Permanent magnet synchronous motor sensorless model prediction control method and system

Abstract

The invention discloses a permanent magnet synchronous motor sensorless model prediction control method and system, and relates to the technical field of motor control. The error angle approximate value of the motor rotor position is obtained through the prediction error construction of the current slope under the fundamental frequency, and is input into the phase-locked loop, and finally the estimation value of the motor rotor position and the rotation speed estimation value are obtained. Compared with a traditional vector control method based on a PI controller, the sensorless model prediction control method for the permanent magnet synchronous motor has the advantages that current response is fast, parameter setting is not needed, and the problem that traditional high-frequency voltage signal injection is poor in application effect in model prediction control is solved; rotor field directional control without a position sensor is realized, and the stability of the system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of motor control, and particularly relates to a sensorless model predictive control method and system for a permanent magnet synchronous motor. Background Art

[0002] Due to advantages such as high efficiency, high power density, long service life, and strong reliability, permanent magnet synchronous motors have been widely used in the fields of industry, household appliances, and new energy vehicles. The speed control system of a permanent magnet synchronous motor usually adopts rotor magnetic field orientation control, and it is necessary to obtain rotor position information and speed information through a position sensor such as an encoder to achieve high-quality torque or speed control. However, the installation of the position sensor not only increases the production cost and volume of the motor, but is also easily affected by environmental factors such as temperature and humidity, reducing the reliability of the system, which greatly limits the application of permanent magnet synchronous motors in special occasions. Therefore, domestic and foreign scholars have carried out extensive research on sensorless control algorithms. Currently, the mainstream sensorless control algorithms are mainly divided into model-based methods and methods based on tracking saliency, and essentially all extract rotor position information from signals generated by the internal or external inverter excitation source of the motor.

[0003] Compared with traditional proportional-integral controllers, model predictive control has advantages such as fast dynamic response, no need for complex parameter tuning, and easy implementation of multi-objective optimization in current tracking control, showing great application value in the field of industrial control. However, current research on applying sensorless control algorithms to the model predictive control of permanent magnet synchronous motors is still quite limited, especially in medium and low speed operating conditions. Since model predictive control does not require an additional modulator, the existing common sensorless control algorithms that extract rotor position by injecting high-frequency voltage signals are not only difficult to implement in model predictive control, but also tend to further deteriorate the current control performance. In addition, the signals generated by the internal excitation source of the motor at medium and low speeds are small, which is also not conducive to extraction.

[0004] Considering that model predictive control itself has an external excitation effect similar to high-frequency voltage signal injection, the rotor position information can be directly reflected in the slope of the stator current without additional voltage injection. Based on this advantage, a novel sensorless control method for obtaining rotor position and speed information through current slope prediction error is proposed, which can obtain high-precision rotor position and speed estimation effects during full-speed operation to promote the practical application of model predictive control in the field of motor control. Summary of the Invention

[0005] To solve the technical problems existing in the background art, the present invention aims to provide a sensorless model predictive control method and system for a permanent magnet synchronous motor, which obtains rotor position and speed information through the prediction error of the current slope. It avoids the defects of the existing common high-frequency voltage signal injection method in model predictive control under medium and low speed conditions, and can obtain high-precision rotor position and speed estimation effects during full-speed operation, so as to promote the practical application of the model predictive control algorithm in the field of motor control.

[0006] To solve the technical problems, the technical solution of the present invention is as follows:

[0007] A sensorless model predictive control method for a permanent magnet synchronous motor, the method comprising:

[0008] S1: Calculate the target value of the q-axis current according to the difference between the preset value of the electrical angular velocity of the motor speed and the estimated value of the electrical angular velocity of the motor speed, and set the target value of the d-axis current to zero;

[0009] S2: Collect three-phase currents, transform the obtained estimated rotor position angle to obtain the d- and q-axis stator currents of the motor at time k, and use the actual output d- and q-axis stator voltages of the inverter at time k to predict the predicted values of the d- and q-axis stator currents of the motor at time k+1 by using the motor current prediction model;

[0010] S3: Respectively predict various different voltage vectors of the two-level voltage source inverter through the motor current prediction model to obtain the predicted values of the d- and q-axis stator currents of the motor at time k+2, and calculate the corresponding cost function from the target value of the q-axis current, the target value of the d-axis current and the usage times of the zero vector within a smaller period;

[0011] S4: Based on the calculation results of step S3, select the voltage vector corresponding to the minimum cost function as the optimal d- and q-axis voltages output by the inverter at time k+1;

[0012] S5: Based on the d- and q-axis voltages output by the inverter at time k-1, predict the d- and q-axis current slopes at time k according to the current slope prediction model, and subtract the approximate values of the measured d- and q-axis current slopes at time k, and finally extract the approximate error angle of the rotor position at time k from the current slope prediction error through a construction method;

[0013] S6: Input the approximate error angle value into the phase-locked loop, obtain the estimated value of the electrical angular velocity of the speed at time k+1 through the proportional-integral controller, and then obtain the estimated value of the electrical angle of the rotor position at time k+1 by integrating the speed estimated value.

