CN116896303B - MRAS-based permanent magnet synchronous motor speed-free sensor control method and system - Google Patents
MRAS-based permanent magnet synchronous motor speed-free sensor control method and system Download PDFInfo
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Classifications
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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
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
- H02P21/18—Estimation of position or speed
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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
- H02P21/13—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
- H02P21/141—Flux estimation
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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
- H02P21/22—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
- H02P21/24—Vector control not involving the use of rotor position or rotor speed sensors
-
- 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
- H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
- H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
- H02P25/022—Synchronous motors
- H02P25/024—Synchronous motors controlled by supply frequency
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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
- H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
- H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
- H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
- H02P27/08—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters with pulse width modulation
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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
- H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
- H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation
Abstract
The invention discloses a permanent magnet synchronous motor speed-sensorless control method and a system based on MRAS, wherein the control method comprises the following steps: taking a stator flux linkage equation as a reference model; obtaining a first adjustable model according to a stator voltage equation and a predefined state space model; obtaining a second adjustable model according to the first adjustable model and the correction compensation parameters; and regulating the reference model and the second adjustable model through a self-adaptive law to obtain the estimated rotating speed of the motor. The determination of the adaptive law is as follows: representing the second adjustable model by an observation estimated value to obtain a stator flux linkage observation model; obtaining an error state equation between the reference rotating speed and the estimated rotating speed of the motor according to the stator flux linkage observation model and the second adjustable model; the error state equation is divided into a form comprising linear time-invariant and nonlinear time-variant, and the self-adaptive law is obtained. The control method and the control system adopting the control method improve the anti-interference capability and stability of the system and the estimation precision of the motor rotating speed.
Description
Technical Field
The invention relates to the technical field of motors, in particular to a permanent magnet synchronous motor speed sensorless control method and system based on MRAS.
Background
In a modern alternating current speed regulation system, a three-phase permanent magnet synchronous motor (Three Phases Permanent Magnet Synchronous Motor) currently takes the dominant role in the fields of industry, manufacturing industry and new energy automobiles, and compared with an asynchronous induction motor, the permanent magnet synchronous motor has the characteristics of higher motor efficiency and power factor, small volume, simple structure, obvious energy-saving effect and the like, so that the permanent magnet synchronous motor gradually replaces the asynchronous induction motor and is paid attention to in the industry.
In the field of new energy automobiles, for example, small motors used in steering booster pumps and air compression inflating pumps, if speed sensors are used for measuring the positions and rotation speed parameters of the small motors, the problems of high difficulty and high cost of installation of a speed sensor interface and wiring exist, and the problem of poor reliability of the speed sensor under complex working conditions also exists. Therefore, the development of the sensorless control strategy of the permanent magnet synchronous motor can not only improve the reliability of the system, but also reduce the cost, and is a future development trend.
The model reference adaptive system (model reference adaptive system, MRAS) is often used for estimating parameters such as motor rotation speed in a speed-free sensor control strategy due to the characteristics of high speed, strong robustness, small jitter and the like. However, the conventional model reference adaptive system is mainly aimed at the surface-mounted permanent magnet synchronous motor, and the motor rotating speed is estimated through the counter electromotive force of the motor, but the method has the problem of low estimation accuracy at low speed, and the method for estimating the permanent magnet synchronous motor rotating speed based on a stator current model or a stator flux linkage model, which is adopted after improvement, improves the estimation accuracy of the rotating speed, but lacks consideration of the problems of anti-interference, stability and the like of the system when the response speed is improved, so that the anti-interference capability and stability of the system are poor.
Disclosure of Invention
In order to improve the anti-interference capability and stability of a speed sensorless control system and improve the estimation accuracy of the motor rotation speed, the application provides a speed sensorless control method and system of a permanent magnet synchronous motor based on MRAS.
In a first aspect, the present application provides a permanent magnet synchronous motor speed sensorless control method based on MRAS, which adopts the following technical scheme: the MRAS-based permanent magnet synchronous motor speed-free sensor control method comprises the following steps:
taking a stator flux linkage equation as a reference model;
obtaining a first adjustable model according to a stator voltage equation and a predefined state space model;
based on the first adjustable model and the correction compensation parameter k c Obtaining a second adjustable model;
the reference model and the second adjustable model are adjusted through a self-adaptive law to obtain the estimated rotating speed of the motor
By adopting the technical scheme, the correction compensation parameter k is introduced into the first adjustable model c The second adjustable model is obtained, the controllability of the first adjustable model is improved, the motor rotation speed is small in shaking and high in convergence speed, the anti-interference capability and stability of the system are improved, and the estimation accuracy of the motor rotation speed is higher.
In a specific embodiment, the determination of the adaptive law is specifically:
representing the second adjustable model by an observation estimated value to obtain a stator flux linkage observation model;
obtaining a motor reference rotating speed omega according to the stator flux linkage observation model and the second adjustable model e And motor estimated rotation speedAn error state equation between;
dividing the error state equation into a form comprising a linear time-invariant feedback system and a nonlinear time-variant feedback system;
and obtaining the self-adaptive law according to the divided error state equation.
