A Method and System for Motor Parameter Identification Based on Multiple Synchronous Rotary Coordinate Transformation Filters

Abstract

This invention discloses a method and system for motor parameter identification based on a multiple synchronous rotating coordinate transformation filter. The method includes: using the motor's voltage and current along the αβ axis, along with position estimation information from a quadrature phase-locked loop (PLL), as inputs to a sliding mode observer to obtain back electromotive force (EMF) observations. The sliding mode algorithm employs a switching function to suppress high-frequency chattering in the back EMF. Furthermore, the harmonic disturbances in the back EMF are filtered out using a multiple synchronous rotating coordinate transformation filter. The fundamental component of the back EMF is then passed through the PLL to obtain the rotor's estimated speed and position information. The proposed multiple synchronous rotating coordinate transformation filter can be applied to sensorless motor control systems, eliminating the fifth and seventh harmonics in the estimated back EMF and exhibiting excellent harmonic suppression. This enables real-time and accurate acquisition of motor position information, improving the control performance of the motor drive system.

Description

Technical Field

[0001] This invention relates to the field of permanent magnet synchronous motor control technology, and in particular to a method and system for identifying motor parameters based on multiple synchronous rotating coordinate transformation filters. Background Technology

[0002] Promoting the improvement of quality and efficiency in the power industry, expanding the industrial chain of high-efficiency and energy-saving electrical equipment, and accelerating the replacement of fossil fuels with electricity on the demand side are crucial. Permanent magnet synchronous motors (PMSMs), due to their simple structural topology and high power density, are widely used in important fields such as aerospace and automotive transportation. With the development of advanced rare earth materials, the vigorous advancement of power electronics technology, and the improvement of motor control algorithms, focusing on the development and research of PMSMs and their drive systems aligns with the fundamental requirements of the current era for energy transformation.

[0003] A PMSM drive system mainly consists of the motor body, power electronic converter, and controller. Regardless of the control algorithm, accurate rotor position information needs to be obtained in real time to ensure high-performance control of the PMSM. This is typically achieved by a mechanical position sensor mounted on one side of the motor shaft. However, these mechanical position sensors operate under relatively harsh conditions and are easily affected, severely reducing the reliability of the PMSM drive system. To overcome these problems, sensorless control technology has been used to control PMSMs. However, sensorless control technology for PMSMs still has many problems to solve, such as harmonic suppression. High-precision, high-robust rotor position observation technology remains the mainstream research direction for scholars both domestically and internationally.

[0004] When the motor operates at medium to high speeds, it is necessary to rely on the fundamental frequency model method to observe the motor's back electromotive force (EMF) or flux linkage, and extract position information by processing the back EMF or flux linkage. Existing methods mainly include: model reference adaptive observation methods, extended Kalman filter-based observation methods, state observer-based observation methods, disturbance observer-based observation methods, and sliding mode observer-based observation methods. Among them, the sliding mode observation method is insensitive to disturbances in internal and external parameters of the system, thus the system has strong robustness. However, due to the use of discontinuous control functions and the inertia and hysteresis of the system, high-frequency chattering will appear in the output state variables, thus affecting the estimation accuracy. In addition, due to the inverter dead-zone effect, the actual voltage deviates from the reference voltage, generating a dead-zone voltage. This dead-zone voltage injected into the motor will introduce harmonic components into the voltage and current, resulting in high-order harmonics in the observed back EMF or flux linkage. When extracting position information through arctangent or PLL, high-order harmonics and DC bias will lead to inaccurate calculated position angles.

[0005] As can be seen from the above, there are still many problems in the existing technologies that need to be solved and further exploration of feasible technologies is needed. For example, inverter nonlinearity and magnetic field spatial harmonics will also cause distortion in the observed back EMF. Among them, dead time and power device voltage drop are important reasons for inverter nonlinearity. The dead voltage generated by the inverter dead time effect will cause harmonic components in the voltage and current when injected into the motor. The magnetization deviation of permanent magnets and rotor eccentricity will cause uneven distribution of magnetic field in space, generating low-order harmonics (such as the 3rd and 5th harmonics), which will cause harmonics in the observed back EMF and thus cause position estimation errors. Summary of the Invention

[0006] This invention aims to solve the technical problem of position estimation error caused by the distortion of observed back electromotive force due to inverter nonlinearity and magnetic field spatial harmonics. To this end, the invention provides a motor parameter identification method based on a multi-synchronous rotating coordinate system transformation filter. Specifically, this invention introduces an improved multi-synchronous rotating coordinate system transformation filter between the sliding mode observer and the orthogonal phase-locked loop, eliminating the fifth and seventh harmonics in the estimated back electromotive force and exhibiting excellent harmonic suppression. This invention overcomes the phase lag problem of traditional sliding mode observers, increases signal estimation accuracy, improves the control reliability of permanent magnet synchronous motors, and has higher system stability and dynamic response. Especially for position, its position observation harmonic suppression effect is excellent.

