A full-speed-range hybrid flux linkage observation method and device based on adaptive weight fuzzy switching

Abstract

The application provides a full-speed-range hybrid flux linkage observation method and device based on adaptive weight fuzzy switching, which can realize smooth fusion of current models and voltage models in a full-speed range, and realize smooth and accurate rotor flux linkage observation in the full-speed range, and comprises the following steps: obtaining a first rotor flux linkage observation value through a voltage model flux linkage observer based on stator current, stator voltage and motor parameters; obtaining a second rotor flux linkage observation value through a current model flux linkage observer; inputting motor speed and stator phase current amplitude as input variables into a preset fuzzy logic controller, and dynamically adjusting a hybrid weight factor by the fuzzy logic controller based on a preset fuzzy rule base; weighting and fusing the first rotor flux linkage observation value and the second rotor flux linkage observation value by using the hybrid weight factor to generate a final hybrid rotor flux linkage observation value; and calculating a rotor position angle and a rotor angular velocity of the motor based on the hybrid rotor flux linkage observation value.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, and in particular to a method and apparatus for observing full-speed domain hybrid flux linkage based on adaptive weighted fuzzy switching. Background Technology

[0002] In high-performance motor vector control systems, accurate acquisition of rotor flux linkage information, including its amplitude and spatial position, is essential for achieving precise torque control and field orientation. Currently, methods for acquiring rotor flux linkage information mainly fall into two categories: flux linkage observation methods based on current models and those based on voltage models.

[0003] The flux linkage observation method based on the current model has high accuracy when the motor is running at low speed, but its performance is highly dependent on motor parameters, such as stator resistance and direct-axis inductance. However, these motor parameters can change in actual operation due to factors such as temperature variations and magnetic circuit saturation, leading to a decrease in the observation accuracy of the current model in the medium and high speed range.

[0004] The flux linkage observation method based on the voltage model obtains flux linkage information by integrating the back electromotive force. It does not rely on volatile rotor-related parameters and has good robustness to changes in motor parameters, achieving high accuracy during medium- and high-speed operation. However, when the motor is running at low speed, the back electromotive force signal is very weak. The inherent DC bias and initial value drift problems in the pure integration stage can lead to severe distortion of the integration results, or even cause the flux linkage observation to fail.

[0005] To address the aforementioned issues, existing technologies have proposed a hybrid flux observer scheme. The common approach is to use a current model in the low-speed region and a voltage model in the high-speed region, switching between them at a pre-defined fixed speed point. However, this hard-switching strategy has significant drawbacks: First, the selection of the switching point is difficult to be precise; the optimal switching point changes under different loads and operating conditions, and a fixed switching point cannot adapt to all conditions. Second, near the switching point, fluctuations in flux observations are prone to occur, causing torque fluctuations or even instability in the control system. Third, the observation robustness across the entire speed range is poor. Summary of the Invention

[0006] To address the aforementioned issues, this invention provides a method and apparatus for full-speed domain hybrid flux observation based on adaptive weighted fuzzy switching. This method enables smooth fusion of the current model and voltage model across the entire speed range, eliminates switching points, avoids flux jumps and torque fluctuations, and achieves smooth and accurate rotor flux observation across the entire speed range.

[0007] The technical solution is as follows: a full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching, characterized by the following steps: Step 1: Obtain the real-time operating parameters of the motor, including the stator current and stator voltage; Step 2: Run the hybrid flux observer that includes a voltage model flux observer and a current model flux observer. Based on the stator current, stator voltage and motor parameters, calculate the first rotor flux observation value through the voltage model flux observer; calculate the second rotor flux observation value through the current model flux observer. Step 3: Input the motor speed and stator phase current amplitude as input variables into a preset fuzzy logic controller. The fuzzy logic controller dynamically adjusts the hybrid weight factor based on the input variables according to a preset fuzzy rule base. Step 4: Use the hybrid weighting factor to weight and fuse the first rotor flux linkage observation value and the second rotor flux linkage observation value to generate the final hybrid rotor flux linkage observation value; Step 5: Based on the observed values ​​of the hybrid rotor flux linkage, calculate the rotor position angle and rotor angular velocity of the motor.

[0008] Furthermore, in step 1, the three-phase current of the motor is sampled using a Hall sensor. , , After Clark transformation, the three-phase stationary coordinate system is transformed into a two-phase stationary coordinate system, and the stator current components are obtained. , The stator voltage component is obtained by reconstructing the inverter PWM duty cycle and the DC bus voltage. , .