In a specific embodiment, the stator flux linkage equation is:
wherein: l (L) d L is the d-axis inductance component of the stator q For the stator q-axis inductance component, i d I is the stator d-axis current component q For stator q-axis current component, ψ f Is a magnetic linkage of a permanent magnet,for stator d-axis flux linkage component reference, < ->A reference value for the q-axis flux linkage component of the stator;
the stator voltage equation is:
wherein: u (u) d For the stator d-axis voltage component, u q For the stator q-axis voltage component, R s Is stator resistance omega e For motor reference speed, ψ d As the actual value of the d-axis flux linkage component of the stator, ψ q Is the stator q-axis flux linkage component actual value.
In a specific embodiment, the predefined state space model is:
wherein u' d 、u′ q For a defined constant representation, u' d For the stator d-axis voltage component u d Is represented by the constant of u' q For the stator q-axis voltage component u q Is a constant representation of (1);
the first adjustable model obtained according to the stator voltage equation and the predefined state space model is as follows:
based on the first adjustable model and the correction compensation parameter k c The second adjustable model obtained is:
in a specific embodiment, the stator flux linkage observation model is:
wherein,for stator d-axis flux linkage component estimation, < +.>For stator q-axis flux linkage component estimation, k c To correct the compensation parameters, u' d 、u′ q For a defined constant representation, u' d For the stator d-axis voltage component u d Is represented by the constant of u' q For the stator q-axis voltage component u q Constant representation of->For stator d-axis flux linkage component reference, < ->A reference value for the q-axis flux linkage component of the stator;
the error state equation is:
the error state equation is divided into a form comprising a linear time-invariant feedback system and a nonlinear time-variant feedback system, wherein the form comprises the following steps:
wherein Ae is a linear time-invariant part, -W is a nonlinear time-variant part, e is a stator flux linkage error,
in a specific embodiment, the obtaining of the estimated rotational speed of the motorThereafter, the method further comprises the following steps:
estimating the rotational speed of the motorAnd feeding back the self-adaptive law to the second adjustable model to realize the real-time adjustment of the self-adaptive law.
By adopting the technical scheme, the obtained motor estimated rotating speedAnd the parameters in the model are updated in real time, so that the estimation accuracy of the motor rotating speed is improved.
In a specific embodiment, the obtaining of the estimated rotational speed of the motorThereafter, the method further comprises the following steps:
estimating rotational speed from the motorIntegrating to obtain the rotor estimated angle +.>
By adopting the technical scheme, the rotor estimation angle is further obtained through integration of the motor estimation rotating speed, and the estimation accuracy of the rotor angle is ensured.
In a specific embodiment, the rotational speed is estimated from the motorIntegrating to obtain the rotor estimated angle +.>The calculation formula of (2) is as follows:
in a second aspect, the present application provides a control system based on a speed sensor of an MRAS permanent magnet synchronous motor, which adopts the following technical scheme:
the control system comprises a reference model unit, an adjustable model unit, a self-adaptive law unit and an integration unit;
the reference model unit is used for taking the stator flux linkage equation as a reference model;
an adjustable model unit for obtaining a first adjustable model according to a stator voltage equation and a predefined state space model, and correcting the compensation parameter k according to the first adjustable model c Obtaining a second adjustable model;
an adaptive law unit for adjusting the reference model and the second adjustable model to obtain an estimated rotation speed of the motor
Integral sheetA unit for estimating the rotation speed according to the motorIntegrating to obtain the rotor estimated angle +.>
By adopting the technical scheme, the correction compensation parameter k is introduced into the first adjustable model c The second adjustable model is obtained, the controllability of the first adjustable model is improved, the motor rotation speed shaking is small, the convergence speed is high, the anti-interference capability and the stability of the system are improved, and the estimation accuracy of the motor rotation speed and the rotor angle is higher.
In a specific implementation manner, the control system further comprises a rotating speed ring, an MTPA unit, a current ring, a first conversion unit, a SVPWM unit, an inverter and a second conversion unit, wherein the permanent magnet synchronous motor is respectively connected with the inverter and the second conversion unit;
the rotating speed ring, the MTPA unit and the current ring are all connected with the self-adaptive law unit; the first conversion unit and the second conversion unit are both connected with the integration unit; the MTPA unit is also connected with the rotating speed ring and the current ring respectively; the current loop is also respectively connected with the adjustable model unit, the first conversion unit and the second conversion unit; the SVPWM unit is respectively connected with the first conversion unit and the inverter; the reference model unit and the adjustable model unit are both connected with the second conversion unit.
In summary, the technical scheme of the application at least comprises the following beneficial technical effects:
1. by introducing a correction compensation parameter k in the first adjustable model c The second adjustable model is obtained, the controllability of the first adjustable model is improved, the motor rotation speed shaking is small, the convergence speed is high, the anti-interference capability and the stability of the system are improved, and the estimation accuracy of the motor rotation speed and the rotor angle is higher.
