Position estimation method for dual three-phase permanent magnet synchronous motor based on flux linkage model correction

Abstract

The present application belongs to the technical field of motor control, and particularly relates to a double three-phase permanent magnet synchronous motor position estimation method based on flux linkage model correction, comprising: S1, inputting the reference voltage and actual current of a subspace into a sliding mode back electromotive force observer to output observed back electromotive forces of an alpha axis and a beta axis; S2, integrating the observed back electromotive forces obtained in S1 to obtain voltage flux linkage of the subspace; S3, calculating current flux linkage based on the online extracted rotor position estimation value and permanent magnet flux linkage, and taking the current flux linkage as a correction term to correct the voltage flux linkage obtained in S2 by weighted fusion to obtain comprehensive flux linkage; S4, performing normalization processing on the comprehensive flux linkage obtained in S3 to calculate an equivalent position error; inputting the equivalent position error into a quadrature phase-locked loop to obtain rotor position and speed information. The method can realize high-precision and strong-robustness rotor position estimation in sensorless control of a double three-phase permanent magnet synchronous motor.

Description

Technical Field

[0001] This invention belongs to the field of motor control technology, and in particular relates to a position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction. Background Technology

[0002] In the field of sensorless control of dual three-phase permanent magnet synchronous motors, model-based solutions have become the mainstream research direction. These methods typically involve two core steps: first, constructing an observer model based on reference voltage and sampled current to achieve effective estimation of the back electromotive force (EMF); second, extracting rotor position and speed information from the estimated EMF using a position demodulation algorithm.

[0003] However, in practical engineering applications, the observer model is difficult to perfectly match the real motor system. Various non-ideal factors lead to significant harmonic components in the estimated back EMF, severely affecting the accuracy of position estimation. These non-ideal factors mainly include: inverter dead-time effect, current sampling error, motor parameter mismatch, and other unmodeled disturbances.

[0004] Existing technical solutions have significant limitations in suppressing the aforementioned disturbances. On the one hand, while traditional low-pass filtering methods can attenuate high-frequency harmonic components to some extent, they inevitably introduce phase delay and amplitude attenuation, leading to a lag in position estimation results, which is particularly pronounced under dynamic operating conditions. On the other hand, when there is a lack of targeted compensation mechanisms, harmonic components in the back electromotive force will be directly transmitted to the position demodulation stage, causing a significant increase in steady-state position error, aggravated speed fluctuations, and even unstable or completely failed estimations under conditions such as low-speed operation or sudden load changes.

[0005] The root cause of the aforementioned problems lies in the unique structural characteristics of the dual three-phase permanent magnet synchronous motor system. Compared to traditional three-phase systems, dual three-phase permanent magnet synchronous motors have two mutually coupled three-phase windings, resulting in more complex harmonic subplane coupling relationships and richer current harmonic components. This complex electromagnetic coupling makes it difficult for traditional single-observer models to accurately capture the true dynamic characteristics of the motor, while simple filtering compensation strategies cannot effectively distinguish between useful signals and interference components. Furthermore, the multivariate coupling characteristics of the dual three-phase system cause the impact of parameter mismatch on estimation accuracy to exhibit a nonlinear amplification effect, further increasing the difficulty of solving the problem.

[0006] Therefore, how to achieve high-precision and robust rotor position estimation in sensorless control of dual three-phase permanent magnet synchronous motors has become an urgent problem to be solved. Summary of the Invention

[0007] To address the aforementioned shortcomings of existing technologies, the present invention aims to provide a position estimation method for dual three-phase permanent magnet synchronous motors based on flux linkage model correction, which can achieve high-precision and robust rotor position estimation in sensorless control of dual three-phase permanent magnet synchronous motors.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] The position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction includes the following steps:

[0010] S1, will The reference voltage and actual current of the subspace are input to a sliding mode back EMF observer, which outputs the observed back EMF along the α-axis and β-axis; the sliding mode back EMF observer incorporates adaptive parameters. and ,in, The linear feedback gain used to adjust the sliding mode back EMF observer to suppress chattering. Used to adjust the update rate of the adaptive variables inside the sliding mode back EMF observer;

[0011] S2. Integrating the observed back electromotive force obtained from S1, we get... Voltage flux linkage in subspace;

[0012] S3. Calculate the current flux based on the online extracted rotor position estimate and permanent magnet flux linkage, and use the current flux linkage as a correction term to perform weighted fusion correction on the voltage flux linkage obtained in S2 to obtain the comprehensive flux linkage;

[0013] S4. Normalize the composite flux obtained in S3 and calculate the equivalent position error; input the equivalent position error into the proportional-integral regulator in the quadrature phase-locked loop, and estimate the rotor position and speed information through the quadrature phase-locked loop.

