Dynamic flux fuzzy prediction torque control method for three-phase permanent magnet synchronous motor

Abstract

The invention relates to the technical field of motor driving control, and discloses a three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method. According to the method, a three-phase two-level permanent magnet synchronous motor mathematical model is established, and a flux weighting factor is dynamically adjusted in combination with fuzzy control, so that the torque and flux linkage of the motor are accurately controlled. Through two-step prediction and cost function optimization, an optimal voltage vector is selected, and in combination with a dead-beat torque control principle, an output pulse signal is optimized. According to the method, the real-time performance and robustness of the system are improved, the torque ripple and flux linkage fluctuation are effectively restrained, the total harmonic distortion of the output current is reduced, and the method is suitable for efficient and stable operation of the motor under the complex working condition.

Description

Technical Field

[0001] The present invention relates to the technical field of motor drive control, and in particular to a dynamic magnetic flux fuzzy prediction torque control method for a three-phase permanent magnet synchronous motor. Background Art

[0002] Three-phase permanent magnet synchronous motors (PMSMs), with their outstanding advantages of high efficiency, energy saving, and high power density, have been widely used in a wide range of fields, including new energy vehicles, precision industrial automation equipment, and various smart home appliances. As these applications continue to place increasing demands on motor performance, PMSM control technology faces a series of severe challenges, and various traditional control methods have exposed numerous shortcomings in their application.

[0003] In terms of vector control, traditional vector control relies on a precise mathematical model of the motor and is highly dependent on motor parameters. However, in actual operation, PMSM parameters vary with operating conditions such as temperature, speed, and load. For example, motor winding resistance increases with temperature, and the permanent magnet flux also fluctuates due to temperature. This causes the preset mathematical model to mismatch the actual motor state, resulting in inaccurate field orientation, reduced torque control accuracy, motor output torque fluctuations, and reduced operating efficiency. While direct torque control simplifies the control structure, it suffers from large torque and flux ripple. It uses a hysteresis comparator to select the voltage vector, making it impossible to precisely control the torque and flux change rate, resulting in significant torque ripple during motor operation. Torque ripple not only causes motor vibration and noise, affecting smooth operation, but also reduces the motor's mechanical life. It also increases current total harmonic distortion (THD), resulting in poor dynamic response. Finite set model predictive control (FCS-MPC) is a new control method. This control method, based on the mathematical model of the motor, predicts the motor's future state and selects the optimal vector from a limited set of voltage vectors within each sampling period to achieve control of motor torque, magnetic flux, and other targets. Due to its fast dynamic response, ability to effectively handle multiple constraints, and the lack of complex modulation steps, it is widely used in motor control applications at different power levels. However, in traditional model predictive control, the cost function used for judgment is highly dependent on fixed weighting factors and lacks adaptability to instantaneous changes in speed, torque, and other parameters. Once these parameters change, motor control performance will be severely degraded. Summary of the Invention

[0004] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a dynamic flux fuzzy predictive torque control method for a three-phase permanent magnet synchronous motor to solve the problems of poor motor control adaptability and strong parameter dependence in the prior art.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for dynamic flux fuzzy prediction torque control of a three-phase permanent magnet synchronous motor, the method comprising: The mathematical model of three-phase two-level permanent magnet synchronous motor is established to determine the corresponding relationship between the inverter switching state and the output voltage vector, and the discretization process is used to obtain Predicted values ​​of flux linkage and stator current at each moment; based on Calculation of predicted flux and stator current at each moment The torque prediction value at the moment is obtained by performing two-step prediction. The predicted value of flux linkage, stator current and torque at the moment; A dynamic flux weighted optimization fuzzy model predictive controller is constructed. The real-time motor speed and torque are used as fuzzy control input variables to dynamically adjust the flux weighting factor. The controller divides the fuzzy subsets according to the speed and torque variation range and generates dynamic weighting factors adapted to different operating conditions based on a preset fuzzy rule table. based on The predicted flux value, stator current value, torque value and cost function at the moment are traversed through the non-zero voltage vectors of the inverter, and the optimal voltage vector that minimizes the cost function is selected; Based on the deadbeat torque control principle, the duty cycle of the optimal voltage vector is calculated, the optimal voltage vector action time is allocated to optimize the output pulse signal, and the inverter switching state is controlled to achieve rapid tracking of the motor torque and flux.