Drawings
FIG. 1 is an overall flow chart of a method of sensorless control of a permanent magnet synchronous motor in an embodiment of the present application;
FIG. 2 is a partial block diagram of a permanent magnet synchronous motor sensorless control system in an embodiment of the present application;
FIG. 3 is a block diagram of the overall structure of a sensorless control system for a permanent magnet synchronous motor in an embodiment of the present application;
FIG. 4 is a simulated waveform diagram of a motor under light and sudden load conditions in an embodiment of the present application;
FIG. 5 is a simulated waveform diagram of a motor in an embodiment of the present application when a single parameter of the motor deviates;
fig. 6 is a simulated waveform diagram of a motor when multiple parameters deviate in an embodiment of the present application.
Reference numerals illustrate:
1. a reference model unit; 2. an adjustable model unit; 3. an adaptive law unit; 4. an integrating unit; 5. a rotational speed ring; 6. an MTPA unit; 7. a current loop; 8. a first conversion unit; 9. an SVPWM unit; 10. an inverter; 11. and a second conversion unit.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Embodiment one:
the embodiment discloses a permanent magnet synchronous motor speed-free sensor control method based on MRAS, referring to FIG. 1, specifically comprising the following steps:
s100: the stator flux linkage equation is used as a reference model.
Specifically, the stator flux linkage equation, i.e. the reference model, is:
wherein: l (L) d L is the d-axis inductance component of the stator q For the stator q-axis inductance component, i d I is the stator d-axis current component q For stator q-axis current component, ψ f Is a magnetic linkage of a permanent magnet,for stator d-axis flux linkage component reference, < ->The stator q-axis flux linkage component reference value is given, wherein the stator d-axis flux linkage component reference value and the stator q-axis flux linkage component reference value are also target values.
S200: and obtaining a first adjustable model according to the stator voltage equation and a predefined state space model.
Specifically, the stator voltage equation is:
wherein: u (u) d For the stator d-axis voltage component, u q For the stator q-axis voltage component, R s Is stator resistance omega e For the reference rotational speed of the motor, i.e. the target rotational speed of the motor, ψ d As the actual value of the d-axis flux linkage component of the stator, ψ q Is the stator q-axis flux linkage component actual value.
Specifically, the predefined state space model is:
in the formula (3), u' d 、u′ q For a defined constant representation, u' d For defining the stator d-axis voltage component u d Is represented by the constant of u' q For defining the stator q-axis voltage component u q Is represented by a constant of (a).
Rewriting the stator voltage equation of the formula (2) according to the expression of the state space model of the formula (3), wherein the rewritten first adjustable model is:
s300: based on the first adjustable model and the correction compensation parameter k c A second tunable model is obtained.
Specifically, because the first adjustable model in the formula (4) is a substantially pure integration link, the problems that an integration initial value, direct current bias and a stator resistance are changed by the running temperature of the motor exist, when the d-axis flux linkage component and the q-axis flux linkage component of the stator are estimated, the estimation error is large, part of poles of a system are positioned on a complex plane virtual axis when the motor runs at a low speed, the system is easy to oscillate, the motor rotation speed is difficult to converge, and the stability is poor.
Therefore, in order to improve the stability and estimation accuracy of the first adjustable model of the formula (4), the coupling of the stator d-axis flux linkage component and the stator q-axis flux linkage component in the dynamic process is weakened, and a correction compensation parameter k is introduced on the basis of the formula (4) c As formula (5):
when k is c For positive, selecting proper parameters to enable a stable area on the left side of a pole reciprocating plane of the system to move, wherein the characteristic value of a transfer function of the system is a negative real part, so that the formula (5) is sorted, and a second adjustable model is obtained as a formula (6):
thus, by introducing the correction compensation parameter k into the first adjustable model as described above c The second adjustable model is obtained, the controllability of the first adjustable model is improved, the motor rotation speed is small in shaking and high in convergence speed, the anti-interference capability and stability of the system are improved, and the estimation accuracy of the motor rotation speed is higher.
S400: obtaining the estimated rotating speed of the motor through self-adaptive law adjustment according to the reference model of the formula (1) and the second adjustable model of the formula (6)
Further, the estimated rotation speed of the motor is obtainedAfterwards, the motor is estimated at speed +.>Integrating to obtain the rotor estimated angle +.>
Further, the estimated rotation speed of the motor is obtainedAfterwards, the motor estimated speed is also added>And feeding back the self-adaptive rule to the second adjustable model to realize real-time adjustment of the self-adaptive rule.
