Model prediction-based asynchronous motor weak magnetic speed regulation method and device and server

Abstract

This invention provides a model-based prediction-based method, device, and server for field weakening speed control of an asynchronous motor, relating to the technical field of motor control. The method includes: performing coordinate transformation and voltage reconstruction processing based on operating state information to obtain the actual stator voltage value for the current control cycle; predicting the stator voltage for multiple consecutive control cycles based on the operating state information and a preset rotor flux direct-axis component prediction model to obtain predicted stator voltage values; using prediction errors to perform feedback correction processing on the predicted stator voltage values ​​to obtain corrected target stator voltage predicted values; calculating the optimal current increment based on the target stator voltage predicted values; updating the direct-axis current reference value for the current control cycle based on the optimal current increment; and outputting the updated direct-axis current reference value to complete the field weakening speed control for the current control cycle. This invention can significantly improve the parameter robustness of field weakening control.

Description

Technical Field

[0001] This invention relates to the technical field of motor control, and in particular to a method, apparatus and server for field weakening speed regulation of asynchronous motors based on model prediction. Background Technology

[0002] Currently, asynchronous motors are widely used in new energy vehicles and rail transit traction fields due to their advantages such as robust and reliable structure, low cost, and wide speed range. When the motor operates above its rated speed, it is limited by the DC bus voltage, and the stator voltage cannot continue to increase. Field weakening control is required to improve the torque output capability in the high-speed range. Related technologies suggest that existing asynchronous motor field weakening control is mainly divided into two categories: one is based on motor parameters and mathematical models to calculate the d-axis current reference value. However, when the motor operates in the field weakening region, the rotor resistance and mutual inductance change with temperature and magnetic saturation, and parameter mismatch can easily lead to a decrease in control effect or even system instability. The other category uses voltage closed-loop control, which adjusts the magnetic field current in real time through a PI regulator. However, PI parameters require extensive experimental tuning, and overshoot and response lag are prone to occur when operating conditions change. Moreover, PI regulation is a lag control, and the adjustment speed is slow when the load changes abruptly, resulting in speed fluctuations. Although model predictive control has been gradually applied in the field of power electronics, existing solutions have not been adapted to the high dynamic response and strong robustness requirements of field weakening speed regulation, and it is also difficult to balance control performance and real-time requirements on a general-purpose DSP platform. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a model prediction-based asynchronous motor field weakening speed control method, device and server, which can significantly improve the parameter robustness of field weakening control and solve the problem of system instability caused by parameter changes and PI tuning in traditional methods.

[0004] In a first aspect, embodiments of the present invention provide a model-based prediction method for field weakening speed regulation of an asynchronous motor. The method includes: acquiring the operating state information of the asynchronous motor, and performing coordinate transformation and voltage reconstruction processing based on the operating state information to obtain the actual value of the stator voltage in the current control cycle. The operating state information includes: stator three-phase current, DC bus voltage, and pulse width modulation three-phase duty cycle. Based on the operating state information and a preset rotor flux direct-axis component prediction model, the stator voltage for multiple consecutive control cycles in the future is predicted to obtain a predicted stator voltage value. The prediction error is calculated based on the actual stator voltage value and the predicted stator voltage value to perform feedback correction processing on the predicted stator voltage value, thereby obtaining a corrected target stator voltage prediction value. The optimal current increment is calculated based on the target stator voltage prediction value, and the direct-axis current reference value for the current control cycle is updated based on the optimal current increment to output the updated direct-axis current reference value, thus completing the field weakening speed regulation control for the current control cycle.

[0005] In one embodiment, the steps of acquiring the operating status information of the asynchronous motor and performing coordinate transformation and voltage reconstruction processing based on the operating status information to obtain the actual value of the stator voltage in the current control cycle include: acquiring the pulse width modulation three-phase duty cycle and the DC bus voltage of the asynchronous motor in the current control cycle, and performing voltage reconstruction processing based on the DC bus voltage and the pulse width modulation three-phase duty cycle to obtain the actual value of the stator voltage in the three-phase stationary coordinate system; sequentially performing Clark transformation and Park transformation on the actual value of the stator voltage in the three-phase stationary coordinate system to obtain the direct-axis voltage component and quadrature-axis voltage component in the synchronous rotating coordinate system, and synthesizing the actual value of the stator voltage in the current control cycle based on the direct-axis voltage component and the quadrature-axis voltage component.

[0006] In one embodiment, before the step of predicting the stator voltage for multiple consecutive control cycles based on the operating status information and a preset rotor flux direct-axis component prediction model to obtain the stator voltage prediction value, the method includes: acquiring the stator three-phase current of the asynchronous motor, and sequentially performing Clark transformation and Park transformation on the stator three-phase current to obtain the stator current direct-axis component and stator current quadrature-axis component in the synchronous rotating coordinate system.

[0007] In one embodiment, the step of predicting the stator voltage for multiple consecutive control cycles based on operating status information and a preset rotor flux direct-axis component prediction model to obtain the stator voltage prediction value includes: sending the stator current direct-axis component and the rotor flux direct-axis component of the previous control cycle to the rotor flux direct-axis component prediction model for iterative calculation to obtain the predicted value of the rotor flux direct-axis component of the current control cycle; and calculating the stator voltage prediction value of the current control cycle based on the predicted value of the rotor flux direct-axis component of the current control cycle, the stator current direct-axis component, the stator current quadrature-axis component, and the synchronous angular velocity of the current control cycle.

[0008] In one embodiment, the step of calculating the prediction error based on the actual stator voltage value and the predicted stator voltage value, and using the prediction error to perform feedback correction processing on the predicted stator voltage value to obtain the corrected target stator voltage prediction value, includes: calculating the difference between the actual stator voltage value and the predicted stator voltage value of the current control cycle to obtain the prediction error of the current cycle; multiplying the prediction error of the current cycle with a preset feedback correction coefficient matrix to determine the error correction value; and superimposing the error correction value with the predicted stator voltage value to obtain the corrected target stator voltage prediction value.

