Permanent magnet synchronous motor control method and system based on online parameter identification

Abstract

The invention discloses a permanent magnet synchronous motor control method and system based on online parameter identification, and relates to the field of motor control, and the control method comprises the steps: building a motor mathematical model of a permanent magnet synchronous motor in a static coordinate system, and the mathematical model is used for describing the relation between stator voltage and current and rotor electrical angle and electrical angular velocity; constructing a flux linkage observer, estimating a rotor flux linkage by observing the stator voltage and current, and calculating the position and speed of the rotor based on the rotor flux linkage; a recursive least square method is adopted to carry out online parameter identification, and motor parameters are updated in real time to compensate the influence of parameter changes on the control precision. According to the position-sensorless control method provided by the invention, accurate rotor position and speed estimation can be realized, and efficient control is realized. In combination with a gain adjustment and compensation method, the system can automatically adjust a control strategy according to different working conditions during motor operation, and the stability, precision and robustness of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a permanent magnet synchronous motor control method based on online parameter identification. Background Art

[0002] With the advancement of motor technology, permanent magnet synchronous motors (PMSMs) have become widely used in industrial automation, electric vehicles, wind power generation, household appliances, and robotics due to their high efficiency, compactness, low noise, and high power density. Traditional PMSM control systems typically rely on position sensors (such as rotary encoders or Hall sensors) to obtain rotor position and speed information for precise control. However, position sensors not only increase system hardware costs but can also affect system reliability and performance due to installation, maintenance, or accuracy limitations. Therefore, developing sensorless control methods has become an important research direction for PMSM control.

[0003] Sensorless control methods not only reduce system costs but also improve motor reliability and adaptability. With the continuous advancement of intelligent and automated technologies, sensorless control technology has shown broad application prospects in various industrial control, smart home, transportation, and renewable energy systems. This is particularly true in applications with high space and cost requirements, such as electric vehicles and robotics, where sensorless control technology will become a future research and application trend.

[0004] To achieve sensorless control, flux observers are widely used in PMSMs. By monitoring input signals such as stator current and voltage, flux observers derive rotor flux, thereby indirectly estimating rotor position and speed. The key to flux observers is their ability to provide accurate rotor state estimation in real time, eliminating direct reliance on expensive and failure-prone sensors. This approach not only reduces hardware costs but also improves the reliability of motor control systems.

[0005] The performance of a PMSM is influenced by its electrical parameters (such as stator resistance, stator inductance, and permanent magnet flux), which can vary due to factors such as temperature, aging, and load variations. Traditional control methods typically assume these motor parameters are known or fixed. However, in practice, variations in motor parameters can lead to reduced control accuracy or performance degradation. Therefore, real-time online identification of motor parameter variations has become an important research topic in sensorless control systems.

[0006] Online parameter identification compensates for errors caused by motor parameter variations by adjusting the parameters in the motor control model in real time. This approach can effectively improve the stability and robustness of sensorless control, especially in applications with large load disturbances and large motor parameter variations.

[0007] Recursive least squares (RLS) is a widely used algorithm for online parameter identification. The RLS algorithm updates motor parameter estimates in real time by minimizing the weighted sum of squared errors of all past measurement data. This method offers excellent real-time performance and accuracy, enabling accurate estimation of key motor parameters (such as stator resistance and inductance) during parameter changes. Compared to traditional least squares methods, the RLS algorithm does not require the storage of large amounts of historical data, making it suitable for online parameter estimation in real-time control systems.

[0008] Based on online parameter identification and a flux observer, the proposed sensorless control method achieves accurate rotor position and speed estimation, enabling efficient control by adjusting motor input signals (such as voltage and current). Combined with gain adjustment and compensation methods, the system automatically adjusts the control strategy based on the motor's operating conditions, improving system stability, accuracy, and robustness. This method maintains excellent control performance, especially under sudden changes in motor load, temperature, and motor parameter variations. Summary of the Invention

[0009] In view of the above problems in the prior art, the present invention is proposed.

