free sensor control method and system |
| CN117040339A (en) * | 2023-10-09 | 2023-11-10 | 潍柴动力股份有限公司 | Vector decoupling method, device and equipment for permanent magnet synchronous motor and storage medium |
| CN117040339B (en) * | 2023-10-09 | 2024-01-16 | 潍柴动力股份有限公司 | Vector decoupling method, device and equipment for permanent magnet synchronous motor and storage medium |
| CN117767829A (en) * | 2023-12-25 | 2024-03-26 | 赛力斯汽车有限公司 | Closed-loop control method and device for motor rotation speed, electronic equipment and storage medium |
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