[0006] Preferably, in a possible implementation manner of the first aspect, the three-phase two-level permanent magnet synchronous motor mathematical model includes a stator voltage equation, a flux equation and a torque equation, wherein the stator voltage equation is The rotating coordinate system is expressed as

[0007]

[0008] The magnetic flux equation is expressed as:

[0009] The torque equation is expressed as

[0010]

[0011] in 、 The stator voltage is The components in the rotated coordinate system, 、 The stator current is The components in the rotated coordinate system, 、 The stator flux is The components in the rotated coordinate system, is the stator resistance, is the motor angular velocity, is the permanent magnet flux, and for Shaft inductance component, is the electromagnetic torque, is the number of motor pole pairs.

[0012] Preferably, in a possible implementation of the first aspect, the discretization process is performed to obtain The predicted flux value and the corresponding predicted stator current value at each moment include: The stator voltage equation is discretized using the first-order Euler equation to obtain:

[0013] And the stator current prediction value is calculated based on the flux prediction value:

[0014] in and express The predicted value of magnetic flux at the moment The component below the coordinate axis, 、 for The stator voltage at The components in the rotating coordinate system are calculated using the stator voltage equation, 、 for The stator current at The components in the rotated coordinate system, and express The predicted value of magnetic flux at the moment The component below the coordinate axis, is the sampling period, 、 for The stator current at Components in the rotated coordinate system.

[0015] Preferably, in a possible implementation of the first aspect, The torque prediction value at the moment is:

[0016] The predicted magnetic flux at the moment is:

[0017] The predicted value of the stator current at the moment is:

[0018] The torque prediction value at the moment is:

[0019] in Indicates The torque prediction value at time t, and express The predicted value of magnetic flux at the moment The component below the coordinate axis, 、 for The stator voltage at The components in the rotating coordinate system are calculated using the stator voltage equation, and Respectively The predicted value of stator current at time The axis weight, Indicates The torque prediction value at the moment.

[0020] Preferably, in a possible implementation of the first aspect, the dynamic flux weighted optimization fuzzy model predictive controller collects the motor speed signal and torque signal in real time as dual input variables, and maps the input quantity to a preset fuzzy domain through fuzzy processing, wherein the speed fuzzy subset and the torque fuzzy subset are divided into five levels: NB, NS, ZO, PS, and PB, and the controller performs inference operations based on the fuzzy rules defined in the fuzzy rule base.

[0021] Preferably, in a possible implementation of the first aspect, the fuzzy rule is represented by fuzzy linguistic variables of input variables and output variables, The fuzzy rules are expressed as

[0022] in is the fuzzy subset of torque, is the fuzzy subset of the speed, is the fuzzy subset of dynamic weighting factors, For the The fuzzy level corresponding to the torque in the fuzzy rules, For the The fuzzy level corresponding to the speed in the fuzzy rules, For the The fuzzy level of the dynamic weighting factor output by the fuzzy rule.

[0023] Preferably, in a possible implementation of the first aspect, the cost function is defined as:

[0024] in is the torque reference value, and is the flux linkage reference value The constant reference component in the coordinate system, Indicates The torque prediction value at time t, and express The predicted value of magnetic flux at the moment The component below the coordinate axis, is the dynamic weighting factor.

[0025] Preferably, in a possible implementation manner of the first aspect, the process of selecting the optimal voltage vector includes: Traverse the six non-zero voltage vectors of the three-phase inverter and calculate the The moment-by-moment flux linkage prediction value and torque prediction value are iteratively optimized through the cost function, and the voltage vector that minimizes the cost function value is selected as the optimal voltage vector.

[0026] Preferably, in a possible implementation manner of the first aspect, the optimal voltage vector satisfies:

[0027] in is the optimal voltage vector action time, limited to Within the interval, is the torque change slope when zero vector is applied, is the torque change slope when the optimal voltage vector acts, is the torque reference value, Indicates The torque prediction value at time t, is the sampling period, and the action time of the optimal voltage vector and the zero vector is finally allocated through PWM modulation.