[0009] Furthermore, through the Park transformation, the rotor position angle known from the previous moment is... Then, the two-phase stationary coordinate system is transformed into a two-phase rotating coordinate system to obtain the stator current components. , .

[0010] Furthermore, in step 2, the first rotor flux linkage observation value is calculated using the voltage model flux linkage observer. The specific formula is as follows: (1) (2) in, The stator voltage is in a two-phase stationary coordinate system. Used to refer to , , For stator resistance, The stator current is in a two-phase stationary coordinate system. Used to refer to , , The total magnetic flux linkage in a two-phase stationary coordinate system. The value is obtained by integrating equation (1). It is the q-axis inductance. For the first rotor flux linkage observation value obtained under the two-phase stationary coordinate system voltage model, in the formula, the superscript 's' indicates the two-phase stationary coordinate system, the superscript 'r' indicates the two-phase rotating coordinate system, the subscript 's' represents stator-related variables, and the subscript 'r' represents rotor-related variables. Used to refer to and , and These are the observed values ​​of the first rotor flux linkage.

[0011] Furthermore, in step 2, the observed value of the second rotor flux linkage is calculated based on the current model flux linkage observer. The specific formula is as follows: (3) (4) (5) (6) (7) (8) (9) Here is the Park transformation matrix. The stator current is in a two-phase stationary coordinate system. Let be the stator current in the two-phase rotating coordinate system d, q axes. For d-axis inductance, For rotor flux linkage, It is the q-axis inductance. For the stator flux linkage in the d-axis current model of a two-phase rotating coordinate system, The stator flux linkage in the q-axis current model of a two-phase rotating coordinate system. This is the inverse transformation matrix of Park. The stator flux linkage value is given by the two-phase stationary coordinate system current model. The second rotor flux linkage is the observed value under the two-phase stationary coordinate system current model.

[0012] Furthermore, in step 3, the stator phase current amplitude is obtained using the following formula: (10) in, This refers to the stator phase current amplitude. , This refers to the stator current component; The motor speed is obtained using the following formula: (11) (12) in, This refers to the motor speed. and These are the observed values ​​of the first rotor flux linkage.

[0013] Furthermore, in step 3, regarding the motor speed... Define a fuzzy subset {low speed, medium speed, high speed} for the stator phase current amplitude. The load is represented by the stator phase current amplitude. A fuzzy subset {light load, heavy load} is defined. Different membership functions are defined based on different fuzzy subsets to calculate the motor speed. and stator phase current amplitude Based on the membership degrees of different fuzzy subsets, and according to different motor speeds and stator current amplitude A fuzzy rule base is established; fuzzy inference is performed based on the fuzzy rule base, and the centroid method is used for defuzzification to obtain the hybrid weight factor.

[0014] Furthermore, in step 3, the fuzzy rule base is preset as follows: When the motor speed is low and the load is light, increase the hybrid weighting factor to give more weight to the second rotor flux observation value. When the motor speed is low and the load is heavy, the hybrid weighting factor is set to an intermediate value to reduce the influence of the second rotor flux observation value. When the motor speed is high, the mixing weighting factor is reduced, and the observation value of the first rotor flux is given more weight. When the motor speed is medium speed, the hybrid weighting factor is set to an intermediate value to balance the first rotor flux linkage observation value and the second rotor flux linkage observation value.

[0015] Furthermore, in step 4, the first rotor flux linkage observation and the second rotor flux linkage observation are weighted and fused using the hybrid weighting factor, specifically implemented through the following formula: (13) in, This represents the final hybrid rotor flux linkage observation in a two-phase stationary coordinate system. This is the first rotor flux linkage observation value. This is the observation value of the second rotor flux linkage.

[0016] Furthermore, in step 5, the final hybrid rotor flux observation is input into the phase-locked loop (PLL), which tracks the flux vector and outputs the rotor position angle and rotor angular velocity.

[0017] Furthermore, in step 5, the final hybrid rotor flux linkage observation values ​​obtained in step 4 are subjected to arctangent calculation in a two-phase stationary coordinate system to obtain rotor position angle information, as follows: (14) Among them, , The final hybrid rotor flux linkage observation values ​​in the two-phase stationary coordinate system are obtained through... Reference , .

