Permanent magnet synchronous motor sensorless control method and system

Abstract

The invention provides a permanent magnet synchronous motor sensorless control method and system, and belongs to the technical field of permanent magnet synchronous motor control, and the method comprises the steps: obtaining a stator current and a stator voltage reference value of a permanent magnet synchronous motor, and converting the stator current to obtain a stator current under an alpha-beta axis; inputting the stator voltage reference value and the stator current under the alpha-beta axis into an improved unscented Kalman filter for observation, wherein the improved unscented Kalman filter comprises an unscented Kalman filter; the unscented Kalman filter outputs the optimized flux linkage parameter, the estimated value of the electrical angle of the rotor position, the estimated value of the electrical angular speed of the rotor and the load torque; and estimating the position and the speed of the rotor based on the output of the unscented Kalman filter so as to control the operation of the motor.

Description

Technical Field

[0001] This invention belongs to the field of permanent magnet synchronous motor control technology, and particularly relates to a sensorless control method and system for permanent magnet synchronous motors. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In certain applications, such as new energy vehicles, flywheel energy storage, aerospace, and high-speed air compressors, high-performance motor drives are required. Permanent magnet synchronous motors (PMSMs) offer advantages such as compact structure, high efficiency, and high power density, and are widely used in robotics, smart manufacturing, and electric vehicles. In these applications, accurate rotor position information is crucial for improving control performance. Although mechanical encoders can provide position signals, they increase system cost and complexity. Furthermore, under extreme operating conditions, encoder failure can lead to a sharp decline in motor control performance, or even cause converter overcurrent or motor damage. Therefore, sensorless control technology has been extensively researched, offering higher reliability and lower hardware costs for PMSM drivers.

[0004] Existing permanent magnet synchronous motors mainly employ the following methods for position control, and these methods present related technical challenges: (1) Sensorless control does not directly measure the position and speed information of the rotor. Instead, it estimates the position and speed of the rotor by measuring the terminal voltage and phase current of the motor and based on the mathematical model of the motor (back EMF model, flux linkage model, etc.). System noise and sampling noise will seriously affect the estimation accuracy of sensorless control of permanent magnet synchronous motor (PMSM). The fundamental reason is that these noises pollute the basic signals used for position estimation and interfere with the stable convergence of the observer or estimation algorithm.

[0005] (2) The dynamic behavior of permanent magnet synchronous motor system cannot be described by simple linear differential equations. The relationship between its output and input is complex and does not satisfy superposition and homogeneity. Therefore, permanent magnet synchronous motor system is a strongly nonlinear system. Most existing observers are linear observers, which leads to a decrease in estimation accuracy.

[0006] (3) In the traditional sensorless control of permanent magnet synchronous motor based on motor model, the back EMF or flux information is usually obtained by an observer; then the observed back EMF or flux information is input into the phase-locked loop to extract the rotor position and speed information. The phase-locked loop architecture is not an algorithm for directly estimating position, but an excellent tracking loop for accurately extracting position and speed information from noise. This technology limits the dynamic response speed of the system.

[0007] Furthermore, traditional methods often rely on precise current information to reconstruct the back EMF and effective flux linkage. However, current sensors introduce sampling noise, which affects the estimation accuracy of these methods. After obtaining the back EMF or effective flux linkage, traditional methods use a phase-locked loop (PLL) architecture to obtain rotor position and speed information, but this limits the system's dynamic response speed and its ability to withstand load torque disturbances.

[0008] (4) The core idea of ​​traditional sensorless control is model-based estimation. Whether it is open-loop calculation or closed-loop observer, the basis is the mathematical model of the motor. Under harsh working conditions, the parameters of the motor will change. When the nominal parameters in the model are inconsistent with the actual parameters of the motor, the calculated back EMF will be inaccurate. Using inaccurate back EMF information will lead to incorrect estimation of position and speed. Therefore, using a fixed and idealized model to estimate a real object with time-varying and nonlinear parameters will lead to a decrease in observation accuracy or even divergence once the model does not match reality. Summary of the Invention

[0009] To overcome the shortcomings of the prior art, the present invention provides a sensorless control method and system for permanent magnet synchronous motors, which can achieve optimal state estimation considering system noise and measurement noise. Even when the model parameters are inaccurate, it can effectively improve the system's estimation accuracy of rotor position and speed.