[0014] Further, the motor current prediction model is:

[0015]

[0016] wherein, u d (k), u q (k), i d (k) and i q (k) respectively represent the stator voltages and stator currents of the motor on the d-axis and q-axis at the k-th moment, i d (k + 1) and i q (k + 1) respectively represent the predicted values of the stator currents of the motor on the d-axis and q-axis at the (k + 1)-th moment, represents the estimated value of the electrical angular velocity of the motor speed at the k-th moment, T s represents the control period, L d , L q , ψ r and R s respectively represent the d-axis stator inductance parameter, q-axis stator inductance parameter, flux linkage parameter and stator resistance parameter of the motor.

[0017] Furthermore, the cost function g i is specifically:

[0018]

[0019] nh ≤ k + 2 < nh + h - 1

[0020] wherein, taking h control periods as a small period, n represents the n-th small period, n represents the number of constraints, p represents the penalty coefficient, is the target value of the q-axis current, is the target value of the d-axis current.

[0021] Furthermore, in step S5, the specific steps of extracting the approximate value of the rotor position error angle through the prediction error of the current slope are as follows:

[0022] The instantaneous slope i′ of the stator currents of the motor on the d-q axis dq is expressed as:

[0023] i′ dq = Ai dq + Bu dq + F dq

[0024] wherein,

[0025] A, B, and F respectively represent the state matrix, input matrix, and disturbance vector, describing the system dynamic characteristics and influencing factors related to the current slope; i′ dq , i dq , u dqrespectively represent the state differential vector, the state vector, and the input vector, i′ d 、i′ q respectively represent the instantaneous slopes of the d-axis and q-axis currents, and ω represents the actual electrical angular velocity of the motor;

[0026] Due to the error between the estimated rotor position and the actual rotor position, through rotation transformation, the actual values of the instantaneous slopes of the d-q axis stator currents in the observed synchronous coordinate system should be rewritten as:

[0027]

[0028] In the formula,

[0029] θ e represents the estimation error of the rotor position, is a matrix representing the input matrix in the observed synchronous coordinate system; is a vector representing the disturbance vector in the observed synchronous coordinate system; R(θ e ) is the matrix describing the rotation transformation; R -1 (θ e ) is the inverse matrix of the rotation transformation matrix; θ e is the estimation error of the rotor position;

[0030] Through discretization by the forward Euler method, the actual value of the instantaneous slope of the current at time k can be obtained as:

[0031]

[0032] Using the average rate of change to approximately obtain i′ dq (k), the specific expression is:

[0033]

[0034] Taking the position error as zero, the predicted value of the instantaneous slope of the current at time k can be expressed as:

[0035]

[0036] For the disturbance term When the zero voltage vector is adopted, at this time the actual value of the current slope only contains the part corresponding to the disturbance term, that is:

[0037]

[0038] At low speeds, the zero voltage vector is adopted more frequently in model predictive control. Replace the disturbance term with the average slope of the current measured when the zero voltage vector was used last time. At this time, the disturbance term at time k-1 Can be approximated as:

[0039]

[0040] When the rotational speed is relatively high, the disturbance term should take the predicted value when the position error is zero, which is:

[0041]

[0042] Observe the predicted slope error Δi′ dq (k) can be expressed as:

[0043]

[0044] In the formula,

[0045] ΔB dq (k - 1) is a matrix, representing the deviation matrix between the input matrix in the observed synchronous coordinate system and the input matrix in the actual synchronous coordinate system at the (k - 1)th moment;

[0046] Through the construction method, when the rotor position error is relatively small, the rotor position angle error can be approximately obtained by the following formula:

[0047]

[0048] In the formula, G(k - 1) = [u q (k - 1) u d (k - 1)].

[0049] Furthermore, in the step S6, the phase - locked loop uses a proportional - integral controller to estimate the electrical angular velocity of the motor rotor, and then obtains the electrical angular position of the rotor through the integration of the electrical angular velocity. The discrete expression of the phase - locked loop is:

[0050]

[0051] In the formula, k i and k p respectively represent the integral gain and the proportional gain of the proportional - integral controller, represents the estimated value of the electrical angular velocity of the rotational speed at the (k + 1)th moment, represents the estimated value of the electrical angular position of the rotor at the (k + 1)th moment.

