Model-free deadbeat predictive current control method for permanent magnet synchronous motor based on current separation

Abstract

The invention belongs to the technical field of motor driving and control application, and provides a permanent magnet synchronous motor model-free and dead-beat predictive current control method based on current separation, and the main scheme is as follows: firstly, a permanent magnet synchronous motor current separation model is introduced, and the relationship between different parts in sampling current change is deduced based on the model; then, a model-free dead-beat control integral control law with extremely low parameter dependence is designed; furthermore, only a series of uncomplicated calculations need to be carried out in a control period, and more accurate predicted current and predicted voltage can be calculated without depending on motor parameters. Therefore, no observer is needed, and errors and oscillation possibly brought by the observer are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of motor drive and control application, and in particular to a model-free deadbeat prediction current control method for a permanent magnet synchronous motor based on current separation. Background Art

[0002] With the rapid development of permanent magnet material technology, permanent magnet materials are being used more and more in the industrial field. Permanent Magnetic Synchronous Motor (PMSM) has been widely used in aerospace, electric vehicles, industrial manufacturing, agricultural production and other fields due to its high power density and high efficiency. Compared with traditional motors, PMSM has the characteristics of simple structure, low rotor loss and high power factor, making PMSM the focus of research in recent years.

[0003] Field Oriented Control (FOC) and Direct Torque Control (DTC) are two classic methods in PMSM control systems. Although the above two methods have good performance, they still have some problems in control. In actual PMSM control systems, the key to determining its current and torque output capabilities is the current control performance. Since the control effect of traditional PMSM control methods is not ideal under conditions such as sudden changes in motor speed and load changes, it is difficult to meet higher requirements. Therefore, advanced control methods are needed in high-performance nonlinear and strongly coupled complex control systems. Model predictive control (MPC) is an advanced intelligent control strategy that can handle nonlinear multi-objective parameters and has fast dynamic response speed. It is suitable for multi-variable and strongly coupled PMSM systems. However, since the MPC control method relies on the accuracy of the motor model, the model parameters (such as stator resistance, flux linkage, torque constant) in actual working conditions are easily affected by factors such as temperature and magnetic saturation, resulting in a decrease in control performance. Model-Free Predictive Control (MFPC) is a data-driven control method that avoids dependence on precise motor models and can better adapt to parameter changes and complex nonlinear systems. Since the deadbeat predictive current control (DPCC) control strategy has better control performance and a simpler calculation process than the model predictive current control (MPCC), model-free deadbeat control (MFDPCC) has been studied by many scholars. MFDPCC estimates the output optimal reference voltage to make the current close to the reference current, and has excellent steady-state performance, but is more sensitive to changes in PMSM model parameters. Some scholars use hyperlocal models and error observers to estimate motor parameter errors, but in fact, they transfer the sensitivity to PMSM model parameters to the sensitivity to observer parameters, and the selection of observer parameters is very complicated.

[0004] The current separation method uses the proposed adaptive strategy to separate the natural response component and the forced response component in the current change, and simultaneously calculates the current change corresponding to the unit voltage vector. By adaptively adjusting these two components, the actual current can easily track the reference current, avoiding the cumbersome parameter adjustment process of the traditional MFDPCC. Some scholars have used the current separation method to reduce the current prediction error, but the prediction error is still large, and the control law parameters are selected as constants based on experience. Summary of the invention

[0005] The purpose of the present invention is to provide a model-free and zero-beat predictive current control method for a permanent magnet synchronous motor based on current separation, aiming to solve the problems of parameter sensitivity and poor robustness due to model-based predictive current control. It can estimate the parameters of the adaptive control law in each cycle, effectively reduce the dependence on the control system parameters, and improve the robustness of the system parameters.

