A common-mode voltage suppression method and system based on model predictive current control

Abstract

The application relates to the technical field of motor drive control, and discloses a common-mode voltage suppression method and system based on model predictive current control, which comprises the following steps: obtaining current values and electrical angular velocities of a permanent magnet synchronous motor at a current time, and obtaining current given values; for Q voltage vectors of an inverter, current prediction models are adopted and combined with the current values and the electrical angular velocities to respectively predict current prediction values at a next time; based on the current prediction values and the current given values, a cost function containing a current tracking error term and a common-mode voltage suppression term is constructed, and based on current deviations and deviation change rates, weight coefficients of the current tracking error term and the common-mode voltage suppression term are determined through a fuzzy controller; the Q voltage vectors and corresponding current prediction values are respectively substituted into the cost function for evaluation, an optimal voltage vector is determined according to a cost function value, and a switching state corresponding to the optimal voltage vector is applied to the inverter. The common-mode voltage can be effectively suppressed while the current tracking performance is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of motor drive control technology, specifically to a common-mode voltage suppression method and system based on model predictive current control. Background Technology

[0002] Permanent Magnet Synchronous Motor Permanent magnet synchronous motors (PMSMs), with their advantages of high power density, high efficiency, and wide speed range, have become core components of drive systems in dehumidifiers, fans, and electric vehicles. To achieve high-performance control of permanent magnet synchronous motors in these applications, voltage source inverters are typically used in conjunction with space vector pulse width modulation (SVPWM) technology. SVPWM technology can effectively improve DC voltage utilization and suppress current harmonics, but it inevitably introduces zero vectors during modulation, resulting in a common-mode voltage with a peak value as high as half of the DC bus voltage. This high-amplitude, high-frequency common-mode voltage not only forms bearing currents through stray capacitance coupling, accelerating the electrochemical corrosion of the motor's mechanical structure, but also causes severe electromagnetic interference. In household appliances such as dehumidifiers and fans, this can affect the service life and reliability of the equipment, and in electric vehicle drive systems, it can threaten the safety of the entire vehicle's electronic equipment. To suppress common-mode voltage, existing technologies have proposed zero-vector-free PWM modulation strategies. By avoiding zero vectors, the amplitude of the common-mode voltage can be effectively limited. However, this usually comes at the cost of current quality, leading to increased current harmonic content and torque ripple, affecting the smoothness of motor operation.

[0003] Current common-mode voltage suppression (CMDS) technologies suffer from the inability to simultaneously achieve both high-quality current and effective CMS suppression. While traditional SVPWM technology ensures excellent current quality, it suffers from significant CMS voltage issues. Zero-vector modulation (ZVPWM) strategies, though effective in suppressing CMS voltage, degrade the current waveform and reduce overall system performance. Furthermore, Model Predictive Current Control (MMDC), an advanced control strategy, predicts future states by establishing a mathematical model of the system and evaluates candidate control schemes using a cost function. It eliminates the need for traditional pulse-width modulation (PWM) stages and current regulators, directly outputting switching states to the inverter and offering rapid dynamic response and the ability to handle multi-constraint problems. However, traditional MMDC primarily focuses on current tracking performance, with its cost function containing only the current tracking error term, neglecting CMS suppression requirements. This makes it difficult to simultaneously achieve high-quality current control and effective CMS suppression. Moreover, even when multiple control objectives are introduced into the cost function, the determination of weighting coefficients among these objectives lacks a systematic approach. Fixed weighting coefficients cannot be adjusted based on operating conditions, making it difficult to achieve an optimal balance between current tracking accuracy and CMS suppression. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a common-mode voltage suppression method based on model predictive current control, comprising: Obtain the current value and electric angular velocity of the permanent magnet synchronous motor at the current moment, and obtain the current setpoint; For the Q voltage vectors of the inverter, a current prediction model is used, combined with the current value and the electric angular velocity, to predict the current value at the next moment; where Q represents the number of voltage vectors determined by the inverter's switching state. Based on the predicted current value and the given current value, a cost function containing a current tracking error term and a common-mode voltage suppression term is constructed, and the weight coefficients of the current tracking error term and the common-mode voltage suppression term are determined by a fuzzy controller based on the current deviation and the deviation change rate. The Q voltage vectors and their corresponding predicted current values ​​are substituted into the cost function for evaluation. The optimal voltage vector is determined based on the cost function value, and the switching state corresponding to the optimal voltage vector is applied to the inverter to achieve common-mode voltage suppression based on model-predicted current control. Based on the predicted current value and the given current value, the cost function that includes a current tracking error term and a common-mode voltage suppression term is constructed by calculating the difference between the predicted current value and the given current value corresponding to each voltage vector, and determining the current tracking error term corresponding to each voltage vector based on the difference. For each voltage vector, a current limiting term is constructed based on the relationship between the magnitude of the current prediction value and the preset current limit value. Based on the current tracking error term and the current limiting term, the common-mode voltage amplitude of the inverter output is calculated according to the relationship between the three-phase bridge arm switch state corresponding to each voltage vector and the DC bus voltage, and the common-mode voltage amplitude is used as the common-mode voltage suppression term. The current tracking error term and the common-mode voltage suppression term are weighted and combined, and combined with the current limiting term, to construct the cost function corresponding to the voltage vector.

[0006] As a preferred embodiment of the common-mode voltage suppression method based on model prediction current control described in this invention, the step of obtaining the current value and electric angular velocity of the permanent magnet synchronous motor at the current moment and obtaining the current setpoint includes measuring the three-phase current of the stator winding of the permanent magnet synchronous motor to obtain the three-phase current value in the natural coordinate system. Measure the rotor position angle of the permanent magnet synchronous motor, and calculate the electric angular velocity based on the rotor position angle and the sampling period; The three-phase current values ​​are transformed from the natural coordinate system to the stationary coordinate system by Clark transformation to obtain the current variables in the stationary coordinate system. The current variables are then transformed to the rotating coordinate system dq by Park transformation. The coordinate transformation matrix of the Park transformation is determined by the rotor position angle to obtain the dq axis current value at the current moment. Based on the maximum torque-to-current ratio control strategy, the dq-axis current setpoint is determined by curve fitting based on the torque setpoint.