[0028] Preferably, in a possible implementation manner of the first aspect, the torque change slope is calculated as:

[0029]

[0030] in is the stator equivalent inductance, is the number of motor pole pairs, is the motor angular velocity, is the reference amplitude of the magnetic flux, is the imaginary unit, is the magnetic flux of the permanent magnet, is the stator resistance, is the motor inductance, For the selected non-zero voltage vector The axis's components.

[0031] The beneficial effects of the present invention are: first, by optimizing the predictive control algorithm, only the non-zero voltage vector of the inverter is traversed for two-step prediction, which greatly reduces the amount of cyclic calculation of model prediction, improves the real-time performance of the system while ensuring control accuracy, and is particularly suitable for high dynamic response scenarios.

[0032] Secondly, a fuzzy control mechanism is introduced, which uses real-time speed and torque as input variables to dynamically adjust the flux weighting factor. This effectively overcomes the traditional method's reliance on the precise mathematical model of the motor, and can maintain stable control under parameter drift or external disturbances, significantly enhancing the system's robustness and adaptability to working conditions.

[0033] In addition, by combining dynamic flux weighted optimization with beat-free duty cycle adjustment, precise tracking and control of torque and flux are achieved, which not only effectively suppresses torque pulsation and flux fluctuation, but also significantly reduces the total harmonic distortion of the output current, thereby improving the power conversion efficiency and motor operation smoothness.

[0034] This control strategy takes into account both the stability of low-speed operation and the dynamic performance of high-speed conditions, providing a better solution for motor control under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A three-phase two-level permanent magnet synchronous motor model diagram is provided for this application.

[0037] Figure 2 A voltage vector diagram of a three-phase two-level inverter is provided for this application.

[0038] Figure 3A block diagram of a dynamic flux weighted dynamic optimization fuzzy model predictive control for a three-phase permanent magnet synchronous motor is provided for this application.

[0039] Figure 4 A traditional model predictive torque control map is provided for this application.

[0040] Figure 5 A dynamic flux weighted optimization fuzzy model predictive torque control diagram is provided for this application.

[0041] Figure 6 The present application provides a speed and torque waveform diagram corresponding to the motor changing from forward to reverse. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] Embodiment 1: The present invention provides a method for dynamic flux fuzzy prediction torque control of a three-phase permanent magnet synchronous motor, comprising: (1) Establishing a mathematical model of a three-phase two-level permanent magnet synchronous motor The three-phase two-level permanent magnet synchronous motor model is as follows Figure 1 As shown, it contains a three-phase inverter module and a permanent magnet synchronous motor. The three-phase two-level inverter has a total of 8 different switch combinations: 000-111, of which 000 and 111 are zero voltage vectors, and the remaining 6 are non-zero voltage vectors. The vector distribution is as follows Figure 2 Assume that the output state control signals of each inverter controller are respectively , , Decision, then

[0044] by For example, when the control signal is 1, it means The switch tube of the upper bridge arm of the phase is turned on. When the control signal is 0, the switch tube of the lower bridge arm is turned on. The status of the other bridge arms is the same. After determining the switch state, the output voltage value of each corresponding phase can be calculated. and space vector voltage :

[0045]

[0046] in is the DC side voltage, Indicates AC test Phase output voltage, The relationship between the switch state and the output level is shown in Table 1.

[0047] Table 1 Correspondence between switch status and output level

[0048] For the permanent magnet synchronous motor part, ignoring the eddy current loss, the stator voltage equation is The mathematical model in the rotating coordinate system is:

[0049] in and Indicates the three-phase voltage of the motor stator The voltage component in the coordinate system, and Indicates the stator three-phase current The current components in the coordinate system, Indicates the resistance value of the stator three-phase winding, is the motor angular velocity, and Indicates that the stator flux is The components in the coordinate system have the following equations:

[0050] in and They are The inductance component in the coordinate system, is the magnetic flux of the permanent magnet, and the torque equation of the permanent magnet synchronous motor is:

[0051] in is the electromagnetic torque, is the number of motor pole pairs.