[0006] The present invention solves the technical problem and adopts the following technical solution: The method for model-free deadbeat predictive current control of a permanent magnet synchronous motor based on current separation comprises the following steps: Acquisition of phase current , , And bus voltage , the sampled phase current is transformed into park, and the dq Stator current in rotating coordinate system , , at the same time, read the k -1) Voltage vector for periodic application , then read the first ( k -1) Predicted voltage calculated by cycle Acting on the inverter; With given speed Subtract feedback speed , obtained after calculation by PI controller dq Reference current in rotating coordinate system , and set d Axis reference current ; The first ( k ) cycle and the first ( k -1) obtained by periodic sampling , Make a difference, get The corresponding current change ; Read in ( k -1) Natural response of current applied periodically , read in ( k -1) Current prediction error integral for periodic applications , read in ( k -1) Unit voltage and current change in cycle application ; Read the first k -1) Cycle-calculated adaptive integral control law predicts current , the current prediction error per unit time is calculated , and calculate the first ( k) Current prediction error integral for periodic applications ; Using the adaptive integral control law, calculate the first ( k ) Periodic adaptive integral coefficient ; Set the adaptive integral coefficient Threshold and , as an adaptive adjustment Parameters: The adaptive integral control law is used to calculate the k ) The natural response of current to cyclic application ; Calculate the ( k )Period unit voltage and current change , thus calculating ( k +1) Cycle Prediction Current and( k +1) Predicted voltage at the moment , prepare for the inverter output of the next cycle; Repeat the above steps in sequence to form a closed-loop control.

[0007] As a further optimization, the permanent magnet synchronous motor dq The mathematical model in the rotating coordinate system is: , In the formula , They are dq Stator current in the axis rotating coordinate system, , They are dq The stator voltage in the axis rotation coordinate system, L d , L q yes dq Inductance in the axis rotation coordinate system, , R s and They are the flux linkage of the permanent magnet, the stator resistance and the electrical angular velocity of the rotor.

[0008] As a further optimization, the forward Euler method is used to calculate the permanent magnet synchronous motor dq The mathematical model in the rotating coordinate system is discretized and expressed as: , in, T s is the control period, ( k )and( k+1) respectively correspond to the first ( k ) and ( k +1) control cycles.

[0009] As a further optimization, the current changes It is expressed as: , In the formula, For the first k -1) sampling current of a control cycle, For the first k ) control cycle sampling current.

[0010] As a further optimization, the current prediction error per unit time is It is expressed as: , in, For the first k -1) The predicted current calculated for each control cycle; The said k ) Current prediction error integral for periodic applications It is expressed as: , in, For the first k -1) Integral of the current prediction error for periodic applications.

[0011] As a further optimization, the adaptive integral control law is expressed as: , in, For the first k ) cycle control law adaptive integral coefficient, for The upper threshold of for The lower threshold of .

[0012] As a further optimization, the adaptive integral coefficient is set Threshold and , as an adaptive adjustment The parameters are: when When to ; when When to ; when When The value of remains unchanged.

[0013] As a further optimization, the k )Period unit voltage and current change It is expressed as: , in, For the first k -1) The predicted voltage obtained by periodic calculation; Said k +1) Cycle Prediction Current and( k +1) Predicted voltage at the moment It is expressed as: , in, is the reference current.

[0014] The beneficial effects of the present invention are: 1. The present invention obtains current change information directly from the change of sampled current, does not rely on any motor model, and improves the reliability of the control system.

[0015] 2. The adaptive control law proposed in the present invention can automatically update the integral parameter in each cycle, thereby improving the accuracy of predicting the current in each cycle.

[0016] 3. The present invention only needs to set a threshold value for the integral parameter of the control law within the stable domain, and there is almost no parameter adjustment process, which greatly simplifies the complexity of the control system and improves the versatility of the control system.