[0010] Therefore, the problem to be solved by the present invention is how to accurately estimate the rotor position and speed.

[0011] To solve the above technical problems, in the first aspect, the present invention provides the following technical solutions: a permanent magnet synchronous motor control method based on online parameter identification, which includes establishing a motor mathematical model of the permanent magnet synchronous motor in a stationary coordinate system, wherein the mathematical model is used to describe the relationship between the stator voltage, current and the rotor electrical angle and electrical angular velocity; constructing a flux observer to estimate the rotor flux by observing the stator voltage and current, and calculate the rotor position and speed based on the rotor flux; using the recursive least squares method for online parameter identification, and updating the motor parameters in real time to compensate for the influence of parameter changes on control accuracy.

[0012] As a preferred solution of the permanent magnet synchronous motor control method based on online parameter identification of the present invention, the expression of the motor mathematical model is:

[0013]

[0014] Where u α ,u β are the α and β axis voltages, i α ,i β is the αβ axis current, R is the stator phase resistance, ψ is the permanent magnet flux, ω e is the rotor electrical angular velocity, θe is the rotor electrical angle. Since it is a surface-mounted permanent magnet synchronous motor, the motor inductance L=L d =L q .

[0015] As a preferred solution of the permanent magnet synchronous motor control method based on online parameter identification of the present invention, the magnetic flux components of the α-axis and β-axis rotors are respectively:

[0016]

[0017] Where ψ α , ψ β is the rotor flux of the permanent magnet synchronous motor on the α-axis and β-axis;

[0018] The expression of the flux observer is:

[0019] ψ α =∫(u α -i α R)dt-(L·i α )

[0020] ψ β =∫(u β -i β R)dt-(L·i β )

[0021] Based on ψ α , ψ β , calculate the rotor position

[0022] As a preferred solution of the permanent magnet synchronous motor control method based on online parameter identification of the present invention, the integral operation of the flux observer adopts a low-pass filter instead of a pure integrator, and its transfer function is: where ω c is the angular velocity corresponding to the cutoff frequency of the low-pass filter, and satisfies ω e >>ω c To reduce the integration error.

[0023] As a preferred solution of the permanent magnet synchronous motor control method based on online parameter identification of the present invention, the cost function of the recursive least squares method is:

[0024]

[0025] Among them, y i is the observed output; x i is the input vector; θ is the parameter to be estimated, including the stator resistance R, the stator inductance L and the permanent magnet flux ψ.

[0026] As a preferred solution of the permanent magnet synchronous motor control method based on online parameter identification of the present invention, the parameter updating step of the recursive least squares method includes:

[0027] Calculate the prediction error:

[0028] Calculate the gain vector: Among them, P k-1 is the covariance matrix, and λ is the forgetting factor.

[0029] Update parameters: θ k =θ k-1 +K k e k ;

[0030] Update the covariance matrix:

[0031] As a preferred solution of the permanent magnet synchronous motor control method based on online parameter identification described in the present invention, the forgetting factor λ has a value range of 0<λ<1 and is used to dynamically adjust the influence weight of historical data on new parameter estimation.

[0032] As a preferred solution of the permanent magnet synchronous motor control method based on online parameter identification described in the present invention, when the motor starts or the load suddenly changes, λ is set to <0.95 to speed up the parameter update speed; during steady-state operation, λ is set to ≥0.98 to improve the estimation accuracy.

[0033] As a preferred solution of the permanent magnet synchronous motor control method based on online parameter identification of the present invention, the flux observer eliminates the DC offset by the following formula:

[0034]

[0035] Wherein, Y(t) is the output of the low-pass filter, and k is the step input amplitude.

[0036] In a second aspect, the present invention further provides a permanent magnet synchronous motor control system based on online parameter identification, which is applicable to the above-mentioned permanent magnet synchronous motor control method based on online parameter identification.

