Model-parameter-free and position-sensor-free predictive control method for permanent magnet synchronous motor

Abstract

The invention relates to the technical field of permanent magnet synchronous motor predictive control, and particularly provides a permanent magnet synchronous motor model-parameter-free and position-sensor-free predictive control method, which comprises the following steps of: sampling stator current of a permanent magnet synchronous motor to obtain stator current data of three latest continuous sampling moments; calculating the current difference of adjacent sampling moments based on the stator current data of three continuous moments; calculating an estimated rotor position of the motor based on the current differential data; inputting the estimated rotor position into a filter for filtering to obtain a smooth rotor position and a rotating speed estimation value; and carrying out closed-loop operation on the rotor position and the rotating speed estimation value to a model-free predictive control algorithm. The position of the rotor is calculated according to the latest three stator current measurement data, no motor parameter is needed in the calculation process, the parameter robustness of a control system is greatly improved, and the method can be combined with various existing model-free current prediction control methods to realize model-free parameter and position-sensor-free prediction control of the permanent magnet synchronous motor.

Description

Technical Field

[0001] The present invention relates to the technical field of permanent magnet synchronous motor predictive control, and in particular to a model-parameter-free and position sensor-free predictive control method for a permanent magnet synchronous motor. Background Art

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in transmission systems due to their high power density and reliability. Among high-performance control methods for PMSMs, model predictive current control (MPC) has attracted widespread attention due to its fast response and simple implementation. It utilizes a discrete motor model to predict future variables. Accurate model parameters and reliable position information are crucial for MPC. In practical applications, motor parameters may deviate from their nominal values. For example, inductance may change due to magnetic saturation, while resistance and flux may vary at different temperatures. Furthermore, installing a position sensor results in a larger motor, higher hardware costs, and reduced reliability in harsh environments.

[0003] In response to the problem of high parameter sensitivity, existing research methods for improving the robustness of model predictive control parameters can be roughly divided into three categories, including online parameter identification, disturbance observer method, and model-free predictive current control method. Among them, online parameter identification can obtain accurate motor parameters in real time, but due to the coupling between motor parameters and the lack of rank problem, it is difficult to accurately identify all motor parameters at the same time. The disturbance observer method is simple in principle and easy to implement, but it requires additional adjustment of the observer gain. Both methods can improve the parameter robustness of model predictive control, but still require a relatively accurate motor model to ensure the initialization of the control algorithm. In contrast, the model-free predictive current control method can achieve closed-loop control without any motor parameters, but existing model-free predictive control methods all require position sensors to achieve high-performance current regulation.

[0004] Due to the weak saliency of surface-mounted motors, existing position sensorless control techniques often estimate rotor position based on motor models, which is highly dependent on the accuracy of motor parameters. To improve system robustness, existing research has proposed online parameter adjustment or position error correction strategies. However, because model-free predictive current control algorithms assume that model parameters are completely unknown, these schemes are not suitable for model-free predictive current control. In existing research, model-free sensorless predictive current control is implemented based on online estimation of the last three state transitions. However, the rotor position is derived based on rotor anisotropy, which is not applicable to surface-mounted motors. Summary of the Invention

[0005] In order to solve the problem that the model-free predictive current control algorithm of the surface-mounted permanent magnet synchronous motor still requires a position sensor, the present invention proposes a rotor position observation method based on stator current differential. This method calculates the rotor position based only on the three most recent stator current measurement data. On the one hand, no motor parameters are required in the calculation process, which greatly improves the parameter robustness of the control system. On the other hand, this method can be combined with various existing model-free current predictive control methods to realize model-free parameter and position sensor-free predictive control of the permanent magnet synchronous motor.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A model-free parameter-free position sensor-free predictive control method for a permanent magnet synchronous motor comprises the following steps:

[0008] S1: Sample the stator current of the permanent magnet synchronous motor and obtain the stator current data of the last three consecutive sampling moments;

[0009] S2: Based on the stator current data at three consecutive moments, calculate the stator current difference at adjacent sampling moments;

[0010] S3: Estimate the motor rotor position based on the current differential data;

[0011] S4: filtering the estimated motor rotor position to obtain a smoothed estimated rotor position and estimated speed;

[0012] S5: Closing the filtered estimated rotor position and estimated speed into the model-free predictive control algorithm.

[0013] Furthermore, the model-free parameter and position sensor-free predictive control method for a permanent magnet synchronous motor according to claim 1 is characterized in that the stator current data in step S1 is expressed in a complex vector form:

[0014]

[0015] in, is the stator current vector in the stationary coordinate system, i α is the α-axis stator current, i β is the β-axis stator current.