[0007] As a preferred embodiment of the common-mode voltage suppression method based on model-predictive current control described in this invention, the method for predicting the current prediction value at the next moment for the Q voltage vectors of the inverter using a current prediction model and combining the current value and electric angular velocity includes: based on the stator voltage equation of the permanent magnet synchronous motor in the rotating coordinate system dq, discretizing it using the first-order forward Euler method to establish a current prediction model, wherein the current prediction model includes stator resistance, dq-axis inductance, and permanent magnet flux linkage as motor parameters; The Q voltage vectors are converted into voltage components in the rotating coordinate system dq through coordinate transformation, resulting in Q dq-axis voltage components. The Q dq-axis voltage components, the current dq-axis current value at the current moment, the electric angular velocity, and the motor parameters are respectively substituted into the current prediction model for calculation. For each voltage vector, the corresponding d-axis current prediction value and q-axis current prediction value at the next moment are output to form the dq-axis current prediction values ​​corresponding to the Q voltage vectors.

[0008] As a preferred embodiment of the common-mode voltage suppression method based on model predictive current control described in this invention, the step of determining the weight coefficients of the current tracking error term and the common-mode voltage suppression term by a fuzzy controller based on the current deviation and the deviation change rate includes calculating the q-axis current deviation based on the q-axis current setpoint in the dq-axis current setpoint and the q-axis current value in the dq-axis current value at the current moment, and using the change in the q-axis current deviation relative to the previous moment as the deviation change rate of the q-axis current. The q-axis current deviation and the rate of change of the q-axis current deviation are used as input variables of the fuzzy controller. Fuzzy inference is performed according to the preset fuzzy rule base. The weight coefficients of the current tracking error term and the weight coefficients of the common-mode voltage suppression term are output and updated in the cost function.

[0009] As a preferred embodiment of the common-mode voltage suppression method based on model predictive current control described in this invention, wherein: the step of performing fuzzy inference according to a preset fuzzy rule base and outputting the weight coefficients of the current tracking error term and the common-mode voltage suppression term includes defining the output range of the weight coefficients of the current tracking error term and the output range of the weight coefficients of the common-mode voltage suppression term; The q-axis current deviation, the rate of change of the q-axis current deviation, the weighting coefficient of the current tracking error term, and the weighting coefficient of the common-mode voltage suppression term are discretized into a preset fuzzy set, which includes negative large, negative small, zero, positive small, and positive large. Based on the relationship between the q-axis current deviation and the rate of change of the q-axis current deviation, the weighting coefficients of the current tracking error term and the common-mode voltage suppression term are determined through a preset fuzzy rule table.

[0010] As a preferred embodiment of the common-mode voltage suppression method based on model predictive current control described in this invention, the method includes: substituting the Q voltage vectors and their corresponding predicted current values ​​into the cost function for evaluation, determining the optimal voltage vector based on the cost function value, and applying the switching state corresponding to the optimal voltage vector to the inverter; and substituting the predicted dq-axis current values ​​corresponding to the Q voltage vectors into the cost function to obtain the cost function values ​​corresponding to the Q voltage vectors. Select the cost function value with the smallest value from the cost function values ​​corresponding to the Q voltage vectors, and determine the corresponding voltage vector as the first optimal voltage vector; After the first optimal voltage vector is determined, the voltage vector with the second smallest cost function value is selected as the second optimal voltage vector; Based on the current tracking error corresponding to the first optimal voltage vector and the second optimal voltage vector, allocate the action time of the first optimal voltage vector and the second optimal voltage vector within a control cycle; Based on the said operating time, the switching states corresponding to the first optimal voltage vector and the second optimal voltage vector are sequentially applied to the inverter within the control cycle.

[0011] A common-mode voltage suppression system based on model predictive current control, wherein: The current acquisition module acquires the current value and electric angular velocity of the permanent magnet synchronous motor at the current moment, and also acquires the current setpoint. The current prediction module uses a current prediction model, combined with the current value and electric angular velocity, to predict the current value at the next moment for each of the Q voltage vectors of the inverter; where Q represents the number of voltage vectors determined by the inverter's switching state. The cost function module constructs a cost function containing a current tracking error term and a common-mode voltage suppression term based on the predicted current value and the given current value, and determines the weight coefficients of the current tracking error term and the common-mode voltage suppression term through a fuzzy controller based on the current deviation and the deviation change rate. The common-mode voltage suppression module substitutes the Q voltage vectors and their corresponding current prediction values ​​into the cost function for evaluation, determines the optimal voltage vector based on the cost function value, and applies the switching state corresponding to the optimal voltage vector to the inverter to achieve common-mode voltage suppression based on model-predicted current control.

[0012] A computer device includes: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, it implements the steps of the method described in any one of the present invention.

[0013] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of the present invention.

[0014] The beneficial effects of this invention are as follows: The common-mode voltage suppression method based on model predictive current control provided by this invention introduces a common-mode voltage suppression term into the cost function of model predictive current control, incorporating current tracking error and common-mode voltage amplitude into a unified optimization framework. A fuzzy controller is used to adjust the weighting coefficients based on the q-axis current deviation and the rate of change of the deviation, ensuring that control accuracy is prioritized when the current tracking error is large, and enhancing the common-mode voltage suppression effect when the current tends to stabilize, thereby achieving a balance between current quality and common-mode voltage suppression. A dual-vector model predictive current control strategy is introduced in the voltage vector selection stage. By selecting two optimal voltage vectors in each control cycle and rationally allocating their action time, steady-state current fluctuations and harmonic content are effectively reduced. The output control strategy not only limits the common-mode voltage amplitude to within one-sixth of the DC bus voltage but also reduces current harmonics. This method effectively suppresses common-mode voltage while ensuring current tracking performance, reducing bearing current and electromagnetic interference, improving the reliability and electromagnetic compatibility of permanent magnet synchronous motors in applications such as dehumidifiers, fans, and electric vehicles, extending equipment lifespan, and ensuring the safety of electronic equipment. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1The first embodiment of the present invention provides an overall flowchart of a common-mode voltage suppression method based on model prediction current control.

[0017] Figure 2 A schematic diagram of the rotational speed waveform provided for the first embodiment of the present invention.

[0018] Figure 3 A schematic diagram of the torque waveform provided for the first embodiment of the present invention.

[0019] Figure 4 A schematic diagram of three-phase current provided for the first embodiment of the present invention.

[0020] Figure 5 This is a schematic diagram of fast Fourier transform analysis of phase current under the common-mode voltage suppression method based on model prediction current control provided in the first embodiment of the present invention.