[0009] In one implementation, the step of calculating the optimal current increment based on the target stator voltage prediction value includes: constructing an optimization objective function based on the target stator voltage prediction value, and performing extremum solving on the optimization objective function according to the transfer matrix between the direct-axis component of the stator current and the direct-axis component of the rotor flux linkage to obtain the optimal current increment.

[0010] In one implementation, the step of updating the direct-axis current reference value of the current control cycle according to the optimal current increment, and outputting the updated direct-axis current reference value to complete the field weakening speed control of the current control cycle includes: superimposing the direct-axis current reference value of the current control cycle with the optimal current increment to obtain the direct-axis current reference value of the next control cycle; and outputting the direct-axis current reference value of the next control cycle as a direct-axis current command to the current loop, so that the current loop generates a corresponding voltage control signal according to the direct-axis current command.

[0011] Secondly, embodiments of the present invention also provide a model-based prediction-based asynchronous motor field weakening speed control device. The device includes: an information acquisition module, which acquires the operating status information of the asynchronous motor, and performs coordinate transformation and voltage reconstruction processing based on the operating status information to obtain the actual value of the stator voltage in the current control cycle. The operating status information includes: stator three-phase current, DC bus voltage, and pulse width modulation three-phase duty cycle; a prediction correction module, which performs prediction processing on the stator voltage for multiple consecutive control cycles in the future based on the operating status information and a preset rotor flux direct-axis component prediction model to obtain the stator voltage prediction value, and calculates the prediction error based on the actual stator voltage value and the predicted stator voltage value to perform feedback correction processing on the predicted stator voltage value using the prediction error to obtain the corrected target stator voltage prediction value; and an optimization update module, which calculates the optimal current increment based on the target stator voltage prediction value, and updates the direct-axis current reference value in the current control cycle based on the optimal current increment to output the updated direct-axis current reference value, thereby completing the field weakening speed control in the current control cycle.

[0012] Thirdly, embodiments of the present invention also provide a server, including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement any of the methods provided in the first aspect.

[0013] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement any of the methods provided in the first aspect.

[0014] The embodiments of the present invention bring the following beneficial effects: This invention provides a model-based prediction-based method, device, and server for field weakening speed control of an asynchronous motor. The method first acquires the operating status information of the asynchronous motor, and then performs coordinate transformation and voltage reconstruction based on the operating status information to obtain the actual stator voltage value for the current control cycle. Next, based on the operating status information and a preset rotor flux direct-axis component prediction model, it predicts the stator voltage for multiple consecutive control cycles to obtain predicted stator voltage values. The prediction error is calculated based on the actual and predicted stator voltage values, and the prediction error is used to perform feedback correction on the predicted stator voltage value to obtain a corrected target stator voltage prediction value. Finally, the optimal current increment is calculated based on the target stator voltage prediction value, and the direct-axis current reference value for the current control cycle is updated based on the optimal current increment. The updated direct-axis current reference value is then output to complete the field weakening speed control for the current control cycle. This invention significantly improves the parameter robustness of field weakening control and solves the problem of system instability caused by parameter changes and PI tuning in traditional methods.

[0015] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A schematic flowchart of a model prediction-based asynchronous motor field weakening speed regulation method provided in an embodiment of the present invention; Figure 2 A schematic diagram of a model predictive control algorithm provided in an embodiment of the present invention; Figure 3 A schematic diagram illustrating the specific process of a model-predictive asynchronous motor field weakening speed regulation method provided in an embodiment of the present invention; Figure 4 A schematic diagram of a motor response curve provided in an embodiment of the present invention; Figure 5A schematic diagram of a d-axis current component provided in an embodiment of the present invention; Figure 6 A schematic diagram illustrating the field weakening principle of an asynchronous motor based on model predictive control, provided for an embodiment of the present invention; Figure 7 A schematic diagram of the structure of an asynchronous motor field weakening speed regulation device based on model prediction provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Currently, asynchronous motors have become the mainstream choice for industrial applications such as high-speed machine tool spindles, new energy vehicle drives, and rail transit traction due to their comprehensive advantages, including robust and reliable structure, no risk of permanent magnet demagnetization, low cost, strong overload capacity, wide field weakening speed regulation range, and high high-speed operation efficiency. This places higher demands on the torque output capability and dynamic response performance of the motor in the high-speed operating range. When the motor operates above the rated speed, the stator voltage amplitude cannot continue to increase due to the physical constraints of DC bus voltage, motor rated voltage, and inverter capacity. Field weakening control is required to maximize the torque and power output capability in the high-speed operating range under voltage and current constraints, ensuring that the system has good dynamic and static characteristics.

[0021] Currently, the mainstream asynchronous motor field weakening control technology is mainly divided into two categories: Parameter-dependent field weakening method: Based on motor parameters and a mathematical model, the reference value of the stator d-axis current is calculated to achieve optimal field weakening control. This method requires accurate motor parameters, including rotor resistance, mutual inductance, stator / rotor inductance, etc., and the control effect is directly affected by the accuracy of these parameters.

[0022] Voltage-closed-loop field weakening method: This method utilizes the d-axis and q-axis voltages in a synchronous rotating coordinate system to construct a voltage regulator, which adjusts the magnetic field current and torque current components in real time; or it uses the zero-vector action time in the SVPWM modulation algorithm as the field weakening trigger condition, automatically reducing the d-axis current reference value when the command voltage exceeds the output limit. This type of method does not rely on motor parameters, achieving field weakening optimization through automatic controller adjustment. Some improved schemes also introduce load torque compensation to improve the operating efficiency in the field weakening region.