[0018] This invention employs a fuzzy logic controller to generate continuously changing hybrid weighting factors, enabling soft switching between current and voltage models. The smooth transition of the hybrid weighting factors eliminates jumps in flux linkage observations, effectively suppressing torque ripple and ensuring stable motor operation across the entire speed range. The fuzzy controller's hybrid weighting factors no longer rely on a single fixed speed threshold but are dynamically adjusted based on real-time motor speed and stator current amplitude. Under conditions most unfavorable to the current model, such as low-speed heavy load, the weighting factors can be intelligently reduced, improving the robustness and accuracy of the observer under complex conditions. Furthermore, by employing a fuzzy rule base, this invention can more comprehensively describe the optimal hybrid strategy across the entire motor operating plane, achieving optimal observation performance across the entire speed range. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of a full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching in the embodiment. Figure 2 This is a schematic diagram illustrating the principle of the method in the embodiment; Figure 3 This is a schematic diagram of the hybrid flux observer in the embodiment; Figure 4 This is a flowchart of step 3 in the embodiment; Figure 5 This is a schematic diagram of the phase-locked loop in step 4 of the embodiment. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0021] See Figure 1The present invention provides a full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching, comprising at least the following steps: Step 1: Obtain the real-time operating parameters of the motor, including the stator current and stator voltage; Step 2: Run the hybrid flux observer that includes a voltage model flux observer and a current model flux observer. Based on the stator current, stator voltage and motor parameters, calculate the first rotor flux observation value through the voltage model flux observer; calculate the second rotor flux observation value through the current model flux observer. Step 3: Input the motor speed and stator phase current amplitude as input variables into the preset fuzzy logic controller. The fuzzy logic controller dynamically adjusts the hybrid weight factor based on the preset fuzzy rule base and the input variables. Step 4: Use a hybrid weighting factor to weight and fuse the first rotor flux linkage observation and the second rotor flux linkage observation to generate the final hybrid rotor flux linkage observation. Step 5: Based on the observed values ​​of the hybrid rotor flux linkage, calculate the rotor position angle and rotor angular velocity of the motor.

[0022] In one specific embodiment of the present invention, in step 1, the three-phase current of the motor is sampled by a Hall sensor. , , After Clark transformation, the three-phase stationary coordinate system is transformed into a two-phase stationary coordinate system, and the stator current components are obtained. , The stator voltage component is obtained by reconstructing the inverter PWM duty cycle and the DC bus voltage. , .

[0023] like Figure 2 As shown, in one specific embodiment of the present invention, in step 2, a voltage model flux linkage observer and a current model flux linkage observer are run in parallel in the hybrid flux linkage observer, and the first rotor flux linkage observation value is calculated by the voltage model flux linkage observer. The specific formula is as follows: (1) (2) in, The stator voltage is in a two-phase stationary coordinate system. Used to refer to , , For stator resistance, The stator current is in a two-phase stationary coordinate system. Used to refer to , Used to simplify formula expression. The total magnetic flux linkage in a two-phase stationary coordinate system. The value is obtained by integrating equation (1). It is the q-axis inductance. The calculated first rotor flux linkage observation value under the two-phase stationary coordinate system voltage model is given in the formula. In the formula, the superscript 's' indicates the two-phase stationary coordinate system, the superscript 'r' indicates the two-phase rotating coordinate system, the subscript 's' represents stator-related variables, and the subscript 'r' represents rotor-related variables. In the embodiment... Used to refer to and , and These are the observed values ​​of the first rotor flux linkage.

[0024] In step 2 of the embodiment, the observed value of the second rotor flux linkage is calculated based on the current model flux linkage observer. The current model is calculated based on the definition of magnetic flux linkage, using the rotor position angle known from the previous moment. By using the Park transformation, and then transforming the two-phase stationary coordinate system to the two-phase rotating coordinate system, the stator current components are obtained. , This can be used in current models and control systems, as shown in the following formula: (3) (4) In a two-phase rotating coordinate system, the relationship between the stator flux linkage and the rotor flux linkage is as follows: (5) (6) The stator flux in the two-phase rotating coordinate system is transformed back to the two-phase stationary coordinate system using the inverse Park transformation: (7) (8) The second rotor flux observation value calculated by the current model flux observer is obtained: (9) in, Here is the Park transformation matrix. The stator current is in a two-phase stationary coordinate system. Let be the stator current in the two-phase rotating coordinate system d, q axes. For d-axis inductance, For rotor flux linkage, It is the q-axis inductance. For the stator flux linkage in the d-axis current model of a two-phase rotating coordinate system, The stator flux linkage in the q-axis current model of a two-phase rotating coordinate system. This is the inverse transformation matrix of Park. The stator flux linkage value is given by the two-phase stationary coordinate system current model. The second rotor flux linkage is the observed value under the two-phase stationary coordinate system current model.

