A gain-variable flux linkage observation algorithm for a PMSM motor

Abstract

The application discloses a gain-variable flux linkage observation algorithm for a PMSM motor and relates to the technical field of permanent magnet motor control, and comprises a variable gain module. Different more applicable models need to be selected for permanent magnet synchronous motors in different application occasions, and the coordinate system of the application needs to be considered when the model is selected. The d-q coordinate system form of the permanent magnet synchronous motor is simple. Based on the condition that the rotor flux linkage is basically close at high and low speeds, the flux linkage is taken as an observation object. The observer gain is improved on the basis of the traditional nonlinear flux linkage observation model, high-precision observation of the rotor position is realized, the accuracy of the rotor position estimation is higher, the motor operation is more stable, the algorithm is all fixed-pointed, the calculation complexity is low, the performance requirement of the microcontroller is low, the algorithm effect is stable, and the negative influence of the signal-to-noise ratio and other factors at low speed is avoided.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of permanent magnet motor control, and particularly relates to a gain-variable flux linkage observation algorithm for a PMSM motor. BACKGROUND

[0002] Permanent magnet synchronous motors are widely applied to industrial production, and are gradually applied to some other industries, so that the application range of the permanent magnet synchronous motor is wider, and the main reason is that the motor has high efficiency. In order to improve the utilization efficiency of the motor, a sensorless control algorithm of the permanent magnet synchronous motor needs to be used, and the core is to quickly and accurately obtain rotor position and speed information. Among a plurality of position observation algorithms applied to the permanent magnet synchronous motor, a nonlinear flux linkage observer shows excellent performance.

[0003] However, the traditional nonlinear flux linkage observation algorithm still has the defects of slow convergence speed, easy overshoot, large observation angle error and the like. These defects result in problems such as step loss, vibration and howling of the motor during operation, and greatly increase the control difficulty of the motor. SUMMARY

[0004] In view of the problems of the existing gain-variable flux linkage observation algorithm for the PMSM motor, the application is provided.

[0005] Therefore, the application aims to provide a gain-variable flux linkage observation algorithm for a PMSM motor, and solve the problem of permanent magnet motor control.

[0006] In order to achieve the above-mentioned purpose, the application provides the following technical scheme.

[0007] A gain-variable flux linkage observation algorithm for a PMSM motor comprises a variable gain module. Different models of the permanent magnet synchronous motor need to be selected according to different application occasions, and the coordinate system of the application needs to be considered when the model is selected. The d-q coordinate system of the permanent magnet synchronous motor is simple, and the following formula is as follows:

[0008]

[0009] u d , u q are d-q axis components of the stator voltage, i d , i q are d-q axis components of the stator current, R is the resistance of the stator, omega e is the electrical angular velocity, L d , L q are d-q axis inductance components, is rotor flux linkage For the convenience of calculation, the mathematical model in α-β coordinate system is generally used in flux linkage model to design algorithm, the above formula is obtained by inverse Park transformation, and the formula is as follows:

[0010]

[0011] Wherein u α , u β is the stator voltage in the stationary coordinate system α-β, i α , i β is the stator current in the stationary coordinate system α-β, θ e is the rotor position information, and each inductance in the α-β coordinate system satisfies the following formula:

[0012]

[0013] For surface-mounted permanent magnet synchronous motor, the difference between cross-axis and direct-axis reluctance is very small, and the difference between corresponding cross-axis and direct-axis inductance is also very small, so L d = L q , so the formula is obtained as follows:

[0014] L α = L β = L = L s

[0015] L s is the stator inductance of permanent magnet synchronous motor, the formula is obtained by simplifying formula

[0016] to obtain the formula as follows:

[0017]

[0018] Preferably, for the convenience of subsequent observation, two state variables are defined, and the state variables are as follows:

[0019]

[0020] y = -Ri αβ + u αβ

[0021] The state variable y only contains measurable stator current and voltage, and does not contain any position quantity related to rotor speed and position, therefore, it is known and the essence is the back electromotive force of PMSM; x is essentially a state variable about flux linkage, and the back electromotive force can be obtained by differentiating the state variable x of flux linkage, and and can be obtained

[0022]

[0023] The state variable x contains the permanent magnet flux linkage and rotor position information, and the integral of y can obtain the estimated flux linkage value, thus the idea of the nonlinear flux linkage model is that the difference between the estimated flux linkage amplitude and the actual flux linkage amplitude is taken as a compensation term of the estimated flux linkage component, so that the flux linkage component can be observed.

[0024] Preferably, the nonlinear flux linkage model is constructed as the following formula:

[0025]

[0026] Wherein, Err is the amplitude difference between the estimated flux linkage and the actual flux linkage, that is, as follows:

[0027]

[0028] As a defined vector function, it can be known that it satisfies the formula:

[0029] The model of the vector function is the amplitude of the flux linkage, and gamma is the gain of the nonlinear flux linkage model observer, which plays a decisive role in the model, and the present application improves the gain. Integrating, the value of the state variable x can be obtained, and according to the formula The vector function contains the rotor position information, that is, it can be known that

[0030] Preferably, it can be known from the above that the observed rotor electric angle is as follows: Herein, x1 and x2 are state variables in the stationary coordinate system.

