Position-sensing-free multi-vector predictive control method and system for permanent magnet synchronous motor

Abstract

The invention provides a permanent magnet synchronous motor position-sensing-free multi-vector predictive control method and system, and belongs to the technical field of permanent magnet synchronous motor measurement predictive control, and the method comprises the steps: obtaining a current predictive equation based on a motor discrete domain voltage response equation; approximating a reference voltage vector by using two adjacent commutation vectors, and respectively endowing the reference voltage vector with action time, namely a first action time and a second action time; constructing a quadratic form cost function and solving the quadratic form cost function to obtain the optimal action time; an injected high-frequency voltage signal is defined, injection signal representation in the static reference system is obtained, then a sector where the injection signal is located is obtained, and action time of two adjacent vectors in the sector is determined; after the voltage signal is injected, the generated high-frequency current response representable is obtained, and a rotor position error is obtained based on the high-frequency current response representable; and the observation error is extracted by adopting a coherent demodulation technology.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of permanent magnet synchronous motor measurement prediction control, and particularly relates to a permanent magnet synchronous motor position sensorless multi-vector prediction control method and system. BACKGROUND

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

[0003] Permanent magnet synchronous motors have the advantages of high power density, excellent efficiency and compact structure, and have become the first choice for AC servo control systems. At present, they have been widely used in offshore wind turbine generators, electric vehicles, industrial automation equipment and household appliances. However, in order to achieve high-performance driving, the permanent magnet synchronous motor (PMSM) system usually relies on a mechanical encoder to provide accurate rotor position feedback; the mechanical encoder is not only expensive, but also prone to failure, making it difficult to meet the industry's demand for high reliability and low cost. Therefore, a high-precision driving system based on a permanent magnet synchronous motor requires accurate rotor position and speed information to achieve precise closed-loop control. As an important element for obtaining rotor position information, the mechanical encoder is large in size, high in cost and prone to damage, which seriously restricts the use of permanent magnet synchronous motors in some working conditions. Therefore, it is necessary to develop a high-performance permanent magnet synchronous motor position sensorless control scheme.

[0004] The position sensorless control technology of permanent magnet synchronous motor is generally divided into saliency-based and model-based. The saliency-based method generally injects a specific high-frequency excitation signal into the motor and calculates the rotor position and speed based on the motor response characteristics. This method is suitable for zero-speed and low-speed conditions. The model-based method is based on the observation and processing of motor back-EMF or flux linkage information to extract rotor position information, thereby achieving accurate control in the medium and high speed range.

[0005] At present, most permanent magnet synchronous motor position sensorless control schemes still follow the traditional linear cascade structure, which is difficult to achieve ideal results in multi-input, multi-output and multi-target control scenarios. Therefore, it is of important research value and broad development prospects to apply model predictive control to the position sensorless control architecture.

[0006] Currently, the position sensorless predictive control method can be roughly divided into two categories: the first category directly extracts the rotor position information by using the inherent characteristics of model predictive control. This category usually obtains accurate rotor position information by differentiating the current ripple signal generated by MPC. Although the first category can fully exert the dynamic characteristics of MPC, it is extremely sensitive to sensor noise, and noise interference significantly affects the observation accuracy. The second category combines MPC with traditional mature schemes to improve overall control performance. This category can be combined with model observation method or salient pole method. The second category cannot completely fit the accurate injection information due to the lack of classic pulse width modulation module in the widely used limited set model predictive control, and it is difficult to seamlessly integrate with high frequency signal injection (HFSI) technology, which has poor low speed running capability, thereby restricting the promotion and application of position sensorless predictive control strategy at zero speed and low speed.

[0007] In summary, most permanent magnet synchronous motor position sensorless control schemes are based on linear cascade controller architecture, which has shortcomings when dealing with multiple input, multiple output and multiple target control requirements. SUMMARY

[0008] To overcome the problem of unstable operation of the above-mentioned prior art position sensorless predictive control technology at zero speed and low speed, the present application provides a permanent magnet synchronous motor position sensorless multi-vector predictive control method, which uses two additional voltage vector signals to fit the high-frequency injection voltage, and realizes stable position sensorless control.

[0009] To achieve the above-mentioned purpose, one or more embodiments of the present application provide the following technical solutions: In a first aspect, a permanent magnet synchronous motor position sensorless multi-vector predictive control method is disclosed, comprising: obtaining a voltage response equation of the 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; using two adjacent commutation vectors to approximate the reference voltage vector, and respectively assigning their action times, which are the first action time and the second action time; constructing a quadratic cost function and solving to obtain the optimal action time; defining an injected high-frequency voltage signal, obtaining an injected signal representation in a stationary reference frame, and obtaining the sector where the injected signal is located, to determine the action time of the two adjacent vectors in the sector; After injecting the voltage signal, the generated high-frequency current response can be represented as, and the rotor position error can be obtained based on the high-frequency current response representation; The observation error is extracted by using coherent demodulation technology.