[0023] Model predictive control (MPC) is an advanced control algorithm based on a model of the controlled system. Its core consists of three parts: a predictive model, rolling optimization, and feedback correction. MPC predicts future states based on the system's dynamic model and current state. Within each control cycle, it solves for the optimal control value based on a performance index function. Finally, feedback correction corrects model errors, achieving rapid and stable control of the system. In recent years, with the development of high-performance digital processors, MPC has been gradually applied in power electronics fields such as PWM rectifiers and high-performance motor control.

[0024] The existing solution has the following problems: Insufficient parameter robustness: Parameter-dependent field weakening methods require extremely high accuracy of motor parameters. However, when the motor operates in the field weakening region, parameters such as rotor resistance and mutual inductance will change significantly with temperature and magnetic saturation, which can easily lead to a decrease in control performance or even system instability.

[0025] PI parameter dependency problem: The control performance of the voltage closed-loop field weakening method is highly dependent on the PI regulator parameters. However, the PI parameters need to be tuned through a large number of experiments in combination with the motor parameters and system characteristics. The parameters have poor adaptability and are prone to problems such as overshoot and response lag when the system operating conditions change.

[0026] Limited disturbance suppression capability: Traditional PI control is a lag regulation. When the system experiences large disturbances such as sudden load changes or voltage fluctuations, the regulation speed is slow, which can easily lead to speed fluctuations and torque pulsations, making it difficult to meet the dynamic performance requirements of high-performance transmission systems.

[0027] Based on this, the asynchronous motor field weakening speed regulation method, device and server based on model prediction provided by the present invention can significantly improve the parameter robustness of field weakening control and solve the problem of system instability caused by parameter changes and PI tuning in traditional methods.

[0028] See Figure 1 The diagram shows a flow chart of a model-predictive field weakening speed control method for an asynchronous motor. This method mainly includes the following steps S102 to S106: Step S102: Obtain the operating status information of the asynchronous motor, and perform coordinate transformation and voltage reconstruction processing based on the operating status information to obtain the actual value of the stator voltage in the current control cycle. The operating status information includes: stator three-phase current, DC bus voltage and pulse width modulation three-phase duty cycle.

[0029] In one implementation, the stator three-phase current is the three-phase current signal on the stator winding of the asynchronous motor, acquired by a Hall current sensor, and sent to the controller after analog-to-digital conversion. This current is used for both rotor flux observation and calculation, as well as current closed-loop control. The DC bus voltage is the voltage value on the DC side of the inverter, acquired by a voltage sensor. This voltage is used for voltage reconstruction calculation and determines the maximum voltage amplitude that the inverter can output, serving as the physical boundary of voltage constraints in field weakening control. The pulse width modulation three-phase duty cycle is the duty cycle command of the pulse width modulation signal output by the controller, i.e., the PWM three-phase duty cycle Ta, Tb, Tc. This duty cycle command reflects the on-time ratio of the switching transistors of each phase bridge arm of the inverter within the current control cycle.

[0030] Voltage reconstruction processing refers to using the mathematical relationship between the DC bus voltage and the three-phase duty cycle of pulse width modulation to deduce the actual three-phase output voltage value of the inverter, without the need for additional voltage sensors. Coordinate transformation processing involves converting the voltage and current signals in the three-phase stationary coordinate system to the synchronous rotating coordinate system through Clarke transform and Park transform, facilitating the conversion of sinusoidal AC quantities into DC quantities for control. Clarke transform is a mathematical transformation from the three-phase stationary coordinate system to a two-phase stationary coordinate system, and Park transform is a mathematical transformation from the two-phase stationary coordinate system to the synchronous rotating coordinate system. The combined use of these two transformations simplifies subsequent prediction calculations in the rotating coordinate system. Voltage reconstruction yields the actual stator voltage value in the three-phase stationary coordinate system. Coordinate transformation then yields the direct-axis and quadrature-axis voltage components in the synchronous rotating coordinate system. Finally, the direct-axis and quadrature-axis voltage components are synthesized to obtain the stator voltage amplitude, which is the actual stator voltage value for the current control cycle.

[0031] Step S104: Based on the operating status information and the preset rotor flux direct-axis component prediction model, the stator voltage for multiple consecutive control cycles in the future is predicted to obtain the stator voltage prediction value. The prediction error is calculated based on the actual stator voltage value and the predicted stator voltage value, and the prediction error is used to perform feedback correction processing on the stator voltage prediction value to obtain the corrected target stator voltage prediction value.

[0032] In one implementation, the stator voltage for multiple consecutive control cycles is predicted based on the operating status information and a preset rotor flux direct-axis component prediction model to obtain the predicted stator voltage value. The rotor flux direct-axis component prediction model uses the rotor flux direct-axis component as the control system state variable. It establishes a state recursive relationship through the voltage equation and flux equation of the asynchronous motor in the synchronous rotating coordinate system. The input is the stator current direct-axis component and the rotor flux direct-axis component of the previous cycle, and the output is the predicted value of the rotor flux direct-axis component of the current cycle.

[0033] The prediction process for a single cycle is iterated multiple times. Each iteration uses the calculation result of the previous cycle as the known quantity for the current cycle, thereby obtaining the stator voltage change trend for multiple future cycles, which is then used for subsequent rolling optimization. The specific calculation method for the stator voltage prediction value is as follows: First, based on the predicted value of the rotor flux direct-axis component, the stator current direct-axis component, and the stator current quadrature-axis component for the current cycle, the direct-axis stator flux component and the quadrature-axis stator flux component are calculated. Then, the direct-axis stator voltage component and the quadrature-axis stator voltage component are calculated. The stator voltage amplitude is synthesized from the two voltage components as the stator voltage prediction value for that cycle.

[0034] The prediction error is calculated based on the actual and predicted stator voltage values. This error is then used to perform feedback correction on the predicted stator voltage, resulting in a corrected target stator voltage prediction. The prediction error is the difference between the actual and predicted stator voltage values, reflecting the combined effects of model parameter mismatch and measurement noise on prediction accuracy. Feedback correction involves multiplying the prediction error of the current period by a preset feedback correction coefficient matrix and then adding this matrix to the predicted stator voltage values ​​for future periods to correct the impact of model bias on future predictions. The corrected target stator voltage prediction is the stator voltage prediction value corrected for the prediction error. This value is closer to the actual voltage change trend than the original prediction and serves as input for subsequent optimization steps.