[0007] Therefore, the present invention provides the following technical solution:

[0008] On one hand, the present invention provides a method for identifying motor parameters based on a multiple synchronous rotating coordinate transformation filter, comprising the following steps:

[0009] Step 1: Sample the stator voltage in the two-phase stationary coordinate system of the motor drive system. and current components Then, based on the sliding mode observer, the back electromotive force estimate is obtained. ;

[0010] Step 2: Estimate the back electromotive force value The input is a multi-synchronous rotating coordinate system transformation filter to obtain the filtered fundamental back electromotive force; the multi-synchronous rotating coordinate system transformation filter adopts a multi-harmonic observer cross-feedback network based on SRFT (Synchronous Rotating Frame Transformation);

[0011] Step 3: Input the filtered fundamental back EMF into a quadrature phase-locked loop and normalize it to obtain the rotational speed. and rotor position .

[0012] Further optionally, the multi-synchronous rotating coordinate system transformation filter filters the fundamental wave, the negative fifth harmonic, and the positive seventh harmonic, corresponding to orders +1, -5, and +7, respectively.

[0013] The mathematical model of the SRFT-based multi-harmonic observer cross-feedback network is expressed as follows:

[0014]

[0015] In the formula, These represent the back electromotive force components of orders +1, -5, and +7 after negative feedback. These are the transfer functions for orders +1, -5, and +7, respectively. These are the estimated values ​​of the back electromotive force components of orders +1, -5, and +7 in the two-phase stationary coordinate system, respectively.

[0016] Further optionally, the multi-synchronous rotating coordinate system transformation filter achieves harmonic filtering through multiple cycles;

[0017] The processing procedure for each cycle is as follows:

[0018] For each order, the coordinate transformation matrix corresponding to the resonant frequency is calculated. Based on the coordinate transformation matrix, the back electromotive force component after negative feedback of each order is transformed into the DC component obtained by low-pass filtering in the synchronous rotating coordinate system.

[0019] The DC component is synchronously rotated and inversely transformed to a rotating coordinate system for negative feedback.

[0020] The estimated back electromotive force component of each order after the inverse transformation is extracted and negatively fed back to other orders before entering the next cycle.

[0021] The negative feedback is the back electromotive force estimate. Subtract the estimated values ​​of the current back EMF components of other orders to obtain the back EMF components of each order after negative feedback, which are then used to estimate the back EMF components of the current order in the next cycle.

[0022] Further, optionally, the formula for the coordinate transformation matrix is:

[0023]

[0024] in, This is the position estimate for the corresponding order h. Rotor position; This is the coordinate transformation matrix corresponding to the resonant frequency.

[0025] Further optionally, the overall transfer function of the multi-synchronous rotating coordinate system transformation filter is:

[0026]

[0027] In the formula, This is the fundamental back electromotive force. These are the transfer functions for orders +1, -5, and +7, respectively, where s is a Laplace variable representing the complex frequency domain;

[0028]

[0029] In the formula, the observed rotational speed corresponds to the order h. ,parameter ,coefficient , It is the cutoff frequency of the LPF. This is an estimated value for the rotational speed. correspond .

[0030] Further optionally, the back electromotive force estimate When composed of the fundamental wave, the negative fifth harmonic, and the positive seventh harmonic, the corresponding mathematical model is expressed as follows:

[0031]

[0032] In the formula, The fundamental and h-th harmonics represent the fundamental and h-th harmonic amplitudes of the back electromotive force components, respectively. The h-th harmonic corresponds to the negative fifth and positive seventh harmonics. It represents the initial phase of the back electromotive force components corresponding to the fundamental wave and the h-th harmonic; t is time. These are the estimated values ​​for the fundamental frequency rotational speed and the h-th harmonic rotational speed, respectively.

[0033] Secondly, the present invention provides a motor control method based on the above-mentioned motor parameter identification method, comprising:

[0034] The estimated rotational speed is obtained using steps 1-3. and rotor position estimate ;

[0035] Then take the estimated speed value and rotor position estimate The signal is used for IPMSM vector control, and finally a PWM drive signal is obtained. The PWM drive signal controls the switching of the inverter switch to obtain the inverter voltage to drive the IPMSM, thereby realizing sensorless control of the IPMSM.

[0036] In three aspects, the control system based on the above-mentioned motor parameter identification method provided by the technical solution of the present invention includes at least: a sliding mode observer (SMO), a multi-synchronous rotating coordinate system transformation filter, an orthogonal phase-locked loop (PLL), a sampling and processing module, an inner current loop, an outer speed loop, a speed loop PI regulator, an inner current loop PI regulator, and an SVPWM modulation module.