[0010] As a further technical solution, the predicted current obtained based on the current prediction equation 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.

[0011] As a further technical solution, two adjacent commutation vectors are used to approximate the reference voltage vector, specifically: The components and electrical angles of the i-th vector in the stationary coordinate system are set, and the projection coefficients of the vector in the rotating coordinate system are obtained based on the above parameters; The components and electrical angles of the i+1-th vector in the stationary coordinate system are set, and the projection coefficients of the vector in the rotating coordinate system are obtained based on the above parameters.

[0012] 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 solved.

[0013] As a further technical solution, the rotor position error is contained in the envelope of the q-axis current high-frequency component.

[0014] As a further technical solution, coherent demodulation technology is used to extract the observation error, specifically: After multiplying the q-axis current high-frequency component with the synchronous carrier, it can be decomposed into a baseband component and a double-frequency component; The error signal can be extracted by a low-pass filter; After the error signal is extracted, the error signal is adjusted to zero by a proportional-integral controller, so that the assumed reference frame coincides with the actual reference frame.

[0015] In a second aspect, a permanent magnet synchronous motor position sensorless multi-vector prediction control system is disclosed, comprising: The current prediction equation acquisition module is configured to: obtain the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system; obtain the motor discrete domain voltage response equation based on the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system; obtain the 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 are respectively assigned with action time, which are the first action time and the second action time; construct a quadratic cost function and solve to obtain the optimal action time; The observation error extraction module is configured to: The high-frequency voltage signal is defined, the injected signal representation in the stationary reference frame is obtained, the sector where the injected signal is located is obtained, and the action time of two adjacent vectors in the sector is determined; After the voltage signal is injected, the generated high-frequency current response representation is obtained, and the rotor position error is obtained based on the high-frequency current response representation; The observation error is extracted by using a coherent demodulation technique.

[0016] The above one or more technical solutions have the following beneficial effects: The position sensorless control framework based on the multi-vector model predictive control architecture is used, and then the position sensorless multi-vector predictive control of the permanent magnet synchronous motor is realized based on the high-frequency signal injection. The method uses two additional voltage vector signals to fit the high-frequency injection voltage, and realizes stable position sensorless control.

[0017] The embodiment of the technical scheme combines the multi-vector model predictive control with the high-frequency signal injection method in the position sensorless control, realizes the position sensorless control of the permanent magnet synchronous motor based on the multi-vector model predictive control, uses two additional voltage vectors to fit the injection voltage in the multi-vector solving framework, realizes reliable zero-low speed domain position sensorless predictive control, can significantly improve the position observation accuracy, and enhances the position sensorless running ability of the permanent magnet synchronous motor in the zero-low speed domain with load. In the case of lower control frequency, the control performance is effectively improved.

[0018] The advantages of the additional aspects of the application will be partially given in the following description, partially become obvious from the following description, or be known by the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0019] The drawings accompanying the specification of the application form part of the application and serve to provide further understanding of the application, the illustrative embodiments of the application and their description serve to explain the application without constituting an inappropriate limitation thereof.

[0020] Figure 1 : Permanent magnet synchronous motor position sensorless multi-vector predictive control system based on high-frequency signal injection Figure 2 : Voltage vector schematic diagram of traditional finite set model predictive control Figure 3 : Voltage vector schematic diagram of the method proposed in the application Figure 4 : Schematic diagram of the distribution relationship of each coordinate system Figure 5 : Schematic diagram of introducing the position error into the PI controller DETAILED DESCRIPTION

[0021] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. 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.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application.

[0023] In the case of no conflict, the embodiments in the application and the features in the embodiments can be combined with each other.

[0024] PMSM: Permanent magnet synchronous motor.

[0025] MPC: Model predictive control.

[0026] HFSI: High frequency signal injection.

[0027] Model predictive control (MPC) has the advantages of fast dynamic response, multi-objective optimization and simple structure. Therefore, the position sensorless control architecture based on predictive control has significant development potential. There is a difficult problem in combining model predictive control with high frequency signal injection method.

[0028] Embodiment one The overall control block diagram is shown in Figure 1 The embodiment discloses a permanent magnet synchronous motor position sensorless multi-vector model predictive control method, which comprises the following steps: Step one: PMSM multi-vector model predictive control strategy based on Lagrange multiplier method.

[0029] Firstly, the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system can be obtained as follows: (1) In the formula, u d , u q , i d , i q , ω e , ψ f , L d , L q , and R s respectively represent the stator voltage component and the stator current component in the rotating coordinate system, the electrical angular velocity of the rotor, the permanent magnet flux linkage amplitude of the motor, the stator inductance in the rotating coordinate system, and the stator resistance.