[0014] Compared with the prior art, the present invention has the following advantages:

[0015] 1. Effectively suppresses chattering in sliding mode observers. This is achieved by introducing adaptive parameters. Adjusting the linear feedback gain significantly reduces the inherent high-frequency chattering problem of traditional sliding mode observers. Compared with the simple fixed-gain design in existing technologies, this method can dynamically adjust the feedback strength according to the system state, thereby greatly improving system stability while ensuring observation accuracy.

[0016] 2. Implement adaptive observer parameter adjustment. Adaptive parameters. It can automatically adjust the update rate of the internal variables of the observer according to the operating conditions, overcoming the shortcomings of traditional fixed parameter observers with poor adaptability under different load and speed conditions, so that the observer can maintain good observation performance in the entire operating range.

[0017] 3. Significantly improves position estimation accuracy. By using the current flux linkage as a correction term to perform weighted fusion correction on the voltage flux linkage, the influence of model uncertainty, parameter drift, and unmodeled disturbances on position estimation is effectively compensated. Compared with the single voltage flux linkage integration method in the prior art, this method can obtain more accurate and stable rotor position information.

[0018] 4. Position error suppression can be achieved without adding any additional hardware. This method is entirely based on software algorithms, eliminating the need for additional position sensors or complex hardware compensation circuits. Compared to traditional solutions that require additional hardware sensors, this significantly reduces system cost and complexity while maintaining excellent position estimation performance.

[0019] 5. Enhanced system robustness and anti-interference capability. Through the synergistic effect of flux linkage model correction and quadrature phase-locked loop, the system's resistance to non-ideal factors such as inverter dead-zone effect and current sampling error is significantly improved. Compared with the simple filtering methods in existing technologies, this method can maintain stable and reliable position estimation under complex operating conditions.

[0020] In summary, this method can effectively suppress jitter and, through correction, significantly reduce the interference of model uncertainty, parameter drift, and unmodeled disturbances on the position estimation signal, thus achieving high-precision and robust rotor position estimation in sensorless control of dual three-phase permanent magnet synchronous motors.

[0021] Preferably, in step S1, the sliding mode back EMF observer is defined by the following set of equations:

[0022] ;

[0023] in, and They are respectively shaft and The actual current of the shaft; and They are respectively shaft and Stator current estimate for the shaft; and These are the resistance and inductance of a dual three-phase permanent magnet synchronous motor, respectively. and They are respectively shaft and Reference voltage of the shaft; and They are respectively shaft and The back electromotive force of the axis is observed; For the nonlinear gain of the back EMF observer; and For the sliding mode surface variables of the back EMF observer; and These are the adaptive parameters for the back EMF observer; and For the adaptive variable of the back EMF observer; It is a symbolic function.

[0024] This setup, through a dual adaptive mechanism, collaboratively suppresses sliding mode chattering and improves the observer's robustness to parameter mismatch and external disturbances. The linear feedback strength is dynamically adjusted to reduce high-frequency chattering while ensuring the convergence speed. The update rate of the sliding mode surface variables is adaptively adjusted to avoid overshoot or response hysteresis caused by excessively fast / slow updates under traditional fixed gain. Together, these two mechanisms enable the observer to stably output high signal-to-noise ratio back EMF estimates even under conditions such as motor parameter drift and sudden load changes, laying the foundation for subsequent high-precision position demodulation. This sliding mode back EMF observer structure significantly improves the accuracy and dynamic adaptability of back EMF estimation in sensorless control of dual three-phase permanent magnet synchronous motors without increasing hardware costs.

[0025] Preferably, shaft and Shaft reference voltage and Through the The reference voltage of the subspace current regulator is obtained by processing.

[0026] Preferably, in step S2, The voltage flux linkage in the subspace is calculated using the following formula:

[0027] ;

[0028] in, and for In subspace shaft and Voltage flux linkage of the shaft; and They are respectively shaft and The back electromotive force of the axis is observed.