[0052] A sensorless model predictive control system for a permanent magnet synchronous motor, the system is applied to any one of the above - mentioned methods, and the system includes:

[0053] Speed regulation module: Calculate the target value of the q - axis current according to the difference between the preset value of the electrical angular velocity of the motor speed and the estimated value of the electrical angular velocity of the motor speed, and set the target value of the d - axis current to zero;

[0054] Current acquisition and coordinate transformation module: It acquires three-phase currents, and according to the estimated rotor position angle, it conducts coordinate transformation to obtain the stator currents of the d-axis and q-axis of the motor at time k. Through the actual output stator voltages of the d-axis and q-axis of the motor at time k, it uses the motor current prediction model to predict the predicted values of the stator currents of the d-axis and q-axis of the motor at time k + 1;

[0055] Voltage vector prediction module: It respectively predicts various different voltage vectors of the two-level voltage source inverter through the motor current prediction model to obtain the predicted values of the stator currents of the d-axis and q-axis of the motor at time k + 2, and calculates the corresponding cost function from the target value of the q-axis current, the target value of the d-axis current, and the usage times of zero vectors within a smaller period;

[0056] Optimal voltage vector selection module: Based on the calculation result of step S3, it selects the voltage vector corresponding to the minimum cost function as the optimal d-axis and q-axis voltages output by the inverter at time k + 1;

[0057] Current slope prediction and error calculation module: Based on the d-axis and q-axis voltages output by the inverter at time k - 1, it predicts the d-axis and q-axis current slopes at time k according to the current slope prediction model, and subtracts the approximate values of the measured d-axis and q-axis current slopes at time k. Finally, it extracts the approximate error angle of the rotor position at time k from the current slope prediction error through a construction method;

[0058] Phase-locked loop and electrical angular velocity estimation module: It inputs the approximate error angle into the phase-locked loop, obtains the estimated value of the electrical angular velocity of the rotational speed at time k + 1 through a proportional-integral controller, and then obtains the estimated value of the electrical angle of the rotor position at time k + 1 through integrating the estimated value of the rotational speed.

[0059] A computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements a sensorless model predictive control method for a permanent magnet synchronous motor described in any one of the above.

[0060] A computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements a sensorless model predictive control method for a permanent magnet synchronous motor described in any one of the above.

[0061] Compared with the prior art, the advantages of the present invention are as follows:

[0062] The present invention adopts model predictive control to replace the traditional PI controller in current tracking control, and compensates for the one-beat delay generated during inverter control, avoiding the complex and cumbersome parameter tuning process of the current inner loop in the traditional rotor magnetic field orientation control of permanent magnet synchronous motors, and obtaining a faster current dynamic response performance.

[0063] The present invention utilizes the external excitation effect similar to high-frequency voltage signal injection inherent in model predictive control. By extracting the rotor position error information through the current slope prediction error, without the need for additional high-frequency voltage injection, full-speed sensorless control of the permanent magnet synchronous motor can be achieved only by constraining the usage times of zero vectors within a small period in the cost function. This not only reduces the impact on the current control effect but also requires less computational effort and is easy to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 、Structure block diagram of a sensorless model predictive control method for a permanent magnet synchronous motor according to the present invention;

[0065] Figure 2 、Specific flowchart of a sensorless model predictive control method for a permanent magnet synchronous motor according to the present invention;

[0066] Figure 3 、Position relationship diagram between the actual synchronous coordinate system and the observed synchronous coordinate system;

[0067] Figure 4 、Comparison simulation waveform diagram of the estimated rotor speed and the actual value under zero and low speeds using the control method of the present invention;

[0068] Figure 5 、Comparison simulation waveform diagram of the estimated rotor position and the actual value under zero and low speeds using the control method of the present invention;

[0069] Figure 6 、Simulation waveform diagram of the estimated rotor position error under zero and low speeds using the control method of the present invention.

[0070] Figure 7 、Comparison simulation waveform diagram of the estimated rotor speed and the actual value under medium and high speeds using the control method of the present invention;

[0071] Figure 8 、Partial comparison simulation waveform diagram of the estimated rotor position and the actual value under medium and high speeds using the control method of the present invention;

[0072] Figure 9 、Simulation waveform diagram of the estimated rotor position error under medium and high speeds using the control method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0073] The following describes the specific embodiments of the present invention in conjunction with the embodiments:

[0074] It should be noted that the structures, ratios, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the implementation conditions of the present invention. Any modification of the structure, change of the proportional relationship or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0075] Meanwhile, the terms such as "upper", "lower", "left", "right", "middle" and "one" cited in this specification are only for the convenience of clear narration and are not used to limit the scope of implementation of the present invention. The change or adjustment of their relative relationship, without substantial change in the technical content, should also be regarded as the scope of implementation of the present invention.