Specifically, since the reference model of the formula (1) does not include the rotation speed parameter to be identified, the second adjustable model of the formula (6) includes the rotation speed parameter to be identified, and the d-axis flux linkage component actual value ψ in the second adjustable model of the formula (6) is adjusted through an adaptive law d Reference value of stator d-axis flux linkage component in proximity reference modelLet the actual value ψ of the stator q-axis flux linkage component in the second adjustable model of (6) q Reference value for stator q-axis flux linkage component in approach reference model +.>Thus, the estimated rotation speed of the motor can be obtained>By estimating the rotational speed of the motor +.>Real timeAnd (3) feeding back the estimated rotation speed to the second adjustable model in the formula (6), so that the real-time adjustment of the estimated rotation speed is realized, and the estimation accuracy of the motor rotation speed is improved.
In the following step S400, the process of determining the adaptive law and calculating the motor estimated rotation speed and the rotor estimated angle will be specifically described:
s410: expressing the second adjustable model of the formula (6) by an observation estimated value to obtain a stator flux linkage observation model as follows:
in the formula (7), the amino acid sequence of the compound,for stator d-axis flux linkage component estimation, i.e. stator d-axis flux linkage component actual value ψ d Representation in observation model, +.>For stator q-axis flux linkage component estimation, i.e. stator q-axis flux linkage component actual value ψ q A representation in an observation model; />For estimating the rotational speed of the motor, also the motor reference rotational speed omega e Representation in the observation model.
S420: according to the stator flux linkage observation model of the formula (7) and the second adjustable model of the formula (6), subtracting the formula (7) from the formula (6) to obtain the motor reference rotation speed omega e And motor estimated rotation speedAn error state equation between:
s430: in order to correct the compensation parameter k c Selecting a proper oneObtaining a proper self-adaptive law by dividing the error state equation of the formula (8) into a form comprising a linear time-invariant feedback system and a nonlinear time-variant feedback system, and then the error state equation of the formula (8) can be abbreviated as:
in the formula (9), ae is a linear time-invariant part, -W is a nonlinear time-variant part, e is a stator flux linkage error,
when the error state equation of the formula (9) satisfies the following condition: the linear time-invariant feedback system satisfies the lyapunov first method, and the linear time-variant feedback system satisfies the Popov integral inequality, the system at this time is progressively stable.
Specifically, according to Lyapunov stability theory, when all eigenvalues λ of matrix A have negative real parts, the linear time-invariant system asymptotically stabilizes, while when k c For positive, it can be guaranteed that all eigenvalues λ of matrix a have negative real numbers, when the system is stable.
According to the Popov hyperstability theory, if the system is stable, the conditions must be satisfied: for a limited positive number, then->I.e. the system is progressively stable.
Therefore, the Popov integral inequality is solved in an inverse way, so that the self-adaptive law can be obtained, and the specific process of the inverse solving is as follows:
s440: will beSubstituting the integral inequality-> In (c), formula (10) is obtained:
is arranged according to the proportional integral formThe expression of (2) is:
will beSubstituted +.>Simplified process, can obtain +.>The simplifying result of (2) is:
will beThe simplified result of (2) is substituted into formula (11), and the adaptive law is obtained as follows:
in the formula (12), k i 、k p For PI controller coefficients, k i To integrate the adjustment coefficient, k p For scaling the coefficient, k i ＝K 1 ，k p ＝K 2 Equation (12) is the final determined adaptive law.
Specifically, in formula (12)Can be represented by +.>Obtained +.>Can be represented by the formula (7)Obtaining; ψ in formula (12) d 、Ψ q Respectively using +.in reference model type (1)>Instead, the estimated rotational speed of the motor can be obtained>The calculation formula of (2) is as follows:
estimating the rotational speed of the motor obtained by the formula (13)Integrating to obtain the rotor estimated angle +.>Rotor estimation angle->The calculation formula of (2) is as follows:
therefore, by the MRAS-based permanent magnet synchronous motor speed sensorless control method of the embodiment, the motor estimated rotating speed is obtainedAnd rotor estimation angle +.>When the estimated rotational speed of the motor is obtained +.>After that, the motor estimated speed can also be +.>And feeding back the self-adaptive regulation to a second adjustable model, namely in the formula (6), so as to realize the real-time regulation of the self-adaptive regulation and improve the estimation accuracy of the motor rotating speed and the rotor angle.
Embodiment two:
the embodiment discloses a control system based on a MRAS permanent magnet synchronous motor speed-free sensor, the control system of the embodiment adopts the control method of the MRAS permanent magnet synchronous motor speed-free sensor of the embodiment I, and referring to FIG. 2, the control system comprises a reference model unit 1, an adjustable model unit 2, a self-adaptive law unit 3 and an integral unit 4; the following describes the operation of the system in detail:
a reference model unit 1 for taking the stator flux linkage equation as a reference model.
An adjustable model unit 2 for obtaining a first adjustable model according to a stator voltage equation and a predefined state space model, and correcting the compensation parameter k according to the first adjustable model c A second tunable model is obtained.
An adaptive law unit 3, configured to adjust the reference model and the second adjustable model in real time through an adaptive law to obtain an estimated rotation speed of the motor
An integration unit 4 for estimating the rotational speed from the motorIntegrating to obtain the rotor estimated angle +.>
Referring to fig. 3, the control system further includes a rotation speed ring 5, an MTPA unit 6, a current ring 7, a first conversion unit 8, a SVPWM unit 9, an inverter 10, and a second conversion unit 11, and a Permanent Magnet Synchronous Motor (PMSM) is connected to the inverter 10 and the second conversion unit 11, respectively.
