A Method and System for Sensorless Multi-Vector Predictive Control of Permanent Magnet Synchronous Motors

Abstract

This invention proposes a sensorless multi-vector predictive control method and system for permanent magnet synchronous motors (PMSMs), belonging to the field of measurement and predictive control technology for PMSMs. The method includes: obtaining a current prediction equation based on the discrete-domain voltage response equation of the motor; approximating a reference voltage vector using two adjacent commutation vectors and assigning them respective application times, designated as the first application time and the second application time; constructing a quadratic cost function and solving for the optimal application time; defining the injected high-frequency voltage signal, obtaining the injected signal representation in the stationary reference frame, determining the sector where the injected signal is located, and determining the application times of two adjacent vectors within that sector; obtaining the generated high-frequency current response representation after the voltage signal is injected, and obtaining the rotor position error based on the high-frequency current response representation; and using coherent demodulation technology to extract the observation error.

Description

Technical Field

[0001] This invention belongs to the field of measurement and predictive control technology for permanent magnet synchronous motors, and particularly relates to a method and system for sensorless multi-vector predictive control of 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] Permanent magnet synchronous motors (PMSMs) combine high power density, excellent efficiency, and compact structure, making them the preferred choice for AC servo control systems. Currently, they are widely used in offshore wind turbines, electric vehicles, industrial automation equipment, and home appliances. However, to achieve high-performance drive, PMSM systems typically rely on mechanical encoders to provide precise rotor position feedback. Mechanical encoders are not only expensive but also prone to failure, making it difficult to meet the industry's demands for high reliability and low cost. Therefore, high-precision drive systems based on PMSMs require accurate rotor position and speed information to achieve precise closed-loop control. However, mechanical encoders, as crucial components for acquiring rotor position information, are large, expensive, and easily damaged, severely limiting the use of PMSMs in certain operating conditions. Therefore, there is a need to develop a high-performance, sensorless control scheme for PMSMs.

[0004] Sensorless control techniques for permanent magnet synchronous motors are generally divided into saliency-based and model-based methods. The saliency-based method typically injects a specific high-frequency excitation signal into the motor and calculates the rotor position and speed based on the motor's response characteristics; this method is suitable for zero-speed and low-speed conditions. The model-based method, on the other hand, extracts rotor position information by observing and processing the motor's back electromotive force or flux linkage information, thereby achieving precise control in the medium- and high-speed range.

[0005] Currently, most sensorless control schemes for permanent magnet synchronous motors still use traditional linear cascade structures, which struggle to achieve ideal results in multi-input, multi-output, and multi-objective control scenarios. Therefore, applying model predictive control to sensorless control architectures has significant research value and broad development prospects.

[0006] Currently, sensorless predictive control methods can be broadly categorized into two types: The first type directly extracts rotor position information using the inherent characteristics of model predictive control (MPC). This type of method typically obtains accurate rotor position information by differentiating the current ripple signal generated by the MPC. While this first type of method can fully utilize the dynamic characteristics of the MPC, it is extremely sensitive to sensor noise, and noise interference significantly affects the observation accuracy. The second type combines MPC with traditional mature solutions to improve overall control performance. This type of method can be integrated with model observation methods or combined with salient pole methods. However, because the widely used finite set model predictive control lacks a classical pulse width modulation module, it cannot fully fit accurate injection information, making seamless integration with high-frequency signal injection (HFSI) technology difficult. It also suffers from poor low-speed operation capability, thus hindering the promotion and application of sensorless predictive control strategies under zero-speed and low-speed conditions.

[0007] In summary, most position-sensorless control solutions for permanent magnet synchronous motors are based on a linear cascaded controller architecture, which is insufficient for handling multi-input, multi-output, and multi-objective control requirements. Summary of the Invention

[0008] To overcome the problem of unstable operation in the zero-low speed domain of the existing sensorless predictive control technology, this invention provides a sensorless multi-vector predictive control method for permanent magnet synchronous motors. By using two additional voltage vector signals to fit the high-frequency injected voltage, stable sensorless control is achieved.

[0009] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0010] Firstly, a sensorless multi-vector predictive control method for permanent magnet synchronous motors is disclosed, including:

[0011] Obtain the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system;

[0012] The discrete domain voltage response equation of the permanent magnet synchronous motor is obtained based on the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system.