[0076] Embodiment 1:

[0077] As Figure 1 shown, the embodiment of the present invention provides the following technical solution. A sensorless model predictive control method for a permanent magnet synchronous motor, the flow chart is as Figure 2 shown, and specifically includes the following steps:

[0078] S1: Obtain the q-axis current reference through the speed regulator according to the difference between the given speed ω * (k) and the estimated speed ω(k), and the d-axis current reference is

[0079] S2: Collect the three-phase current i abc (k), and transform it according to the estimated rotor position angle θ(k) to obtain the d-axis current i d (k) and the q-axis current i q (k) at time k. Considering that there is a one-beat delay in the inverter output, it is necessary to predict the corresponding i d (k + 1) and i q (k + 1) at time k + 1 according to the d-axis and q-axis voltages u d (k) and u q (k) of the inverter output at time k through the motor current prediction model. The current prediction model is obtained by discretizing the motor current state equation through the forward Euler method, and its specific expression can be expressed as:

[0080]

[0081] In the formula, u d (k), u q (k), i d (k) and i q (k) respectively represent the d-axis and q-axis stator voltages and stator currents of the motor at time k, and i d ​(k + 1) and i q (k + 1) respectively represent the predicted values of the stator currents of the motor on the d and q axes at the (k + 1)th moment. represents the estimated value of the electrical angular velocity of the motor at the kth moment, T s represents the control period, L d 、L q 、ψ r and R s respectively represent the d-axis stator inductance parameter, q-axis stator inductance parameter, flux linkage parameter, and stator resistance parameter of the motor;

[0082] S3: According to the motor current prediction model, predict i d (k + 2) and i q (k + 2) for the 7 different voltage vectors of the two-level voltage source inverter respectively, and from and the predicted i d (k + 2), i q (k + 2) and the usage times of the zero vectors within a smaller period, calculate the corresponding cost function g i . The specific expression of the cost function is:

[0083]

[0084] nh ≤ k + 2 < nh + h - 1

[0085] In the formula, taking h control periods as a small period, n represents the nth small period, m represents the number of constraints, and p represents the penalty coefficient.

[0086] S4: Select the voltage vector corresponding to the minimum cost function as the optimal d and q axis voltages u d (k + 1) and u q (k + 1) output by the inverter at the (k + 1)th moment;

[0087] S5: From the d and q axis voltages u d (k - 1) and u q (k - 1) output by the inverter at the (k - 1)th moment, obtain the d and q axis current slopes at the kth moment according to the current slope prediction model and and make a difference with the approximate values i′ d (k) and i′ q (k) of the measured d and q axis current slopes at the kth moment. Finally, through a construction method, obtain the approximate error angle θ e (k) of the rotor position at the kth moment from the current slope prediction error. The specific steps to extract the approximate error angle of the rotor position from the prediction error of the current slope are as follows:

[0088] The instantaneous slope i′ of the stator currents of the motor on the d - q axisdq It can be expressed as:

[0089] i′ dq = Ai dq + Bu dq + F dq

[0090] In the formula,

[0091] The relationship between the actual synchronous coordinate system and the observed synchronous coordinate system is as Figure 3 shown, where the angle between the actual synchronous coordinate system and the axis of the q-phase winding of the motor is θ, and the angle between the observed synchronous coordinate system and the axis of the a-phase winding of the motor is Due to the error between the estimated rotor position and the actual rotor position, and the angular error θ e between the actual synchronous coordinate system and the observed synchronous coordinate system within a control period can be ignored. Therefore, through rotation transformation, the actual value of the instantaneous slope of the stator current on the d-q axis of the motor in the observed synchronous coordinate system should be rewritten as:

[0092]

[0093] In the formula:

[0094] By discretizing through the forward Euler method, the actual value of the instantaneous slope of the current at time k can be obtained as:

[0095]

[0096] Since it is difficult to measure the instantaneous slope of the d-q axis current at time k, and the control period is small, the average rate of change is used to approximate it. The specific expression is:

[0097]

[0098] Taking the position error as zero, the predicted value of the instantaneous slope of the current at time k

[0099]

[0100] For the disturbance term When the zero voltage vector is adopted, the actual value of the current slope only contains the part corresponding to the disturbance term at this time, that is

[0101]

[0102] Due to the changes in the rotational speed and the angular error θ e in the steady state can be ignored, the disturbance term There will also be no drastic changes. When the speed is low, the model predictive control uses the zero voltage vector at a relatively high frequency. Therefore, the disturbance term can be replaced by the average current slope measured when the zero voltage vector was used last time to reduce the computational burden. At this time, the disturbance term at time k-1 can be approximated as:

[0103]

[0104] When the speed is high, since the disturbance term is large, and the frequency of the zero voltage vector selected by the model predictive control method is significantly reduced compared to when the speed is low. At this time, replacing the disturbance term with the average current slope measured when the zero voltage vector was used last time will cause the prediction error of the current slope to increase when the speed fluctuates, which will in turn affect the rotor position and speed estimation. At this time, the disturbance term should take the predicted value when the position error is zero as:

[0105]

[0106] The predicted slope error Δi′ dq in the observed synchronous coordinate system at time (k) is:

[0107]

[0108] In the formula,

[0109] Through the construction method, when the rotor position error is small, the rotor position angle error can be approximately obtained by the following formula:

[0110]

[0111] In the formula, G(k-1) = [u q (k-1) u d (k-1)].