Further, the MTPA is Maximum Torque Per Amp, which indicates maximum torque current ratio control.
Wherein, the rotating speed ring 5, the MTPA unit 6 and the current ring 7 are all connected with the adaptive law unit 3 and are used for receiving the motor estimated rotating speed output by the adaptive law unit 3The first conversion unit 8 and the second conversion unit 11 are both connected with the integration unit 4 and are used for receiving the rotor estimated angle +.>The current loop 7 is also connected to a second switching unit 11 forIn receiving the stator d-q axis current component output from the second converting unit 11, the stator d-q axis current component is the stator d-axis current component i d And stator q-axis current component i q 。
The operation of the system will be described with reference to fig. 3:
the speed loop 5 comprises a PI controller, and the speed loop 5 is used for estimating the speed according to the motorAnd a given motor reference speed omega e Obtaining a motor rotation speed error e, obtaining a motor reference torque according to the motor rotation speed error e, and outputting the motor reference torque to the MTPA unit 6;
an MTPA unit 6 for estimating the rotation speed according to the motorAnd motor reference torque, and outputs the stator d-q axis reference current component to the current loop 7. Specifically, the stator d-q axis reference current component is the stator d-axis reference current component +.>And stator q-axis reference current component +.>
The current loop 7 comprises a PI controller and a stator voltage decoupling link, and the current loop 7 is used for obtaining a current error between a stator reference value and an actual value according to the stator d-q axis current component and the stator d-q axis reference current component and combining the motor estimated rotating speedThe stator d-q axis voltage component is output to the first converting unit 8. Specifically, the stator d-q axis voltage component is a stator d-axis voltage component u d And stator q-axis voltage component u q 。
A first conversion unit 8 for estimating from the rotorAngle ofThe stator d-q axis voltage component is converted from the synchronous coordinate system to the stationary coordinate system, and the stator α - β axis voltage component is output to the SVPWM unit 9. Specifically, the stator α - β axis voltage component is the stator α axis voltage component u α And stator beta-axis voltage component u β 。
And the SVPWM unit 9 is configured to output a corresponding pulse signal to the inverter 10 by modulating the α - β axis voltage component of the stator in the stationary coordinate system through space vector pulse width modulation.
An inverter 10 for outputting a predetermined DC voltage u based on the pulse signal dc And converting into three-phase alternating current and outputting the three-phase alternating current to the permanent magnet synchronous motor.
And the permanent magnet synchronous motor is used for outputting corresponding ABC triaxial current components to the second conversion unit 11 according to the three-phase alternating current. Specifically, the ABC triaxial current component is i A 、i B 、i C 。
A second conversion unit 11 for estimating an angle based on the rotorThe ABC triaxial current component is converted into a stator d-q axis current component and the stator d-q axis current component is output to the reference model unit 1 and the adjustable model unit 2.
Therefore, through the above operation process of the control system, the reference model unit 1 and the adjustable model unit 2 acquire the stator d-q axis current component output by the second conversion unit 11 again, and the adjustable model unit 2 also acquires the stator d-q axis voltage component output by the current loop 7, so that the accuracy of estimating the motor rotation speed by the system is improved by updating the parameters of the adjustable model unit 2.
Referring to fig. 4 to 6, in order to build a simulation model in a MATLAB/Simulink environment, the method and the system for controlling the permanent magnet synchronous motor without a speed sensor based on the MRAS are adopted to perform robust simulation verification when the rotating speed, the rotor position and the permanent magnet synchronous motor parameters of the permanent magnet synchronous motor are inaccurate. Wherein the simulation modelThe parameters of the carried built-in permanent magnet synchronous motor are shown in table 1, and the compensation parameter k is corrected by adopting a maximum torque current ratio (MTPA) control mode through discretization of the system sampling frequency of 8kHz c Set to 120.
Table 1 permanent magnet synchronous motor parameter table
The system dynamic verification is specifically described as follows:
the permanent magnet synchronous motor (hereinafter referred to as motor) starts running from an initial static state under light load and is speed-regulated to 800r/min, rated torque load is suddenly added at 2 seconds, load is regulated to rated rotation speed at 4 seconds, the motor model output rotation speed is taken as the motor actual rotation speed, the MRAS model output rotation speed is taken as the motor estimated rotation speed, referring to (a) in fig. 4, a waveform comparison chart between the motor actual rotation speed and the motor estimated rotation speed is shown by an abscissa in fig. 4, the time is shown by an s, the rotation speed is shown by an ordinate, and the rotation speed is shown by r/min.