[0029] Preferably, in step S3, the current flux linkage is calculated based on the online extracted rotor position estimate and permanent magnet flux linkage using the following formula:

[0030] ;

[0031] in, and for In subspace shaft and Current flux linkage of the shaft; The permanent magnet flux linkage for a dual three-phase permanent magnet synchronous motor; These are the estimated rotor positions extracted online.

[0032] This setup models the current flux linkage as an analytical expression that relies solely on the amplitude and real-time position estimation of the permanent magnet flux linkage, avoiding the cumulative error and parameter sensitivity issues introduced by traditional methods that rely on inductance parameters or current integrals to estimate the flux linkage. At the same time, this current flux linkage can accurately characterize the ideal flux linkage component dominated by the permanent magnet, thereby constructing a physically meaningful weighted fusion mechanism between the voltage flux linkage and the current flux linkage, effectively compensating for flux linkage deviations caused by observer model mismatch, parameter drift, and unmodeled dynamics.

[0033] Preferably, in step S3, the voltage flux obtained in S2 is weighted and fused to obtain a comprehensive flux, which is achieved by the following formula:

[0034] ;

[0035] in, and for In subspace shaft and The overall magnetic flux linkage of the shaft; For correction factors; and for In subspace shaft and Voltage flux linkage of the shaft.

[0036] This setup, by introducing a closed-loop feedback mechanism, enables the integrated flux linkage to dynamically track the ideal permanent magnet flux linkage trajectory derived from the position estimation, thereby actively suppressing the effects of voltage flux linkage integral drift, high-frequency noise, and unmodeled disturbances. Compared to traditional open-loop integration or simple weighted averaging methods, this structure possesses inherent stability and adaptive compensation capabilities. When the voltage flux linkage deviates from the ideal value due to initial integration error or disturbance, the feedback term automatically drives it to converge to the model's expected value, significantly improving the long-term consistency and robustness of flux linkage estimation, and providing a high signal-to-noise ratio and low-bias flux linkage signal foundation for subsequent position error extraction.

[0037] Preferably, the correction coefficient λ is a preset positive constant.

[0038] Preferably, in step S4, the composite flux obtained in S3 is normalized, and the equivalent position error is calculated using the following formula:

[0039] ;

[0040] in, This is the equivalent position error.

[0041] This setting, through normalization, eliminates the interference of magnetic flux amplitude fluctuations on position error calculation, making... It is sensitive only to flux phase deviation, thus significantly improving the signal-to-noise ratio and robustness of the position error signal. Especially under low-speed or light-load conditions, when the flux amplitude is small and the noise ratio is relatively high, this normalization mechanism can effectively suppress spurious errors caused by amplitude disturbances, ensuring that the feedback loop only responds to the real phase mismatch, and providing high-precision, low-bias error input for sliding mode / PI and other position correction controllers.

[0042] Preferably, the equivalent position error relative to the actual position of the rotor and rotor position estimation value The relationship between them is satisfied:

[0043] .

[0044] Preferably, in step S4, the orthogonal phase-locked loop includes a proportional-integral (PI) regulator and an integrator; the PI regulator takes the equivalent position error as input and outputs the estimated rotor electrical angular velocity; the integrator performs an integral operation on the rotor electrical angular velocity to obtain an updated rotor position estimate. Attached Figure Description

[0045] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0046] Figure 1 This is a flowchart of the method;

[0047] Figure 2 This is a block diagram illustrating the principle of the orthogonal phase-locked loop in this method;

[0048] Figure 3 This is a waveform diagram of the signal carrying position information under rated load in the conventional method of Example 2;

[0049] Figure 4 This is a waveform diagram of the signal carrying position information under the rated load condition of this method in Example 2;

[0050] Figure 5 This is a steady-state experimental waveform diagram under rated load using the conventional method in Example 2;

[0051] Figure 6 This is a steady-state experimental waveform diagram under rated load conditions in Example 2. Detailed Implementation

[0052] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0054] Example 1

[0055] like Figure 1 As shown, this invention provides a position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction, comprising the following steps:

[0056] S1, will The reference voltage and actual current of the subspace are input to a sliding mode back EMF observer, which outputs the observed back EMF along the α-axis and β-axis; the sliding mode back EMF observer incorporates adaptive parameters. and ,in, The linear feedback gain used to adjust the sliding mode back EMF observer to suppress chattering. Used to adjust the update rate of the internal adaptive variables of the sliding mode back EMF observer.