[0052] The stator voltage equation is discretized using the first-order Euler equation to obtain:

[0053] in and express The predicted value of magnetic flux at the moment The component below the coordinate axis, and express The magnetic link at the moment The weight below the axis, is the sampling time, and They are The stator current at The current component under the coordinate axis. We can get:

[0054] in and Respectively The predicted value of the stator three-phase current at the moment The components of the axis. Torque prediction value at this moment:

[0055] In order to solve the problem that the permanent magnet synchronous motor (PMSM) model predictive torque control (MPTC) needs to calculate the flux and torque at the next moment, which causes the switch tube action delay and leads to the deterioration of system performance, delay compensation is used to eliminate the influence of calculation on the model predictive control performance. The specific compensation measure is to use Time variables and As an initial value to predict Moment and , we can get The stator flux and torque equations at time

[0056]

[0057]

[0058] In the above formula and express The predicted value of magnetic flux at the moment The component below the coordinate axis, and Respectively The predicted value of the stator three-phase current at the moment The axis weight, Indicates The torque prediction value at the moment.

[0059] (2) Dynamic flux weighted optimization fuzzy model predictive control method The weighting factor is determined according to the system load and speed. When the load and speed are both small, the system increases the weighting factor of the magnetic flux to ensure that the motor can run smoothly at low speed; when the load is low and the speed is high, the flux error has little effect on the torque control, so the weighting factor of the flux is appropriately reduced; when the load is high and the speed is low, the accuracy of the flux control is critical to ensure stable operation, so the weighting factor of the flux is increased; when the load is high and the speed is high, both require precise control, but the torque has a higher priority, so the weighting factor of the flux is reduced. The fuzzy sets of the input and output variables of the fuzzy controller use five basic categories: ,in Indicates a large negative. Indicates a small negative. represents zero, Indicates positive small, The designed fuzzy control rules are shown in Table 2, and the designed fuzzy control parameters are shown in Table 3: Table 2 Fuzzy control rules

[0060] Table 3 Fuzzy control parameters

[0061] Fuzzy rules are composed of input variables 、 and output variables The fuzzy language variables are used to represent The fuzzy rules are expressed as

[0062] in is the fuzzy subset of torque, is the fuzzy subset of the speed, is the fuzzy subset of dynamic weighting factors, For the The fuzzy level corresponding to the torque in the fuzzy rules, For the The fuzzy level corresponding to the speed in the fuzzy rules, For the The fuzzy controller divides the variable range in Table 3 into five basic categories, judges these categories according to the fuzzy rule Table 2, and then determines the value of the weighting factor.

[0063] (3) Controller design The general cost function of model predictive torque control generally includes torque and flux, and includes a weighting factor. The cost function G is designed as:

[0064] In the formula is the torque reference value, and is the flux reference value at The components in the coordinate system, Indicates The torque prediction value at time t, and express The predicted value of magnetic flux at the moment The component below the coordinate axis, is the dynamic weighting factor.

[0065] According to the deadbeat torque control principle, the action time of the zero voltage vector and the voltage vector with the minimum model prediction cost function is allocated within a sampling period, and the duty cycle of each vector can be optimized. When the selected voltage vector is applied during the control loop, the torque should reach the reference value, which is expressed as:

[0066] In the formula represents the action time of the non-zero vector, represents the sampling time, and The estimated values ​​of the three-phase currents of the inverter representing the torque change slope caused by non-zero vector and zero vector; and It can be expressed as:

[0067]

[0068] in is the stator flux, is the magnetic flux of the permanent magnet, Indicates the resistance value of the stator three-phase winding, is the angular velocity, is the stator equivalent inductance, is the imaginary unit, is the motor inductance, non-zero time It can be expressed as:

[0069] in Restricted to Inside.

[0070] According to the above analysis, the control block diagram of the dynamic flux weighted optimization fuzzy model predictive torque control of the three-phase permanent magnet synchronous motor is as follows: Figure 3 As shown, first get The measured values ​​of the motor angular velocity, stator voltage and current at the moment , and , transform the voltage and current coordinates, convert the angular velocity to get the speed, and traverse the six non-zero voltage vectors of the inverter to predict The stator flux and torque at the moment are predicted in two steps by the obtained values. The predicted value at each moment is obtained through the fuzzy controller rules. To determine the specific cost function, find the voltage vector that minimizes the cost function, calculate the duty cycle, and finally control the three-phase two-level inverter through pulse width modulation of the voltage vector and duty cycle.