[0037] The sampling and processing module is used to sample the stator voltage in the two-phase stationary coordinate system of the motor drive system. and current components And transmit it to the sliding mode observer (SMO);

[0038] The sliding mode observer (SMO) is used to obtain the back electromotive force estimate. And transmit it to the multi-synchronous rotating coordinate system transformation filter;

[0039] The multi-synchronous rotating coordinate system transformation filter estimates the back electromotive force. Filtering is performed to obtain the filtered fundamental back electromotive force, which is then transmitted to the quadrature phase-locked loop.

[0040] The orthogonal phase-locked loop is used to calculate the speed estimate. and rotor position estimate and connected to the outer rotation speed ring;

[0041] The outer speed loop, the speed loop PI regulator, the inner current loop, the inner current loop PI regulator, and the SVPWM modulation module are connected in sequence to finally generate an inverter voltage to drive the IPMSM, which is then input to the motor.

[0042] The present invention provides a PMSM drive system including the above-mentioned control system, characterized in that it includes at least a control system, a permanent magnet synchronous motor body, and an inverter.

[0043] The control system generates a PWM drive signal to control the switching of the inverter transistors, thereby obtaining an inverter voltage to drive the IPMSM, which acts on the permanent magnet synchronous motor body.

[0044] In five aspects, the present invention provides a computer-readable storage medium storing a computer program, which is invoked by a processor to implement the following:

[0045] The steps of the motor parameter identification method based on the above-mentioned multi-synchronous rotating coordinate transformation filter or the steps of the above-mentioned motor control method.

[0046] Beneficial effects

[0047] This invention introduces an improved multi-synchronous rotating coordinate system transformation filter between the sliding mode observer and the orthogonal phase-locked loop, eliminating the fifth and seventh harmonics in the estimated back electromotive force and exhibiting excellent harmonic suppression. This invention overcomes the phase lag problem of traditional sliding mode observers, increases signal estimation accuracy, improves the control reliability of permanent magnet synchronous motors, and provides higher system stability and dynamic response. Furthermore, the introduction of the control algorithm does not increase the system's size or cost.

[0048] To verify feasibility, this invention underwent on-machine testing on a 1.5kW IPMSM. The system included a magnetic powder brake providing the load torque, and measured the actual speed and position via a mechanical shaft connection (PENON-K3808 G) with an incremental encoder, solely for comparing the accuracy of the estimates. Sensorless control was implemented using a DSP (32-bit TMS 320 F2808) with a sampling frequency of 10 kHz. The discretization error of the linear interpolation MSRFT digital delay was negligible. Steady-state and dynamic tests were performed. As shown in the figure, the phase estimation accuracy was improved, the phase lag problem was overcome, the signal estimation accuracy was increased, the reliability of the permanent magnet synchronous motor control was enhanced, and higher system stability and dynamic response performance were achieved. Attached Figure Description

[0049] Figure 1 This is a structural block diagram of the control system of the motor parameter identification method based on multiple synchronous rotating coordinate transformation filters disclosed in this invention;

[0050] Figure 2 This is a block diagram of the position observer structure based on SMO and multiple synchronous rotating coordinate system transformation filter according to an embodiment of the present invention;

[0051] Figure 3 This is a harmonic observer structure based on a multiple synchronous rotating coordinate system transformation filter according to an embodiment of the present invention;

[0052] Figure 4 The Bode plot of the SRFT-based harmonic observer according to an embodiment of the present invention;

[0053] Figure 5 The Bode plot of the harmonic observer based on the multiple synchronous rotating coordinate system transformation filter according to an embodiment of the present invention;

[0054] Figure 6 The Nyquist plot of the SRFT-based harmonic observer used in this embodiment of the invention;

[0055] Figure 7 The image shows the estimated back electromotive force waveform and FFT analysis results of the harmonic observer based on the multi-synchronous rotating coordinate system transformation filter according to an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The technical features involved in the various embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.

[0057] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0058] Unless otherwise defined, 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 application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0059] This invention provides a motor parameter identification method based on a multi-synchronous rotating coordinate transformation filter, belonging to the sensorless control method, which eliminates the need for mechanical position sensors. Furthermore, regarding the chattering phenomenon caused by the sliding mode observer, the multi-synchronous rotating coordinate transformation filter constructed in this invention can accurately eliminate harmonic components in the back EMF without requiring additional compensation measures, reducing the complexity of the IPMSM sensorless control system. It also achieves back EMF harmonic elimination at low switching frequencies, improving rotor position estimation accuracy. The technical solution of this invention is described in detail below using a permanent magnet synchronous motor as an example.