[0112] S6: Input the approximate error angle value θ e (k) into the phase-locked loop, and obtain the speed estimation value at time k+1 through the proportional-integral controller Then obtain the rotor position estimation value at time k+1 by integrating the speed estimation value The discrete expression of the phase-locked loop is:

[0113]

[0114] In the formula, k i and k p respectively represent the integral gain and proportional gain of the proportional-integral controller.

[0115] By setting appropriate integral gain and proportional gain, the phase-locked loop can continuously adjust the estimated rotational speed, so that the estimated rotor position value finally converges to the vicinity of the true value.

[0116] Figures 4 - 6 The simulation results of the control performance of a sensorless model predictive control method for a permanent magnet synchronous motor according to the present invention at zero speed and low speed are shown. In this simulation, the initial value of the given rotational speed is 200 rpm, and it steps to -200 rpm at 0.7 s and steps to 0 rpm again at 2 s. The load torque steps from the initial value of 6.9 N·m to 1.9 N·m at 2.5 s. From Figure 4 It can be seen that by using the sensorless control method of the present invention, the motor can operate normally or perform forward and reverse rotation in the zero-low speed region, and can quickly reach the given rotational speed value. The actual rotational speed value fluctuates within ±5 rpm under steady state, and both the dynamic and steady state performances are good. From Figure 5 It can be seen that by using the sensorless control method of the present invention, the obtained estimated position value can quickly track the actual value under the above working conditions. From Figure 6 It can be seen that by using the sensorless control method of the present invention, the steady-state error between the estimated position electrical angle value and the actual value is within 1 degree. Although the electrical angle estimation error of the rotor position will mutate during the dynamic process, especially during the forward and reverse rotation process, the error of the rotor position is limited at this time. The estimated rotor position can ensure the stable operation of the motor control system, and when the rotational speed reaches the given value, the estimated position value can also quickly converge to the vicinity of the actual value.

[0117] Figures 7 - 9 The simulation results of the control performance of a sensorless model predictive control method for a permanent magnet synchronous motor according to the present invention at medium speed and high speed are shown. In this simulation, the initial value of the given rotational speed is 400 rpm, and it steps to 800 rpm at 0.7 s and steps to 1200 rpm again at 2 s. The load torque steps from the initial value of 6.9 N·m to 1.9 N·m at 2.5 s. From Figure 7 It can be seen that by using the sensorless control method of the present invention, for the given medium and high speed rotational speeds, the motor can quickly reach the given rotational speed value, and the actual rotational speed value fluctuates less under steady state. Both the dynamic and steady state performances of the rotational speed control are good. From Figure 8 and Figure 9 It can be seen that by using the sensorless control method of the present invention, the obtained estimated position electrical angle value can quickly track the actual value under the above working conditions, and the steady-state error with the actual value is small. The estimated rotor position can ensure the stable operation of the motor control system.

[0118] Embodiment 2:

[0119] The present invention provides a sensorless model predictive control system for a permanent magnet synchronous motor, which can be used to implement the above-mentioned sensorless model predictive control method for a permanent magnet synchronous motor. Specifically, it includes:

[0120] Speed Regulation Module: According to the given speed ω * (k) and the estimated speed , the difference is used to obtain the q-axis current reference , and the d-axis current reference is

[0121] Current Acquisition and Transformation Module: Collect the three-phase current i abc (k), and according to the estimated rotor position angle , through coordinate transformation, the d-axis current i d (k) and q-axis current i q (k) at time k are obtained. Then, based on the actual output d and q-axis voltages u d (k) and u q (k) of the inverter at time k, the corresponding i d (k + 1) and i q (k + 1) at time k + 1 are predicted according to the motor current prediction model;

[0122] Voltage Vector Prediction Module: For the 7 different voltage vectors of the two-level voltage source inverter, i d (k + 2) and i q (k + 2) are predicted according to the motor current prediction model, and based on and the predicted i d (k + 2), i q (k + 2), and the usage times of the zero vectors within a smaller period, the corresponding cost function g i is calculated;

[0123] Optimal Voltage Vector Selection Module: Select the voltage vector corresponding to the minimum cost function as the optimal d and q-axis voltages u d (k + 1) and u q (k + 1) output by the inverter at time k + 1;

[0124] Current Slope Prediction and Error Calculation Module: Based on the d-axis and q-axis voltages u d (k - 1) and u q (k - 1) output by the inverter at time k - 1, the d-axis and q-axis current slopes at time k are obtained according to the current slope prediction model and are subtracted from the approximate values i′ d (k) and i′ q (k) of the measured d-axis and q-axis current slopes at time k. Finally, an approximate error angle θ e (k) of the rotor position at time k is extracted from the current slope prediction error through a construction method;