[0021] Figure 6 A schematic diagram of the common-mode voltage provided for the first embodiment of the present invention.

[0022] Figure 7 This is a schematic diagram of common-mode voltage amplification provided for the first embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1 As an embodiment of the present invention, a common-mode voltage suppression method based on model prediction current control is provided, comprising: S1: Obtain the current value and electric angular velocity of the permanent magnet synchronous motor at the current moment, and obtain the current setpoint.

[0025] In this embodiment, the implementation of the model predictive current control algorithm relies on the accurate perception of the real-time operating status of permanent magnet synchronous motors in application scenarios such as dehumidifiers, fans, and electric vehicles. Through sensor acquisition, coordinate transformation, and control strategy calculation, the complete state information and control objectives required by the subsequent current prediction model are obtained. The obtained state information includes not only the motor's current, position, and speed, but also the desired current setpoint needs to be determined according to the control strategy. These state information together constitute the input basis of the model predictive current control algorithm.

[0026] In applications such as dehumidifiers, fans, and electric vehicles, permanent magnet synchronous motors (PMSMs), as high-order nonlinear systems, are characterized by multiple variables and strong coupling, making it difficult to control the excitation torque and electromagnetic torque independently, unlike DC motors. To facilitate analysis and control, coordinate transformation is required to convert the AC system in the natural coordinate system ABC into a simplified DC system in the rotating coordinate system dq, thereby achieving decoupled control. At the same time, considering the reluctance torque characteristic of built-in PMSMs, appropriate control strategies are needed to determine the current setpoint to fully leverage the motor's performance advantages.

[0027] Specifically, the steps for obtaining the current value and electrical angular velocity of the permanent magnet synchronous motor at the current moment, and obtaining the current setpoint, include: measuring the three-phase current of the stator winding of the permanent magnet synchronous motor through a current sensor to obtain the three-phase current value in the natural coordinate system ABC. The accurate measurement of the three-phase current directly affects the accuracy of subsequent current prediction and control performance. Since model predictive current control predicts the current response at the next moment based on the current value at the current moment, the accuracy and real-time performance of the current measurement are crucial. Measurement errors will propagate through the prediction model, affecting the selection of the optimal voltage vector, and thus affecting the control effect of the entire system.

[0028] Secondly, the rotor position angle of the permanent magnet synchronous motor is measured by a position sensor. The electric angular velocity is calculated based on the rotor position angle and the sampling period. .

[0029] It should be noted that the rotor position angle is a necessary condition for realizing vector control and is used for calculating the coordinate transformation matrix of the Park transformation. The electric angular velocity, as an input parameter of the current prediction model, reflects the operating state of the motor and can correctly describe the back electromotive force and coupling effects, thereby improving the accuracy of current prediction. Especially when the motor speed changes rapidly, such as during acceleration, deceleration, or sudden load changes, real-time and accurate electric angular velocity information is of great significance for maintaining prediction accuracy.

[0030] Furthermore, for the obtained three-phase current values, the three-phase current values ​​are transformed from the natural coordinate system ABC to the stationary coordinate system α-β using Clark transformation to obtain the current variables in the stationary coordinate system α-β: in, These are variables such as the motor's voltage, current, or flux linkage; This is the coordinate transformation matrix.

[0031] coordinate transformation matrix Specifically, it can be expressed as: The current variable is transformed into a rotating coordinate system dq using the Park transformation. The coordinate transformation matrix of the Park transformation is determined using the rotor position angle, and the dq-axis current value at the current moment is obtained. in, For d-axis current variables; For q-axis current variables; The coordinate transformation matrix of the Park transformation can be specifically represented as: in, This is the rotor position angle.

[0032] It should be noted that the Park transformation was used to decouple the mathematical model of the three-phase permanent magnet synchronous motor, thus obtaining the d-axis current value at the current moment. and q-axis current value This allows the d-axis and q-axis currents to be controlled independently, thus enabling the control of an AC motor in the same way as a DC motor.

[0033] Meanwhile, this embodiment also employs a maximum torque-to-current ratio control strategy, based on the torque setpoint. The dq-axis current setpoint was determined using curve fitting. and For built-in permanent magnet synchronous motors, due to When adopting During control, the utilization rate of current is not high, so it is necessary to reselect the control strategy to maximize the utilization of reluctance torque.

[0034] To maximize the efficiency of a permanent magnet synchronous motor (PMSM), a control method is needed to enable the motor to generate the maximum torque with the minimum current, i.e., maximum torque-to-current ratio (MTTR) control. MMTTR control can be transformed into an optimization problem of finding the minimum stator current amplitude that meets the torque requirement. Since the calculation of the torque-current relationship involves high order and many parameters, curve fitting is used in practical engineering applications to reduce the computational burden on the control chip. Given different torques, the corresponding torque-current ratio is calculated. and Under the condition that the maximum torque is the rated torque of 14 N·m, the torque setpoint is calculated using MATLAB / Simulink tools. and , By fitting the functional relationship, the polynomial relationship of the fitted curve is obtained: in, This refers to the d-axis current value. This represents the q-axis current value.

[0035] It should be noted that, addressing the problem of difficult independent control of excitation torque and electromagnetic torque caused by strong coupling of multiple variables in the three-phase AC system in traditional permanent magnet synchronous motor control, three-phase current values ​​and rotor position angles are collected by current and position sensors. The AC quantities in the natural coordinate system are converted into DC quantities in the rotating coordinate system using Clark and Park transformations, achieving decoupled control of the d-axis and q-axis currents. Furthermore, considering the unequal d-axis and q-axis inductances of the built-in permanent magnet synchronous motor, a maximum torque-to-current ratio control strategy is adopted, combined with curve fitting to determine the current setpoint. This fully utilizes the contribution of reluctance torque, thereby reducing the computational burden on the control chip and enabling the motor to generate the maximum electromagnetic torque with the minimum stator current amplitude. This improves current utilization efficiency and motor energy conversion efficiency, providing accurate state information and control objectives for subsequent current prediction models.

[0036] S2: For the Q voltage vectors of the inverter, a current prediction model is used, combined with the current value and electric angular velocity, to predict the current value at the next moment; where Q represents the number of voltage vectors determined by the inverter's switching state.