[0010] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: In the first aspect, a sensorless control method for a permanent magnet synchronous motor is disclosed, including: Obtain the reference values ​​of stator current and stator voltage of the permanent magnet synchronous motor, and convert the stator current to obtain the stator current under the αβ axis; The stator voltage reference value and the stator current along the αβ axis are input to an improved unscented Kalman filter for observation. The improved unscented Kalman filter includes an unscented Kalman filter and... Figure 2 The rest of the content; The stator current and the electrical angle of the rotor position are estimated by observing the stator voltage reference value using an unscented Kalman filter. The estimated values ​​of the stator current and the electrical angle of the rotor position under the αβ axis are transformed and processed by the first low-pass filter to obtain the first current estimate. The estimated values ​​of the stator current under the αβ axis and the estimated values ​​of the electrical angle of the rotor position are transformed and processed by the second low-pass filter to obtain the second current estimate. The difference is obtained by subtracting the first current estimate from the second current estimate; The difference is corrected using a correction factor to obtain the corrected value; The estimated value of the permanent magnet flux linkage parameter is obtained by subtracting the initial permanent magnet flux linkage parameter from the correction value and then input into the unscented Kalman filter. The unscented Kalman filter outputs the optimized flux linkage parameter, the estimated value of the electric angle of the rotor position, the estimated value of the rotor electric angular velocity, and the load torque. The rotor position and speed are estimated based on the output of the unscented Kalman filter, thereby controlling the motor operation.

[0011] As a further technical solution, before obtaining the reference values ​​of stator current and stator voltage of the permanent magnet synchronous motor, a permanent magnet synchronous motor model based on the concept of effective flux linkage is established. The permanent magnet synchronous motor model based on the concept of effective flux linkage includes: the flux linkage equation of the permanent magnet synchronous motor based on effective flux linkage.

[0012] As a further technical solution, the flux linkage equation of a permanent magnet synchronous motor based on effective flux linkage is as follows: (1) in This represents the differentiation operation with respect to time. This represents the stator flux linkages along the α and β axes of a permanent magnet synchronous motor. R s Indicates stator resistance. θ e Electrical angles representing the rotor position. u α and u β The values ​​represent the input voltages along the α and β axes. Indicates the effective magnetic flux linkage. L d , L q Inductance along the d-axis and q-axis.

[0013] As a further technical solution, the effective magnetic flux The expression is: (2) in, L d , L q Inductance along the d-axis and q-axis Indicates permanent magnet flux linkage. i d This represents the stator current along the d-axis.

[0014] As a further technical solution, the stator current on the αβ axis is represented based on the effective flux linkage, the electrical angle of the rotor position, and the inductance of the d-axis and q-axis, specifically as follows: (3) in, These represent the stator currents along the α and β axes, respectively. This represents the stator flux linkages along the α and β axes of a permanent magnet synchronous motor. θ e Electrical angles representing the rotor position. Indicates the effective magnetic flux linkage. L d , L q Inductance along the d-axis and q-axis.

[0015] As a further technical solution, state variables are defined in the unscented Kalman filter. x ,enter u With output z， Specifically:

[0016] in, This represents the stator flux linkages along the α and β axes of a permanent magnet synchronous motor. θ e Electrical angles representing the rotor position. ω e The rotor's electric angular velocity, T l For load torque, u α and u β The values ​​represent the input voltages along the α and β axes. These represent the stator currents along the α and β axes, respectively.

[0017] Secondly, a sensorless control system for a permanent magnet synchronous motor is disclosed, comprising: The data acquisition module is configured to: acquire the stator current and stator voltage reference values ​​of the permanent magnet synchronous motor, and convert the stator current to obtain the stator current under the αβ axis; An improved unscented Kalman filter processing module is configured to input stator voltage reference values ​​and stator currents under the αβ axis to an improved unscented Kalman filter for observation, wherein the improved unscented Kalman filter includes an unscented Kalman filter. The stator current and the electrical angle of the rotor position are estimated by observing the stator voltage reference value using an unscented Kalman filter. The estimated values ​​of the stator current and the electrical angle of the rotor position under the αβ axis are transformed and processed by the first low-pass filter to obtain the first current estimate. The estimated values ​​of the stator current under the αβ axis and the estimated values ​​of the electrical angle of the rotor position are transformed and processed by the second low-pass filter to obtain the second current estimate. The difference is obtained by subtracting the first current estimate from the second current estimate; The difference is corrected using a correction factor to obtain the corrected value; The estimated value of the permanent magnet flux linkage parameter is obtained by subtracting the initial permanent magnet flux linkage parameter from the correction value and then input into the unscented Kalman filter. The unscented Kalman filter outputs the optimized flux linkage parameter, the estimated value of the electric angle of the rotor position, the estimated value of the rotor electric angular velocity, and the load torque. The control module is configured to estimate the rotor position and speed based on the output of an unscented Kalman filter, thereby controlling the motor operation.