[0125] Phase - Locked Loop and Angular Velocity Estimation Module: The approximate error angle θ e (k) is input into the phase - locked loop, and an estimated value of the electrical angular velocity of the rotational speed at time k + 1 is obtained through a proportional - integral controller Then, an estimated value of the electrical angle of the rotor position at time k + 1 is obtained by integrating the estimated value of the rotational speed

[0126] Embodiment 3:

[0127] This embodiment provides a terminal device, which includes a processor and a memory. The memory is used to store a computer program. The computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general - purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field - Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; The processor described in the embodiments of the present invention can be used for the operation of a sensorless model predictive control method for a permanent magnet synchronous motor, including the following steps:

[0128] S1: Obtain the q-axis current reference by the speed regulator based on the difference between the given speed ω * (k) and the estimated speed The d-axis current reference is

[0129] S2: Collect the three-phase current i abc (k), and obtain the d-axis current i at time k and the q-axis current i d (k) through coordinate transformation according to the estimated rotor position angle q Then, predict the corresponding i d (k + 1) and i q (k + 1) at time k+1 according to the actual output d- and q-axis voltages u d (k) and u q (k) of the inverter at time k using the motor current prediction model;

[0130] S3: Predict i d (k + 2) and i q (k + 2) for the seven different voltage vectors of the two-level voltage source inverter respectively according to the motor current prediction model, and calculate the corresponding cost function g from d (k + 2), i q (k + 2) obtained by prediction, and the usage times of the zero vectors within a smaller period; i ;

[0131] S4: Select the voltage vector corresponding to the minimum cost function as the optimal d- and q-axis voltages u d (k + 1) and u q (k + 1) output by the inverter at time k+1;

[0132] S5: Obtain the d- and q-axis current slopes d (k) and q (k) at time k according to the current slope prediction model from the d- and q-axis voltages u and output by the inverter at time k - 1, and subtract them from the approximate values i' d (k) and i' q (k) of the measured d- and q-axis current slopes at time k. Finally, extract the approximate error angle θ e (k) of the rotor position at time k from the current slope prediction error through a construction method;

[0133] S6: The approximate error angle θ e ​(k) is input into the phase-locked loop, and the estimated electrical angular velocity of the rotational speed at the (k + 1)th moment is obtained through a proportional-integral controller Then, the estimated electrical angle of the rotor position at the (k + 1)th moment is obtained by integrating the estimated rotational speed

[0134] Embodiment 4:

[0135] This embodiment provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by a processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0136] One or more instructions stored in the computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the method for sensorless model predictive control of a permanent magnet synchronous motor in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by a processor to perform the following steps:

[0137] S1: Through a speed regulator, according to the given rotational speed ω * (k) and the estimated rotational speed The difference to obtain the q-axis current reference The d-axis current reference is

[0138] S2: Collect the three-phase current i abc (k), and through coordinate transformation according to the estimated rotor position angle To obtain the d-axis current i d (k) and the q-axis current i q (k) at the kth moment, and then through the actual output d and q-axis voltages u d (k) and u q (k) of the inverter at the kth moment, predict the corresponding i d (k + 1) and i q (k + 1) at the (k + 1)th moment according to the motor current prediction model;

[0139] S3: Predict \(i_{(k + 2)}\) and \(i_{(k + 2)}\) for 7 different voltage vectors of the two-level voltage source inverter according to the motor current prediction model, and calculate the corresponding cost function \(g\) from \(\omega\) and the predicted \(i_{(k + 2)}\), \(i_{(k + 2)}\), as well as the usage times of zero vectors in a smaller period; d \((k + 2)\) and \(i\) q (k + 2), and from \(\omega\) and the predicted \(i\) d (k + 2), \(i\) q (k + 2) and the usage times of zero vectors in a smaller period to calculate the corresponding cost function \(g\) i ;

[0140] S4: Select the voltage vector corresponding to the minimum cost function as the optimal \(d\)-axis and \(q\)-axis voltages \(u_{(k + 1)}\) and \(u_{(k + 1)}\) output by the inverter at time \(k + 1\); d \((k + 1)\) and \(u\) q (k + 1);

[0141] S5: From the \(d\)-axis and \(q\)-axis voltages \(u_{(k - 1)}\) and \(u_{(k - 1)}\) output by the inverter at time \(k - 1\), obtain the \(d\)-axis and \(q\)-axis current slopes \(\frac{di_d(k)}{dt}\) and \(\frac{di_q(k)}{dt}\) at time \(k\) according to the current slope prediction model, and subtract them from the approximate values \(i_d'(k)\) and \(i_q'(k)\) of the measured \(d\)-axis and \(q\)-axis current slopes at time \(k\). Finally, extract the approximate error angle value \(\theta(k)\) of the rotor position at time \(k\) from the current slope prediction error through a construction method; d \((k - 1)\) and \(u\) q (k - 1), obtain the \(d\)-axis and \(q\)-axis current slopes \(\frac{di_d(k)}{dt}\) and \(\frac{di_q(k)}{dt}\) at time \(k\) according to the current slope prediction model and and subtract them from the approximate values \(i_d'(k)\) and \(i_q'(k)\) of the measured \(d\)-axis and \(q\)-axis current slopes at time \(k\). Finally, extract the approximate error angle value \(\theta(k)\) of the rotor position at time \(k\) from the current slope prediction error through a construction method; d (k) and \(i'\) q (k) to obtain the approximate error angle value \(\theta(k)\) of the rotor position at time \(k\); e (k);