[0060] Both the estimation method and the control method provided by the technical solution of this invention require the use of a sliding mode observer and a multiple synchronous rotating coordinate system transformation filter. Therefore, their principles are briefly described as follows:

[0061] Sliding mode observer:

[0062] The permanent magnet synchronous motor is sampled to obtain at least the real-time stator current. The real-time stator current is then transformed into coordinates to obtain the stator current in the two-phase stationary coordinate system. Finally, the estimated value of the stator current is obtained by using the voltage equation. .

[0063] The voltage equations for the PMSM in the two-phase stationary coordinate system (αβ coordinate system) are as follows:

[0064]

[0065] in, Stator voltage, for of Axial components, Axial components; For stator current, for of Axial components, Axial components; back electromotive force of Axial components, Axial components; For dq axis inductance, For stator resistance, For rotational speed, For rotor position, For the magnetic flux linkage of the rotor magnet, This represents a differential operator.

[0066] in:

[0067]

[0068] in, To expand the amplitude of the back electromotive force.

[0069] The back EMF calculation process involves inputting the stator current and stator voltage in a two-phase stationary coordinate system, along with the rotational speed observations estimated by a quadrature phase-locked loop, into a sliding mode observer to obtain the observed back EMF value. This yields the sliding mode observer voltage equation based on the extended back EMF, used for estimating the rotor position:

[0070]

[0071] Stator current estimate added , Stator current estimate Axial components, Axial components; back electromotive force observations , The back electromotive force observation value Axial components, Shaft component; speed estimate The control function of the sliding mode observer is defined as follows: , To represent the sliding mode gain, and to maintain system stability, the following must be satisfied:

[0072]

[0073]

[0074] In the formula, For stator current estimation error Axial components, Axis components; A and B are custom coefficient matrix symbols.

[0075] It should be understood that the stator current observations obtained based on the sliding mode observer get closer and closer to the actual stator current value. When the stator current observations converge to the actual stator current value (the current observations approach the sliding mode surface), the back EMF obtained at this time is regarded as the final back EMF observation value and participates in the subsequent position estimation. It should be understood that the data when it does not converge can also be passed into the subsequent filter, the difference being that it is not used to directly estimate the position corresponding to the current moment.

[0076] It should also be understood that the above-mentioned sliding mode observer was selected in the embodiment of the present invention using a permanent magnet synchronous motor as an example. In other feasible embodiments, the selection of a sliding mode observer capable of observing back electromotive force based on the changes in motor type and motor model also falls within the protection scope of the present invention.

[0077] The harmonics in the back electromotive force (EMF) were analyzed, including the negative-sequence fifth harmonic, the positive-sequence seventh harmonic, and higher harmonics. Considering the low-pass characteristics of the sliding mode observer (SMO) and the phase-locked loop (PLL), high-frequency noise was neglected, and the back EMF was rewritten as follows:

[0078]

[0079] in, This represents the fundamental and h-th order harmonic amplitudes of the back electromotive force components corresponding to the fundamental and h-th order harmonics. The subscript h indicates the harmonic order, corresponding to the negative fifth and positive seventh harmonics. It represents the initial phase of the back electromotive force components corresponding to the fundamental wave and the h-th harmonic; t is time. These are the fundamental frequency speed estimate and the h-th harmonic speed estimate, respectively. Both harmonics will prevent the error from converging to zero, meaning that the position estimate will deviate from the actual position to some extent, reducing the performance of sensorless control.

[0080] Normalizing the position signal error of the PLL yields:

[0081]

[0082]

[0083] In the formula, This is a location estimate. These are the estimated phase values ​​corresponding to the fundamental and harmonic frequencies. All are user-defined parameter symbols. From the above formula, it can be seen that the desired position estimation error... The error is zero, but the two harmonics will prevent the error from converging to zero, which means that the position estimate will deviate from the actual position to some extent, reducing the performance of sensorless control.

[0084] Due to the influence of inverter nonlinearity and magnetic field space harmonics, the estimated back EMF is distorted. Therefore, the technical solution of this invention introduces a multi-synchronous rotating coordinate system transformation filter. The filtering module is used to input the back EMF observation value into the constructed multi-synchronous rotating coordinate system transformation filter to obtain the filtered fundamental back EMF.