[0057] In practical implementation, the sliding mode back EMF observer is defined by the following set of equations:

[0058] ;

[0059] in, and They are respectively shaft and The actual current of the shaft; and They are respectively shaft and Stator current estimate for the shaft; and These are the resistance and inductance of a dual three-phase permanent magnet synchronous motor, respectively. and They are respectively shaft and Reference voltage of the shaft; and They are respectively shaft and The back electromotive force of the axis is observed; For the nonlinear gain of the back EMF observer; and For the sliding mode surface variables of the back EMF observer; and These are the adaptive parameters for the back EMF observer; and For the adaptive variable of the back EMF observer; It is a symbolic function.

[0060] in, shaft and Shaft reference voltage and Through the The reference voltage of the subspace current regulator is obtained by processing.

[0061] In practice, shaft and Shaft reference voltage and Calculate using the following steps:

[0062] First, obtain Reference voltage output by subspace current regulator , ;

[0063] Then, using the rotor position estimate at the current moment... ,right , Perform the inverse Park transform to calculate the state at rest. The voltage components in the coordinate system are used to obtain the reference voltage. and :

[0064] ;

[0065] ;

[0066] After that, what will be obtained , It serves as the input reference voltage for subsequent modulation algorithms (such as SVPWM or SPWM).

[0067] In practical implementation, if it is necessary to adapt to the voltage output capability of the inverter, the amplitude can be limited or normalized according to the actual system requirements. This processing is a conventional method known to those skilled in the art and does not affect the implementation of the core solution of this invention.

[0068] Through a dual adaptive mechanism, sliding mode chattering is synergistically suppressed and the robustness of the observer to parameter mismatch and external disturbances is improved: The linear feedback strength is dynamically adjusted to reduce high-frequency chattering while ensuring the convergence speed. The update rate of the sliding mode surface variables is adaptively adjusted to avoid overshoot or response hysteresis caused by excessively fast / slow updates under traditional fixed gain. Together, these two mechanisms enable the observer to stably output high signal-to-noise ratio back EMF estimates even under conditions such as motor parameter drift and sudden load changes, laying the foundation for subsequent high-precision position demodulation. This sliding mode back EMF observer structure significantly improves the accuracy and dynamic adaptability of back EMF estimation in sensorless control of dual three-phase permanent magnet synchronous motors without increasing hardware costs.

[0069] S2. Integrating the observed back electromotive force obtained from S1, we get... Voltage flux linkage in subspace.

[0070] In practice, The voltage flux linkage in the subspace is calculated using the following formula:

[0071] ;

[0072] in, and for In subspace shaft and Voltage flux linkage of the shaft; and They are respectively shaft and The back electromotive force of the axis is observed.

[0073] S3. Calculate the current flux linkage based on the online extracted rotor position estimate and permanent magnet flux linkage, and use the current flux linkage as a correction term to perform weighted fusion correction on the voltage flux linkage obtained in S2 to obtain the comprehensive flux linkage.

[0074] In practice, the current flux linkage is calculated based on the online extracted rotor position estimate and permanent magnet flux linkage, using the following formula:

[0075] ;

[0076] in, and for In subspace shaft and Current flux linkage of the shaft; The permanent magnet flux linkage for a dual three-phase permanent magnet synchronous motor; These are the estimated rotor positions extracted online.

[0077] Modeling the current flux linkage as an analytical expression that depends solely on the amplitude and real-time position estimation of the permanent magnet flux linkage avoids the cumulative error and parameter sensitivity issues introduced by traditional methods that rely on inductance parameters or current integrals to estimate the flux linkage. At the same time, this current flux linkage can accurately characterize the ideal flux linkage component dominated by the permanent magnet, thereby constructing a physically meaningful weighted fusion mechanism between the voltage flux linkage and the current flux linkage, effectively compensating for flux linkage deviations caused by observer model mismatch, parameter drift, and unmodeled dynamics.