[0142] S6: Input the approximate error angle value \(\theta(k)\) into the phase-locked loop, obtain the estimated electrical angular velocity \(\omega_{est}(k + 1)\) of the rotational speed at time \(k + 1\) through a proportional-integral controller, and then obtain the estimated electrical angle \(\theta_{est}(k + 1)\) of the rotor position at time \(k + 1\) by integrating the estimated rotational speed value; e (k) into the phase-locked loop, obtain the estimated electrical angular velocity \(\omega_{est}(k + 1)\) of the rotational speed at time \(k + 1\) through a proportional-integral controller and then obtain the estimated electrical angle \(\theta_{est}(k + 1)\) of the rotor position at time \(k + 1\) by integrating the estimated rotational speed value

[0143] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0144] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0145] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0147] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those of ordinary skill in the art.

[0148] Many other changes and modifications can be made without departing from the concept and scope of the present invention. It should be understood that the present invention is not limited to a specific embodiment, and the scope of the present invention is defined by the appended claims.

Claims

1. A sensorless model predictive control method for a permanent magnet synchronous motor, characterized in that The method includes: S1: Calculate the target value of the q-axis current according to the difference between the preset value of the electrical angular velocity of the motor speed and the estimated value of the electrical angular velocity of the motor speed, and set the target value of the d-axis current to zero; S2: Collect the three-phase current, transform the obtained estimated rotor position angle to obtain the d-axis and q-axis stator currents of the motor at time k, and use the actually output d-axis and q-axis stator voltages of the inverter at time k. Predict the predicted values of the d-axis and q-axis stator currents of the motor at time k + 1 by using the motor current prediction model; S3: Predict the various different voltage vectors of the two-level voltage source inverter through the motor current prediction model to obtain the predicted values of the d-axis and q-axis stator currents of the motor at time k + 2, and calculate the corresponding cost function from the target value of the q-axis current, the target value of the d-axis current, and the number of times the zero vector is used within a smaller period; S4: Based on the calculation results in step S3, select the voltage vector corresponding to the minimum cost function as the optimal d-axis and q-axis voltages output by the inverter at time k + 1; S5: Based on the d-axis and q-axis voltages output by the inverter at time k - 1, predict the d-axis and q-axis current slopes at time k according to the current slope prediction model, and subtract the approximate values of the actually measured d-axis and q-axis current slopes at time k. Finally, extract the approximate error angle of the rotor position at time k from the current slope prediction error through a construction method; S6: Input the approximate error angle into the phase-locked loop, obtain the estimated value of the electrical angular velocity of the speed at time k + 1 through the proportional-integral controller, and then obtain the estimated electrical angle of the rotor position at time k + 1 by integrating the speed estimated value.

2. The sensorless model predictive control method for a permanent magnet synchronous motor according to claim 1, wherein The motor current prediction model is: where \(u\) d (k), \(u\) q (k), \(i\) d (k) and \(i\) q (k) represent the stator voltages and stator currents of the d - axis and q - axis of the motor at time k respectively, and \(i\) d (k + 1) and \(i\) q (k + 1) represent the predicted values of the stator currents of the d - axis and q - axis of the motor at time k + 1 respectively, represents the estimated value of the electrical angular velocity of the motor speed at time k, \(T\) s represents the control period, \(L\) d , \(L\) q , \(\psi\) r and \(R\) s represent the d - axis stator inductance parameter, q - axis stator inductance parameter, flux linkage parameter and stator resistance parameter of the motor respectively.

3. A sensorless model predictive control method for a permanent magnet synchronous motor according to claim 1, wherein The cost function g i Specifically: nh ≤ k + 2 < nh + h - 1 In the formula, h control cycles are taken as a small cycle, n represents the nth small cycle, n represents the number of constraint times, and p represents the penalty coefficient. is the target value of the q-axis current. is the target value of the d-axis current.