[0017] 4. The present invention does not require any error observer, thus avoiding current oscillation and harmonic errors that may be caused by the observer and improving the stability of the control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a block diagram of the model-free deadbeat predictive current control principle in an embodiment of the present invention; Figure 2 This is a flow chart of MFDPCC control in an embodiment of the present invention; Figure 3 This is a waveform diagram of the MFDPCC experiment in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations. Example

[0020] See also Figure 1 The model-free deadbeat prediction current control principle block diagram includes a speed outer loop PI regulator, a current prediction module, an inverter, a current sensor and a permanent magnet synchronous motor. The present embodiment provides a model-free deadbeat prediction current control method for a permanent magnet synchronous motor based on current separation, including the following steps: Step 1: Collect phase current through current sensor , , And bus voltage , the sampled phase current is transformed into park, and the dq Stator current in rotating coordinate system , , at the same time, read the k -1) Voltage vector for periodic application , then read the first ( k -1) Predicted voltage calculated by cycle Acts on the inverter.

[0021] Step 2: Use a given speed Subtract feedback speed , obtained after calculation by PI controller dq Reference current in rotating coordinate system , and set d Axis reference current .

[0022] Step 3: k ) cycle and the first ( k -1) obtained by periodic sampling , Make a difference, get The corresponding current change , specifically expressed as: , In the formula, For the first k ) control cycle sampling current, For the first k -1) The sampled current of a control cycle. This quantity can be obtained only by sampling the current without any motor model.

[0023] Step 4: Read the k -1) Natural response of current applied periodically , in the ( k -1) Current prediction error integral for periodic applications , in the ( k -1) Unit voltage and current change in cycle application If the information read is 0, use a smaller number, such as 0.01, to prevent calculation problems.

[0024] Step 5: Read the k -1) Cycle-calculated adaptive integral control law predicts current , the current prediction error per unit time is calculated , and calculate the first ( k ) Current prediction error integral for periodic applications ， Current prediction error Specifically expressed as: , in, For the first k -1) The predicted current calculated for each control cycle; Current prediction error integral Specifically expressed as: , in, For the first k -1) Integral of the current prediction error for periodic applications, T s To control the cycle.

[0025] The adaptive integral control law in this embodiment is used because the parameter setting process for conventional control laws or models with observers is relatively complicated in actual experiments, and the current prediction error is greatly affected by these parameters. Taking the conventional control law as an example, even in the same motor, the general parameters are usually designed as constants, and there will be a large difference in the size of the predicted current error under different reference speed settings. Therefore, in this embodiment, the parameters of the control law can be directly estimated using the information of the sampled current, which greatly reduces the sensitivity of the control system to the parameters. At this time, the use of adaptive parameters for model-free predictive control can obtain a more accurate predicted voltage and reduce the predicted current error.

[0026] Step 6: Using the adaptive integral control law proposed in this embodiment, calculate the ( k ) Periodic adaptive integral coefficient , the specific derivation is as follows: Permanent magnet synchronous motor dq The mathematical model in the rotating coordinate system is: , In the formula , They are dq Stator current in the axis rotating coordinate system, , They are dq The stator voltage in the axis rotation coordinate system, L d , L q yes dq Inductance in the axis rotation coordinate system, , R s and They are the flux linkage of the permanent magnet, the stator resistance and the electrical angular velocity of the rotor.

[0027] The forward Euler method is used to discretize the above formula: , in, T s is the control period, ( k )and( k +1) respectively correspond to the first ( k ) and ( k +1) control cycles.

[0028] According to the deadbeat control principle, the actual current is made to track the reference current at the beginning of the next control cycle after the predicted voltage is applied to the inverter. Then, the predicted voltage can be expressed by the above formula as: , in, , yes dq Axis reference current.

[0029] Considering the one-step delay in the control system, the first ( k ) and ( k +1) The result calculated in the control cycle will be loaded in the next control cycle, so a one-step delay compensation is required: , According to the current separation method, the change of the sampling current includes two parts: , in, is the natural response to current changes and can be considered a constant in steady state. is the forced response to voltage changes.

[0030] According to the PMSM discrete model, and It can be expressed as: , in, , , are all related to the PMSM model, in particular, It can be expressed as: , at this time, The meaning of can be regarded as the forced response of current change to unit voltage, recorded as unit voltage current change. At this time, redefine for : , in, For the first k ) control cycle, that is, the voltage vector applied to the inverter in the ( k -1) The predicted voltage calculated in the control cycle, In the k ) control cycles will cause the current to force response .