[0030] For formula (1), the discrete domain voltage response equation of the motor can be obtained by first-order Euler discretization as follows: (2) Where Ts represents the sampling period, and k represents the current sampling time.

[0031] Moving the k+1th current of equation (2) to the left side of the equation and other terms to the right side of the equation, the current prediction equation can be obtained from equation (2) as: (3) In the formula, i p d (k+1) and i p q (k+1) represent the stator current prediction value in the rotating coordinate system, and the formula (3) is subsequently brought into formula (6). Formulas (1)-(3) are the formulas used for predicting current control in the rotating coordinate system. As can be seen from formula (3), 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.

[0032] In the multi-vector model predictive control (MPC) framework, one or two non-zero vectors are usually combined with zero vectors in a single commutation period to synthesize the optimal voltage vector. The present embodiment uses two adjacent voltage vectors to approximate the reference voltage vector, and assigns them action times t1 and t2, respectively. The two adjacent voltage vectors are the inherent properties of the two-level converter.

[0033] Let the component of the i-th vector in the stationary coordinate system be (V α,i , V β,i ), and the electrical angle be θe, then the projection coefficient of the vector in the rotating coordinate system can be expressed as: (4) Where θ e is the electrical angle of the motor, and a d , a q are the projection coefficients of the i-th vector in the rotating coordinate system.

[0034] Similarly, the projection coefficient of the i+1-th vector can be expressed as: (5) Where b d , b q are the projection coefficients of the i-th vector in the rotating coordinate system.

[0035] Formulas (4) and (5) are used to find the optimal action vector, as shown in formulas (8) and (9).

[0036] Therefore, a quadratic cost function can be constructed as shown in the following formula: (6) Where J is the cost function, i p is the stator current prediction value, and i *The stator current target value.

[0037] At this time, the optimal action time should be obtained to minimize the value of the cost function J, and the following is obtained: (7) Where: (8) In addition:

[0038] Therefore, the optimal action time can be solved as: (9) In addition:

[0039] The optimal action time is used for subsequent control, and the two times are the action times of the two adjacent vectors.

[0040] Step two: high-frequency injected voltage vector fitting.

[0041] As mentioned above, the core of the multi-vector model predictive control is to combine two optimal non-zero vectors with zero vectors to synthesize the target voltage vector in a single control cycle. However, in order to extract the rotor position information from the high-frequency response of the motor, a high-frequency excitation signal must be additionally injected into the system. The injected high-frequency voltage signal is defined as follows: (10) In the formula, V h And ω h are the amplitude and frequency of the injected voltage, respectively. Figure 4 The correspondence between the real coordinate system and the observation coordinate system is shown. Therefore, in the stationary reference frame, the injected signal can be expressed as: (11) From which the sector where the injected signal is located is as follows: (12) Where i represents the i-th sector where the injected vector is located. Subsequently, the action time of the two adjacent vectors in the sector can be determined according to the following formula: (13) Based on the determined action vector and its action time, the high-frequency signal is injected. After injecting the voltage signal, the generated high-frequency current response can be expressed as: (14) In the formula, the symbol "^" indicates that the quantity is in the estimated reference frame. L Σ And L Δrespectively, which are defined as follows: (15) From equation (14), it can be seen 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: (16) Step 3: Coherent demodulation position extraction.

[0042] The present application uses coherent demodulation technology to extract the observation error corresponding to equation (16) above. After multiplying the high frequency component of the q-axis current by the synchronous carrier, it can be decomposed into a baseband component and a double frequency component, which can be expressed as: (17) These error signals can be extracted by a low pass filter (LPF), and the processing flow is as follows: (18) After extracting the f( e ), 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.

[0043] Referring to FIG. 1, the position error is introduced into the PI controller, which converges to 0, and the output is the accurate rotor speed and rotor position. Figure 5 Referring again to FIG. 1, the system response prediction and optimal action vector selection are the two main parts of step 1, the high frequency injection voltage generation is equation (10) of step 2, and the signal processing and position observer are step 3.

[0044] Figure 1 It should be noted that the present embodiment is a control framework, and the closed-loop control with the observer is achieved by observing the state quantity of the system and feeding it back to the control loop. The final output is the switching signal of the drive inverter, which is the calculated switching vector and optimal action time in the present embodiment, as shown in equation (9). The present embodiment obtains the three-phase current of the motor at the current time through the current sensor, and obtains the rotor position and speed from the high frequency response in the current, which is then used in the closed-loop control.