[0016] Furthermore, the stator current differential calculation formula in step S2 is:

[0017]

[0018] in, and are the stator current differences at the kth moment and (k-1)th moment, and are the basic voltage vectors of the two-level inverter at the (k-1)th moment and the (k-2)th moment, and are the rotor electrical angles at the (k-1)th moment and the (k-2)th moment respectively.

[0019] Furthermore, the current differential model coefficients are calculated as:

[0020]

[0021] in, and To estimate the model coefficients, is the stator voltage vector, is the stator current differential, Estimate the rotor position at the (k-1)th moment.

[0022] Furthermore, in step S3, the calculation formula for the estimated rotor position of the motor is:

[0023]

[0024] in, is the estimated motor speed, T sc is the sampling period, and X is only related to the current differential and voltage vector.

[0025] Furthermore, the formula for calculating X is:

[0026]

[0027] in, and Yes real and imaginary parts, and They are The real and imaginary parts of .

[0028] Furthermore, in step S4, the estimated rotor position and the estimated speed are calculated as follows:

[0029]

[0030] Among them, G θ (s) is the filter transfer function.

[0031] Furthermore, the present invention also provides a model-free parameter and position sensor-free predictive control system for a permanent magnet synchronous motor, comprising a current sampling module, an operation module, a filtering module and a closed-loop control module; the current sampling module is used to obtain stator current data at three consecutive sampling moments; the operation module can calculate the stator current differential and the estimated rotor position of the motor based on the obtained stator current data; the filtering module is used to filter the estimated rotor position of the motor; the closed-loop control module is used to implement model-free predictive closed-loop control; wherein, the current sampling module, the operation module, the filtering module and the closed-loop control module are all implemented using the predictive control method as described above.

[0032] Furthermore, the present invention also provides an electronic device, which includes at least one processor; and a memory communicatively connected to the processor; wherein the memory stores instructions that can be executed by the processor, and the instructions are executed by the processor to enable the processor to execute the predictive control method as described above.

[0033] The present invention has the following technical effects:

[0034] 1. The present invention estimates rotor position by processing stator current data from the three most recent consecutive sampling moments and calculating the current differential between adjacent sampling moments. Compared with traditional methods, position estimation only requires the most recent three stator current measurements, significantly improving the system's response speed and real-time performance. Experimental results demonstrate that the present invention not only has low rotor position estimation error in steady-state conditions but also exhibits excellent rotor position tracking capabilities during dynamic motor acceleration and deceleration, as well as addition and subtraction of rated load, verifying the high efficiency of rotor information updates.

[0035] 2. The rotor position calculation process does not rely on motor model parameters. Only current differential data and voltage vectors are required to achieve position estimation. Therefore, it is highly robust to changes in motor parameters (such as inductance, flux linkage, temperature, etc.).

[0036] 3. The rotor position and speed are estimated using a standard phase-locked loop filter. Its transfer function is simple, and performance requirements can be met by simply adjusting the filter gain. This significantly reduces the complexity of control system parameter adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0038] Figure 1 This is a system control block diagram of a model-free parameter and position sensor-free predictive control method for a permanent magnet synchronous motor according to the present invention;

[0039] Figure 2 This is a first test result diagram of the present invention;

[0040] Figure 3 This is a second test result diagram of the present invention;

[0041] Figure 4 This is a third test result diagram of the present invention;

[0042] Figure 5 This is a fourth test result diagram of the present invention;

[0043] Figure 6 This is the fifth test result diagram of the present invention. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0045] In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "front end", "back end", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0046] It should also be noted that, in the description of this application, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to direct connections, indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0047] Example 1:

[0048] Reference Figure 1 The present invention provides a model-free parameter and position sensor-free predictive control method for a permanent magnet synchronous motor, comprising the following steps:

[0049] S1: Sample the stator current of the permanent magnet synchronous motor and obtain the stator current data of the last three consecutive sampling moments;

[0050] Specifically, IF control is used to start the motor in open loop, keep the stator current amplitude constant, and set the motor d-axis current amplitude to I d , the rotor speed follows the given value:

[0051]

[0052] in The reference speed is given for open-loop startup, and β is the motor startup acceleration. Calculate the rotor position:

[0053]

[0054] θ r is the estimated rotor position during open-loop startup.

[0055] The three-phase stator current i of the motor A, B, and C is obtained through the current sensor a,b,c After Clarke coordinate transformation, the stator current in the two-phase stationary coordinate system is obtained and written in the form of a complex vector

[0056] Further, S2: based on the stator current data at three consecutive moments, the current difference at adjacent sampling moments is calculated;

[0057] Specifically, calculate the current difference between the kth moment and the (k-1)th moment and

[0058] Furthermore, step S3: estimating the rotor position of the motor according to the current differential data;

[0059] Specifically, calculate the current differential model coefficients and

[0060]

[0061] in, is the voltage vector acting at the (k-1)th moment.