Sudden load: after the motor is started, the speed is regulated to 800r/min, the load of rated torque is suddenly added in 2 seconds, and the speed is restored to 800r/min within 0.25 seconds; the load is suddenly added until the motor is stable, and the root mean square deviation of the motor rotation speed is 4.2632r/min; the root mean square difference of the motor rotation speed after stabilization is 2.8184r/min. Referring to fig. 4 (b), in order to compare the waveform of the actual angle of the rotor with the waveform of the estimated angle of the rotor when the rated load is suddenly applied, the time is represented by the abscissa in fig. 4 (b), the angle is represented by the ordinate in s, and the instantaneous error between the actual angle of the rotor and the estimated angle of the rotor is 1.4% at the maximum. Referring to fig. 4 (c), which shows time in s and current in a unit of a and a in fig. 4 (c) which shows current in a unit of s and q in a unit of a, it can be seen that the motor stator d-q current becomes small in impact in 2 seconds and operates stably.
And (3) carrying out speed regulation: the motor is in load speed regulation from 800r/min to rated rotation speed 1500r/min in 0.7 seconds at 4 seconds, the root mean square difference of the rotation speed of the motor in the speed regulation process is 2.4083r/min, and the root mean square difference of the rotation speed after stabilization is 2.1894r/min. Referring also to (c) of fig. 4, the motor stator d-q axis current behaves at 4 seconds, and the current changes smoothly.
Therefore, according to the simulation result of fig. 4, the system dynamic response is fast, the motor estimated rotation speed fluctuation is small, the control expectation is met, and when the control method of the application is adopted for estimating parameters such as the motor rotation speed, the method has the advantages of high feasibility, high system dynamic response speed and high stability.
The system robustness is verified in a simulation mode, wherein the simulation mode comprises simulation verification of single parameter deviation of the motor and simulation verification of multiple parameter deviations of the motor.
In practical application, the permanent magnet synchronous motor has the condition that the actual parameters are inconsistent with the off-line calibration parameters, and under different working conditions, the motor parameters are influenced by the environment and the dynamic characteristics to cause the motor parameters to change within a certain range; after the controller parameters are required to be solidified by a reliable system, the motor parameters can still maintain the control performance under the condition of floating in a certain range, and good robustness is the key of whether the control system can be practically applied.
The following is a simulation verification of the single parameter deviation of the motor:
in order to verify that the control system keeps stable control performance when single parameter identification of the motor has errors or changes; the d-axis inductance component L of the stator in Table 1 d Inductance component L of stator q axis q Stator resistor R s Permanent magnet flux linkage ψ f Respectively deviate intoThe resulting deviation effective ranges and error data are shown in table 2.
TABLE 2 Single parameter deviation tables
Referring to fig. 5, for the motor's performance waveform when the individual parameters deviate according to the data of table 2, fig. 5 (a) is the motor parameters, respectivelyThe corresponding waveform diagrams of the estimated rotating speed and the actual rotating speed of the motor, the estimated angle and the actual angle of the rotor and the d-q axis current component of the stator; in fig. 5 (b) is the motor parameter +.>The corresponding waveform diagrams of the estimated rotating speed and the actual rotating speed of the motor, the estimated angle and the actual angle of the rotor and the d-q axis current component of the stator; in fig. 5 (c) is the motor parameter +.>The corresponding waveform diagrams of the estimated rotating speed and the actual rotating speed of the motor, the estimated angle and the actual angle of the rotor and the d-q axis current component of the stator; in fig. 5, (d) is the motor parameter +.>And the waveform diagram of the corresponding motor estimated rotating speed and the actual rotating speed of the motor, the rotor estimated angle and the actual rotor angle and the stator d-q axis current component.
In the simulation diagram of fig. 5, the abscissa in the waveform diagram of the estimated rotation speed of the motor and the actual rotation speed of the motor represents time, the unit is s, the ordinate represents rotation speed, and the unit is r/min; in the waveform diagram of the estimated angle of the rotor and the actual angle of the rotor, the abscissa represents time, the unit is s, the ordinate represents angle, and the unit is rad; in the waveform diagram of the stator d-q axis current component, the abscissa represents time in s and the ordinate represents current in a.
Therefore, as can be seen from fig. 5, the motor rotation speed estimation is stable and converged, the fluctuation range is smaller, the rotor angle estimation is accurate, the stator current error is small, the control requirement is met, the reliability is high, and the system can still keep stable within a certain single parameter deviation range.
The following is a simulated verification of the deviation of a plurality of parameters of the motor:
in the practical application scenario, a plurality of parameters of the motor are changed simultaneously, so that robustness verification of simultaneous deviation of the plurality of parameters is needed, when errors or changes occur in parameter identification, the control system enables a plurality of parameters which keep stable performance to deviate from simulation examples on the basis of the parameters of the motor in the table 1, the simulation examples are shown in the table 3, and statistical data corresponding to the obtained results are shown in the table 4 after the parameters deviate from the table 3.