[0018] The above one or more technical solutions have the following beneficial effects: The method proposed in this invention can achieve optimal state estimation considering both system noise and measurement noise, effectively improving the estimation accuracy of rotor position and speed. Since permanent magnet synchronous motors are highly nonlinear systems, the unscented Kalman filter used in this invention can improve the estimation accuracy under highly nonlinear conditions. The unscented Kalman filter used in this invention incorporates load torque into the observed state variables, effectively improving the system's dynamic response speed and resistance to load torque disturbances; simultaneously, the proposed adaptive correction strategy enhances the system's parameter robustness, thereby improving the system's adaptability to harsh operating conditions.

[0019] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 : Structure diagram of the proposed permanent magnet synchronous motor control system; Figure 2 Block diagram of the proposed improved unscented Kalman filter; Figure 3 : Flowchart of the proposed improved unscented Kalman filter implementation. Detailed Implementation

[0022] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0023] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0024] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0025] Example 1 This embodiment discloses a sensorless control method for a permanent magnet synchronous motor based on an improved unscented Kalman filter. It employs a vector control architecture and uses an improved unscented Kalman filter to observe position and velocity. The control structure diagram is shown below. Figure 1 As shown; the block diagram of the improved unscented Kalman filter proposed in this invention is as follows. Figure 2 As shown, the estimated value of the d-axis current is The estimated value of the d-axis current is Vector control of permanent magnet synchronous motors is currently a relatively mature control scheme.

[0026] Figure 1 In this study, the control loop of the permanent magnet synchronous motor adopts a vector control architecture. The input is a given speed value, and the output is the switching state of the converter, which controls the operation of the motor. The control loop requires rotor position and speed information to achieve high-performance control of the motor. By improving the unscented Kalman filter to become a position and speed observation link, the rotor angle and speed are estimated by collecting stator current and voltage information and fed back to the control loop, realizing sensorless control and thus completing the high-performance control of the permanent magnet synchronous motor. Figure 2 In this process, the output variable of the unscented Kalman filter is the estimated value of the stator current. The actual stator current value and the estimated stator current value are transformed to the dq coordinate system using the rotor angle estimate. The transformed value is then passed through a low-pass filter (LPF) to filter out high-frequency noise components. The difference between the above d-axis current estimates is integrated and fed back to the system parameters for updating, thereby improving the robustness of the observation loop parameters. Figure 3 The paper describes the specific formula implementation process of the improved unscented Kalman filter, including the implementation process of the standard unscented Kalman filter and the proposed adaptive feedback correction strategy.

[0027] The following section details the implementation process of the observation loop—the proposed improved unscented Kalman filter. The specific steps of the above method include: Step 1: Data acquisition module, including: acquiring the stator current and stator voltage reference values ​​of the permanent magnet synchronous motor, and converting the stator current to obtain the stator current under the αβ axis; Step 2: Observation steps using the improved unscented Kalman filter: Input the stator voltage reference value and the stator current under the αβ axis into the improved unscented Kalman filter for observation. The improved unscented Kalman filter includes an unscented Kalman filter. The stator current and the electrical angle of the rotor position are estimated by observing the stator voltage reference value using an unscented Kalman filter. The estimated values ​​of the stator current and the electrical angle of the rotor position under the αβ axis are transformed and processed by the first low-pass filter to obtain the first current estimate. The estimated values ​​of the stator current under the αβ axis and the estimated values ​​of the electrical angle of the rotor position are transformed and processed by the second low-pass filter to obtain the second current estimate. The difference is obtained by subtracting the first current estimate from the second current estimate; The difference is corrected using a correction factor to obtain the corrected value; The estimated value of the permanent magnet flux linkage parameter is obtained by subtracting the initial permanent magnet flux linkage parameter from the correction value and then input into the unscented Kalman filter. The unscented Kalman filter outputs the optimized flux linkage parameter, the estimated value of the electric angle of the rotor position, the estimated value of the rotor electric angular velocity, and the load torque. Step 3: Control Steps: The rotor position and speed are estimated based on the output of the unscented Kalman filter, thereby controlling the motor operation.