[0035] Step S106: Calculate the optimal current increment based on the target stator voltage prediction value, and update the direct-axis current reference value of the current control cycle according to the optimal current increment, so as to output the updated direct-axis current reference value and complete the field weakening speed regulation control of the current control cycle.

[0036] In one implementation, the optimal current increment is calculated based on the target stator voltage prediction value. The target stator voltage prediction value is a stator voltage prediction sequence after feedback correction, which reflects the stator voltage change trend over multiple future control cycles. The optimal current increment is the optimal current increment obtained after optimization and limiting. The optimal current increment is calculated as follows: An optimization objective function is constructed using the target stator voltage prediction value tracking a preset voltage reference value as the optimization objective, where the voltage reference value is a voltage upper limit value given based on the DC bus voltage; the extremum of the optimization objective function is solved by combining the transfer matrix between the direct-axis components of the stator current and the rotor flux linkage, resulting in the direct-axis current increment that minimizes the optimization objective function; and the direct-axis current increment is then limited to obtain the optimal current increment. The limiting process is used to prevent excessive single adjustment from causing system oscillation and to ensure the stability of the field weakening regulation process.

[0037] The direct-axis current reference value for the current control cycle is updated based on the optimal current increment, and the updated direct-axis current reference value is output to complete the field weakening speed control for the current control cycle. The direct-axis current reference value is the core adjustment variable of field weakening control. By adjusting the magnitude of the direct-axis current reference value, the strength of the rotor flux linkage can be changed, thereby adjusting the stator voltage amplitude to achieve stable operation at higher speeds within the bus voltage constraint range. This update process involves superimposing the direct-axis current reference value of the current control cycle with the optimal current increment to obtain the direct-axis current reference value for the next control cycle. This superposition process is repeated in each control cycle to achieve rolling updates of the direct-axis current reference value, ensuring continuous and smooth field weakening regulation. The updated direct-axis current reference value is output to the current loop. The current loop generates corresponding voltage control signals based on the direct-axis current command and the quadrature-axis current command. These voltage control signals are pulse-width modulated and drive the inverter to control the asynchronous motor, completing the field weakening speed control for the current control cycle. The above process is executed cyclically in each control cycle, enabling the asynchronous motor to operate continuously and stably in the high-speed field weakening region.

[0038] The model prediction-based asynchronous motor field weakening speed control method provided in this embodiment of the invention can overcome the dependence of traditional field weakening control on motor parameters and PI parameters, improve the parameter robustness of the field weakening control system, reduce the impact of parameter changes on control performance, improve the dynamic response speed of field weakening control, enhance the system's anti-disturbance capability, and ensure the stability of operation in the field weakening region. In addition, it can simplify the engineering implementation difficulty of the field weakening controller, implement a lightweight model predictive control algorithm on a general DSP platform, and take into account both control performance and real-time requirements.

[0039] See Figure 2 The diagram shows a model predictive control algorithm. This invention provides a model predictive control algorithm, including: a prediction module, a feedback correction module, and a rolling optimization module. The signal flow direction is shown, and the input is a reference value. The output is a control quantity. Acting on the controlled system, the controlled system outputs By feeding back to the prediction model and feedback correction stage, this invention introduces model predictive control into the field weakening control stage of an asynchronous motor. By constructing a simplified field weakening prediction model for the asynchronous motor and combining rolling optimization and feedback correction mechanisms, the optimal d-axis current reference value is solved in real time, achieving optimal field weakening control under voltage constraints. Prediction model construction: Based on the mathematical model of the asynchronous motor with rotor field orientation, the rotor flux linkage state equation is discretized by the first-order Euler method. Combining the time scale characteristics of field weakening control, the prediction rules of d-axis current, q-axis current and speed are simplified, and a stator voltage prediction model is constructed to realize multi-step prediction of stator voltage based on d-axis current increment.

[0040] Feedback correction: The stator voltage feedback value obtained by voltage reconstruction is used to correct the prediction model error, reduce the impact of model mismatch and measurement interference on prediction accuracy, and improve control robustness.

[0041] Rolling optimization: Taking the stator voltage tracking the reference voltage as the optimization objective, a quadratic objective function is constructed, and the optimal d-axis current increment is obtained by solving it. The d-axis current reference value is updated in each prediction cycle to achieve rolling optimal control.

[0042] During the field weakening speed regulation process of an asynchronous motor, the rotor flux linkage can be changed by adjusting the d-axis current, and the stator flux linkage changes synchronously, thereby reducing the stator terminal voltage and enabling the motor operating point to break through the rated speed limit, achieving stable operation at higher speeds under voltage constraints.

[0043] See Figure 3 The diagram shows a specific process flow of a model-based asynchronous motor field weakening speed regulation method. This embodiment of the invention also provides a model-based prediction algorithm. First, for the asynchronous motor field weakening speed regulation method, the three-phase current and DC bus voltage of the motor are collected, and the real-time value of the stator voltage is obtained through voltage reconstruction technology. Second, the stator voltage for multiple future cycles is predicted using a rotor flux discretization prediction model. Then, the stator voltage prediction error is corrected through a feedback correction loop to improve model accuracy. Next, an optimization objective function is constructed with stator voltage tracking reference voltage and control increment smoothing as optimization objectives, and the optimal d-axis current increment is solved. Finally, the d-axis current reference value is updated and output to the current loop, completing the rolling optimization of one control cycle. The above process is repeated in each control cycle to achieve fast and robust control of the asynchronous motor field weakening zone. See (1) to (7) for details: (1) The motor controller collects the stator three-phase current. , , DC bus voltage .