[0085] Multiple synchronous rotating coordinate system transformation filter:

[0086] Figure 3 This invention presents a harmonic observer structure based on a multi-synchronous rotating coordinate system transformation filter, employing a cross-feedback network. In this embodiment, the preferred harmonic orders are h=1, -5, 7. This is because the -5th and 7th harmonics are dominant and need to be filtered out. Based on the above formula, those skilled in the art will understand that this filter can filter out specified harmonic components to obtain the fundamental back electromotive force component. Specifically, the observed back electromotive force value is input into the multi-synchronous rotating coordinate system transformation filter, which internally has three orders: +1, -5, and +7. Only the resonant frequency changes with the order of each order. A SRFT-based multi-harmonic observer cross-feedback network is used. Each observer observes its own frequency fundamental or harmonic. The cross-feedback network is a feasible structure that collaboratively adjusts parameters at different frequencies, allowing the fundamental component to be obtained even under severely distorted back electromotive forces, thus eliminating multiple harmonic components. The SRFT-based multi-harmonic observer cross-feedback network is shown in the following equation:

[0087]

[0088] This represents the back electromotive force component corresponding to the h-th order after negative feedback. It is the back electromotive force estimate of order h. Let h = +1, -5, and +7 be the transfer functions for orders +1, -5, and +7, respectively.

[0089] The working mechanism of each order synchronous rotating coordinate system transformation filter is as follows: First, it is converted to DC through coordinate transformation, as expressed in the following expression:

[0090]

[0091]

[0092] in, This is the position estimate for the corresponding order h. This is the coordinate transformation matrix corresponding to order h. The back electromotive force in the fundamental or harmonic coordinate system under a synchronously rotating coordinate system of order h. Axial components, The axial components; the negative fifth and positive seventh harmonics can be extracted using the above equation and a low-pass filter as follows: The h-th fundamental or harmonic component, after being low-pass filtered in a synchronous rotating coordinate system, is the h-th back electromotive force fundamental or harmonic component.

[0093]

[0094] in, The low-pass filter cutoff frequency is used to extract the DC signal, which is then input into the low-pass filter for higher-order filtering to extract the corresponding order signal. Finally, the coordinates are transformed back to the corresponding order frequency for convenient negative feedback. The expression is as follows:

[0095]

[0096] in, It is the estimated back electromotive force component of frequency h in the stationary coordinate system. The extracted harmonic negative feedback is sent to other axis systems for elimination.

[0097] In summary, the multi-synchronous rotating coordinate system transformation filter achieves harmonic filtering through multiple cycles.

[0098] The processing procedure for each cycle is as follows:

[0099] For each order, the coordinate transformation matrix corresponding to the resonant frequency is calculated. Based on the coordinate transformation matrix, the back electromotive force component after negative feedback of each order is transformed into the DC component obtained by low-pass filtering in the synchronous rotating coordinate system.

[0100] The DC component is synchronously rotated and inversely transformed to a rotating coordinate system for negative feedback.

[0101] The estimated back electromotive force component of each order in the stationary coordinate system after the inverse transformation is extracted and negatively fed back to other orders before entering the next cycle.

[0102] Based on the above reasoning, the multi-synchronous rotating coordinate system transformation filter is explained from a holistic perspective.

[0103] Single-order SRFT transfer function:

[0104]

[0105] In the formula, the observed rotational speed corresponds to the order h. ,parameter ,coefficient , It is the cutoff frequency of the LPF, and its Bode plot is as follows: Figure 4 It can be seen that SRFT is It possesses bandpass characteristics, which can suppress unwanted components at other frequencies. The formula is used to calculate... . correspond The extracted harmonic signals obtained from each order are input to other orders for filtering. Through multiple cycles, the final harmonic filtering is achieved, yielding the target fundamental signal. Therefore, the overall transfer function... for:

[0106]

[0107] Substituting the single-order transfer function yields:

[0108]

[0109] Among them, custom parameters satisfy:

[0110]

[0111] Overall Bode plot as follows Figure 5 As shown in the figure, the proposed multi-synchronous rotating coordinate system transformation filter can eliminate the fifth and seventh harmonics, transforming the observed signal into the required filtered waveform, ensuring the accuracy of the estimation, and realizing sensorless position estimation. Simplified, we can obtain:

[0112]

[0113] Treating the structure as a negative feedback system, we obtain the open-loop transfer function:

[0114]

[0115] Among them, custom parameters :

[0116] .

[0117] Drawing Nyquist plots as follows Figure 6 Observations show that frequency does not affect the system's stability, and the system remains stable, indicating that it possesses good stability and response speed. It should be understood that, using the aforementioned techniques, the observed back electromotive force filtered by a multiple synchronous rotating coordinate system transformation filter eliminates the fifth and seventh harmonics in the estimated back electromotive force, demonstrating excellent harmonic suppression.

[0118] Based on this, the present invention provides a method for identifying motor parameters based on a multiple synchronous rotating coordinate transformation filter, comprising the following steps:

[0119] Step 1: Sample the stator voltage in the two-phase stationary coordinate system of the motor drive system. and current components Then, based on the sliding mode observer, the back electromotive force estimate is obtained. .