[0078] In practice, the voltage flux obtained from S2 is weighted and fused to obtain the comprehensive flux, which is achieved through the following formula:

[0079] ;

[0080] in, and for In subspace shaft and The overall magnetic flux linkage of the shaft; For correction factors; and for In subspace shaft and The voltage flux linkage of the shaft. Wherein, the correction coefficient λ is a preset positive constant.

[0081] By introducing a closed-loop feedback mechanism, the integrated flux linkage dynamically tracks the ideal permanent magnet flux linkage trajectory derived from the position estimation, thereby actively suppressing the effects of voltage flux linkage integral drift, high-frequency noise, and unmodeled disturbances. Compared with traditional open-loop integration or simple weighted averaging methods, this structure has inherent stability and adaptive compensation capabilities. When the voltage flux linkage deviates from the ideal value due to initial integral error or disturbance, the feedback term automatically drives it to converge to the model's expected value, significantly improving the long-term consistency and robustness of flux linkage estimation, and providing a high signal-to-noise ratio and low-bias flux linkage signal foundation for subsequent position error extraction.

[0082] S4. Normalize the composite flux obtained in S3 and calculate the equivalent position error; input the equivalent position error into the proportional-integral regulator in the quadrature phase-locked loop, and estimate the rotor position and speed information through the quadrature phase-locked loop.

[0083] The principle of orthogonal phase-locked loop is as follows: Figure 2As shown. The quadrature phase-locked loop includes a proportional-integral (PI) controller and an integrator; the PI controller takes the equivalent position error as input and outputs an estimated rotor electrical angular velocity; the integrator integrates the rotor electrical angular velocity to obtain an updated rotor position estimate.

[0084] In practice, the equivalent position error is calculated using the following formula:

[0085] ;

[0086] in, This is the equivalent position error.

[0087] Normalization eliminates the interference of magnetic flux amplitude fluctuations on position error calculation, thus... It is sensitive only to flux phase deviation, thus significantly improving the signal-to-noise ratio and robustness of the position error signal. Especially under low-speed or light-load conditions, when the flux amplitude is small and the noise ratio is relatively high, this normalization mechanism can effectively suppress spurious errors caused by amplitude disturbances, ensuring that the feedback loop only responds to the real phase mismatch, and providing high-precision, low-bias error input for sliding mode / PI and other position correction controllers.

[0088] The equivalent position error relative to the actual position of the rotor and rotor position estimation value The relationship between them is satisfied:

[0089] .

[0090] Compared with existing technologies, this invention introduces adaptive parameters. Adjusting the linear feedback gain significantly reduces the inherent high-frequency chattering problem of traditional sliding mode observers. Compared to the simple fixed-gain design in existing technologies, this method can dynamically adjust the feedback strength according to the system state, greatly improving system stability while ensuring observation accuracy. In addition, adaptive parameters... This method can automatically adjust the update rate of the observer's internal variables according to the operating conditions, overcoming the poor adaptability of traditional fixed-parameter observers under different load and speed conditions, and enabling the observer to maintain good observation performance across the entire operating range. Furthermore, by using the current flux linkage as a correction term to perform weighted fusion correction on the voltage flux linkage, it effectively compensates for the impact of model uncertainties, parameter drift, and unmodeled disturbances on position estimation. Compared to the single voltage flux linkage integration method in existing technologies, this method can obtain more accurate and stable rotor position information.

[0091] This method is entirely based on software algorithms, requiring no additional position sensors or complex hardware compensation circuits. Compared to traditional solutions that necessitate additional hardware sensors, it significantly reduces system cost and complexity while maintaining excellent position estimation performance. Furthermore, the synergistic effect of flux linkage model correction and quadrature phase-locked loops significantly improves the system's resilience to non-ideal factors such as inverter dead-zone effects and current sampling errors. Compared to simple filtering methods in existing technologies, this method maintains stable and reliable position estimation under complex operating conditions.

[0092] This method can effectively suppress jitter and, through correction, significantly reduce the interference of model uncertainty, parameter drift, and unmodeled disturbances on the position estimation signal, thus achieving high-precision and robust rotor position estimation in sensorless control of dual three-phase permanent magnet synchronous motors.

[0093] Implementation 2

[0094] To help those skilled in the art better understand the effects of this method, the following explanation is provided.