4. A sensorless model predictive control method for a permanent magnet synchronous motor according to claim 1, characterized in that, In step S5, the specific steps of extracting the approximate rotor position error angle from the prediction error of the current slope are as follows: The instantaneous slope i′ of the d-q axis stator current of the motor dq is expressed as: i′ dq = Ai dq + Bu dq + F dq In the formula, A, B, and F respectively represent the state matrix, input matrix, and disturbance vector, which describe the system dynamic characteristics and influencing factors related to the current slope; i′ dq 、i dq 、u dq represent the state differential vector, the state vector, and the input vector respectively. i′ d 、i′ q represent the instantaneous slopes of the d-axis and q-axis currents respectively. ω represents the actual electrical angular velocity of the motor; Through rotation transformation, rewrite the actual value of the instantaneous slope of the d-q axis stator current of the motor in the observed synchronous coordinate system as: In the formula, θ e represents the estimated error of the rotor position; is a matrix, representing the input matrix in the observed synchronous coordinate system; is a vector, representing the disturbance vector in the observed synchronous coordinate system; R(θ e ) is the matrix describing the rotation transformation; R -1 (θ e ) is the inverse matrix of the rotation transformation matrix; θ e is the estimated error of the rotor position; Through forward Euler discretization, the actual value of the instantaneous slope of the current at time k can be obtained as: The average rate of change is used to approximately obtain the value of i′ dq (k), and the specific expression is as follows: Take the position error as zero, and the predicted value of the instantaneous slope of the current at time k can be expressed as: For the disturbance term When the zero voltage vector is adopted, the actual value of the current slope only contains the part corresponding to the disturbance term at this time, that is: At low speeds, the model predictive control uses the zero voltage vector with a relatively high frequency. The disturbance term is replaced by the average current slope measured when the zero voltage vector was last used. At this time, the disturbance term at time k-1 can be approximated as: When the speed is relatively high, the disturbance term should take the predicted value when the position error is zero as: The predicted slope error Δi′ in the observed synchronous coordinate system dq (k) can be expressed as: In the formula, ΔB dq (k - 1) is a matrix, representing the deviation matrix between the input matrix in the observed synchronous coordinate system and the input matrix in the actual synchronous coordinate system at time k - 1; Through a construction method, when the rotor position error is relatively small, the rotor position angle error can be approximately obtained by the following formula: where G(k - 1) = [u q (k - 1) u d (k - 1)].

5. A sensorless model predictive control method for a permanent magnet synchronous motor according to claim 1, characterized in that In step S6, the phase-locked loop uses a proportional-integral controller to estimate the electrical angular velocity of the motor rotor, and then obtains the electrical angle of the rotor position by integrating the electrical angular velocity. The discrete expression of the phase-locked loop is: where k i and k p represent the integral gain and the proportional gain of the proportional-integral controller respectively, represents the estimated electrical angular velocity of the rotational speed at the (k + 1)-th moment, represents the estimated electrical angle of the rotor position at the (k + 1)-th moment.

6. A sensorless model predictive control system for a permanent magnet synchronous motor, characterized in that, The system is applied to the method described in any one of claims 1-5. The system includes: Speed adjustment module: Calculate the target value of the q-axis current according to the difference between the preset value of the electrical angular velocity of the motor speed and the estimated value of the electrical angular velocity of the motor speed, and set the target value of the d-axis current to zero; Current acquisition and coordinate transformation module: It acquires three-phase currents, and based on the estimated rotor position angle, it conducts coordinate transformation to obtain the stator currents of the d-axis and q-axis of the motor at time k. Through the actual output stator voltages of the d-axis and q-axis of the motor at time k, it uses the motor current prediction model to predict the predicted values of the stator currents of the d-axis and q-axis of the motor at time k + 1; Voltage vector prediction module: It respectively predicts various different voltage vectors of the two-level voltage source inverter through the motor current prediction model to obtain the predicted values of the stator currents of the d-axis and q-axis of the motor at time k + 2, and calculates the corresponding cost function from the target value of the q-axis current, the target value of the d-axis current, and the usage times of zero vectors within a smaller period; Optimal voltage vector selection module: Based on the calculation result of step S3, it selects the voltage vector corresponding to the minimum cost function as the optimal d-axis and q-axis voltages output by the inverter at time k + 1; Current slope prediction and error calculation module: Based on the d-axis and q-axis voltages output by the inverter at time k - 1, it predicts the d-axis and q-axis current slopes at time k according to the current slope prediction model, and subtracts the approximate values of the measured d-axis and q-axis current slopes at time k. Finally, it extracts the approximate error angle of the rotor position at time k from the current slope prediction error through a construction method; Phase-locked loop and electrical angular velocity estimation module: It inputs the approximate error angle into the phase-locked loop, obtains the estimated value of the electrical angular velocity of the rotational speed at time k + 1 through a proportional-integral controller, and then obtains the estimated value of the electrical angle of the rotor position at time k + 1 through integrating the estimated value of the rotational speed.

7. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements a sensorless model predictive control method for a permanent magnet synchronous motor according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the program is executed by the processor, it implements a sensorless model predictive control method for a permanent magnet synchronous motor according to any one of claims 1 to 5.

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