TABLE 3 multiparameter bias table
Table 4 results statistics table
Referring to fig. 6, in order to simulate the motor speed, rotor angle and stator current waveforms when the motor is simultaneously varied with a plurality of parameters under the three conditions of numbers 1, 2 and 3, fig. 6 (a) corresponds to the case of number 1 in table 3, namely, when the parameters areUnchanged (I/O)>The corresponding waveform diagrams of the estimated rotating speed and the actual rotating speed of the motor, the estimated angle and the actual angle of the rotor and the d-q axis current component of the stator; (b) in FIG. 6 corresponds to the case of sequence number 2 in Table 3, i.e., when the parameter +.>Unchanged (I/O)>When the corresponding motor is estimatedCalculating a waveform diagram of the current components of the rotating speed and the actual rotating speed of the motor, the estimated angle and the actual angle of the rotor and the d-q axis of the stator; (c) in FIG. 6 corresponds to the case of number 3 in Table 3, i.e., when the parameter +.> And the waveform diagram of the corresponding motor estimated rotating speed and the actual rotating speed of the motor, the rotor estimated angle and the actual rotor angle and the stator d-q axis current component.
In the simulation diagram of fig. 6, the abscissa in the waveform diagram of the estimated rotation speed of the motor and the actual rotation speed of the motor represents time, the unit is s, the ordinate represents rotation speed, and the unit is r/min; in the waveform diagram of the estimated angle of the rotor and the actual angle of the rotor, the abscissa represents time, the unit is s, the ordinate represents angle, and the unit is rad; in the waveform diagram of the stator d-q axis current component, the abscissa represents time in s and the ordinate represents current in a.
Therefore, as can be seen from fig. 6, when the parameter identification of the plurality of motors is inaccurate or the parameters deviate, the motor rotation speed and the motor position, namely the rotor angle, can be estimated through the control method of the embodiment, the motor position, namely the rotor angle, gradually converges after the additional load, the system response speed is high, the robustness is strong, and the feasibility of the built-in permanent magnet synchronous motor in the practical application scene is high.
Therefore, simulation verification shows that the speed-sensorless control method for the permanent magnet synchronous motor has low dependence on actual parameters of the motor, is suitable for the actual working condition that the parameters of the motor change, and improves the robustness and the system stability of vector control of the permanent magnet synchronous motor.
The foregoing are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in any way, therefore: all equivalent changes in structure, shape and principle of this application should be covered in the protection scope of this application.
Claims (7)
1. The MRAS-based permanent magnet synchronous motor speed-free sensor control method is characterized by comprising the following steps of:
taking a stator flux linkage equation as a reference model;
obtaining a first adjustable model according to a stator voltage equation and a predefined state space model;
based on the first adjustable model and the correction compensation parameter k c Obtaining a second adjustable model;
the reference model and the second adjustable model are adjusted through a self-adaptive law to obtain the estimated rotating speed of the motor
The determination of the adaptive law is specifically as follows:
representing the second adjustable model by an observation estimated value to obtain a stator flux linkage observation model;
obtaining a motor reference rotating speed omega according to the stator flux linkage observation model and the second adjustable model e And motor estimated rotation speedAn error state equation between;
dividing the error state equation into a form comprising a linear time-invariant feedback system and a nonlinear time-variant feedback system;
obtaining a self-adaptive law according to the divided error state equation;
the stator voltage equation is:
wherein: u (u) d For the stator d-axis voltage component, u q For the stator q-axis voltage component, R s Is stator resistance omega e For motor reference speed, ψ d As the actual value of the d-axis flux linkage component of the stator, ψ q The actual value of the q-axis flux linkage component of the stator; i.e d To fixSub-d-axis current component, i q Is the stator q-axis current component;
the predefined state space model is:
wherein: u (u) ′ d 、u ′ q For a defined constant representation method, u ′ d For the stator d-axis voltage component u d Constant representation of u ′ q For the stator q-axis voltage component u q Is a constant representation of (1);
the first adjustable model obtained according to the stator voltage equation and the predefined state space model is as follows:
based on the first adjustable model and the correction compensation parameter k c The second adjustable model obtained is:
the stator flux linkage observation model is as follows:
wherein,for stator d-axis flux linkage component estimation, < +.>For the stator q-axis flux linkage component estimation, u ′ d 、u ′ q For a defined constant representation method, u ′ d For the stator d-axis voltage component u d Constant representation of u ′ q For the stator q-axis voltage component u q Is a constant representation of (1);for stator d-axis flux linkage component reference, < ->A reference value for the q-axis flux linkage component of the stator; />Estimating a rotational speed for the motor;
the error state equation is:
dividing the error state equation into a form comprising a linear time-invariant feedback system and a nonlinear time-variant feedback system, wherein the error state equation comprises the following components:
wherein Ae is a linear time-invariant part, -W is a nonlinear time-variant part, e is a stator flux linkage error,
setting k c Positive, making all eigenvalues λ of matrix a have negative real numbers, then the adaptationThe law is:
wherein k is i To integrate the adjustment coefficient, k p Is a scaling factor.