[0044] (2) Utilizing the three-phase duty cycle of PWM (i.e., pulse width modulation three-phase duty cycle) , , The actual value of the stator voltage in the three-phase stationary coordinate system is indirectly reconstructed. , In one implementation, the pulse width modulation (PWM) three-phase duty cycle and the DC bus voltage of the asynchronous motor in the current control cycle can be obtained. Voltage reconstruction processing is then performed based on the DC bus voltage and the PWM three-phase duty cycle to obtain the actual stator voltage value in the three-phase stationary coordinate system. Subsequently, the actual stator voltage value in the three-phase stationary coordinate system is subjected to Clarke transform and Park transform sequentially to obtain the direct-axis voltage component and quadrature-axis voltage component in the synchronous rotating coordinate system. Based on the direct-axis voltage component and the quadrature-axis voltage component, the actual stator voltage value for the current control cycle is synthesized.

[0045]

[0046] (3) , Obtained through Clarke transform Axis voltage components , Axis voltage components The d-axis voltage component is then obtained through Parker transformation. (i.e., direct-axis voltage component), q-axis voltage component (i.e., quadrature-axis voltage components), and synthesize the stator voltage. .

[0047] ,

[0048] ,

[0049]

[0050] in, For synchronous electrical angle, , For extreme logarithms, For rotor mechanical angle, It represents the slip electrical angular velocity.

[0051] (4) Using the d-axis component of the rotor flux linkage as the state variable of the control system, predict the future. In one implementation, the stator voltage for each cycle can be obtained by acquiring the three-phase stator current of the asynchronous motor and sequentially performing Clarke transform and Park transform on the three-phase stator current to obtain the direct-axis component and quadrature-axis component of the stator current in the synchronous rotating coordinate system. Then, the direct-axis component of the stator current and the direct-axis component of the rotor flux linkage from the previous control cycle are sent to the rotor flux linkage direct-axis component prediction model for iterative calculation to obtain the predicted value of the direct-axis component of the rotor flux linkage for the current control cycle. Finally, based on the predicted value of the direct-axis component of the rotor flux linkage, the direct-axis component of the stator current, the quadrature-axis component of the stator current, and the synchronous angular velocity of the current control cycle, the predicted value of the stator voltage for the current control cycle is calculated.

[0052] (4-1) Rotor flux orientation control is adopted. , The d-axis component of the rotor flux linkage in the current cycle is calculated by combining the voltage equation and flux linkage equation of the asynchronous motor in the synchronous rotating coordinate system. (That is, the direct-axis component of the rotor flux linkage), see the following rotor flux linkage direct-axis component prediction model:

[0053] in, For rotor flux linkage, For the d-axis component of the rotor flux linkage, For the q-axis component of the rotor flux linkage, This is the current control cycle number. This represents the d-axis component of the rotor flux linkage from the previous cycle. This refers to the iteration cycle of the model prediction algorithm. The rotor time constant, For mutual inductance between stator and rotor, This refers to the d-axis component of the stator current in the current cycle (i.e., the direct-axis component of the stator current). From stator current , The coordinates are obtained through step (3).

[0054] (4-2) Use the stator d-axis current and speed information to predict the stator voltage.

[0055] First, calculate the stator flux linkage components along the d-axis and q-axis. , : ,

[0056] Secondly, calculate the d-axis and q-axis stator voltage components. , : ,

[0057] Finally, the predicted stator voltage value is calculated. :

[0058] in, For rotor inductance, For stator inductance, Leakage inductance coefficient, For stator resistance, The current cycle synchronous angular velocity, This refers to the q-axis current component of the current period (i.e., the quadrature-axis component of the stator current). From stator current , The coordinates are obtained through step (3).

[0059] (4-3) Assume the prediction step size is Repeat execution Repeat steps (4-1) and (4-2) above.

[0060] (5) Using the reconstructed stator voltage as the measured value, a prediction error correction link is constructed to correct the predicted stator voltage value. In one embodiment, the difference between the actual stator voltage value and the predicted stator voltage value of the current control cycle can be calculated to obtain the prediction error of the current cycle. Then, the prediction error of the current cycle is multiplied by a preset feedback correction coefficient matrix to determine the error correction value. The error correction value is then superimposed with the predicted stator voltage value to obtain the corrected target stator voltage prediction value.

[0061]

[0062] in, This represents the prediction error for the current cycle. For the current cycle number One stator voltage prediction value, For the current cycle number The corrected stator voltage prediction value This is the feedback correction coefficient matrix.

[0063] (6) Taking the stator voltage tracking the reference voltage and the smoothing of the control increment as the optimization objectives, the optimal current increment for real-time control is obtained. And limit the amplitude [0, In one implementation, an optimization objective function can be constructed based on the predicted target stator voltage value. The optimal current increment is obtained by solving the extremum of the objective function using the transfer matrix between the direct-axis components of the stator current and the rotor flux linkage.

[0064] in, The voltage reference value is given according to the DC bus voltage. To predict the step size is The step shown in step (4-1) and The transfer matrix between them To measure output voltage Tracking reference voltage The error weight coefficient matrix, To measure control increment The control coefficient matrix for smoothness.

[0065] (7) The optimal d-axis current increment obtained by the solution is superimposed with the current d-axis current reference value to obtain the d-axis current reference value (i.e., the direct-axis current reference value) for the next cycle. In one embodiment, the direct-axis current reference value of the current control cycle can be superimposed with the optimal current increment to obtain the direct-axis current reference value for the next control cycle, and the direct-axis current reference value of the next control cycle can be output as a direct-axis current command to the current loop so that the current loop generates the corresponding voltage control signal according to the direct-axis current command:

[0066] Meanwhile, to reduce the computational load of the model prediction algorithm and meet real-time control requirements, the present invention adopts the following simplified design: Assumption The q-axis current remains constant within each prediction cycle, while the increments of the d-axis current and rotational speed remain consistent with the previous prediction cycle, significantly simplifying the computational complexity of the prediction model.