[0031] Preferably, in the nonlinear flux linkage model, the difference between the estimated flux linkage amplitude and the actual flux linkage amplitude is multiplied by a fixed observer gain to obtain a compensation term of the estimated flux linkage component, so as to correct the flux linkage component to gradually approach the actual value. In order to reduce the estimation error, improve the approximation speed, and enhance the convergence of the model, a linear model is established by changing the fixed gain into a new variable gain model. The model takes the rotor flux linkage as a threshold, so that the observer gain gamma can be dynamically adjusted according to the flux linkage error value. When the flux linkage error value is greater than a given threshold, the observer gain will be dynamically adjusted in a linear relationship, otherwise the observer will gradually converge with the initial gain. K1 represents the influence degree of the flux linkage error on the observer gain, k2 determines the threshold point at which the observer gain starts to change, and gamma0 is the initial observer gain value.

[0032] In the above technical solution, the present application provides the technical effects and advantages:

[0033] 1. The application is based on the condition that the rotor flux linkage is basically close at high and low speeds, taking the flux linkage as the observation object, improving the observer gain based on the traditional nonlinear flux linkage observation model, and realizing high-precision observation of the rotor position.

[0034] 2. The application has higher accuracy in estimating the rotor position, so that the motor runs more smoothly, and the algorithm is all fixed-point, with low computational complexity and low performance requirements for microcontrollers, so that the algorithm effect is stable, avoiding the negative effects of signal-to-noise ratio and other factors at low speed. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0036] Fig. 1 The improved observation algorithm flow chart of the present application;

[0037] Fig. 2 The observer gain model before and after improvement of the present application;

[0038] Fig. 3 The observation angle and actual angle value of the present application;

[0039] Fig. 4 The actual speed curve diagram under the variable gain model of the present application;

[0040] Fig. 5 The actual speed curve diagram under the traditional nonlinear flux linkage model of the present application. DETAILED DESCRIPTION

[0041] In order to make those skilled in the art better understand the technical solutions of the present application, the present application will be further described in detail below with reference to the drawings.

[0042] The embodiment of the present application discloses a gain-variable flux linkage observation algorithm for PMSM motor.

[0043] The present application provides a gain-variable flux linkage observation algorithm for PMSM motor as shown in Figs. 1-3 The present application provides a gain-variable flux linkage observation algorithm for PMSM motor as shown in

[0044]

[0045] u d , u q are d-q axis components of stator voltage, i d , i q are d-q axis components of stator current, R is resistance of stator, ω e is electrical angular velocity, L d , L q are d-q axis inductance components, is rotor permanent magnet flux linkage For convenience of calculation, in the flux linkage model, a mathematical model in α-β coordinate system is generally used for algorithm design, the above formula is obtained by inverse Park transformation, and the formula is as follows:

[0046]

[0047] wherein u α , u β are stator voltages in stationary coordinate system α-β, i α , i β are stator currents in stationary coordinate system α-β, θ e is rotor position information, and each inductance in α-β coordinate system satisfies the following formula:

[0048]

[0049] For surface-mounted permanent magnet synchronous motor, the difference of cross-axis and direct-axis reluctance is very small, and the difference of corresponding cross-axis and direct-axis inductance is also very small, so L d can be considered as L q , so the formula is obtained as follows:

[0050] L α = L β = L = L s

[0051] L s is stator inductance of permanent magnet synchronous motor, by simplifying the formula

[0052] so as to obtain the formula:

[0053]

[0054] wherein, for convenience of subsequent observation, two state variables are defined, and the state variables are as follows:

[0055]

[0056] y = -R i αβ + u αβ

[0057] The state variable y only contains measurable stator current and voltage, and does not contain any position quantity related to rotor speed and position, thus it is known and is essentially the back electromotive force of the PMSM; x is essentially a state variable of the flux linkage, and the back electromotive force can be obtained by differentiating the state variable x of the flux linkage, and according to and The following can be obtained

[0058]

[0059] The state variable x contains the permanent magnet flux linkage and rotor position information, and the estimated flux linkage value can be obtained by integrating y, thus the idea of the nonlinear flux linkage model is that the difference between the estimated flux linkage amplitude and the actual flux linkage amplitude is taken as a compensation term of the estimated flux linkage component, so that the flux linkage component can be observed.