[0013] Then, the current prediction equation is obtained based on the discrete domain voltage response equation of the motor;

[0014] Two adjacent commutation vectors are used to approximate the reference voltage vector, and their respective application times are assigned as the first application time and the second application time; a quadratic cost function is constructed and solved to obtain the optimal application time;

[0015] Define the injected high-frequency voltage signal, obtain the injected signal representation in the stationary reference frame, obtain the sector where the injected signal is located, and determine the action time of two adjacent vectors in the sector;

[0016] After the voltage signal is injected, the resulting high-frequency current response can be obtained, and the rotor position error can be obtained based on the high-frequency current response.

[0017] Coherent demodulation techniques are used to extract observation errors.

[0018] As a further technical solution, based on the current prediction equation, the predicted current consists of two parts: one part is related to the current converter output voltage at the current moment, and the other part is independent of the voltage.

[0019] As a further technical solution, two adjacent commutation vectors are used to approximate the reference voltage vector, specifically:

[0020] Define the components and electrical angles of the i-th vector in the stationary coordinate system, and obtain the projection coefficients of the vector in the rotating coordinate system based on the above parameters.

[0021] Define the components and electrical angles of the (i+1)th vector in the stationary coordinate system, and obtain the projection coefficients of the vector in the rotating coordinate system based on the above parameters.

[0022] As a further technical solution, for the constructed quadratic cost function, the optimal action time is obtained to minimize the value of the cost function J, and the optimal action time is thus obtained.

[0023] As a further technical solution, the rotor position error is included in the envelope of the high-frequency component of the q-axis current.

[0024] As a further technical solution, coherent demodulation technology is used to extract observation errors, specifically:

[0025] Multiplying the high-frequency component of the q-axis current by the synchronous carrier wave allows it to be decomposed into baseband components and second harmonic components.

[0026] Error signals can be extracted using a low-pass filter;

[0027] After the error signal is extracted, it is adjusted to zero by a proportional-integral controller, so that the assumed reference frame coincides with the actual reference frame.

[0028] Secondly, a sensorless multi-vector predictive control system for permanent magnet synchronous motors is disclosed, including:

[0029] The current prediction equation acquisition module is configured as follows:

[0030] Obtain the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system;

[0031] The discrete domain voltage response equation of the permanent magnet synchronous motor is obtained based on the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system.

[0032] Then, the current prediction equation is obtained based on the discrete domain voltage response equation of the motor;

[0033] The optimal action time acquisition module is configured as follows:

[0034] Two adjacent commutation vectors are used to approximate the reference voltage vector, and their respective application times are assigned as the first application time and the second application time; a quadratic cost function is constructed and solved to obtain the optimal application time;

[0035] The observation error extraction module is configured as follows:

[0036] Define the injected high-frequency voltage signal, obtain the injected signal representation in the stationary reference frame, obtain the sector where the injected signal is located, and determine the action time of two adjacent vectors in the sector;

[0037] After the voltage signal is injected, the resulting high-frequency current response can be obtained, and the rotor position error can be obtained based on the high-frequency current response.

[0038] Coherent demodulation techniques are used to extract observation errors.

[0039] The above one or more technical solutions have the following beneficial effects:

[0040] A sensorless control framework based on a multi-vector model predictive control architecture is proposed, and then sensorless multi-vector predictive control of a permanent magnet synchronous motor is achieved based on high-frequency signal injection. This method uses two additional voltage vector signals to fit the high-frequency injected voltage, achieving stable sensorless control.

[0041] This embodiment combines multi-vector model predictive control with the high-frequency signal injection method in sensorless control to achieve sensorless control of permanent magnet synchronous motors based on multi-vector model predictive control. By using two additional voltage vectors to fit the injected voltage in the multi-vector solution framework, reliable zero-low-speed domain sensorless predictive control is achieved, which can significantly improve the position observation accuracy, enhance the zero-low-speed domain load sensorless operation capability of permanent magnet synchronous motors, and effectively improve control performance at lower control frequencies.

[0042] 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

[0043] 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.