[0067] The matrix operations for optimization are expanded and pre-programmed to reduce online computation time.

[0068] In one implementation, see Figure 4 The diagram shown is a schematic of a motor response curve and Figure 5 The diagram shown represents a d-axis current component. Figure 4 and Figure 5 The figures show the motor acceleration response curve and the d-axis current component variation curve, respectively. It can be seen that under the model-predictive field weakening control strategy, the motor acceleration response curve is smooth and converges quickly, achieving stable operation over a wide speed range. Simultaneously, by adjusting the d-axis excitation current in real time through rolling optimization, the high-speed stable operation boundary of the motor is significantly improved, and the dynamic disturbance rejection performance in the field weakening range is enhanced.

[0069] The asynchronous motor field weakening control method based on model predictive control proposed in this invention has the following significant advantages compared with traditional field weakening control technology: Excellent parameter robustness: The model parameter error is automatically corrected through the feedback correction process, resulting in small fluctuations in the weak magnetic region and solving the problem of high-speed instability in traditional methods.

[0070] Smooth speed regulation over a wide range: There is no shock when switching from the base speed to the weak magnetic field zone, and the dynamic response is consistent across the entire speed range, avoiding overshoot and fluctuation problems during switching using traditional methods.

[0071] Flexible performance adaptability: By adjusting the values ​​of the error weighting coefficient matrix Q and the control weighting coefficient matrix R, a trade-off can be made between dynamic response speed and system stability.

[0072] In practical applications, see Figure 6 The diagram shows a field weakening principle for an asynchronous motor based on model predictive control. The main controller is a TI TMS320F28035 with a 60MHz operating frequency, responsible for controlling algorithm execution, signal sampling, and PWM generation. The power section uses a three-phase IGBT inverter with a bus voltage of 80V and an IGBT switching frequency of 5kHz. The signal detection section uses a Hall current sensor to detect the three-phase stator current, a 256-line incremental quadrature encoder to detect the motor rotor position for field orientation and speed calculation, and a voltage sensor to detect the DC bus voltage.

[0073] Software algorithm implementation. The control software is implemented in a DSP, and the specific execution flow is as follows: 1. System initialization: After power-on, complete the initialization of DSP peripherals, motor parameters, control variables, PI controller parameters and MPC controller parameters, and set the sampling interrupt period to 200μs (corresponding to a 5kHz sampling frequency).

[0074] 2. Main Control Program: Samples three-phase stator current, obtains d-axis and q-axis current feedback values ​​through Clarke transform and Parker transform; reads encoder pulses, calculates the actual motor speed and rotor position angle; reconstructs stator voltage feedback value based on DC bus voltage and current PWM duty cycle; executes speed loop control with a 1ms cycle, using PI control, and outputs q-axis current reference value; executes field weakening MPC control with a 1ms cycle, updates d-axis current reference value; limits the q-axis current reference value output by the speed loop; executes current loop control, using PI control for d- and q-axis current controllers, outputs d- and q-axis voltage commands; executes SVPWM modulation, generates six IGBT drive signals, and updates the PWM compare register value.

[0075] Experimental verification steps: S-acceleration experiment: Given a torque, record the speed, stator voltage, and d-axis current response curves to verify the dynamic response performance.

[0076] Parameter robustness test: The inductance and mutual inductance parameters of the motor stator and rotor are set to deviate from the actual values, and the no-load acceleration test is repeated to compare the control effect.

[0077] Parameter adjustment experiment: Modify the values ​​of weight coefficient matrices Q and R respectively, test the control performance under different parameters, and select the optimal parameter combination according to the application scenario.

[0078] Through the above implementation steps, the asynchronous motor field weakening control of the present invention can be realized, and the dynamic performance and robustness are superior to those of traditional PI control.

[0079] In summary, this invention can eliminate the influence of parameter errors through a feedback correction mechanism. The prediction error is calculated using the difference between the actual and predicted stator voltage values, and the predicted value is corrected using a feedback correction coefficient matrix. When motor parameters change due to temperature or magnetic saturation, this correction mechanism can automatically compensate for prediction deviations caused by model mismatch, making the control effect independent of parameter accuracy and solving the problem of instability inherent in traditional parameter-dependent methods.

[0080] Furthermore, this invention can replace the PI control structure with a model prediction framework. By replacing the PI regulator in the traditional voltage closed-loop method with a prediction model and rolling optimization, there is no need to tune the PI parameters, thus avoiding overshoot and hysteresis problems caused by poor parameter adaptability.

[0081] This invention can also improve dynamic response capability through the look-ahead mechanism of predictive control. By solving for the optimal current increment online, the control action is performed before the voltage over-limit occurs. Unlike the lag regulation of PI control, this method can complete the adjustment before the disturbance affects the output voltage, effectively suppressing speed fluctuations.

[0082] Regarding the model-prediction-based asynchronous motor field weakening speed control method provided in the foregoing embodiments, this invention provides a model-prediction-based asynchronous motor field weakening speed control device, see [link to relevant documentation]. Figure 7 The diagram shows a model-predictive field weakening speed control device for an asynchronous motor. The device includes the following components: The information acquisition module 702 acquires the operating status information of the asynchronous motor, and performs coordinate transformation and voltage reconstruction processing based on the operating status information to obtain the actual value of the stator voltage in the current control cycle. The operating status information includes: stator three-phase current, DC bus voltage and pulse width modulation three-phase duty cycle. The prediction and correction module 704 predicts the stator voltage for multiple consecutive control cycles based on the operating status information and the preset rotor flux direct-axis component prediction model, obtains the stator voltage prediction value, and calculates the prediction error based on the actual stator voltage value and the predicted stator voltage value, so as to use the prediction error to perform feedback correction processing on the stator voltage prediction value, and obtains the corrected target stator voltage prediction value. The optimization and update module 706 calculates the optimal current increment based on the target stator voltage prediction value, and updates the direct-axis current reference value of the current control cycle according to the optimal current increment, so as to output the updated direct-axis current reference value and complete the field weakening speed regulation control of the current control cycle.