[0031] Therefore, the sampling current change of the current separation method can be expressed as: , Furthermore, the predicted current of the next cycle can be expressed as: , Considering one-step delay compensation, in order to make the next cycle sampling current close to the reference current, the above formula can be used to replace the predicted current with the reference current to derive the predicted voltage. The predicted reference voltage can be expressed as: , It can be seen that the reference voltage prediction formula does not involve any content related to PMSM parameters. At the same time, since the control cycle is short enough, If there is no obvious change in two consecutive cycles, it can be considered .

[0032] In summary, the basic control law of the control system is formed. Considering the robustness of the control system, the current prediction error integral information is added to the control law, and the basic control law can be expressed as: , in, For the firstk ) cycle current prediction error, For the first k ) cycle control law integral coefficient, For the first k -1) The current prediction error integral calculated periodically.

[0033] In this embodiment, the following process proves It can be directly represented by the sampled current information: No. ( k -1) The predicted reference voltage obtained by period calculation can be expressed as: , Thus, k )Natural response to periodic current changes It can be expressed as: , Considering , from the above formula we can get expression: , It can be seen that the control law integral coefficient It can be obtained from the information of current change and can be adaptively adjusted every cycle.

[0034] Step 7: Set the adaptive integral coefficient Threshold and , as an adaptive adjustment Parameters: when When to ; when When to ; when When The value of remains unchanged.

[0035] In this embodiment, since the reference current is not stable during the speed change, it is necessary to set the adaptive integral coefficient Threshold and , as an adaptive adjustment The parameters of the control system are adjusted to keep it stable. First, the stability of the basic control law is analyzed: pass Z Transformation, transfer function of the basic control law for: , List the characteristic equations of the calculation system, we have: , So the characteristic root can be calculated as: , When the system is stable, the characteristic roots must be within the unit circle, and the calculation can be obtained , When the system is stable Value range.

[0036] Next, just make sure and , near the border, set Threshold and In this embodiment, the following judgment is made to adjust , which can keep the system stable: when When to ; when When to ; when When The value of remains unchanged.

[0037] Here, set Threshold and The purpose is to make the adaptive parameters fall within the stable range of the control system. Assuming that the control law parameters are constants, the range of parameter values ​​within the system stable domain calculated from the transfer function of the control law is determined. However, since the reference current may change greatly when the speed is not stable, it may affect the calculation of the adaptive coefficient. Therefore, setting a threshold can prevent the adaptive coefficient from being too large or too small, making the system unstable. At the same time, in this embodiment, the threshold is designed to directly prevent the adaptive parameters from falling into the unstable domain, and the value of the threshold is very easy to determine, as long as it is near the boundary of the system stable domain. The prediction error is automatically adjusted by the adaptive change of the coefficient.

[0038] Step 8: Use the adaptive integral control law to calculate the k ) The natural response of current to cyclic application , It can be expressed as: , Step 9: Calculate the ( k )Period unit voltage and current change , thus calculating ( k +1) Cycle Prediction Current and( k +1) Predicted voltage at the moment , preparing for the inverter output of the next cycle.

[0039] Specifically, the current separation method can be used to deduce : , Furthermore, the predicted current can be expressed as: , Therefore, according to the calculation method mentioned above, the predicted voltage can be expressed as: , Step 10: Repeat steps 1 to 9 to form a closed-loop control. The above MFDPCC control process is as follows: Figure 2 shown.

[0040] Finally, the present embodiment is verified by experiments. The experimental data is used to generate waveforms through MATLAB / Simulink. Figure 3 The experimental results are at a reference speed of 600rpm and a load torque of 5Nm. When the system is stable, dq The axis feedback current coincides with the reference current, which indicates that the MFDPCC method proposed in the present invention has good current tracking performance.