[0044] Figure 1A sensorless multi-vector predictive control system for permanent magnet synchronous motors based on high-frequency signal injection;

[0045] Figure 2 Schematic diagram of predictive control voltage vector using a traditional finite set model;

[0046] Figure 3 : A schematic diagram of the voltage vector of the method proposed in this invention;

[0047] Figure 4 Schematic diagram of the distribution relationship of each coordinate system;

[0048] Figure 5 : Schematic diagram of importing position error into PI controller. Detailed Implementation

[0049] 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.

[0050] 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.

[0051] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0052] PMSM: Permanent Magnet Synchronous Motor.

[0053] MPC: Model Predictive Control.

[0054] HFSI: High Frequency Signal Injection.

[0055] Model predictive control (MPC) offers advantages such as fast dynamic response, multi-objective optimization, and simple structure. Therefore, sensorless control architectures based on predictive control have significant development potential. However, combining MPC with high-frequency signal injection methods presents a challenge.

[0056] Example 1

[0057] Its overall control block diagram is as follows Figure 1 As shown, this embodiment discloses a sensorless multi-vector predictive control method for permanent magnet synchronous motors, including:

[0058] Step 1: PMSM multivector model predictive control strategy based on Lagrange multiplier method.

[0059] First, the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system can be obtained as follows:

[0060] (1)

[0061] In the formula, u d u q with i d i q Let ω represent the stator voltage component and stator current component in the rotating coordinate system, respectively. e ψ represents the electric angular velocity of the rotor. f L represents the amplitude of the permanent magnet flux linkage in the motor. d L q R represents the stator inductance in a rotating coordinate system. s This indicates the stator resistance.

[0062] After first-order Euler discretization of equation (1), the discrete-domain voltage response equation of the motor can be obtained as follows:

[0063] (2)

[0064] Where Ts represents the sampling period and k represents the current sampling time.

[0065] Move the current at time k+1 in equation (2) to the left side of the equation, and move the other terms to the right side. From equation (2), the current prediction equation can be obtained as follows:

[0066] (3)

[0067] In the formula, i p d (k+1) and i p q (k+1) represent the predicted stator current values ​​in the rotating coordinate system. Substituting this formula (3) into formula (6), formulas (1)-(3) are essential formulas for predictive current control in the rotating coordinate system. As can be seen from formula (3), the predicted current consists of two parts: one part is related to the current converter output voltage, and the other part is unrelated to the voltage.

[0068] In the multi-vector model predictive control (MPC) framework, one or two non-zero vectors are typically combined with a zero vector within a single commutation cycle to synthesize the optimal voltage vector. In this example, two adjacent voltage vectors are used to approximate the reference voltage vector, and their application times are assigned t1 and t2, respectively. The two adjacent voltage vectors are inherent properties of the two-level converter.

[0069] Let the component of the i-th vector in the stationary coordinate system be (V α,i V β,i If the electrical angle of the vector is θe, then the projection coefficient of the vector in the rotating coordinate system can be expressed as:

[0070] (4)

[0071] Where θ e Let a be the electrical angle of the motor. d a q These are the projection coefficients of the i-th vector in the rotating coordinate system, representing the perpendicular axis.

[0072] Similarly, the projection coefficient of the (i+1)th vector can be expressed as:

[0073] (5)

[0074] Where b d b q These are the projection coefficients of the i-th vector in the rotating coordinate system, representing the perpendicular axis.

[0075] Formulas (4) and (5) are used to find the optimal action vector, see formulas (8) and (9) for details.

[0076] Therefore, the quadratic cost function can be constructed as shown in the following equation:

[0077] (6)

[0078] Where J is the cost function, i p i is the predicted value of the stator current. * This is the target value for the stator current.

[0079] At this point, we should determine the optimal action time that minimizes the cost function J, and obtain:

[0080] (7)

[0081] in:

[0082] (8)

[0083] in addition:

[0084]

[0085] Therefore, the optimal action time can be determined as:

[0086] (9)

[0087] in addition:

[0088]

[0089] The optimal action time is subsequently used for control; these two times are the action times of two adjacent vectors.

[0090] Step 2: High-frequency injection voltage vector fitting.