[0037] The beneficial effects of the present invention are as follows: the position sensorless control method proposed in the present invention can achieve accurate rotor position and speed estimation, and then achieve efficient control by adjusting the motor input signals (such as voltage and current). Combined with the gain adjustment and compensation methods, the system can automatically adjust the control strategy according to the different operating conditions of the motor, improving the stability, accuracy and robustness of the system. In particular, in the case of sudden changes in motor load, temperature changes and changes in motor parameters, this method can maintain good control performance. This method is used to online identify the stator resistance, stator inductance and permanent magnet flux of the motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 The present invention provides a flow chart of a position sensorless control method for a permanent magnet synchronous motor based on online parameter identification.

[0040] Figure 2 This is a block diagram of a flux observer implementation provided by an example of the present invention.

[0041] Figure 3 This is a flow chart for implementing an online parameter identification principle provided by an example of the present invention.

[0042] Figure 4 This is a rotor speed comparison diagram based on online parameter identification and flux observer control provided by an example of the present invention.

[0043] Figure 5 This is a rotor position comparison diagram based on online parameter identification and flux observer control provided by an example of the present invention.

[0044] Figure 6 This is a resistance identification simulation diagram based on online parameter identification provided by an example of the present invention.

[0045] Figure 7 This is a simulation diagram of permanent magnet flux linkage identification based on online parameter identification provided by an example of the present invention.

[0046] Figure 8 This is a stator inductance identification simulation diagram based on online parameter identification provided by an example of the present invention. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it individually or selectively refer to an embodiment that is mutually exclusive of other embodiments.

[0050] Reference Figures 1 to 8 , which is the first embodiment of the present invention, provides a permanent magnet synchronous motor control method based on online parameter identification, the method comprising:

[0051] Step 1: Establish a mathematical model of the permanent magnet synchronous motor in a stationary coordinate system. The mathematical model is used to describe the relationship between the stator voltage, current and the rotor electrical angle and electrical angular velocity.

[0052] Specifically, the present invention first establishes a mathematical model of a permanent magnet synchronous motor (PMSM) in the αβ stationary coordinate system. By modeling the relationship between the motor's stator voltage, stator current, torque, and speed, the dynamic changes in motor parameters such as stator resistance, inductance, and permanent magnet flux during motor operation are taken into account. This model provides a theoretical basis for the subsequent design of a flux observer and parameter identification algorithm. The mathematical model of the permanent magnet synchronous motor is expressed as:

[0053]

[0054] Where u α ,u β are the α and β axis voltages, i α ,i β is the αβ axis current, R is the stator phase resistance, ψ is the permanent magnet flux, ω e is the rotor electrical angular velocity, θ e is the rotor electrical angle. Since it is a surface-mounted permanent magnet synchronous motor, the motor inductance L=L d =L q .

[0055] Step 2: Construct a flux observer to estimate the rotor flux by observing the stator voltage and current, and calculate the rotor position and speed based on the rotor flux.

[0056] Specifically, a flux observer is designed to estimate the rotor position and speed of the permanent magnet synchronous motor. The following formulas are the flux expressions of the α and β axes respectively:

[0057]

[0058] Where ψ α , ψ β is the rotor flux of the permanent magnet synchronous motor on the α-axis and β-axis.

[0059] The expression of the flux observer is:

[0060] ψ α =∫(u α -i α R)dt-(L·i α )

[0061] ψ β =∫(u β -i β R)dt-(L·i β )

[0062] If the order

[0063] (u α -i α R)=ν1(t)

[0064] (u β -i β R)=ν2(t)

[0065] Then the following Laplace transform is expressed in ψ α and ψ β Available integration terms:

[0066]

[0067] These integral terms are calculated using an integrator based on a low-pass filter (LPF). If the motor’s input electrical angular velocity ω e A speed much larger than the cutoff frequency ω corresponding to the LPF-based integrator c ,but