[0037] In this embodiment, after obtaining the real-time status information of permanent magnet synchronous motors used in scenarios such as dehumidifiers, fans, and electric vehicles, a current prediction model needs to be established to predict the current response under different control inputs. The model predicts the current control and uses a value function to select the voltage vector that minimizes the error between the predicted current and the given current as the optimal voltage vector. Therefore, accurate current prediction is a prerequisite for achieving high-performance control and effectively suppressing common-mode voltage. At the same time, since there are multiple voltage vectors in the inverter, it is necessary to perform current prediction on all candidate voltage vectors to provide complete input for subsequent cost function evaluation.

[0038] Specifically, the method for predicting the current value at the next moment for the Q voltage vectors of the inverter using a current prediction model combined with the current value and electric angular velocity includes: based on the stator voltage equation of the permanent magnet synchronous motor in the rotating coordinate system dq, discretizing it using the first-order forward Euler method to establish a current prediction model. The current prediction model includes stator resistance, dq-axis inductance, and permanent magnet flux linkage as motor parameters. The specific steps for establishing the current prediction model include: According to the mathematical model of the motor, the equation of the stator current in the rotating coordinate system dq is: in, and These are the dq-axis components of the stator voltage, respectively. This refers to the d-axis current value. This represents the q-axis current value. Electric angular velocity; and These are the dq axis inductance components, respectively; Stator resistance; It is a permanent magnet flux linkage.

[0039] To perform digital control, discrete time is first introduced. That is, from time k to time k+1, and the current is discretized for analysis. When the discrete time... When the value is sufficiently small, using first-order forward Euler discretization, we can obtain: in, The time interval is discrete, that is, from time k to time k+1.

[0040] Discretize the equations for stator current in the rotating coordinate system dq and substitute them into the formula after first-order forward Euler discretization to obtain the prediction model for stator current: in, and These are the values ​​of the dq-axis current samples at time k; and These are the predicted dq-axis current values ​​at time k+1; Let be the electric angular velocity at time k; and These represent the voltage output at the next moment.

[0041] Furthermore, the Q voltage vectors are converted into voltage components in the rotating coordinate system dq through coordinate transformation, resulting in Q dq-axis voltage components. Since this embodiment focuses on a three-phase two-level voltage-type inverter and a permanent magnet synchronous motor, and the three-phase two-level voltage-type inverter consists of 6 power switching transistors, the 8 switching states of the three-phase bridge arm correspond to 8 voltage vectors, including 2 zero vectors and 6 non-zero vectors, the number of voltage vectors Q in this embodiment is taken as 8.

[0042] For the aforementioned eight voltage vectors, the current prediction model is calculated by substituting the eight dq-axis voltage components, the current dq-axis current value at the current moment, the electric angular velocity, and the motor parameters into the model. For each voltage vector, the predicted d-axis current value and the predicted q-axis current value for the next moment are output, forming the predicted dq-axis current values ​​for the eight voltage vectors. in, and These are the predicted dq-axis current values ​​for the eight voltage vectors in the next sampling period; and The voltage vector generated by the inverter; Vector index, .

[0043] Through the above calculation process, the predicted dq-axis current values ​​for the next moment are obtained for the eight voltage vectors of the inverter, forming the predicted dq-axis current values ​​for the eight voltage vectors. These predicted current values ​​will be used as inputs for subsequent cost function evaluation to calculate the current tracking error and common-mode voltage suppression effect for each voltage vector, thereby providing a basis for selecting the optimal voltage vector.

[0044] It should be noted that, addressing the limitation of traditional pulse width modulation methods, which can only apply a fixed control strategy at the current moment and cannot predict future current responses, a current prediction model is established based on the stator voltage equation of a permanent magnet synchronous motor. The first-order forward Euler method is introduced into the numerical solution of the current differential equation. The predicted d-axis and q-axis current values ​​for the next moment are calculated for the eight voltage vectors of a three-phase two-level voltage-type inverter. The current prediction model fully considers the influence of stator resistance, d-axis inductance, q-axis inductance, permanent magnet flux linkage, and electric angular velocity on current evolution, providing a complete set of candidate scheme inputs for cost function evaluation. By obtaining the current response through prediction rather than measurement, the controller can evaluate the effect before applying control actions, laying a forward-looking decision-making foundation for subsequent multi-objective optimization and improving the response speed and accuracy of the control strategy.

[0045] S3: Based on the predicted current value and the given current value, construct a cost function that includes a current tracking error term and a common-mode voltage suppression term, and determine the weight coefficients of the current tracking error term and the common-mode voltage suppression term through a fuzzy controller based on the current deviation and the deviation change rate.

[0046] In this embodiment, after obtaining the predicted dq-axis current values ​​corresponding to the eight voltage vectors in step S2, a cost function needs to be constructed to evaluate and optimize each candidate voltage vector. The cost function is the core of model predictive current control, which directly determines the system's control performance, robustness, and computational efficiency. Model predictive current control uses the cost function to select the voltage vector that minimizes the error between the predicted current and the given current as the optimal voltage vector. The cost function of finite control set model predictive current control can integrate multiple optimization control objectives simultaneously, supporting the realization of multi-objective, multi-variable, and nonlinear constraints, so that while ensuring that the current closely tracks the given value, it can also meet the requirements of additional control constraints.

[0047] While traditional model predictive current control (MMDC) offers fast response and the ability to handle multi-constraint problems, its cost function design primarily focuses on current tracking performance, including only the current tracking error term and failing to incorporate common-mode voltage (CMV) as a control objective into the optimization framework. Furthermore, existing CMV suppression methods mainly rely on improved pulse-width modulation (PWM) strategies, such as zero-vector modulation, which reduces CMV amplitude by avoiding the use of zero vectors. However, this approach inherently constrains the modulation strategy, leading to a reduction in the number of selectable voltage vectors and an increase in switching frequency, thus sacrificing current quality. This invention integrates the CMV suppression objective and the current tracking objective into a single cost function. Leveraging the advantages of MDC—no PWM step and direct selection of the optimal switching state—it evaluates the combined effect of all candidate voltage vectors on current tracking and CMV suppression in each control cycle, achieving synergistic optimization of the two objectives from the outset. This overcomes the constraint of fixed vector selection rules in traditional modulation strategies, enabling the controller to flexibly select the optimal voltage vector that ensures both current quality and CMV suppression based on the current operating state, effectively reducing CMV amplitude without sacrificing current quality.