[0091] As mentioned above, the core of multi-vector model predictive control lies in combining two optimal non-zero vectors with a zero vector within a single control cycle to synthesize the target voltage vector. However, to extract rotor position information from the motor's high-frequency response, a high-frequency excitation signal must be injected into the system. The injected high-frequency voltage signal is defined as follows:

[0092] (10)

[0093] In the formula, V h With ω h These represent the amplitude and frequency of the injected voltage, respectively. Figure 4 This demonstrates the correspondence between the real coordinate system and the observed coordinate system. Therefore, in the stationary reference frame, the injected signal can be expressed as:

[0094] (11)

[0095] Therefore, the sector where the injected signal is located can be determined as follows:

[0096] (12)

[0097] Where i represents the i-th sector where the injected vector is located. Then, the duration of action of two adjacent vectors within this sector can be determined using the following formula:

[0098] (13)

[0099] Based on the determined vector and duration of action, a high-frequency signal is injected. The resulting high-frequency current response after injecting this voltage signal can be expressed as:

[0100] (14)

[0101] In the formula, the symbol "^" indicates that the quantity is in the estimation reference frame. L Σ With L Δ Let these represent the average inductance and differential inductance of the motor, respectively, and their definitions are as follows:

[0102] (15)

[0103] From equation (14), we can obtain that the rotor position error is contained in the envelope of the high-frequency component of the q-axis current, which can be expressed as:

[0104] (16)

[0105] Step 3: Extraction of coherent demodulation location.

[0106] This invention employs coherent demodulation technology to extract the observation error corresponding to the above equation (16). After multiplying the high-frequency component of the q-axis current with the synchronization carrier, it can be decomposed into a baseband component and a second harmonic component, which can be specifically expressed as:

[0107] (17)

[0108] These error signals can be extracted using a low-pass filter (LPF), and the processing flow is as follows:

[0109] (18)

[0110] After extracting the f( e After that, the error signal is adjusted to zero by a proportional-integral (PI) controller, so that the assumed reference frame coincides with the actual reference frame.

[0111] See appendix Figure 5 As shown, by importing the position error into the PI controller, it can be made to converge to 0, and the output speed and rotor position can be accurate.

[0112] See appendix again Figure 1 As shown, the system response prediction and optimal action vector selection are the two main parts of step one, the high-frequency injection voltage generation is equation (10) of step two, and the signal processing and position observer are step three.

[0113] It should be noted that, in this embodiment, the sub-technical solution serves as the entire control framework. The closed-loop control with an observer observes the system's state variables and feeds them back into the control loop. The final output is the switching signal driving the converter. In this embodiment, the switching signal is the calculated switching vector and optimal action time, as shown in equation (9). In this embodiment, the sub-technical solution obtains the current three-phase current of the motor using a current sensor and uses the high-frequency response of the current to obtain the rotor position and speed, which are then used in the closed-loop control.

[0114] In this embodiment, the inner control loop employs a multi-vector model predictive control method based on Lagrange multipliers, which can effectively inject high-frequency signals while ensuring high control performance of the inner loop. The position observation section uses coherent demodulation to dynamically extract rotor position information from the high-frequency signal response, which is then provided to the inner control loop to complete closed-loop control.

[0115] Example 2

[0116] 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.

[0117] Example 3

[0118] The purpose of this embodiment is to provide a computer-readable storage medium.

[0119] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.

[0120] Example 4

[0121] The purpose of this embodiment is to provide a position-sensorless multi-vector predictive control system for permanent magnet synchronous motors, including:

[0122] The current prediction equation acquisition module is configured as follows:

[0123] Obtain the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system;

[0124] The discrete domain voltage response equation of the permanent magnet synchronous motor is obtained based on the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system.

[0125] Then, the current prediction equation is obtained based on the discrete domain voltage response equation of the motor;

[0126] The optimal action time acquisition module is configured as follows:

[0127] Two adjacent commutation vectors are used to approximate the reference voltage vector, and their respective application times are assigned as the first application time and the second application time; a quadratic cost function is constructed and solved to obtain the optimal application time;

[0128] The observation error extraction module is configured as follows:

[0129] Define the injected high-frequency voltage signal, obtain the representation of the injected signal in the stationary reference frame, then obtain the sector where the injected signal is located, and determine the action time of two adjacent vectors in that sector;

[0130] After the voltage signal is injected, the resulting high-frequency current response can be obtained, and the rotor position error can be obtained based on the high-frequency current response.