[0062] The current differential model coefficients are low-pass filtered using a first-order low-pass filter. The filter form is as follows:

[0063]

[0064] Among them, ω c is the filter cutoff frequency, T sc is the sampling period, and are the model coefficients after filtering.

[0065] Predict the stator current at the (k+1)th moment corresponding to the eight basic voltage vectors based on the filtered model coefficients

[0066]

[0067] in, is the current sampling value at kth moment, according to the predicted Further predict the stator current at (k+2)th moment

[0068]

[0069] According to the given stator current I d Calculate current reference value as follows:

[0070]

[0071] Use predicted current and reference current Calculate the cost function J m :

[0072]

[0073] The optimal voltage vector is selected by minimizing the cost function, and the switching state of the optimal voltage vector is used to control the inverter thyristor. The inverter outputs three-phase stator current to drive the motor from static start to the set value ω th .

[0074] Determine whether the motor speed reaches the set value ω t h. When the motor speed reaches the set value, calculate the estimated rotor angle of the motor:

[0075]

[0076] in, To estimate the rotor position, is the stator current difference at the kth moment, To estimate the rotational speed, the expression for X is as follows:

[0077]

[0078] Furthermore, step S4: filtering the estimated rotor position of the motor to obtain a smoothed estimated rotor position and estimated speed;

[0079] Specifically, the motor estimated rotor angle in step S3 is sent to a standard phase-locked loop for filtering. The phase-locked loop transfer function is as follows:

[0080]

[0081]

[0082] Where ρ is the phase-locked loop gain.

[0083] Calculate the estimated rotor position and estimated speed:

[0084]

[0085] Furthermore, step S5: closing the filtered rotor position and speed estimation values ​​into a model-free predictive control algorithm.

[0086] Specifically, if the motor speed rises to the set value ω th And maintain the set time t i Then, the model coefficients are updated using the estimated motor speed and filtered rotor position. and At the same time, the difference between the estimated speed and the reference speed is processed by the PI controller to obtain the torque current reference value The PI controller transfer function is as follows:

[0087]

[0088] Among them, k p and k i is the PI controller gain.

[0089] Reference value of excitation current Calculate the stator current reference value in the two-phase stationary coordinate system:

[0090]

[0091] Using the updated model coefficients and Predict the stator current at the (k+2)th moment corresponding to the eight basic voltage vectors

[0092]

[0093] Use current reference value and current prediction value Calculating the cost function The optimal voltage vector is selected by minimizing the cost function, and the switching state of the inverter thyristor is changed according to the optimal vector to further drive the motor to achieve closed-loop control.

[0094] Simulation experiment:

[0095] To verify the effectiveness of the model-free parameter-free position sensor-free predictive control method for a permanent magnet synchronous motor in Example 1, an experimental test was conducted on a surface-mount permanent magnet synchronous motor drive system powered by a two-level inverter. The system parameters are as follows:

[0096]

[0097] To verify the effectiveness of the invention, the experiment first tested the motor's process from open-loop startup to the set speed, and then gradually accelerated to the rated speed. The steady-state performance of the motor was then tested at 10% and 100% of the rated speed. Finally, the dynamic performance of the motor was tested under sudden speed increases and sudden additions and subtractions of the rated load. The test results show that the technology of the invention performs well.

[0098] Reference Figures 2 to 6 The waveform test of this embodiment is as follows:

[0099] Figure 2 The test results of the motor starting from a standstill to 10Hz, then to 50Hz, and then to 100Hz show that the motor starts from a standstill in open loop and switches to closed-loop control smoothly and quickly. When it continues to speed up to 50% and 100% of the rated speed, the speed rises smoothly, the rotor position is accurately observed, and the three-phase stator current waveforms are symmetrical.

[0100] Figure 3 This is the steady-state test result of the motor with a speed of 10Hz under rated load. Figure 4 This is the steady-state test result of the motor with a speed of 100Hz under rated load. Figure 3 Combine Figure 4 It can be seen that the motor can operate stably under rated load conditions of 10 Hz and 100 Hz, and the peak values ​​of the rotor position estimation error are 0.16 rad and 0.07 rad, respectively, indicating that the proposed scheme has good steady-state performance.

[0101] Figure 5 Figure 2 shows the dynamic test results of the motor speed step from 10 Hz to 100 Hz. It can be seen that when the reference speed command increases from 10 Hz to 100 Hz, the rotor accelerates smoothly to the target speed. At the moment of the speed step, the maximum rotor estimation error is about 0.07 rad. Figure 6 The following are the dynamic test results of the motor suddenly adding or subtracting the rated load. When the rated load is added or subtracted at a speed of 100 Hz, the speed will slightly deviate from the reference value but will quickly recover. The average value of the rotor position estimation error is almost zero. The test proves that the technology of the present invention has excellent dynamic response performance.