[0083] The asynchronous motor field weakening speed control device based on model prediction provided in this application embodiment can significantly improve the parameter robustness of field weakening control.

[0084] In one embodiment, when performing the steps of acquiring the operating status information of the asynchronous motor and performing coordinate transformation and voltage reconstruction processing based on the operating status information to obtain the actual value of the stator voltage in the current control cycle, the aforementioned information acquisition module 702 is further configured to: acquire the pulse width modulation three-phase duty cycle and the DC bus voltage of the asynchronous motor in the current control cycle, and perform voltage reconstruction processing based on the DC bus voltage and the pulse width modulation three-phase duty cycle to obtain the actual value of the stator voltage in the three-phase stationary coordinate system; sequentially perform Clark transformation and Park transformation on the actual value of the stator voltage in the three-phase stationary coordinate system to obtain the direct-axis voltage component and quadrature-axis voltage component in the synchronous rotating coordinate system, and synthesize the actual value of the stator voltage in the current control cycle based on the direct-axis voltage component and the quadrature-axis voltage component.

[0085] In one embodiment, before performing the step of predicting the stator voltage for multiple consecutive control cycles based on the operating status information and a preset rotor flux direct-axis component prediction model to obtain the stator voltage prediction value, the information acquisition module 702 is further configured to: collect the stator three-phase current of the asynchronous motor, and sequentially perform Clark transformation and Park transformation on the stator three-phase current to obtain the stator current direct-axis component and stator current quadrature-axis component in the synchronous rotating coordinate system.

[0086] In one embodiment, when performing the step of predicting the stator voltage for multiple consecutive control cycles based on the operating status information and a preset rotor flux direct-axis component prediction model to obtain the stator voltage prediction value, the prediction correction module 704 is further configured to: send the stator current direct-axis component and the rotor flux direct-axis component of the previous control cycle to the rotor flux direct-axis component prediction model for iterative calculation processing to obtain the predicted value of the rotor flux direct-axis component of the current control cycle; and calculate the predicted value of the stator voltage of the current control cycle based on the predicted value of the rotor flux direct-axis component of the current control cycle, the stator current direct-axis component, the stator current quadrature-axis component, and the synchronous angular velocity of the current control cycle.

[0087] In one embodiment, when performing the step of calculating the prediction error based on the actual value and the predicted value of the stator voltage, and using the prediction error to perform feedback correction processing on the predicted value of the stator voltage to obtain the corrected target predicted value of the stator voltage, the prediction correction module 704 is further configured to: calculate the difference between the actual value of the stator voltage in the current control cycle and the predicted value of the stator voltage in the current control cycle to obtain the prediction error of the current cycle; multiply the prediction error of the current cycle by a preset feedback correction coefficient matrix to determine the error correction value; and superimpose the error correction value with the predicted value of the stator voltage to obtain the corrected target predicted value of the stator voltage.

[0088] In one embodiment, when performing the step of calculating the optimal current increment based on the target stator voltage prediction value, the optimization update module 706 is further configured to: construct an optimization objective function based on the target stator voltage prediction value, and perform extreme value solving on the optimization objective function according to the transfer matrix between the direct-axis component of the stator current and the direct-axis component of the rotor flux linkage to obtain the optimal current increment.

[0089] In one embodiment, when performing the step of updating the direct-axis current reference value of the current control cycle according to the optimal current increment, and outputting the updated direct-axis current reference value to complete the field weakening speed regulation control of the current control cycle, the aforementioned optimization update module 706 is further configured to: superimpose the direct-axis current reference value of the current control cycle with the optimal current increment to obtain the direct-axis current reference value of the next control cycle; and output the direct-axis current reference value of the next control cycle as a direct-axis current command to the current loop, so that the current loop generates a corresponding voltage control signal according to the direct-axis current command.

[0090] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0091] This invention provides a server, specifically, the server includes a processor and a storage device; the storage device stores a computer program, which, when run by the processor, executes the method described in any of the above embodiments.

[0092] Figure 8 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. The server 100 includes: a processor 80, a memory 81, a bus 82, and a communication interface 83. The processor 80, the communication interface 83, and the memory 81 are connected through the bus 82. The processor 80 is used to execute executable modules, such as computer programs, stored in the memory 81.

[0093] The memory 81 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 83 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0094] Bus 82 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0095] The memory 81 is used to store programs. After receiving an execution instruction, the processor 80 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 80 or implemented by the processor 80.

[0096] The processor 80 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 80 or by instructions in software form. The processor 80 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 81. The processor 80 reads the information in memory 81 and, in conjunction with its hardware, completes the steps of the above method.

[0097] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0098] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0099] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for field weakening speed regulation of an asynchronous motor based on model prediction, characterized in that, The method includes: The operating status information of the asynchronous motor is obtained, and coordinate transformation and voltage reconstruction are performed based on the operating status information to obtain the actual value of the stator voltage in the current control cycle. The operating status information includes: stator three-phase current, DC bus voltage and pulse width modulation three-phase duty cycle. Based on the operating status information and the preset rotor flux direct-axis component prediction model, the stator voltage for multiple consecutive control cycles in the future is predicted to obtain the stator voltage prediction value. The prediction error is calculated based on the actual stator voltage value and the predicted stator voltage value, and the prediction error is used to perform feedback correction on the predicted stator voltage value to obtain the corrected target stator voltage prediction value. The optimal current increment is calculated based on the target stator voltage prediction value, and the direct-axis current reference value of the current control cycle is updated according to the optimal current increment, so as to output the updated direct-axis current reference value and complete the field weakening speed regulation control of the current control cycle. The step of calculating the optimal current increment based on the target stator voltage prediction value includes: constructing an optimization objective function based on the target stator voltage prediction value, and performing extreme value solving on the optimization objective function according to the transfer matrix between the stator current direct-axis component and the rotor flux direct-axis component to obtain the optimal current increment; The step of constructing an optimization objective function based on the predicted target stator voltage includes: taking stator voltage tracking of the reference voltage and smoothing of control increments as optimization objectives, and obtaining the optimal current increment for immediate control action. And limit the amplitude [0, ]: in, The voltage reference value is given according to the DC bus voltage. To predict the step size is Direct-axis component of stator current With the rotor flux direct axis component The transfer matrix between them To measure output voltage Tracking reference voltage The error weight coefficient matrix, To measure control increment The control coefficient matrix for smoothness.