[0028] In step two, we first establish a permanent magnet synchronous motor model based on the concept of effective flux linkage. This is because commonly used mathematical models based on Kalman filters are mostly based on current models, i.e., using stator current. As the system's state variables, this model leads to erroneous convergence of the Kalman filter, resulting in incorrect rotor position and speed information. Therefore, this embodiment proposes a mathematical model for a permanent magnet synchronous motor based on the concept of effective flux linkage. This model is also applicable to integrated permanent magnet synchronous motors (IPMSMs), i.e., motors where the d-axis and q-axis inductances are unequal. Establishing this model is a necessary step in implementing a Kalman filter, which requires state equations. Perform a one-step prediction and obtain the output equation of the required state. .

[0029] The flux linkage equation of a permanent magnet synchronous motor based on effective flux linkage is shown below: (1) in This represents the differentiation operation with respect to time. This represents the stator flux linkages along the α and β axes of a permanent magnet synchronous motor. R s Indicates stator resistance. θe Electrical angles representing the rotor position. u α and u β The α and β axes represent the input voltages. Indicates the effective magnetic flux linkage. L d , L q This represents the inductance along the d-axis and q-axis. Among them, effective magnetic flux The expression is: (2) in Indicates the effective magnetic flux linkage. L d , L q Inductance representing the d-axis and q-axis, Indicates permanent magnet flux linkage. i d This represents the stator current along the d-axis.

[0030] The stator current on the αβ axis is expressed as: (3) in This represents the stator flux linkages along the α and β axes of a permanent magnet synchronous motor. Indicates the effective magnetic flux linkage. L q q-axis inductance Indicates permanent magnet flux linkage. These represent the stator currents along the α and β axes, respectively. θ e Electrical angle representing the rotor position. See appendix again Figure 2 As shown, the improved unscented Kalman filter uses stator current in its specific processing. And the estimated values ​​obtained through observations using the unscented Kalman filter UKF. .

[0031] (2-1) in Indicates measured value, Represents the estimated value, where Includes the actual measured value of the stator current, which is obtained by a current sensor; This represents the estimated value of the stator current, which is obtained from the output equation of the unscented Kalman filter (see equation (3)).

[0032] The actual stator current value Compared with the estimated stator current value The estimated values ​​of the dq-axis currents are obtained by performing a Park transformation on the rotor position estimates. And the estimated values ​​of the dq-axis current. .

[0033] Park transformation The expression is as follows: (2-2) in, This is the electrical angle estimate of the rotor position.

[0034] Under conditions of parameter mismatch, and There will be a steady-state error, which can be addressed by changing the digital controller. The compensation will be made in the following way, let the current time be... k Time (the following) k (All times are represented by time), first, regarding the error Extract: (2-3) in s Represents the Laplace operator. This represents the bandwidth of the low-pass filter, where Indicates measured value, The value represents the average of the estimated values. After the Park transformation, the stator current along the αβ axis will be transformed to the dq axis, yielding the estimated value of the dq axis current. And the estimated values ​​of the dq-axis current. Therefore, the estimation error on the dq axis can be expressed as: (2-4) in, These represent the estimation errors along the d-axis and q-axis, respectively. In this invention, we utilize the d-axis estimation... To compensate for interference caused by parameter mismatch, an integral negative feedback loop is used to form a closed loop. The original flux linkage parameters in the controller are then updated. This can mitigate the interference of parameter mismatch, and the update equation is expressed as follows: (2-5) in K PM Indicates the correction factor. This indicates the parameter value obtained after correction. The current estimation error along the d-axis represents the error. The above correction strategy can improve the system's robustness to parameter mismatch. The flowchart of the proposed unscented Kalman filter correction is shown below. Figure 3 As shown.

[0035] In one implementation example, the following is a design of a fifth-order unscented Kalman filter based on the proposed model.