[0048] Specifically, based on the predicted current value and the given current value, constructing a cost function that includes a current tracking error term and a common-mode voltage suppression term includes the following steps: First, after predicting the current corresponding to the candidate vectors, a suitable cost function needs to be selected to evaluate the control performance of the prediction algorithm. Since the goal of current prediction control for permanent magnet synchronous motors is to minimize the error between the current feedback value and the setpoint, the steps for constructing the initial cost function are as follows: The difference between the predicted dq-axis current values ​​corresponding to the eight voltage vectors and the given dq-axis current values ​​is calculated, and the current tracking error term corresponding to the voltage vector is determined based on the difference. in, and These are the current setpoints for the dq axes, respectively. This refers to the d-axis current value. This represents the q-axis current value.

[0049] For each voltage vector corresponding to the predicted dq-axis current value, a current limiting term is constructed based on the relationship between the magnitude of the predicted dq-axis current value and the preset current limit value: in, This is a current limiting term; This represents the maximum current value.

[0050] It should be noted that if the current value predicted by the selected voltage vector exceeds the current limit, the current limit term is ∞, thus excluding the corresponding voltage vector; when the constraint condition is met, the current limit term is 0, and optimization is performed only based on the current tracking error term. Finally, the voltage vector with the minimum corresponding cost function g is selected as the optimal vector to make the output current as close as possible to the given current.

[0051] Combining the current tracking error term and the current limiting term mentioned above, the specific formula for the initial cost function is as follows: in, This is the initial cost function.

[0052] Secondly, based on the current tracking error term and the current limiting term, the common-mode voltage amplitude of the inverter output is calculated according to the relationship between the three-phase bridge arm switching state corresponding to each voltage vector and the DC bus voltage. The common-mode voltage amplitude is then used as the common-mode voltage suppression term. The specific steps are as follows: Obtain the three-phase bridge arm switch states Sa, Sb, and Sc corresponding to each voltage vector, where the switch state is 1 when the upper bridge arm is on and 0 when the lower bridge arm is on. Calculate the common-mode voltage value output by the inverter based on the relationship between the three-phase bridge arm switch states and the DC bus voltage. Take the absolute value of the common-mode voltage value to obtain the common-mode voltage amplitude. The common-mode voltage amplitude is then introduced into the cost function as a common-mode voltage suppression term.

[0053] It should be noted that, in order to reduce the amplitude of the common-mode voltage, a common-mode voltage penalty term is added to the cost function. By minimizing the amplitude of the common-mode voltage, the common-mode voltage can be effectively reduced, thereby reducing electromagnetic interference and motor bearing current. Based on the magnitude of the common-mode voltage output under each operating state of the inverter, a common-mode voltage suppression term is provided for the construction of the cost function.

[0054] Furthermore, after obtaining the current tracking error term, the current limiting constraint term, and the common-mode voltage suppression term, a weighted combination is performed on the current tracking error term and the common-mode voltage suppression term. In the motor control system, and Controlled variables belonging to the same type of objective are given the same weighting coefficients. Furthermore, to reduce the amplitude of the common-mode voltage, a common-mode voltage penalty term is added to the cost function. Additionally, by combining the current limiting constraint term, a cost function corresponding to the voltage vector is constructed. : in, The magnitude of the common-mode voltage generated by the inverter under the action of 8 voltage vectors is used as an additional constraint condition. The weighting coefficients for the dq-axis current control target are the weighting coefficients for the current tracking error term. The weighting coefficients for the common-mode voltage control objective, i.e., the weighting coefficients for the common-mode voltage suppression term; and These are the current setpoints for the dq axes, respectively. This refers to the d-axis current value. This is the q-axis current value. and With common-mode voltage amplitude The dimensions are inconsistent, so in this embodiment, the weighting coefficients need to be tuned through experiments.

[0055] It should be noted that the weighting coefficients in the cost function serve to balance the importance of various control objectives. The determination of these weighting coefficients determines the dynamic performance of the system; therefore, they are crucial for achieving predictive current control using the control optimization model. The cost function designed in this invention balances common-mode voltage suppression and current error by adjusting the weighting coefficients to control the proportions of the primary constraint objective (current tracking) and the secondary constraint objective (common-mode voltage suppression) within the cost function. When the current tracking error is large, the weighting coefficient of the dq-axis current control objective is increased. The weighting of the common-mode voltage target is prioritized to ensure control accuracy; when the common-mode voltage amplitude is large, the weighting coefficient of the common-mode voltage control target is increased. The proportion is used to optimize the common-mode voltage.

[0056] For weighting coefficients and Traditionally, a constant weight allocation is determined based on a large number of experiments, which is a complex tuning process. Meanwhile, the permanent magnet synchronous motor control system is essentially a dynamic process that requires multi-objective adjustment of the weights according to different operating conditions and different operating stages. Therefore, this embodiment introduces fuzzy control theory to optimize the allocation of weight coefficients among multiple objectives and determines the weight coefficients in the cost function through fuzzy logic control.

[0057] Specifically, the step of determining the weight coefficients of the current tracking error term and the common-mode voltage suppression term using a fuzzy controller based on the current deviation and the deviation change rate includes: calculating the q-axis current deviation E based on the q-axis current setpoint in the dq-axis current setpoint and the q-axis current value in the dq-axis current value at the current moment; and using the change in the q-axis current deviation relative to the previous moment as the deviation change rate EC of the q-axis current. The q-axis current deviation E and the deviation change rate EC are used as input variables of the fuzzy controller. Fuzzy inference is performed according to a preset fuzzy rule base, and the weight coefficients of the current tracking error term and the common-mode voltage suppression term are output and updated in the cost function.

[0058] Furthermore, the q-axis current deviation E and the rate of change of the q-axis current deviation EC are used as input variables of the fuzzy controller. Fuzzy inference is performed according to the preset fuzzy rule base, and the weighting coefficients of the current tracking error term and the common-mode voltage suppression term are output. The specific steps are as follows: The q-axis current deviation E and the rate of change of the q-axis current deviation EC are used as input variables of the fuzzy controller, with weighting coefficients... and As an output variable, when the difference between the current setpoint and the actual value is large, current following performance should be considered as the primary objective; if the difference is small, the common-mode voltage suppression effect should be considered.

[0059] Define weight coefficients The output range is [0.4, 0.8]. Furthermore, since the common-mode voltage plays an optimizing role in the steady-state current, a weighting coefficient is defined. [0.01, 0.5].