[0120] Step 2: Estimate the back electromotive force The filtered fundamental back electromotive force is obtained by inputting a multi-synchronous rotating coordinate system transformation filter; the multi-synchronous rotating coordinate system transformation filter adopts a multi-harmonic observer cross-feedback network based on SRFT.

[0121] Step 3: Input the filtered fundamental back EMF into a quadrature phase-locked loop and normalize it to obtain the rotational speed. and rotor position .

[0122] Furthermore, the motor control method based on this motor parameter identification method includes:

[0123] First, the estimated rotational speed is obtained using steps 1-3. and rotor position estimate ;

[0124] Secondly, the estimated speed value and rotor position estimate The signal is used for IPMSM vector control, and finally a PWM drive signal is obtained. The PWM drive signal controls the switching of the inverter switch to obtain the inverter voltage to drive the IPMSM, thereby realizing sensorless control of the IPMSM.

[0125] This invention discloses a method for estimating the position of a permanent magnet synchronous motor using a multi-synchronous rotating coordinate system transformation filter. The method mainly comprises three parts: a sliding mode observer, a multi-synchronous rotating coordinate system transformation filter, and an orthogonal phase-locked loop (PLL). Based on the dSPACE experimental platform development process and corresponding supporting hardware, the platform mainly consists of an inverter rectifier circuit, a dSPACE system, and a drive motor. After the motor control algorithm is built using MATLAB / Simulink, it is compiled and downloaded to dSPACE for implementation. The control board connected to dSPACE is used for power supply to various power sources and for various hardware protections. The photoelectric encoder at the end of the motor shaft is used to acquire the rotor position signal, but it is only used for comparative analysis in the experiment and does not participate in the actual control of the motor. The magnetic powder brake, coaxially connected to the motor, provides the load torque to the motor. The stator voltage extracted by the sensor... Input the sliding mode observer and obtain the estimated stator current based on the voltage equation above. , respectively with the extracted actual stator current The difference is obtained by subtraction. The observed back electromotive force is obtained using a sliding mode gain observer. The input is a multi-synchronous rotating coordinate system transformation filter, which has three orders: +1, -5, and +7. Only the resonant frequency changes with each order. A cross-feedback network of multi-harmonic observers based on SRFT is used. Each observer observes its own fundamental or harmonic wave. The cross-feedback network is a feasible structure that collaboratively adjusts parameters at different frequencies. Each order first performs a coordinate transformation corresponding to the resonant frequency. The DC signal is extracted, input into a low-pass filter for high-order filtering, and the corresponding order signal is extracted. This signal is then negativeed, and finally, a reverse coordinate transformation is performed. Returning to the corresponding order frequency, the extracted compensation signal is obtained for each order. The signal is then fed into other orders for filtering. Due to limited extraction capabilities, the desired precision cannot be achieved in a single cycle. Multiple cycles are then used to achieve final harmonic removal, yielding the target fundamental signal. Ultimately, the target fundamental frequency signal will be... The input is a quadrature phase-locked loop (PLL), which is then normalized and processed by a quadrature transform PI controller to obtain the rotational speed. The rotor position is then obtained through integration. Closed-loop estimation also requires inputting the estimated rotational speed and position into the sliding mode observer and the multiple synchronous rotating coordinate system transformation filter.

[0126] Therefore, this invention overcomes the phase lag problem of traditional sliding mode observers, increases the accuracy of signal estimation, improves the control reliability of permanent magnet synchronous motors, and has higher system stability and dynamic response. Figure 7 The estimated back EMF waveform and FFT analysis results of the harmonic observer based on the multi-synchronous rotating coordinate system transformation filter of this invention are shown in the embodiments of the present invention. It can be seen that after using the filter of the present invention, the back EMF waveform becomes significantly smoother and close to the standard sine wave. The FFT analysis results show that the fifth and seventh harmonics are significantly reduced, achieving a very good filtering effect.

[0127] In some embodiments, such as Figure 1 and 2 As shown, the control system applying this motor parameter identification method includes at least: a sliding mode observer (SMO), a multiple synchronous rotating coordinate system transformation filter, an orthogonal phase-locked loop (PLL), a sampling and processing module, an inner current loop, an outer speed loop, a speed loop PI regulator, an inner current loop PI regulator, and an SVPWM modulation module.

[0128] The sampling and processing module is used to sample the stator voltage in the two-phase stationary coordinate system of the motor drive system. and current components The data is then transmitted to the sliding mode observer (SMO); the SMO is used to obtain the back electromotive force estimate. The data is then transmitted to the multi-synchronous rotating coordinate system transformation filter; the multi-synchronous rotating coordinate system transformation filter estimates the back electromotive force. The filter is applied to obtain the fundamental back electromotive force, which is then transmitted to the quadrature phase-locked loop (PLL). The PLL is used to calculate the speed estimate. and rotor position estimate It is connected to the outer speed loop; the outer speed loop, the speed loop PI regulator, the inner current loop, the inner current loop PI regulator, and the SVPWM modulation module are connected in sequence to finally generate an inverter voltage to drive the IPMSM, which is then input to the motor.