[0036] By selecting the stator flux Rotor electric angular velocity ω e Rotor electrical angle and load torque T l As the state variables of the UKF, a nonlinear equation can be obtained, which is the state equation of the permanent magnet synchronous motor, i.e., formula (1); because Refer to formula (5).

[0037] First, define the state variables of the proposed Kalman filter model. x ,enter u With output z for: (4) Furthermore, the state equation based on UKF can be defined as follows: (5) in, These are the system's state variables, inputs, and outputs, respectively. express The derivative, assuming system noise ω and measuring noise v They are white noise, which conforms to the principle of having covariance. Q and R The Gaussian distribution, also known as the standard normal distribution, Q Represents system noise ω covariance, R Represents measurement noise v covariance, The state nonlinear function and the output nonlinear function are respectively obtained from formula (1) and formula (3).

[0038] (6) in, N p Indicates the number of pole pairs of the motor. T e This represents the electromagnetic torque of the motor. B This represents the viscous friction coefficient of the motor. J This represents the moment of inertia of the motor. This represents the stator flux linkages along the α and β axes of a permanent magnet synchronous motor. R s Indicates stator resistance. θ e Electrical angles representing the rotor position. uα and u β The α and β axes represent the input voltages. Indicates the effective magnetic flux linkage. L d , L q This represents the inductance along the d-axis and q-axis.

[0039] And the output equation h ( x This can be represented as: This equation is the state output equation of the permanent magnet synchronous motor, i.e., formula (3), because the output variable adopted is ; (7) UKF needs to pass Further predictions can be achieved through Euler discretization, as shown in the following equation. Discretization, that is, discretizing formula (6), can then be implemented in a digital controller to obtain the predicted value at the next moment.

[0040] (8) in, T s This indicates the distance from the step, which is equivalent to the cycle of a digital controller. k Indicates in k time, x k+1 Indicates in k The state variable at time +1 Indicates by The prediction formula obtained by one-step discretization.

[0041] This embodiment proposes an improved observation architecture for an unscented Kalman filter, which is mentioned in this invention. Figure 1 The implementation process of the "improved unscented Kalman filter" is as follows: Figure 2 As shown. Figure 2 The implementation steps of the "unscented Kalman filter" are shown below. The overall implementation steps of the "improved unscented Kalman filter" using the formula are as follows. Figure 3 As shown.

[0042] about Figure 2 The steps for implementing an unscented Kalman filter include: (1) For state variables x The initialization of 0 and covariance matrix P0 is done manually at t = 0. After entering the loop, the state variables and covariance matrix calculated in the previous step are saved.

[0043] (2) Perform an unscented transformation on the state variables of the previous cycle to obtain the Sigma point set. ; (9) in, This represents the estimated value of the state variable. Represents the sampling coefficient. k -1 indicates k At time -1, P is the posterior estimated state covariance matrix.

[0044] (3) The sigma points obtained in step (2) are further predicted using equation (10) to obtain the prior estimates of the state variables. ; (10) Among them, u k-1 for k Voltage input value at time -1.

[0045] (4) Obtain the prior estimates of the state variables by weighted averaging. Prior estimates of the sum and covariance matrix ; (11) (12) in, Weight coefficients representing the state mean. The weighting coefficients represent the variance calculations. i Represents the first point set of the matrix i The column, n, represents the dimension of the state variables, i.e., the number of state variables selected, which is 5 in this case. Q The covariance matrix represents the system noise.

[0046] (5) Based on the new predicted value, use the unscented transformation again to obtain a new Sigma point set. ; (13) (6) Take the Sigma point set obtained in step (5) Substitute into the observation equation The measured values ​​of the observed values ​​are obtained. ; (14) (7) Take the estimated Sigma point set obtained in step (6) from the observations of the unscented Kalman filter. We perform a weighted summation to obtain the mean of the estimated values. And calculate the covariance matrix of the output value and the covariance matrix between the state variables and the output value: (15) (16) (17) in, The state covariance matrix is ​​the prior estimate. This represents the average of the measured estimates, where the superscript T indicates the transpose of the matrix. k express k Time. P xz Represents state variables x With output z The covariance matrix between them, P zz Output z The covariance matrix, R This represents the covariance matrix of the measurement noise.

[0047] (8) Calculate the Kalman gain K k (18) (9) Calculate the state update and covariance matrix update of the system (19) (20) in, These are the prior estimates of the state variables. Indicates measured value, This represents the average of the estimated values. This represents the updated state covariance matrix.