[0060] Discretize the input and output variables, and form a fuzzy set of input and output: {NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large)}.

[0061] Based on the above requirements, the fuzzy rules for establishing the weight function in this embodiment are shown in Table 1.

[0062] Table 1. Fuzzy rule table for weighting functions

[0063] It should be noted that the first value in each cell of Table 1 corresponds to a weighting coefficient. The fuzzy output, the second value corresponds to the weight coefficient. The fuzzy output; for example, when E is PB and EC is PB, the weighting coefficients... The output is PB, with weighting coefficients. The output is NB, which means that the current deviation is large and increasing rapidly. In this case, the current tracking performance should be prioritized and the weight of common-mode voltage suppression should be reduced.

[0064] Furthermore, the q-axis current deviation E and the rate of change of the q-axis current deviation EC are used as input variables of the fuzzy controller. Fuzzy inference is performed according to a preset fuzzy rule base, and the weighting coefficients of the current tracking error term are output. Weighting coefficient of the common-mode voltage rejection term And update it in the cost function.

[0065] It should be noted that by using fuzzy logic control to determine the weight coefficients in the cost function, the weights can be adjusted for multiple objectives according to different operating conditions and stages, avoiding the tuning process of determining a constant weight allocation based on a large number of experiments, as is required in traditional methods. After optimizing the weight coefficients in the cost function with fuzzy logic control, the voltage vector that minimizes the cost function is selected in subsequent steps, and the corresponding switching state is applied to the inverter. This achieves the goal of effectively reducing common-mode voltage while maintaining current tracking performance, providing a basis for selecting the optimal voltage vector based on the cost function value. In addition, the cost function comprehensively considers current tracking performance and common-mode voltage suppression effect, and the weight coefficients are adjusted through the fuzzy controller, enabling the system to maintain good control performance under different operating conditions.

[0066] S4: Substitute the Q voltage vectors and their corresponding current prediction values ​​into the cost function for evaluation, determine the optimal voltage vector based on the cost function value, and apply the switching state corresponding to the optimal voltage vector to the inverter to achieve common-mode voltage suppression based on model-predicted current control.

[0067] In this embodiment, after obtaining the cost function containing the current tracking error term and the common-mode voltage suppression term, as well as the determined weighting coefficients, it is necessary to evaluate all candidate voltage vectors, select the optimal control scheme, and apply it to the inverter. In model predictive current control, the eight voltage vectors in step S2 are compared and optimized using the cost function, and the voltage vector that minimizes the cost function value is selected as the optimal voltage vector, thereby achieving common-mode voltage suppression while ensuring current tracking performance. Since single-vector model predictive current control selects only one voltage vector in each control cycle, compared with the continuous modulation method, the steady-state current fluctuation is obvious and the current harmonic content is high, affecting system efficiency and electromagnetic compatibility. Therefore, this invention adopts dual-vector model predictive current control, selecting two effective voltage vectors in each control cycle and calculating their optimal action time to improve the response speed of the current loop and reduce current harmonics, thereby enabling the motor to have a good control effect.

[0068] Specifically, substituting the Q voltage vectors and their corresponding predicted current values ​​into the cost function for evaluation, determining the optimal voltage vector based on the cost function value, and applying the switching state corresponding to the optimal voltage vector to the inverter includes substituting the predicted dq-axis current values ​​corresponding to the 8 voltage vectors in step S2 into the cost function in step S3. In this process, the cost function values ​​corresponding to the eight voltage vectors are obtained. These cost function values ​​reflect the comprehensive performance of each voltage vector at the current control moment, including the current tracking effect and the common-mode voltage suppression effect.

[0069] Furthermore, the voltage vector with the smallest cost function value is selected from the cost function values ​​corresponding to the eight basic voltage vectors, and the corresponding voltage vector is determined as the first optimal voltage vector. This makes the output current as close as possible to the given current.

[0070] It should be noted that the first optimal voltage vector is the optimal voltage vector selected in the traditional model predictive current control, which can achieve the best overall effect of current tracking error and common-mode voltage suppression at the current control moment; while on the basis of the optimal voltage vector selected by the traditional model predictive current control, the present invention needs to perform a voltage vector selection to determine the second optimal voltage vector in order to realize dual-vector model predictive current control.

[0071] Furthermore, after determining the first optimal voltage vector, the voltage vector with the second smallest cost function value is selected as the second optimal voltage vector. .

[0072] According to the first optimal voltage vector With the second optimal voltage vector The corresponding current tracking error is addressed by allocating the action time of the first optimal voltage vector and the second optimal voltage vector within one control cycle. This allows the predicted current to be closer to the given current within one control cycle, while maintaining the common-mode voltage suppression effect. Simultaneously, the sum of the action time of the first optimal voltage vector and the action time of the second optimal voltage vector is made equal to the control cycle, ensuring that the inverter continuously outputs an effective control voltage throughout the entire control cycle.

[0073] Furthermore, based on the said application time, the switching states corresponding to the first optimal voltage vector and the second optimal voltage vector are sequentially applied to the inverter within the control cycle. Specifically, within the control cycle, firstly, based on the application time of the first optimal voltage vector, the first optimal voltage vector is... The corresponding switching state is applied to the inverter; after the first optimal voltage vector has finished acting, the second optimal voltage vector is applied according to its duration. The corresponding switching state is applied to the inverter; by applying it sequentially, the combined effect of the two voltage vectors is achieved within one control cycle.

[0074] It should be noted that each voltage vector corresponds to a specific set of three-phase bridge arm switching states. By applying the switching states to the power switching transistors of the inverter, the inverter can be controlled to output the corresponding voltage vector, thereby achieving precise control of the permanent magnet synchronous motor. When the next control cycle arrives, the above process of collecting current and position information, predicting current, evaluating the cost function, and selecting and applying a new optimal voltage vector is repeated to form a closed-loop control, making the predicted current closer to the given current and reducing current fluctuations and harmonic content. At the same time, through multi-objective optimization of the cost function, the common-mode voltage amplitude is effectively limited while ensuring current tracking performance, reducing the risk of bearing erosion and electromagnetic conduction interference, and improving the reliability and electromagnetic compatibility of the motor drive system.

[0075] On the other hand, this embodiment also provides a common-mode voltage suppression system based on model predictive current control, which includes: The current acquisition module acquires the current value and electric angular velocity of the permanent magnet synchronous motor at the current moment, and also acquires the current setpoint.