[0131] Coherent demodulation techniques are used to extract observation errors.

[0132] Example 5

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 method for position-sensorless multi-vector predictive control of permanent magnet synchronous motor, characterized in that, The method comprises the following steps: obtaining a voltage response equation of a permanent magnet synchronous motor in a rotating coordinate system; obtaining a motor discrete domain voltage response equation based on the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system; obtaining a current prediction equation based on the motor discrete domain voltage response equation; two adjacent commutation vectors are used to approximate the reference voltage vector, and the two adjacent commutation vectors are respectively given an action time, which is a first action time and a second action time; wherein two adjacent commutation vectors are used to approximate the reference voltage vector, and specifically: the components of the i-th vector in the stationary coordinate system are set, the electrical angle is set, and the projection coefficient of the vector in the rotating coordinate system is obtained based on the set parameters; the components of the i+1-th vector in the stationary coordinate system are set, the electrical angle is set, and the projection coefficient of the vector in the rotating coordinate system is obtained based on the set parameters; based on the obtained projection coefficient, a quadratic cost function is constructed and solved to obtain the optimal action time; defining an injected high-frequency voltage signal, obtaining the injected signal in the stationary reference frame, determining the sector of the injected signal, and determining the action time of the two adjacent vectors in the sector; after injecting the voltage signal, a high-frequency current response generated is obtained, and a rotor position error is obtained based on the high-frequency current response; a coherent demodulation technique is used to extract the observation error, specifically: the q-axis current high-frequency component is multiplied by the synchronous carrier, and then decomposed into a baseband component and a double-frequency component; the error signal is extracted through a low-pass filter; after the error signal is extracted, the error signal is adjusted to zero through a proportional-integral controller, so that the rotor position angle is obtained.

2. The method of claim 1, wherein the method is characterized by, Based on the current prediction equation, the predicted current is composed of two parts: one part is related to the current time converter output voltage, and the other part is not related to the voltage.

3. The method of claim 1, wherein the method further comprises: For the constructed quadratic cost function, the optimal action time is obtained to minimize the value of the cost function J, and the optimal action time is solved.

4. The method of claim 1, wherein the method is characterized by, The rotor position error is contained in the envelope of the q-axis current high-frequency component.

5. A position-sensorless multi-vector predictive control system for permanent magnet synchronous motor, characterized in that, The method comprises the following steps: The current prediction equation acquisition module is configured to: obtain a voltage response equation of a permanent magnet synchronous motor in a rotating coordinate system; obtain a motor discrete domain voltage response equation based on the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system; obtain a current prediction equation based on the motor discrete domain voltage response equation; The optimal action time acquisition module is configured to: two adjacent commutation vectors are used to approximate the reference voltage vector, and the two adjacent commutation vectors are respectively given an action time, which is a first action time and a second action time; wherein two adjacent commutation vectors are used to approximate the reference voltage vector, and specifically: the components of the i-th vector in the stationary coordinate system are set, the electrical angle is set, and the projection coefficient of the vector in the rotating coordinate system is obtained based on the set parameters; the components of the i+1-th vector in the stationary coordinate system are set, the electrical angle is set, and the projection coefficient of the vector in the rotating coordinate system is obtained based on the set parameters; based on the obtained projection coefficient, a quadratic cost function is constructed and solved to obtain the optimal action time; The observation error extraction module is configured to: The injected high-frequency voltage signal is defined, a representation of the injected signal in a stationary reference frame is obtained, the sector in which the injected signal is located is determined, and the action time of two adjacent vectors in the sector is determined; After the injection of the voltage signal, a generated high-frequency current response is obtained, and a rotor position error is obtained based on the high-frequency current response; A coherent demodulation technique is used to extract the observation error, specifically: The high-frequency component of the q-axis current is multiplied by a synchronous carrier, and then decomposed into a baseband component and a double-frequency component; The error signal is extracted through a low-pass filter; After the error signal is extracted, the error signal is adjusted to zero through a proportional-integral controller, thereby obtaining a rotor position angle.

6. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method of any one of claims 1 to 4.

7. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor, when executing the program, implements the steps of the method of any one of claims 1 to 4.

8. A computer readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by a processor, executes the steps of the method of any one of claims 1 to 4.

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