2. The asynchronous motor field weakening speed regulation method based on model prediction according to claim 1, characterized in that, The step of obtaining the operating status information of the asynchronous motor, and performing coordinate transformation and voltage reconstruction processing based on the operating status information to obtain the actual value of the stator voltage in the current control cycle includes: The pulse width modulation three-phase duty cycle and the DC bus voltage of the asynchronous motor are obtained in the current control cycle. Voltage reconstruction processing is performed based on the DC bus voltage and the pulse width modulation three-phase duty cycle to obtain the actual value of the stator voltage in the three-phase stationary coordinate system. The actual stator voltage value in the three-phase stationary coordinate system is sequentially subjected to Clark transformation and Park transformation to obtain the direct-axis voltage component and quadrature-axis voltage component in the synchronous rotating coordinate system. Based on the direct-axis voltage component and the quadrature-axis voltage component, the actual stator voltage value of the current control cycle is synthesized.

3. The asynchronous motor field weakening speed regulation method based on model prediction according to claim 1, characterized in that, Before the step of predicting the stator voltage for multiple consecutive control cycles based on the operating status information and a preset rotor flux direct-axis component prediction model to obtain the stator voltage prediction value, the following steps are included: The stator three-phase current of the asynchronous motor is collected, and the stator three-phase current is subjected to Clark transformation and Park transformation in sequence to obtain the direct-axis component and quadrature-axis component of the stator current in the synchronous rotating coordinate system.

4. The asynchronous motor field weakening speed regulation method based on model prediction according to claim 1, characterized in that, The step of predicting the stator voltage for multiple consecutive control cycles based on the operating status information and a preset rotor flux direct-axis component prediction model to obtain the predicted stator voltage value includes: The stator current direct-axis component and the rotor flux direct-axis component of the previous control cycle are sent to the rotor flux direct-axis component prediction model for iterative calculation to obtain the predicted value of the rotor flux direct-axis component of the current control cycle. The predicted value of the stator voltage for the current control cycle is calculated based on the predicted value of the rotor flux direct-axis component, the stator current direct-axis component, the stator current quadrature-axis component, and the synchronous angular velocity for the current control cycle.

5. The asynchronous motor field weakening speed regulation method based on model prediction according to claim 1, characterized in that, The step of calculating and processing the prediction error based on the actual value of the stator voltage and the predicted value of the stator voltage, and using the prediction error to perform feedback correction processing on the predicted value of the stator voltage to obtain the corrected target predicted value of the stator voltage, includes: The difference between the actual value of the stator voltage in the current control cycle and the predicted value of the stator voltage in the current control cycle is calculated to obtain the prediction error of the current cycle. The product of the prediction error of the current period and the preset feedback correction coefficient matrix is ​​determined as the error correction value. The error correction value is then superimposed on the stator voltage prediction value to obtain the corrected target stator voltage prediction value.

6. The asynchronous motor field weakening speed regulation method based on model prediction according to claim 1, characterized in that, The step of updating the direct-axis current reference value of the current control cycle based on the optimal current increment, and outputting the updated direct-axis current reference value to complete the field weakening speed control of the current control cycle includes: The direct-axis current reference value of the current control cycle is superimposed with the optimal current increment to obtain the direct-axis current reference value of the next control cycle. The direct-axis current reference value of the next control cycle is output as a direct-axis current command to the current loop, so that the current loop generates a corresponding voltage control signal according to the direct-axis current command.

7. A model-predictive field-weakening speed control device for asynchronous motors, characterized in that, The device includes: The information acquisition module acquires the operating status information of the asynchronous motor, and performs coordinate transformation and voltage reconstruction processing based on the operating status information to obtain the actual value of the stator voltage in the current control cycle. The operating status information includes: stator three-phase current, DC bus voltage and pulse width modulation three-phase duty cycle. The prediction and correction module predicts the stator voltage for multiple consecutive control cycles based on the operating status information and the preset rotor flux direct-axis component prediction model, obtains the stator voltage prediction value, and calculates the prediction error based on the actual stator voltage value and the predicted stator voltage value, so as to use the prediction error to perform feedback correction processing on the predicted stator voltage value, and obtains the corrected target stator voltage prediction value. The optimization and update module calculates the optimal current increment based on the target stator voltage prediction value, and updates the direct-axis current reference value of the current control cycle according to the optimal current increment, so as to output the updated direct-axis current reference value and complete the field weakening speed regulation control of the current control cycle. The step of calculating the optimal current increment based on the target stator voltage prediction value includes: constructing an optimization objective function based on the target stator voltage prediction value, and performing extreme value solving on the optimization objective function according to the transfer matrix between the stator current direct-axis component and the rotor flux direct-axis component to obtain the optimal current increment; The step of constructing an optimization objective function based on the predicted target stator voltage includes: taking stator voltage tracking of the reference voltage and smoothing of control increments as optimization objectives, and obtaining the optimal current increment for immediate control action. And limit the amplitude [0, ]: in, The voltage reference value is given according to the DC bus voltage. To predict the step size is Direct-axis component of stator current With the rotor flux direct axis component The transfer matrix between them To measure output voltage Tracking reference voltage The error weight coefficient matrix, To measure control increment The control coefficient matrix for smoothness.

8. A server, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

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