[0048] This embodiment proposes a Kalman filter model based on the concept of effective flux linkage, which can prevent erroneous convergence of traditional Kalman filters. Based on the above model, a fifth-order unscented Kalman filter based on the effective flux linkage model is proposed, which is more suitable for strongly nonlinear systems such as permanent magnet synchronous motors, improving the system's resistance to load torque disturbances and enhancing its dynamic performance. A parameter correction strategy suitable for the above Kalman filter is proposed, improving the system's robustness to parameter mismatch.

[0049] This embodiment of the sub-technical solution improves the system's estimation accuracy of rotor position and speed, achieves optimal estimation under system noise and measurement noise, effectively improves the system's parameter robustness, effectively improves the system's dynamic characteristics of speed response, and effectively improves the system's ability to resist load torque disturbances.

[0050] The core of this embodiment's sub-technical solution is to address the urgent need to improve the dynamic characteristics, resistance to load torque disturbances, resistance to measurement noise, and resistance to parameter mismatch in existing sensorless control systems. It proposes a sensorless control method and system for permanent magnet synchronous motors based on an improved unscented Kalman filter. Compared to existing sensorless control strategies, this solution innovatively proposes a flux linkage model suitable for Kalman filters, applicable to both built-in and surface-mounted permanent magnet synchronous motors, preventing erroneous convergence of traditional Kalman filters that use current as the state variable. Based on this flux linkage model, an improved fifth-order unscented Kalman filter model is proposed, enhancing the parameter robustness of sensorless control of permanent magnet synchronous motors.

[0051] Example 2 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0052] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0053] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.

[0054] Example 4 The purpose of this embodiment is to provide a sensorless control system for a permanent magnet synchronous motor, including: The data acquisition module is configured to: acquire the stator current and stator voltage reference values ​​of the permanent magnet synchronous motor, and convert the stator current to obtain the stator current under the αβ axis; An improved unscented Kalman filter processing module is configured to input stator voltage reference values ​​and stator currents under the αβ axis to an improved unscented Kalman filter for observation, wherein the improved unscented Kalman filter includes an unscented Kalman filter. The stator current and the electrical angle of the rotor position are estimated by observing the stator voltage reference value using an unscented Kalman filter. The estimated values ​​of stator current and rotor position electrical angle under the αβ axis are transformed and processed by a low-pass filter to obtain the first current estimate. The estimated values ​​of the stator current under the αβ axis and the estimated values ​​of the electrical angle of the rotor position are transformed and processed by a low-pass filter to obtain the second current estimate. The difference is obtained by subtracting the first current estimate from the second current estimate; The difference is corrected using a correction factor to obtain the corrected value; The estimated value of the permanent magnet flux linkage parameter is obtained by subtracting the initial permanent magnet flux linkage parameter from the correction value and then input into the unscented Kalman filter. The unscented Kalman filter outputs the optimized flux linkage parameter, the estimated value of the electric angle of the rotor position, the estimated value of the rotor electric angular velocity, and the load torque. The control module is configured to estimate the rotor position and speed based on the output of an unscented Kalman filter, thereby controlling the motor operation.

[0055] Example 5 The purpose of this embodiment is to provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions involved in any of the above embodiments. The steps and methods involved in the apparatus of the above embodiments correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0056] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0057] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A sensorless control method for a permanent magnet synchronous motor, characterized in that, include: Obtain the reference values ​​of stator current and stator voltage of the permanent magnet synchronous motor, and convert the stator current to obtain the stator current under the αβ axis; The stator voltage reference value and the stator current under the αβ axis are input to an improved unscented Kalman filter for observation. The improved unscented Kalman filter includes an unscented Kalman filter. The stator current and the electrical angle of the rotor position are estimated by observing the stator voltage reference value using an unscented Kalman filter. The estimated values ​​of the stator current and the electrical angle of the rotor position under the αβ axis are transformed and processed by the first low-pass filter to obtain the first current estimate. The estimated values ​​of the stator current under the αβ axis and the estimated values ​​of the electrical angle of the rotor position are transformed and processed by the second low-pass filter to obtain the second current estimate. The difference is obtained by subtracting the first current estimate from the second current estimate; The difference is corrected using a correction factor to obtain the corrected value; The estimated value of the permanent magnet flux linkage parameter is obtained by subtracting the initial permanent magnet flux linkage parameter from the correction value and then input into the unscented Kalman filter. The unscented Kalman filter outputs the optimized flux linkage parameter, the estimated value of the electric angle of the rotor position, the estimated value of the rotor electric angular velocity, and the load torque. The rotor position and speed are estimated based on the output of the unscented Kalman filter, thereby controlling the motor operation.