2. The MRAS-based permanent magnet synchronous motor sensorless control method of claim 1, wherein: the stator flux linkage equation is:
wherein: l (L) d L is the d-axis inductance component of the stator q For the stator q-axis inductance component, i d I is the stator d-axis current component q For stator q-axis current component, ψ f Is a magnetic linkage of a permanent magnet,for stator d-axis flux linkage component reference, < ->Is a stator q-axis flux linkage component reference value.
3. The MRAS-based permanent magnet synchronous motor sensorless control method of claim 1, wherein: the estimated rotating speed of the motor is obtainedThereafter, the method further comprises the following steps:
estimating the rotational speed of the motorFeedback to the second adjustable model to realize the real-time self-adaptive lawAnd (5) adjusting.
4. The MRAS-based permanent magnet synchronous motor sensorless control method of claim 1, wherein: the estimated rotating speed of the motor is obtainedThereafter, the method further comprises the following steps:
estimating rotational speed from the motorIntegrating to obtain the rotor estimated angle +.>
5. The MRAS-based permanent magnet synchronous motor sensorless control method of claim 4, wherein: estimating rotational speed from the motorIntegrating to obtain the rotor estimated angle +.>The calculation formula of (2) is as follows:
6. the utility model provides a control system based on MRAS PMSM does not have speed sensor which characterized in that: the control system comprises a reference model unit (1), an adjustable model unit (2), an adaptive law unit (3) and an integration unit (4);
a reference model unit (1) for taking the stator flux linkage equation as a reference model;
adjustable model unit(2) For obtaining a first adjustable model according to a stator voltage equation and a predefined state space model, and correcting the compensation parameter k according to the first adjustable model c Obtaining a second adjustable model;
an adaptive law unit (3) for obtaining an estimated rotation speed of the motor by adjusting the reference model and the second adjustable model through an adaptive law
An integration unit (4) for estimating the rotational speed from the motorIntegrating to obtain the rotor estimated angle +.>
The determination of the adaptive law is specifically as follows:
representing the second adjustable model by an observation estimated value to obtain a stator flux linkage observation model;
obtaining a motor reference rotating speed omega according to the stator flux linkage observation model and the second adjustable model e And motor estimated rotation speedAn error state equation between;
dividing the error state equation into a form comprising a linear time-invariant feedback system and a nonlinear time-variant feedback system;
obtaining a self-adaptive law according to the divided error state equation;
the stator voltage equation is:
wherein: u (u) d For the stator d-axis voltage component, u q For the stator q-axis voltage component, R s Is stator resistance omega e For motor reference speed, ψ d As the actual value of the d-axis flux linkage component of the stator, ψ q The actual value of the q-axis flux linkage component of the stator; i.e d I is the stator d-axis current component q Is the stator q-axis current component;
the predefined state space model is:
wherein: u (u) ′ d 、u ′ q For a defined constant representation method, u ′ d For the stator d-axis voltage component u d Constant representation of u ′ q For the stator q-axis voltage component u q Is a constant representation of (1);
the first adjustable model obtained according to the stator voltage equation and the predefined state space model is as follows:
based on the first adjustable model and the correction compensation parameter k c The second adjustable model obtained is:
the stator flux linkage observation model is as follows:
wherein,for stator d-axis flux linkage component estimation, < +.>For the stator q-axis flux linkage component estimation, u ′ d 、u ′ q For a defined constant representation method, u ′ d For the stator d-axis voltage component u d Constant representation of u ′ q For the stator q-axis voltage component u q Is a constant representation of (1);for stator d-axis flux linkage component reference, < ->A reference value for the q-axis flux linkage component of the stator; />Estimating a rotational speed for the motor;
the error state equation is:
dividing the error state equation into a form comprising a linear time-invariant feedback system and a nonlinear time-variant feedback system, wherein the error state equation comprises the following components:
wherein Ae is a linear time-invariant part, -W is a nonlinear time-variant part, e is a stator flux linkage error,
setting k c If positive, making all eigenvalues lambda of matrix a have negative real numbers, the adaptive law is:
wherein k is i To integrate the adjustment coefficient, k p Is a scaling factor.
7. The MRAS permanent magnet synchronous motor speed sensorless control system of claim 6, wherein: the control system further comprises a rotating speed ring (5), an MTPA unit (6), a current ring (7), a first conversion unit (8), an SVPWM unit (9), an inverter (10) and a second conversion unit (11), wherein the permanent magnet synchronous motor is respectively connected with the inverter (10) and the second conversion unit (11);
the rotating speed ring (5), the MTPA unit (6) and the current ring (7) are connected with the self-adaptive law unit (3); the first conversion unit (8) and the second conversion unit (11) are connected with the integration unit (4); the MTPA unit (6) is also respectively connected with the rotating speed ring (5) and the current ring (7); the current ring (7) is also respectively connected with the adjustable model unit (2), the first conversion unit (8) and the second conversion unit (11); the SVPWM unit (9) is respectively connected with the first conversion unit (8) and the inverter (10); the reference model unit (1) and the adjustable model unit (2) are connected with the second conversion unit (11).
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