[0025] In step 3, the motor speed and stator phase current amplitude are used as input variables. In this embodiment, the stator phase current amplitude is obtained by the following formula: (10) in, This refers to the stator phase current amplitude. , This refers to the stator current component; The motor speed is obtained using the following formula: (11) (12) in, This refers to the motor speed. and These are the observed values ​​of the first rotor flux linkage.

[0026] In step 3, regarding the motor speed Define a fuzzy subset {low speed, medium speed, high speed} for the stator phase current amplitude. The load is represented by the stator phase current amplitude. A fuzzy subset {light load, heavy load} is defined. Different membership functions, such as triangular, trapezoidal, or Gaussian membership functions, are defined based on different fuzzy subsets to calculate the motor speed. and stator phase current amplitude Based on the membership degrees of different fuzzy subsets, and according to different motor speeds and stator current amplitude A fuzzy rule base is established; fuzzy inference is performed based on the fuzzy rule base, and the centroid method is used for defuzzification to obtain the hybrid weight factor.

[0027] In step 3 of the embodiment, the fuzzy rule base is preset as follows: When the motor speed is low and the load is light, increase the hybrid weighting factor to give more weight to the second rotor flux observation value. When the motor speed is low and the load is heavy, the hybrid weighting factor is set to an intermediate value to reduce the influence of the second rotor flux observation value. When the motor speed is high, the mixing weighting factor is reduced, and the observation value of the first rotor flux is given more weight. When the motor speed is medium speed, the hybrid weighting factor is set to an intermediate value to balance the first rotor flux linkage observation value and the second rotor flux linkage observation value.

[0028] The fuzzy rule base in this embodiment is shown in Table 1. The rules in this embodiment are based on expert knowledge and can leverage the advantages of the two models under different working conditions.

[0029] Table 1 Fuzzy Rule Base 1 The motor is running at low speed and the load is light. Fuzzy weight K=0.9 2 The motor is running at low speed and the load is heavy. Fuzzy weight K=0.5 3 Medium speed of motor Fuzzy weight K=0.5 4 The motor is running at high speed and the load is light. Fuzzy weight K=0.1 5 The motor operates at high speed and is under heavy load. Fuzzy weight K=0.1 The following explains the fuzzy rule base in Table 1: At low speeds and light loads, the current model is most accurate, therefore its weight K should be close to 1, set to 0.9 in this example; Rules 4 and 5 indicate that at high speeds, the voltage model is more reliable, therefore the weight of the current model should be close to 0, set to 0.1 in this example; Rule 2 indicates that at low speeds and heavy loads, motor parameters change significantly, and the accuracy of the current model decreases, therefore its weight needs to be appropriately reduced. The weight values ​​in the fuzzy rule base can be optimized based on experimental results for different motors and application scenarios.

[0030] The inference process typically uses the minimum operation to determine the activation intensity of each rule, merges the fuzzy output results of all activated rules to form a total fuzzy output region, and then uses the centroid method to calculate the centroid of the region, and outputs the abscissa value corresponding to the centroid as the final mixing weight factor K.

[0031] In this embodiment, a continuously varying hybrid weighting factor K is used to weight and fuse the current model and the voltage model. The weighting factor continuously changes between 0 and 1, eliminating the problem of flux linkage observation jumps at fixed switching points in traditional hard switching methods. The smooth switching of this invention reduces flux linkage observation errors, torque ripple, and makes the system run more smoothly. The weight allocation of this invention no longer depends on a fixed speed threshold, but is dynamically adjusted according to both real-time speed and load factors. Especially under low-speed heavy-load conditions, the current model is most severely affected by stator resistance and inductance parameter errors. Traditional methods rely entirely on the current model, resulting in persistently high flux linkage observation errors. However, this invention reduces the weight K and appropriately increases the proportion of the voltage model, which significantly reduces flux linkage observation errors and significantly improves the overall robustness of the system. This invention also employs a fuzzy rule library, which can more comprehensively describe the optimal hybrid strategy across the entire motor operating plane, achieving optimal observation performance across the entire speed domain. The fuzzy rule library is intuitively designed and easy to optimize and adjust according to the characteristics of different motors. The membership function and fuzzy rules can be flexibly set according to actual application requirements, without the need for complex mathematical models and extensive parameter identification work. It is highly versatile and applicable to various types of motor control systems, such as permanent magnet synchronous motors and induction motors.