[0076] The current prediction module uses a current prediction model to predict the current value at the next moment for each of the Q voltage vectors of the inverter, combining the current value and the electric angular velocity; where Q represents the number of voltage vectors determined by the inverter's switching state.

[0077] The cost function module constructs a cost function containing a current tracking error term and a common-mode voltage suppression term based on the predicted current value and the given current value, and determines the weight coefficients of the current tracking error term and the common-mode voltage suppression term through a fuzzy controller based on the current deviation and the deviation change rate.

[0078] The common-mode voltage suppression module substitutes the Q voltage vectors and their corresponding current prediction values ​​into the cost function for evaluation, determines the optimal voltage vector based on the cost function value, and applies the switching state corresponding to the optimal voltage vector to the inverter to achieve common-mode voltage suppression based on model-predicted current control.

[0079] If the above 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, or the part that contributes to the prior art, or a part 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.

[0080] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0081] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0082] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0083] Example 2, refer to Figures 2-7 As an embodiment of the present invention, a common-mode voltage suppression method based on model predictive current control is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0084] To test the performance of the common-mode voltage suppression strategy based on model predictive current control, a simulation model was built on the MATLAB / Simulink platform in this embodiment. The motor was set to start under no-load conditions, with a sudden 5 N·m load applied at 0.2 s. Without considering the inverter dead zone, the simulated waveforms under the common-mode voltage suppression strategy based on model predictive current control were obtained as follows: Figures 2-5 As shown. Figure 2 The image shows a waveform of the rotational speed, where the horizontal axis represents time and the vertical axis represents rotational speed. Figure 2 As can be seen, during the startup phase, the motor recovers to the target speed from a standstill in 0.012s, demonstrating a fast dynamic response. Furthermore, when a 5 N·m load is suddenly applied in 0.2s, the speed experiences a brief drop (not exceeding 50 r / min), but quickly recovers to the target speed within approximately 0.005s, with a steady-state error of almost zero. This demonstrates the excellent load disturbance resistance capability of this invention. Figure 3 The figure shows the torque waveform, where the horizontal axis represents time and the vertical axis represents torque. It can be seen that when the common-mode voltage suppression strategy using model predictive current control is adopted, the torque fluctuation during no-load operation is controlled within ±0.5 N·m. After a sudden load change, the output torque follows the target value of 5 N·m within approximately 0.003 s, with an overshoot of less than 0.8 N·m. This indicates that the multi-objective optimization of the cost function can ensure torque control accuracy while suppressing common-mode voltage. Figure 4 The figure shows the waveforms of three-phase currents. The horizontal axis represents time, and the vertical axis represents current. The figure displays the three-phase currents. , and The waveform exhibits good symmetry, presenting a relatively smooth sine wave with minimal current distortion, indicating that this invention, through a dual-vector model predictive current control strategy, effectively improves current quality by rationally allocating the action time of the two optimal voltage vectors within each control cycle. Furthermore, from... Figure 5 The spectrum shows that the horizontal axis represents frequency and the vertical axis represents the percentage of each harmonic amplitude to the fundamental frequency. The fundamental component has the largest amplitude and is dominant, while the higher harmonic components have smaller amplitudes and show a gradual attenuation trend. The total harmonic distortion (THD) of the three-phase stator current is reduced to 6.33%, indicating that the present invention effectively suppresses current harmonics and maintains good current waveform quality.

[0085] To verify the effectiveness of the model-predictive current control-based common-mode voltage suppression strategy in suppressing common-mode voltage, the simulated common-mode voltage waveform was obtained as follows: Figure 6 and Figure 7 As shown, the horizontal axis represents time, and the vertical axis represents common-mode voltage. ;in, Figure 6 The common-mode voltage waveforms of the entire simulation process are shown in the figure. It can be seen from the figure that when a common-mode voltage suppression strategy based on model predictive current control is adopted, the use of zero vectors is reduced, and the common-mode voltage... Within a fluctuation of ±12.5V, high-amplitude common-mode voltage is significantly suppressed; furthermore, Figure 7 for Figure 6 The enlarged view shows that the common-mode voltage jumps regularly between ±12.5V and 0V, with a regular waveform and no abnormal spikes or glitches, which verifies that the present invention can effectively limit the common-mode voltage amplitude to within ±Udc / 6, achieving the expected common-mode voltage suppression effect.

[0086] In addition, in order to fully verify the advantages of the common-mode voltage suppression strategy based on model prediction current control over traditional algorithms, this embodiment also sets the same simulation conditions in MATLAB / Simulink to verify the key performance indicators of the three modulation strategies. The specific comparison results are shown in Table 2.

[0087] Table 2 Performance Comparison Table

[0088] As shown in Table 2, SVPWM is a highly efficient modulation strategy widely used in inverter drives, offering high DC bus voltage utilization and lower current harmonics. However, due to the use of zero vectors, the peak common-mode voltage of SVPWM is ±Udc / 2, which can easily lead to bearing current and EMI problems. AZSPWM1 is an improved PWM modulation strategy specifically designed to suppress common-mode voltage in inverter systems. AZSPWM1 can suppress the common-mode voltage amplitude to within ±Udc / 6 by disabling zero vectors, but at the expense of current quality, affecting the smoothness of motor operation. In contrast, the common-mode voltage suppression strategy based on model predictive current control proposed in this invention constrains the common-mode voltage through a cost function and selects the optimal switching combination, thus limiting the common-mode voltage amplitude to ±Udc / 6 while maintaining good current quality.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A common-mode voltage suppression method based on model predictive current control, characterized in that, include: Obtain the current value and electric angular velocity of the permanent magnet synchronous motor at the current moment, and obtain the current setpoint; For the Q voltage vectors of the inverter, a current prediction model is used, combined with the current value and electric angular velocity, to predict the current value at the next moment. Where Q represents the number of voltage vectors determined by the inverter's switching state; Based on the predicted current value and the given current value, a cost function containing a current tracking error term and a common-mode voltage suppression term is constructed, and the weight coefficients of the current tracking error term and the common-mode voltage suppression term are determined by a fuzzy controller based on the current deviation and the deviation change rate. The Q voltage vectors and their corresponding predicted current values ​​are substituted into the cost function for evaluation. The optimal voltage vector is determined based on the cost function value, and the switching state corresponding to the optimal voltage vector is applied to the inverter to achieve common-mode voltage suppression based on model-predicted current control. Based on the predicted current value and the given current value, the cost function that includes a current tracking error term and a common-mode voltage suppression term is constructed by calculating the difference between the predicted current value and the given current value corresponding to each voltage vector, and determining the current tracking error term corresponding to each voltage vector based on the difference. For each voltage vector, a current limiting term is constructed based on the relationship between the magnitude of the current prediction value and the preset current limit value. Based on the current tracking error term and the current limiting term, the common-mode voltage amplitude of the inverter output is calculated according to the relationship between the three-phase bridge arm switch state corresponding to each voltage vector and the DC bus voltage, and the common-mode voltage amplitude is used as the common-mode voltage suppression term. The current tracking error term and the common-mode voltage suppression term are weighted and combined, and combined with the current limiting term, to construct the cost function corresponding to the voltage vector.