[0068]

[0069] The LPF-based integrator does not allow any DC offset to gradually increase the output. Suppose there is an input function X(s) = k / s corresponding to the step input x(t) = k*u(t). Using the above transfer function of the LPF-based integrator We get:

[0070]

[0071] According to the previous equation, the step response of the LPF integrator will asymptotically stabilize at the DC value. However, the step response of a pure integrator is a ramp function. Using a high-pass filter at the output of the LPF integrator can completely eliminate the DC offset in the input. The following formula is the torque expression:

[0072]

[0073] Where T e is the electrical torque of the rotor, and P is the number of motor pole pairs.

[0074] ψ α =(∫(u α -i α R)dt-σLi α

[0075] ψ β =(∫(u β -i β R)dt-σLi β

[0076]

[0077] θ e =∫ω e dt

[0078]

[0079] Where ω m is the mechanical angular velocity of the motor, N r is the motor speed, and the rotor position can be obtained by the inverse tangent function.

[0080] Step 3: Use the recursive least squares method to perform online parameter identification and update the motor parameters in real time to compensate for the impact of parameter changes on control accuracy.

[0081] Specifically, we use recursive least squares (RLS) for online parameter identification. Recursive least squares (RLS) is an optimization algorithm for estimating system parameters online. Unlike classic least squares (LS), RLS dynamically updates its estimated values as new data is acquired, rather than recalculating them based on all historical data.

[0082] In the least squares method, a set of parameters θ is found to minimize the sum of squared errors:

[0083]

[0084] Where y k is the observed output at the kth moment, x kis the observation input vector at the kth moment, and θ is the parameter to be estimated.

[0085] Recursive least squares differs from traditional least squares in that it continuously updates existing estimates as new data arrives. Therefore, the RLS algorithm doesn’t need to store all historical data, but instead updates parameters based on previous estimates and the current new data.

[0086] Assume that the input vector at the current moment is x k , the observed value is y k , and the parameter we want to estimate is θ k , then the basic goal of RLS is to minimize the following cost function:

[0087]

[0088] In order to recursively calculate the minimum value of this cost function, we do not need to recalculate the sum of squared errors of historical data every time, but recursively calculate the parameters θ and the covariance matrix P k to update.

[0089] First, calculate the prediction error e at the current moment k :

[0090]

[0091] Where θ k-1 is the parameter estimate at the previous moment.

[0092]

[0093] Where K k is the gain vector that adjusts the current parameter estimate, P k-1 is the parameter covariance matrix at the previous moment, which indicates the uncertainty of the estimate, and λ is the forgetting factor, which controls the influence of historical data.

[0094] By K k and e k , the current parameter estimate θ can be updated k :

[0095] θ k =θ k-1 +K k e k

[0096] Whenever new data arrives, we calculate the error based on the current error e k and gain K k To adjust the estimate θ of the previous moment k-1 , thus obtaining a more accurate estimate.

[0097] Covariance matrix P k represents the uncertainty of the parameter estimate, which changes with each update. The update formula of the covariance matrix is:

[0098]

[0099] In this way, RLS is able to gradually reduce uncertainty and adjust the accuracy of the estimate in real time based on new data.

[0100] This embodiment also provides a permanent magnet synchronous motor control system based on online parameter identification, which is applicable to the above-mentioned control method.

[0101] In summary, the embodiment of this patent is based on a position sensorless control method for a permanent magnet synchronous motor (PMSM) based on online parameter identification, combining a flux observer with a recursive least squares (RLS) algorithm to achieve precise control of the motor without a position sensor. First, a mathematical model of the motor in the αβ stationary coordinate system is established, and a flux observer is designed. By observing the stator voltage and current, the rotor flux is estimated, and the rotor position and speed of the motor are indirectly obtained. Secondly, online parameter identification is performed in combination with the recursive least squares method. By minimizing the output error, the key parameters of the motor (such as stator resistance, d-axis and q-axis inductance, etc.) are estimated in real time, effectively compensating for the impact of changes in motor parameters on system performance. In order to further improve the robustness and stability of the system, this method also designs a gain adjustment and compensation strategy to dynamically adjust the observer gain value to ensure that the motor can maintain high-precision control under different operating conditions. The advantage of this method is that it does not rely on traditional rotor position sensors. Through intelligent identification and observation technology, it optimizes the control performance of the motor, improves the reliability and response speed of the system, and has broad application prospects.