[0032] In step 4 of the embodiment, the two magnetic flux observations obtained in step 2 are weighted and fused using the mixing weighting factor K obtained in step 3, as specifically expressed by the following formula: (13) in, This represents the final hybrid rotor flux linkage observation in a two-phase stationary coordinate system. This is the first rotor flux linkage observation value. The second rotor flux linkage observation value is obtained through... Reference , ,in , This represents the final hybrid rotor flux linkage observation in a two-phase stationary coordinate system.

[0033] In step 5 of the embodiment, the final hybrid rotor flux linkage observation value is input into the phase-locked loop (PLL), and the PLL will... , As input, the estimated rotor angle is continuously adjusted by an internal PI controller until the error between the estimated flux linkage and the actual flux linkage approaches zero, thus accurately tracking the flux linkage vector. The output of the phase-locked loop is the final rotor position angle. and rotor angular velocity .

[0034] In another embodiment of the present invention, in addition to using a phase-locked loop in step 5, the final hybrid rotor flux linkage observation values ​​obtained in step 4 can also be subjected to arctangent calculation in a two-phase stationary coordinate system to obtain rotor position angle information, as follows: (14) Among them, , The final hybrid rotor flux linkage observation values ​​in the two-phase stationary coordinate system are obtained through... Reference , .

[0035] The method in this embodiment has low computational complexity and is suitable for implementation on low-cost microcontrollers, but it still meets performance requirements in most industrial applications.

[0036] In an embodiment of the present invention, a hybrid magnetic flux observation device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0037] In an embodiment of the present invention, a computer-readable storage medium is also provided, on which a program is stored, which, when executed by a processor, implements the full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching as described above.

[0038] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, computer-readable storage media, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0039] The embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, computer apparatuses, or computer program products according to embodiments of the invention. These computer program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams.

[0040] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in the flowchart.

[0041] In an embodiment of the present invention, a computer program product is also provided, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0042] In practical applications, the aforementioned computer program products include, but are not limited to: fuel cell systems, fuel cell stack systems, smartphones, desktop computers, laptops, tablets, host computers, and server platforms, etc., without specific limitations.

[0043] The above provides a detailed description of the application of the full-speed domain hybrid flux linkage observation method, hybrid flux linkage observation device, computer-readable storage medium, and computer program product based on adaptive weighted fuzzy switching provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A full-speed domain hybrid flux linkage observation method based on adaptive weighted fuzzy switching, characterized in that, Includes the following steps: Step 1: Obtain the real-time operating parameters of the motor, including the stator current and stator voltage; Step 2: Run the hybrid flux observer that includes the voltage model flux observer and the current model flux observer. Based on the stator current, stator voltage and motor parameters, calculate the first rotor flux observation value through the voltage model flux observer. The observed value of the second rotor flux was obtained by calculating the flux observation value using the current model flux observer; Step 3: Input the motor speed and stator phase current amplitude as input variables into a preset fuzzy logic controller. The fuzzy logic controller dynamically adjusts the hybrid weight factor based on the input variables according to a preset fuzzy rule base. Step 4: Use the hybrid weighting factor to weight and fuse the first rotor flux linkage observation value and the second rotor flux linkage observation value to generate the final hybrid rotor flux linkage observation value; Step 5: Based on the observed values ​​of the hybrid rotor flux linkage, calculate the rotor position angle and rotor angular velocity of the motor; In step 3, regarding the motor speed Define a fuzzy subset {low speed, medium speed, high speed}, and represent the load by the stator phase current amplitude. For the stator phase current amplitude... Define a fuzzy subset {light load, heavy load}, and define different membership functions based on different fuzzy subsets to calculate the motor speed. and stator phase current amplitude Based on the membership degrees of different fuzzy subsets, and according to different motor speeds and stator current amplitude A fuzzy rule base is established; fuzzy inference is performed based on the fuzzy rule base, and the centroid method is used for defuzzification to obtain the hybrid weight factor. In step 3, the fuzzy rule base is preset as follows: When the motor speed is low and the load is light, increase the hybrid weighting factor to give more weight to the second rotor flux observation value. When the motor speed is low and the load is heavy, the hybrid weighting factor is set to an intermediate value to reduce the influence of the second rotor flux observation value. When the motor speed is high, the mixing weighting factor is reduced, and the observation value of the first rotor flux is given more weight. When the motor speed is medium speed, the hybrid weighting factor is set to an intermediate value to balance the first rotor flux linkage observation value and the second rotor flux linkage observation value.