2. The sensorless control method for a permanent magnet synchronous motor as described in claim 1, characterized in that, Before obtaining the reference values ​​of stator current and stator voltage of the permanent magnet synchronous motor, a permanent magnet synchronous motor model based on the concept of effective flux linkage is established. The permanent magnet synchronous motor model based on the concept of effective flux linkage includes: the flux linkage equation of the permanent magnet synchronous motor based on effective flux linkage.

3. The sensorless control method for a permanent magnet synchronous motor as described in claim 1, characterized in that, The flux linkage equation of a permanent magnet synchronous motor based on effective flux linkage is shown below: (1) in This represents the differentiation operation with respect to time. This represents the stator flux linkages along the α and β axes of a permanent magnet synchronous motor. R s Indicates stator resistance. θ e Electrical angles representing the rotor position. u α and u β The values ​​represent the input voltages along the α and β axes. Indicates the effective magnetic flux linkage. L d , L q Inductance along the d-axis and q-axis.

4. The sensorless control method for a permanent magnet synchronous motor as described in claim 1, characterized in that, The effective magnetic flux The expression is: (2) in, L d , L q Inductance representing the d-axis and q-axis, Indicates permanent magnet flux linkage. i d This represents the stator current along the d-axis.

5. The sensorless control method for a permanent magnet synchronous motor as described in claim 1, characterized in that, The stator current on the αβ axis is represented by the effective flux linkage, the electrical angle of the rotor position, and the inductance along the d and q axes, specifically as follows: (3) in, These represent the stator currents along the α and β axes, respectively. This represents the stator flux linkages along the α and β axes of a permanent magnet synchronous motor. θ e Electrical angles representing the rotor position. Indicates the effective magnetic flux linkage. L d , L q This represents the inductance along the d-axis and q-axis.

6. The sensorless control method for a permanent magnet synchronous motor as described in claim 1, characterized in that, Defining state variables in an unscented Kalman filter x ,enter u With output z， Specifically: in, This represents the stator flux linkages along the α and β axes of a permanent magnet synchronous motor. θ e Electrical angles representing the rotor position. ω e The rotor's electric angular velocity, T l For load torque, u α and u β The values ​​represent the input voltages along the α and β axes. These represent the stator currents along the α and β axes, respectively.

7. A sensorless control system for a permanent magnet synchronous motor, characterized in that, include: The data acquisition module is configured to: acquire the stator current and stator voltage reference values ​​of the permanent magnet synchronous motor, and convert the stator current to obtain the stator current under the αβ axis; An improved unscented Kalman filter processing module is configured to input stator voltage reference values ​​and stator currents under the αβ axis to an improved unscented Kalman filter for observation, wherein the improved unscented Kalman filter includes an unscented Kalman filter. The stator current and the electrical angle of the rotor position are estimated by observing the stator voltage reference value using an unscented Kalman filter. The estimated values ​​of the stator current and the electrical angle of the rotor position under the αβ axis are transformed and processed by the first low-pass filter to obtain the first current estimate. The estimated values ​​of the stator current under the αβ axis and the estimated values ​​of the electrical angle of the rotor position are transformed and processed by the second low-pass filter to obtain the second current estimate. The difference is obtained by subtracting the first current estimate from the second current estimate; The difference is corrected using a correction factor to obtain the corrected value; The estimated value of the permanent magnet flux linkage parameter is obtained by subtracting the initial permanent magnet flux linkage parameter from the correction value and then input into the unscented Kalman filter. The unscented Kalman filter outputs the optimized flux linkage parameter, the estimated value of the electric angle of the rotor position, the estimated value of the rotor electric angular velocity, and the load torque. The control module is configured to estimate the rotor position and speed based on the output of an unscented Kalman filter, thereby controlling the motor operation.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it performs the steps of the method described in any one of claims 1-6 above.

Images