[0095] To compare the effectiveness of this method Figure 3 and Figure 4 The signal waveforms carrying position information under a rated load of 1 Nm are compared between the traditional method and the method presented in this paper. It can be found that the signal waveform of the method has lower harmonic content and is more sinusoidal.

[0096] Figure 5 and Figure 6 The steady-state experimental waveforms of the traditional method and the method presented in this paper are compared under a rated load of 1 Nm. From top to bottom, the figures represent motor speed, speed estimation error, and position estimation error, respectively.

[0097] It can be observed that although both methods can achieve sensorless speed closed-loop control, the position steady-state error of this method is 5.6°, while the steady-state estimation error of the traditional method is 8.5°, which proves the superiority of this method.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction, characterized in that, Includes the following steps: S1, will The reference voltage and actual current of the subspace are input to a sliding mode back EMF observer, which outputs the observed back EMF on the α-axis and β-axis. The sliding mode back EMF observer incorporates adaptive parameters. and ,in, The linear feedback gain used to adjust the sliding mode back EMF observer to suppress chattering. Used to adjust the update rate of the adaptive variables inside the sliding mode back EMF observer; S2. Integrating the observed back electromotive force obtained from S1, we get... Voltage flux linkage in subspace; S3. Calculate the current flux based on the online extracted rotor position estimate and permanent magnet flux linkage, and use the current flux linkage as a correction term to perform weighted fusion correction on the voltage flux linkage obtained in S2 to obtain the comprehensive flux linkage; S4. Normalize the composite flux obtained in S3 and calculate the equivalent position error; input the equivalent position error into the proportional-integral regulator in the quadrature phase-locked loop, and estimate the rotor position and speed information through the quadrature phase-locked loop.

2. The position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction according to claim 1, characterized in that, In step S1, the sliding mode back EMF observer is defined by the following set of equations: ； in, and They are respectively shaft and The actual current of the shaft; and They are respectively shaft and Stator current estimate for the shaft; and These are the resistance and inductance of a dual three-phase permanent magnet synchronous motor, respectively. and They are respectively shaft and Reference voltage of the shaft; and They are respectively shaft and The back electromotive force of the axis is observed; For the nonlinear gain of the back EMF observer; and For the sliding mode surface variables of the back EMF observer; and These are the adaptive parameters for the back EMF observer; and For the adaptive variable of the back EMF observer; It is a symbolic function.

3. The position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction according to claim 2, characterized in that, shaft and Shaft reference voltage and Through the The reference voltage of the subspace current regulator is obtained by processing.

4. The position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction according to claim 1, characterized in that, In step S2, The voltage flux linkage in the subspace is calculated using the following formula: ； in, and for In subspace shaft and Voltage flux linkage of the shaft; and They are respectively shaft and The back electromotive force of the axis is observed.

5. The position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction according to claim 1, characterized in that, In step S3, the current flux is calculated based on the online extracted rotor position estimate and permanent magnet flux linkage, using the following formula: ； in, and for In subspace shaft and Current flux linkage of the shaft; The permanent magnet flux linkage for a dual three-phase permanent magnet synchronous motor; These are the estimated rotor positions extracted online.

6. The position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction according to claim 5, characterized in that, In step S3, the voltage flux obtained in S2 is weighted and fused to obtain the composite flux, which is achieved by the following formula: ； in, and for In subspace shaft and The overall magnetic flux linkage of the shaft; For correction factors; and for In subspace shaft and Voltage flux linkage of the shaft.

7. The position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction according to claim 6, characterized in that, The correction coefficient λ is a preset positive constant.

8. The position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction according to claim 6, characterized in that, In step S4, the composite flux obtained in S3 is normalized, and the equivalent position error is calculated using the following formula: ； in, This is the equivalent position error.

9. The position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction according to claim 8, characterized in that, The equivalent position error relative to the actual position of the rotor and rotor position estimation value The relationship between them is satisfied: 。 10. The position estimation method for a dual three-phase permanent magnet synchronous motor based on flux linkage model correction according to claim 1, characterized in that, In step S4, the quadrature phase-locked loop includes a proportional-integral (PI) controller and an integrator; the PI controller takes the equivalent position error as input and outputs the estimated rotor electrical angular velocity; the integrator performs an integral operation on the rotor electrical angular velocity to obtain an updated rotor position estimate.

Images