[0129] In some embodiments, the present invention provides a PMSM drive system including the above-mentioned control system, characterized in that: it includes at least a control system, a permanent magnet synchronous motor body, and an inverter; wherein, the control system generates a PWM drive signal to control the switching of the inverter switch tube, thereby obtaining an inverter voltage to drive the IPMSM, which acts on the permanent magnet synchronous motor body.

[0130] In some embodiments, the present invention provides a computer-readable storage medium storing a computer program, which is invoked by a processor to implement the following:

[0131] The steps of the motor parameter identification method based on the above-mentioned multi-synchronous rotating coordinate transformation filter or the steps of the above-mentioned motor control method.

[0132] The specific process of implementing the motor parameter identification method based on multiple synchronous rotating coordinate transformation filters is as follows:

[0133] Step 1: Sample the stator voltage in the two-phase stationary coordinate system of the motor drive system. and current components Then, based on the sliding mode observer, the back electromotive force estimate is obtained. .

[0134] Step 2: Estimate the back electromotive force The filtered fundamental back electromotive force is obtained by inputting a multi-synchronous rotating coordinate system transformation filter; the multi-synchronous rotating coordinate system transformation filter adopts a multi-harmonic observer cross-feedback network based on SRFT.

[0135] Step 3: Input the filtered fundamental back EMF into a quadrature phase-locked loop and normalize it to obtain the rotational speed. and rotor position .

[0136] The specific process for implementing the motor control method is as follows:

[0137] Step 1: Sample the stator voltage in the two-phase stationary coordinate system of the motor drive system. and current components Then, based on the sliding mode observer, the back electromotive force estimate is obtained. .

[0138] Step 2: Estimate the back electromotive force The filtered fundamental back electromotive force is obtained by inputting a multi-synchronous rotating coordinate system transformation filter; the multi-synchronous rotating coordinate system transformation filter adopts a multi-harmonic observer cross-feedback network based on SRFT.

[0139] Step 3: Input the filtered fundamental back EMF into a quadrature phase-locked loop and normalize it to obtain the rotational speed. and rotor position .

[0140] Step 4: Estimate the rotational speed and rotor position estimate The signal is used for IPMSM vector control, and finally a PWM drive signal is obtained. The PWM drive signal controls the switching of the inverter switch to obtain the inverter voltage to drive the IPMSM, thereby realizing sensorless control of the IPMSM.

[0141] Please refer to the explanation of the method above for the specific implementation process of each step.

[0142] The readable storage medium is a computer-readable storage medium, which can be an internal storage unit of the hardware and software device described in any of the foregoing embodiments, such as the hard drive or memory of the controller. The readable storage medium can also be an external storage device of the controller, such as a plug-in hard drive, Smart MediaCard (SMC), Secure Digital (SD) card, or Flash Card equipped on the controller. Further, the readable storage medium can include both internal storage units and external storage devices of the controller. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0143] Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application refers to flowchart illustrations and / or instructions executed by a processor of a method, apparatus (system), and computer program product according to embodiments of this application to create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams. These computer program instructions may also be stored in a computer-readable storage medium capable of directing a computer or other programmable data processing apparatus 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 one or more flowchart illustrations and / or one or more block diagrams. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks of a block diagram.

[0145] It should be emphasized that the examples described in this invention are illustrative rather than limiting. Therefore, this invention is not limited to the examples described in the specific embodiments. Any other embodiments derived by those skilled in the art based on the technical solutions of this invention, without departing from the spirit and scope of this invention, whether modifications or substitutions, are also within the protection scope of this invention.

Claims

1. A method for identifying motor parameters based on a multiple synchronous rotating coordinate transformation filter, characterized in that: Includes the following steps: Step 1: Sample the stator voltage in the two-phase stationary coordinate system of the motor drive system. and current components Then, based on the sliding mode observer, the back electromotive force estimate is obtained. ; Step 2: Estimate the back electromotive force value The filtered fundamental back electromotive force is obtained by inputting a multi-synchronous rotating coordinate system transformation filter; the multi-synchronous rotating coordinate system transformation filter adopts a multi-harmonic observer cross-feedback network based on SRFT. Step 3: Input the filtered fundamental back EMF into a quadrature phase-locked loop and normalize it to obtain the rotational speed. and rotor position ; If the multi-synchronous rotating coordinate system transformation filter filters the fundamental wave, the negative fifth harmonic, and the positive seventh harmonic, corresponding to orders +1, -5, and +7 respectively, then the mathematical model of the SRFT-based multi-harmonic observer cross-feedback network is expressed as follows: ； In the formula, These represent the back electromotive force components of orders +1, -5, and +7 after negative feedback. These are the transfer functions for orders +1, -5, and +7, respectively. These are the estimated values ​​of the back electromotive force components of orders +1, -5, and +7 in the two-phase stationary coordinate system, respectively. The overall transfer function of the multi-synchronous rotating coordinate system transformation filter is: ； In the formula, This is the fundamental back electromotive force. These are the transfer functions for orders +1, -5, and +7, respectively, where s is a Laplace variable representing the complex frequency domain; ； In the formula, the observed rotational speed corresponds to the order h. ,parameter ,coefficient , It is the cutoff frequency of the LPF. This is an estimated value for the rotational speed. correspond .