[0102] Example 2:

[0103] Embodiment 2 provides a frequency hopping carrier signal detection system based on time-frequency analysis, wherein the signal detection system includes a current sampling module, a calculation module, a filtering module and a closed-loop control module;

[0104] The current sampling module is used to obtain stator current data at three consecutive sampling moments;

[0105] The calculation module can calculate the stator current difference and the estimated rotor position of the motor based on the acquired stator current data;

[0106] The filtering module is used to filter the estimated rotor position of the motor;

[0107] The closed-loop control module is used to implement model-free predictive closed-loop control;

[0108] Among them, the current sampling module, the calculation module, the filtering module and the closed-loop control module are all implemented by the predictive control method in the first embodiment.

[0109] Example 3:

[0110] Embodiment three provides an electronic device, which includes at least one processor; and a memory communicatively connected to the processor; wherein the memory stores instructions that can be executed by the processor, and the instructions are executed by the processor to enable the processor to execute the predictive control method in embodiment one.

[0111] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A model-free parameter and position sensor-free predictive control method for a permanent magnet synchronous motor, characterized in that: The steps include: S1: Sampling the stator current of the permanent magnet synchronous motor to obtain the stator current data of the last three consecutive sampling moments; S2: Based on the stator current data at three consecutive moments, the stator current difference at adjacent sampling moments is calculated; S3: Estimate the motor rotor position based on the current differential data; S4: filtering the estimated motor rotor position to obtain a smoothed estimated rotor position and estimated speed; S5: Closing the filtered estimated rotor position and estimated speed into a model-free predictive control algorithm.

2. The method for predictive control of a permanent magnet synchronous motor without model parameters and without position sensor according to claim 1, characterized in that: The stator current data in step S1 is expressed in a complex vector form, in, is the stator current vector in the stationary coordinate system, i α is the α-axis stator current, i β is the β-axis stator current.

3. The method for predictive control of a permanent magnet synchronous motor without model parameters and without position sensor according to claim 2, characterized in that: The stator current differential calculation formula in step S2 is: in, and are the stator current differences at the kth and (k-1)th moments, respectively. and are the basic voltage vectors of the two-level inverter at the (k-1)th moment and the (k-2)th moment, respectively. and are the rotor electrical angles at the (k-1)th moment and the (k-2)th moment respectively, m and n are the model coefficients containing the motor parameters, and their expressions are as follows: Among them, T sc is the sampling period, u dc is the DC bus voltage, L s is the stator inductance, R s is the stator resistance, ω r is the rotor electrical angular velocity, is the rotor flux, is the q-axis stator current.

4. The method for predictive control of a permanent magnet synchronous motor without model parameters and without position sensor according to claim 3 is characterized in that: The current differential model coefficients are calculated as: in, and To estimate the model coefficients, is the stator voltage vector, is the stator current differential, Estimate the rotor position for the (k-1)th moment.

5. The method for predictive control of a permanent magnet synchronous motor without model parameters and without position sensor according to claim 4, characterized in that: In step S3, the calculation formula for the estimated rotor position of the motor is: in, is the estimated motor speed, T sc is the sampling period, and X is only related to the current differential and voltage vector.

6. The method for predictive control of a permanent magnet synchronous motor without model parameters and without position sensor according to claim 5, characterized in that: The formula for calculating X is: in, and Yes The real and imaginary parts, and They are The real and imaginary parts of .

7. The method for predictive control of a permanent magnet synchronous motor without model parameters and without position sensor according to claim 6, characterized in that: In step S4, the estimated rotor position and estimated speed are calculated as: Among them, G θ (s) is the filter transfer function, which is expressed as follows: Among them, k p and k i is the filter gain.

8. A model-free parameter and position sensor-free predictive control system for a permanent magnet synchronous motor, characterized in that: It includes a current sampling module, a calculation module, a filtering module and a closed-loop control module; The current sampling module is used to obtain stator current data at three consecutive sampling moments; The calculation module can calculate the stator current difference and the estimated rotor position of the motor based on the acquired stator current data; The filter module is used to filter the estimated rotor position of the motor; The closed-loop control module is used to implement model-free predictive closed-loop control; Among them, the current sampling module, the operation module, the filtering module and the closed-loop control module are all implemented by the predictive control method described in any one of claims 1-7.

9. An electronic device, characterized in that: The electronic device includes at least one processor; and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor can execute the predictive control method described in any one of claims 1-7.

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