[0060] The nonlinear flux linkage model is constructed according to the following formula:

[0061]

[0062] Wherein, Err is the amplitude difference between the estimated flux linkage and the actual flux linkage, that is, as follows:

[0063]

[0064] is a defined vector function, so it can be known that it satisfies the formula:

[0065] The model of the vector function is the amplitude of the flux linkage, and γ is the gain of the nonlinear flux linkage model observer, which plays a decisive role in the model, and the present application improves the gain. Integrating the following formula, the value of the state variable x can be obtained: The vector function contains the rotor position information, that is, the following can be known:

[0066] Wherein, from the above, it can be known that the observed rotor electric angle is as follows: Herein, x1 and x2 are state variables in the stationary coordinate system.

[0067] In the nonlinear flux linkage model, the difference between the estimated flux linkage amplitude and the actual flux linkage amplitude is multiplied by a fixed observer gain to obtain a compensation term of the estimated flux linkage component, so as to correct the flux linkage component to gradually approach the actual value. In order to reduce the estimation error, improve the approximation speed, and enhance the convergence of the model, the fixed gain is changed into a new variable gain model, and a linear model is established The model takes the rotor flux linkage as a threshold, so that the observer gain γ can be dynamically adjusted according to the flux linkage error value. When the flux linkage error value is greater than a given threshold, the observer gain will be dynamically adjusted in a linear relationship, otherwise the observer will gradually converge with the initial gain. k1 represents the influence degree of the flux linkage error on the observer gain, k2 determines the threshold point at which the observer gain starts to change, and γ0 is the initial observer gain value.

[0068] The foregoing merely illustrates some exemplary embodiments of the present application, no doubt numerous modifications can be made by those skilled in the art without departing from the spirit and scope of the present application. Therefore, the above drawings and descriptions are illustrative in nature, and should not be construed as limiting the scope of the present application.

Claims

1. A gain variable flux linkage observation algorithm for a PMSM motor, characterized by, Including variable gain module, permanent magnet synchronous motor needs to select different more applicable model in different application occasions, and the coordinate system of its application needs to be considered when selecting the model, and the coordinate system of permanent magnet synchronous motor The coordinate system is simple, such as the following formula: ； , These are the stator voltages. Axial components, , These are the stator currents. Axial components, It is the resistance of the stator. It is electric angular velocity. , They are Shaft inductance component, It refers to the flux linkage of the rotor permanent magnet. For ease of calculation, in the flux linkage model, it is generally used as... The mathematical model in the coordinate system is used for algorithm design. The above equation is transformed by inverse Park to obtain the following formula: ； wherein , is the stator voltage in the stationary coordinate system , , is the stator current in the stationary coordinate system , is the rotor position information, and the inductances in the stationary coordinate system satisfy the following equations: ； For surface-mounted permanent magnet synchronous motor, the difference of cross-axis and direct-axis reluctance is very small, and the difference of corresponding cross-axis and direct-axis inductance is also very small, so it can be considered that Therefore, the formula can be obtained: ； For the stator inductance of the permanent magnet synchronous motor, by the formula Simplifying gives the formula: ； For the convenience of subsequent observation, two state variables are defined, and the state variables are as follows: ； where the state variables Only measurable stator currents and voltages are involved, and no position quantities related to rotor speed and position are involved, so it is known and in essence the back EMF of the PMSM; In essence, the state variables are related to the flux linkage, and we have the state variables The differential, and we can get the back EMF, and according to And We can get State variable The state variable includes the permanent magnet flux linkage and the rotor position information, and is used to The estimated flux linkage value can be obtained by integration, and therefore the idea of the nonlinear flux linkage model is that the difference between the estimated flux linkage amplitude and the actual flux linkage amplitude is used as a compensation term for the estimated flux linkage component, so that the flux linkage component can be observed. The nonlinear flux linkage model is constructed as follows: ； wherein is the estimated amplitude difference between the flux linkage and the actual flux linkage, i.e. as ； For a defined vector function, it can be known that it satisfies the formula: the model of this vector function is the amplitude of the flux linkage, This gain of the nonlinear flux linkage model observer plays a decisive role for the model, for the value of the state variable is obtained by integrating According to the formula the vector function contains the rotor position information, i.e. From the above, the observed rotor electrical angle is given by where are the state variables in the stationary coordinate system, respectively. In the nonlinear flux linkage model, the difference between the estimated flux linkage amplitude and the actual flux linkage amplitude is multiplied by a fixed observer gain to obtain a compensation term of the estimated flux linkage component, thereby correcting the flux linkage component to gradually approach the actual value. In order to reduce the estimation error, improve the approximation speed, and enhance the convergence of the model, a linear model is established by changing the fixed gain to a new variable gain model The model uses the rotor flux linkage as a threshold, so that the observer gain can be dynamically adjusted according to the flux linkage error value. When the flux linkage error value is greater than a given threshold, the observer gain is dynamically adjusted in a linear relationship, otherwise the observer gradually converges with the initial gain, represents the degree of influence of the flux linkage error on the observer gain, determines the threshold point at which the observer gain starts to change, is the initial observer gain value.

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