2. The full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching according to claim 1, characterized in that, In step 1, the three-phase current of the motor is sampled using a Hall sensor. , , After Clark transformation, the three-phase stationary coordinate system is transformed into a two-phase stationary coordinate system, and the stator current components are obtained. , The stator voltage component is obtained by reconstructing the inverter PWM duty cycle and the DC bus voltage. , .

3. The full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching according to claim 1, characterized in that, In step 2, the observed value of the first rotor flux linkage is calculated using the voltage model flux linkage observer. The specific formula is as follows: (1)； (2)； in, The stator voltage is in a two-phase stationary coordinate system. Used to refer to , , For stator resistance, The stator current is in a two-phase stationary coordinate system. Used to refer to , , The total magnetic flux linkage in a two-phase stationary coordinate system. The value is obtained by integrating equation (1). It is the q-axis inductance. For the first rotor flux linkage observation value obtained under the two-phase stationary coordinate system voltage model, in the formula, the superscript 's' indicates the two-phase stationary coordinate system, the superscript 'r' indicates the two-phase rotating coordinate system, the subscript 's' represents stator-related variables, and the subscript 'r' represents rotor-related variables. Used to refer to and , and These are the observed values ​​of the first rotor flux linkage.

4. The full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching according to claim 2, characterized in that, In step 2, the observed value of the second rotor flux linkage is calculated based on the current model flux linkage observer. The rotor position angle known from the previous moment By using the Park transformation, the two-phase stationary coordinate system is transformed into a two-phase rotating coordinate system, and the stator current components are obtained. , The specific formula is as follows: (3)； (4)； In a two-phase rotating coordinate system, the relationship between the stator flux linkage and the rotor flux linkage is as follows: (5)； (6)； The stator flux in the two-phase rotating coordinate system is transformed back to the two-phase stationary coordinate system using the inverse Park transformation: (7)； (8)； The second rotor flux observation value calculated by the current model flux observer is obtained: (9)； Here is the Park transformation matrix. The stator current is in a two-phase stationary coordinate system. Let be the stator current in the two-phase rotating coordinate system d, q axes. For d-axis inductance, For rotor flux linkage, It is the q-axis inductance. For the stator flux linkage in the d-axis current model of a two-phase rotating coordinate system, The stator flux linkage in the q-axis current model of a two-phase rotating coordinate system. This is the inverse transformation matrix of Park. The stator flux linkage value is given by the two-phase stationary coordinate system current model. The second rotor flux linkage is the observed value under the two-phase stationary coordinate system current model.

5. The full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching according to claim 2, characterized in that, In step 3, the stator phase current amplitude is obtained using the following formula: (10)； in, This refers to the stator phase current amplitude. , This refers to the stator current component; The motor speed is obtained using the following formula: (11)； (12)； in, This refers to the motor speed. and These are the observed values ​​of the first rotor flux linkage.

6. The full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching according to claim 1, characterized in that: In step 4, the first rotor flux linkage observation value and the second rotor flux linkage observation value are weighted and fused using the hybrid weighting factor, specifically implemented through the following formula: (13)； in, This represents the final hybrid rotor flux linkage observation in a two-phase stationary coordinate system. This is the first rotor flux linkage observation value. This is the observation value of the second rotor flux linkage.

7. The full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching according to claim 1, characterized in that: In step 5, the final hybrid rotor flux observation is input into the phase-locked loop (PLL), which tracks the flux vector and outputs the rotor position angle and rotor angular velocity.

8. The full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching according to claim 6, characterized in that: In step 5, the final hybrid rotor flux linkage observation values ​​obtained in step 4 are subjected to arctangent calculation in a two-phase stationary coordinate system to obtain rotor position angle information, as follows: (14)； Among them, , The final hybrid rotor flux linkage observation values ​​in the two-phase stationary coordinate system are obtained through... Reference , .

9. A hybrid magnetic flux monitoring device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by the processor, it implements the full-speed domain hybrid flux observation method based on adaptive weighted fuzzy switching as described in any one of claims 1 to 8.

11. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 8.

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