2. The motor parameter identification method according to claim 1, characterized in that: The multi-synchronous rotating coordinate system transformation filter achieves harmonic filtering through multiple cycles; The processing procedure for each cycle is as follows: For each order, the coordinate transformation matrix corresponding to the resonant frequency is calculated. Based on the coordinate transformation matrix, the back electromotive force component after negative feedback for each order is transformed into the synchronous rotating coordinate system, and then the DC component is obtained by low-pass filtering. The DC component is synchronously rotated and inversely transformed to a rotating coordinate system for negative feedback. The estimated back electromotive force component of each order after the inverse transformation is extracted and negatively fed back to other orders before entering the next cycle. The negative feedback is the back electromotive force estimate. Subtract the estimated values ​​of the current back EMF components of other orders to obtain the back EMF components of each order after negative feedback, which are then used to estimate the back EMF components of the current order in the next cycle.

3. The motor parameter identification method according to claim 2, characterized in that: The formula for the coordinate transformation matrix is: ； in, This is the position estimate for the corresponding order h. Rotor position; This is the coordinate transformation matrix corresponding to the resonant frequency.

4. The motor parameter identification method according to claim 1, characterized in that: The back electromotive force estimate When composed of the fundamental wave, the negative fifth harmonic, and the positive seventh harmonic, the corresponding mathematical model is expressed as follows: ； In the formula, The fundamental and h-th harmonics represent the fundamental and h-th harmonic amplitudes of the back electromotive force components, respectively. The h-th harmonic corresponds to the negative fifth and positive seventh harmonics. It represents the initial phase of the back electromotive force components corresponding to the fundamental wave and the h-th harmonic; t is time. These are the estimated values ​​for the fundamental frequency rotational speed and the h-th harmonic rotational speed, respectively.

5. A motor control method based on the motor parameter identification method according to any one of claims 1-4, characterized in that: include: The estimated rotational speed is obtained using steps 1-3. and rotor position estimate ; Then take the estimated speed value and rotor position estimate The signal is used for IPMSM vector control, and finally a PWM drive signal is obtained. The PWM drive signal controls the switching of the inverter switch to obtain the inverter voltage to drive the IPMSM, thereby realizing sensorless control of the IPMSM.

6. A control system based on the motor parameter identification method according to any one of claims 1-4, characterized in that: At least including: Sliding mode observer (SMO), multiple synchronous rotating coordinate system transformation filter, orthogonal phase-locked loop (PLL), sampling and processing module, current inner loop, speed outer loop, speed loop PI regulator, current inner loop PI regulator, SVPWM modulation module; The sampling and processing module is used to sample the stator voltage in the two-phase stationary coordinate system of the motor drive system. and current components And transmit it to the sliding mode observer (SMO); The sliding mode observer (SMO) is used to obtain the back electromotive force estimate. And transmit it to the multi-synchronous rotating coordinate system transformation filter; The multi-synchronous rotating coordinate system transformation filter estimates the back electromotive force. Filtering is performed to obtain the filtered fundamental back electromotive force, which is then transmitted to the quadrature phase-locked loop. The orthogonal phase-locked loop is used to calculate the speed estimate. and rotor position estimate And connected to the outer rotation speed ring; The outer speed loop, the speed loop PI regulator, the inner current loop, the inner current loop PI regulator, and the SVPWM modulation module are connected in sequence to finally generate an inverter voltage to drive the IPMSM, which is then input to the motor.

7. A PMSM drive system comprising the control system of claim 6, characterized in that: It includes at least the control system, the permanent magnet synchronous motor body, and the inverter; The control system generates a PWM drive signal to control the switching of the inverter transistors, thereby obtaining an inverter voltage to drive the IPMSM, which acts on the permanent magnet synchronous motor body.

8. A computer-readable storage medium, characterized in that: The computer program is stored and is invoked by the processor to implement: The steps of the motor parameter identification method based on multiple synchronous rotating coordinate transformation filters as described in any one of claims 1-4, or the steps of the motor control method as described in claim 5.

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