[0102] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A permanent magnet synchronous motor control method based on online parameter identification, characterized in that: include, Establishing a mathematical model of the permanent magnet synchronous motor in a stationary coordinate system, wherein the mathematical model is used to describe the relationship between the stator voltage and current and the rotor electrical angle and electrical angular velocity; constructing a flux observer to estimate the rotor flux by observing the stator voltage and current, and calculating the rotor position and speed based on the rotor flux; The recursive least squares method is used for online parameter identification, and the motor parameters are updated in real time to compensate for the influence of parameter changes on control accuracy.

2. The permanent magnet synchronous motor control method based on online parameter identification according to claim 1, characterized in that: The expression of the motor mathematical model is: Where u α ,u β are the α and β axis voltages, i α ,i β is the αβ axis current, R is the stator phase resistance, ψ is the permanent magnet flux, ω e is the rotor electrical angular velocity, θ e is the rotor electrical angle. Since it is a surface-mounted permanent magnet synchronous motor, the motor inductance L=L d =L q .

3. The permanent magnet synchronous motor control method based on online parameter identification according to claim 2, characterized in that: The magnetic flux components of the α-axis and β-axis rotors are: Where ψ α , ψ β is the rotor flux of the permanent magnet synchronous motor on the α-axis and β-axis; The expression of the flux observer is: ψ α =∫(u α -i α R)dt-(L·i α ) ψ β =∫(u β -i β R)dt-(L·i β ) Based on ψ α , ψ β , calculate the rotor position 4. The permanent magnet synchronous motor control method based on online parameter identification according to claim 3, characterized in that: The integral operation of the flux observer uses a low-pass filter instead of a pure integrator, and its transfer function is: where ω c is the angular velocity corresponding to the cutoff frequency of the low-pass filter, and satisfies ω e >>ω c To reduce the integration error.

5. The permanent magnet synchronous motor control method based on online parameter identification according to claim 4, characterized in that: The cost function of the recursive least squares method is: Among them, y i is the observed output; x i is the input vector; θ is the parameter to be estimated, including the stator resistance R, the stator inductance L and the permanent magnet flux ψ.

6. The permanent magnet synchronous motor control method based on online parameter identification according to claim 5, characterized in that: The parameter updating step of the recursive least squares method includes: Calculate the prediction error: Calculate the gain vector: Among them, P k-1 is the covariance matrix, and λ is the forgetting factor. Update parameters: θ k =θ k-1 +K k e k ; Update the covariance matrix:

7. The permanent magnet synchronous motor control method based on online parameter identification according to claim 6, characterized in that: The forgetting factor λ has a value range of 0<λ<1 and is used to dynamically adjust the influence weight of historical data on new parameter estimation.

8. The permanent magnet synchronous motor control method based on online parameter identification according to claim 7, characterized in that: When the motor starts or the load changes suddenly, set λ<0.95 to speed up the parameter update; during steady-state operation, set λ≥0.98 to improve the estimation accuracy.

9. The permanent magnet synchronous motor control method based on online parameter identification according to claim 8, characterized in that: The flux observer eliminates the DC offset by the following formula: Wherein, Y(t) is the output of the low-pass filter, and k is the step input amplitude.

10. A permanent magnet synchronous motor control system based on online parameter identification, characterized in that: The method is applicable to the permanent magnet synchronous motor control method based on online parameter identification as claimed in any one of claims 1 to 9.

Images