[0041] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A model-free deadbeat predictive current control method for a permanent magnet synchronous motor based on current separation, characterized in that: The steps include: Acquisition of phase current , , And bus voltage , the sampled phase current is transformed into park, and the dq Stator current in rotating coordinate system , , at the same time, read the k -1) Voltage vector for periodic application , then read the first ( k -1) Predicted voltage calculated by cycle Acting on the inverter; With given speed Subtract feedback speed , obtained after calculation by PI controller dq Reference current in rotating coordinate system , and set d Axis reference current ; The first ( k ) cycle and the first ( k -1) obtained by periodic sampling , Make a difference, get The corresponding current change ; Read in ( k -1) Natural response of current applied periodically , read in ( k -1) Current prediction error integral for periodic applications , read in ( k -1) Unit voltage and current change in cycle application ; Read the first k -1) Cycle-calculated adaptive integral control law predicts current , the current prediction error per unit time is calculated , and calculate the first ( k ) Current prediction error integral for periodic applications ; Using the adaptive integral control law, calculate the first ( k ) Periodic adaptive integral coefficient ; Set the adaptive integral coefficient Threshold and , as an adaptive adjustment Parameters: The adaptive integral control law is used to calculate the k ) The natural response of current to cyclic application ; Calculate the ( k )Period unit voltage and current change , thus calculating ( k +1) Cycle Prediction Current and( k +1) Predicted voltage at the moment , prepare for the inverter output of the next cycle; Repeat the above steps in sequence to form a closed-loop control.

2. The method for model-free deadbeat predictive current control of a permanent magnet synchronous motor based on current separation according to claim 1, characterized in that: Permanent magnet synchronous motor dq The mathematical model in the rotating coordinate system is: ， In the formula , They are dq Stator current in the axis rotating coordinate system, , They are dq The stator voltage in the axis rotation coordinate system, L d , L q yes dq Inductance in the axis rotation coordinate system, , R s and They are the flux linkage of the permanent magnet, the stator resistance and the electrical angular velocity of the rotor.

3. The method for model-free deadbeat predictive current control of a permanent magnet synchronous motor based on current separation according to claim 2, characterized in that: The forward Euler method is used to simulate the permanent magnet synchronous motor dq The mathematical model in the rotating coordinate system is discretized and expressed as: ， in, T s is the control period, ( k )and( k +1) respectively correspond to the first ( k ) and ( k +1) control cycles.

4. The method for model-free deadbeat predictive current control of a permanent magnet synchronous motor based on current separation according to claim 1, characterized in that: The current changes It is expressed as: ， In the formula, For the first k -1) sampling current of a control cycle, For the first k ) control cycle sampling current.

5. The method for model-free deadbeat predictive current control of a permanent magnet synchronous motor based on current separation according to claim 4, characterized in that: The current prediction error per unit time It is expressed as: ， in, For the first k -1) The predicted current calculated for each control cycle; The said k ) Current prediction error integral for periodic applications It is expressed as: ， in, For the first k -1) Integral of the current prediction error for periodic applications.

6. The method for model-free deadbeat prediction current control of a permanent magnet synchronous motor based on current separation according to claim 1, characterized in that: The adaptive integral control law is expressed as: ， in, For the first k ) cycle control law adaptive integral coefficient, for The upper threshold of for The lower threshold of .

7. The method for model-free deadbeat predictive current control of a permanent magnet synchronous motor based on current separation according to claim 1, characterized in that: Setting the adaptive integral coefficient Threshold and , as an adaptive adjustment The parameters are: when When to ; when When to ; when When The value of remains unchanged.

8. The method for model-free deadbeat predictive current control of a permanent magnet synchronous motor based on current separation according to claim 1, characterized in that: The said k )Period unit voltage and current change It is expressed as: ， in, For the first k -1) The predicted voltage obtained by periodic calculation; Said k +1) Cycle Prediction Current and( k +1) Predicted voltage at the moment It is expressed as: ， in, is the reference current.

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