2. The common-mode voltage suppression method based on model predictive current control as described in claim 1, characterized in that: The process of obtaining the current value and electric angular velocity of the permanent magnet synchronous motor at the current moment, and obtaining the current setpoint, includes measuring the three-phase current of the stator winding of the permanent magnet synchronous motor to obtain the three-phase current value in the natural coordinate system. Measure the rotor position angle of the permanent magnet synchronous motor, and calculate the electric angular velocity based on the rotor position angle and the sampling period; The three-phase current values ​​are transformed from the natural coordinate system to the stationary coordinate system by Clark transformation to obtain the current variables in the stationary coordinate system. The current variables are then transformed to the rotating coordinate system dq by Park transformation. The coordinate transformation matrix of the Park transformation is determined by the rotor position angle to obtain the dq axis current value at the current moment. Based on the maximum torque-to-current ratio control strategy, the dq-axis current setpoint is determined by curve fitting based on the torque setpoint.

3. The common-mode voltage suppression method based on model predictive current control as described in claim 2, characterized in that: The method for predicting the current prediction value at the next moment for the Q voltage vectors of the inverter by using a current prediction model and combining the current value and electric angular velocity includes: based on the stator voltage equation of the permanent magnet synchronous motor in the rotating coordinate system dq, discretizing it using the first-order forward Euler method to establish a current prediction model; the current prediction model includes stator resistance, dq-axis inductance and permanent magnet flux linkage as motor parameters. The Q voltage vectors are converted into voltage components in the rotating coordinate system dq through coordinate transformation, resulting in Q dq-axis voltage components. The Q dq-axis voltage components, the current dq-axis current value at the current moment, the electric angular velocity, and the motor parameters are respectively substituted into the current prediction model for calculation. For each voltage vector, the corresponding d-axis current prediction value and q-axis current prediction value at the next moment are output to form the dq-axis current prediction values ​​corresponding to the Q voltage vectors.

4. The common-mode voltage suppression method based on model predictive current control as described in claim 3, characterized in that: The step of determining the weighting coefficients of the current tracking error term and the common-mode voltage suppression term by a fuzzy controller based on the current deviation and the deviation change rate includes calculating the q-axis current deviation based on the q-axis current setpoint in the dq-axis current setpoint and the q-axis current value in the dq-axis current value at the current moment, and using the change of the q-axis current deviation relative to the previous moment as the deviation change rate of the q-axis current. The q-axis current deviation and the rate of change of the q-axis current deviation are used as input variables of the fuzzy controller. Fuzzy inference is performed according to the preset fuzzy rule base. The weight coefficients of the current tracking error term and the weight coefficients of the common-mode voltage suppression term are output and updated in the cost function.

5. The common-mode voltage suppression method based on model predictive current control as described in claim 4, characterized in that: The step of performing fuzzy inference based on a preset fuzzy rule base and outputting the weight coefficients of the current tracking error term and the common-mode voltage suppression term includes defining the output range of the weight coefficients of the current tracking error term and the output range of the weight coefficients of the common-mode voltage suppression term. The q-axis current deviation, the rate of change of the q-axis current deviation, the weighting coefficient of the current tracking error term, and the weighting coefficient of the common-mode voltage suppression term are discretized into a preset fuzzy set, which includes negative large, negative small, zero, positive small, and positive large. Based on the relationship between the q-axis current deviation and the rate of change of the q-axis current deviation, the weighting coefficients of the current tracking error term and the common-mode voltage suppression term are determined through a preset fuzzy rule table.

6. The common-mode voltage suppression method based on model predictive current control as described in claim 5, characterized in that: Substituting the Q voltage vectors and their corresponding current prediction values ​​into the cost function for evaluation, determining the optimal voltage vector based on the cost function value, and applying the switching state corresponding to the optimal voltage vector to the inverter, including substituting the dq axis current prediction values ​​corresponding to the Q voltage vectors into the cost function to obtain the cost function values ​​corresponding to the Q voltage vectors; Select the cost function value with the smallest value from the cost function values ​​corresponding to the Q voltage vectors, and determine the corresponding voltage vector as the first optimal voltage vector; After the first optimal voltage vector is determined, the voltage vector with the second smallest cost function value is selected as the second optimal voltage vector; Based on the current tracking error corresponding to the first optimal voltage vector and the second optimal voltage vector, allocate the action time of the first optimal voltage vector and the second optimal voltage vector within a control cycle; Based on the said operating time, the switching states corresponding to the first optimal voltage vector and the second optimal voltage vector are sequentially applied to the inverter within the control cycle.

7. A common-mode voltage suppression system based on model predictive current control using the method described in any one of claims 1-6, characterized in that: The current acquisition module acquires the current value and electric angular velocity of the permanent magnet synchronous motor at the current moment, and acquires the current setpoint. The current prediction module uses a current prediction model to predict the current value at the next moment for each of the Q voltage vectors of the inverter, combined with the current value and the electric angular velocity. Where Q represents the number of voltage vectors determined by the inverter's switching state; The cost function module constructs a cost function containing a current tracking error term and a common-mode voltage suppression term based on the predicted current value and the given current value, and determines the weight coefficients of the current tracking error term and the common-mode voltage suppression term through a fuzzy controller based on the current deviation and the deviation change rate. The common-mode voltage suppression module substitutes the Q voltage vectors and their corresponding current prediction values ​​into the cost function for evaluation, determines the optimal voltage vector based on the cost function value, and applies the switching state corresponding to the optimal voltage vector to the inverter to achieve common-mode voltage suppression based on model-predicted current control.

8. A computer device, comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-6.

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