[0045] It should be noted that the present embodiment is a control framework, and the closed-loop control with the observer is achieved by observing the state quantity of the system and feeding it back to the control loop. The final output is the switching signal of the drive inverter, which is the calculated switching vector and optimal action time in the present embodiment, as shown in equation (9). The present embodiment obtains the three-phase current of the motor at the current time through the current sensor, and obtains the rotor position and speed from the high frequency response in the current, which is then used in the closed-loop control.

[0046] The inner loop of the present embodiment uses 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 part uses coherent demodulation to dynamically extract the rotor position information in the high frequency signal response, which is then provided to the control inner loop to complete the closed-loop control.

[0047] Embodiment Two ​The purpose of the embodiment is to provide a computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0048] Embodiment three The purpose of the embodiment is to provide a computer-readable storage medium.

[0049] A computer-readable storage medium, having stored thereon a computer program, which is executable by a processor to perform the steps of the above method.

[0050] Embodiment four The purpose of the embodiment is to provide a permanent magnet synchronous motor position sensorless multi-vector predictive control system, comprising: The current prediction equation acquisition module is configured to: obtain a voltage response equation of the 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: approximate the reference voltage vector using two adjacent commutation vectors, and respectively assign action times to the two adjacent commutation vectors, which are respectively a first action time and a second action time; construct a quadratic cost function and solve to obtain the optimal action time; The observation error extraction module is configured to: define an injected high-frequency voltage signal, obtain an injected signal representation in a stationary reference frame, and obtain a sector in which the injected signal is located, and determine the action times of two adjacent vectors in the sector; After injecting the voltage signal, a generated high-frequency current response representation is obtained, and the rotor position error is obtained based on the high-frequency current response representation; The coherent demodulation technique is used to extract the observation error.

[0051] Embodiment five The purpose of the embodiment is to provide a computer program product containing instructions, which when executed on a computer, causes the computer to perform the methods and functions involved in any of the above embodiments. The steps and methods involved in the above embodiments correspond to Embodiment One, and the specific implementation can refer to the relevant description part of Embodiment One. The term "computer-readable storage medium" should be understood to include a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium that can store, encode or carry instruction sets for execution by a processor and cause the processor to perform any method in the present application.

[0052] Those skilled in the art should understand that the modules or steps of the present application described above can be realized by a general computer device, or alternatively, they can be realized by program codes executable by a computing device, so that they can be stored in a storage device and executed by a computing device, or they can be respectively manufactured into individual integrated circuit modules, or a plurality of modules or steps among them can be manufactured into a single integrated circuit module. The present application is not limited to any specific combination of hardware and software.

[0053] The specific embodiments of the present application described above in conjunction with the accompanying drawings are not intended to limit the protection scope of the present application. Those skilled in the art should understand that various modifications or changes made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the protection scope of the present application.

Claims

1. A sensorless multi-vector predictive control method for permanent magnet synchronous motors, characterized by: include: Obtain the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system; 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. Then, the current prediction equation is obtained based on the discrete domain voltage response equation of the motor; 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. Construct a quadratic cost function and solve for the optimal action time; 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; 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. Coherent demodulation techniques are used to extract observation errors.

2. The sensorless multi-vector predictive control method for permanent magnet synchronous motors as described in claim 1, characterized in that, 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.

3. The sensorless multi-vector predictive control method for permanent magnet synchronous motors as described in claim 1, characterized in that, The reference voltage vector is approximated by two adjacent commutation vectors, specifically: Define the components of the i-th vector in the stationary coordinate system and the electrical angle. Based on the above parameters, obtain the projection coefficient of the vector in the rotating coordinate system. Define the components of the (i+1)th vector in the stationary coordinate system and the electrical angle. Based on the above parameters, obtain the projection coefficient of the vector in the rotating coordinate system.

4. The sensorless multi-vector predictive control method for permanent magnet synchronous motors as described in claim 1, characterized in that, For the constructed quadratic cost function, the optimal action time is found to minimize the value of the cost function J, and the optimal action time is obtained.

5. The sensorless multi-vector predictive control method for permanent magnet synchronous motors as described in claim 1, characterized in that, The rotor position error is contained in the envelope of the high-frequency component of the q-axis current.

6. The sensorless multi-vector predictive control method for permanent magnet synchronous motors as described in claim 1, characterized in that, Coherent demodulation techniques are used to extract observation errors, specifically: 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. Error signals can be extracted using a low-pass filter; 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.

7. A position-sensorless multi-vector predictive control system for permanent magnet synchronous motors, characterized by including: The current prediction equation acquisition module is configured as follows: Obtain the voltage response equation of the permanent magnet synchronous motor in the rotating coordinate system; 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. Then, the current prediction equation is obtained based on the discrete domain voltage response equation of the motor; The optimal action time acquisition module is configured as follows: 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; The observation error extraction module is configured as follows: 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; 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. Coherent demodulation techniques are used to extract observation errors.

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.

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