[0071] Example 2: The present invention provides a simulation of a dynamic flux fuzzy predictive torque control method for a three-phase permanent magnet synchronous motor. The simulation is performed in MATLAB / Simulink, and the simulation parameters are shown in Table 2.

[0072] Table 3 Simulation parameters

[0073] The simulation results are as follows Figure 4 、 Figure 5 and Figure 6 As shown, the dynamic weighted optimization fuzzy model predictive control of the three-phase permanent magnet synchronous motor introduced by the present invention has more precise control performance, stronger robustness, reduces the THD of the output current, and improves the current quality.

[0074] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method, characterized in that: The method comprises: The mathematical model of three-phase two-level permanent magnet synchronous motor is established to determine the corresponding relationship between the inverter switching state and the output voltage vector, and the discretization process is used to obtain Predicted values ​​of flux linkage and stator current at each moment; based on Calculation of predicted flux and stator current at each moment The torque prediction value at the moment is obtained by performing two-step prediction. The predicted value of flux linkage, stator current and torque at the moment; A dynamic flux-weighted optimization fuzzy model predictive controller is constructed. The motor's real-time speed and torque are used as fuzzy control input variables to dynamically adjust the flux weighting factor of the cost function. The controller divides the fuzzy subsets according to the speed and torque variation range and generates dynamic weighting factors adapted to different operating conditions based on a preset fuzzy rule table. based on The predicted flux value, stator current value, torque value and cost function at the moment are traversed through the non-zero voltage vectors of the inverter, and the optimal voltage vector that minimizes the cost function is selected; Based on the deadbeat torque control principle, the duty cycle of the optimal voltage vector is calculated, the optimal voltage vector action time is allocated to optimize the output pulse signal, and the inverter switching state is controlled to achieve rapid tracking of the motor torque and flux.

2. A three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method as claimed in claim 1, characterized in that: The mathematical model of the three-phase two-level permanent magnet synchronous motor includes the stator voltage equation, the flux equation and the torque equation, wherein the stator voltage equation is The rotating coordinate system is expressed as The magnetic flux equation is expressed as: The torque equation is expressed as in 、 The stator voltage is The components in the rotated coordinate system, 、 The stator current is The components in the rotated coordinate system, 、 The stator flux is The components in the rotated coordinate system, is the stator resistance, is the motor angular velocity, is the permanent magnet flux, and for Shaft inductance component, is the electromagnetic torque, is the number of motor pole pairs.

3. A three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method as claimed in claim 2, characterized in that: Obtained through discretization The predicted flux value and the corresponding predicted stator current value at each moment include: The stator voltage equation is discretized using the first-order Euler equation to obtain: And the stator current prediction value is calculated based on the flux prediction value: in and express The predicted value of magnetic flux at the moment The component below the coordinate axis, 、 for The stator voltage at The components in the rotating coordinate system are calculated using the stator voltage equation, 、 for The stator current at The components in the rotated coordinate system, and express The predicted value of magnetic flux at the moment The component below the coordinate axis, is the sampling period, 、 for The stator current at Components in the rotated coordinate system.

4. A three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method as claimed in claim 3, characterized in that: The torque prediction value at the moment is: The predicted value of magnetic flux at the moment is: The predicted value of the stator current at the moment is: The torque prediction value at the moment is: in Indicates The torque prediction value at time t, and express The predicted value of magnetic flux at the moment The component below the coordinate axis, 、 for The stator voltage at The components in the rotating coordinate system are calculated using the stator voltage equation, and Respectively The predicted value of stator current at time The axis weight, Indicates The torque prediction value at the moment.

5. A three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method as claimed in claim 1, characterized in that: The dynamic flux weighted optimization fuzzy model predictive controller collects the motor speed signal and torque signal in real time as dual input variables, and maps the input quantity to a preset fuzzy domain through fuzzy processing. The speed fuzzy subset and the torque fuzzy subset are both divided into five levels: NB, NS, ZO, PS, and PB. The controller performs inference operations based on the fuzzy rules defined in the fuzzy rule base.

6. A three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method as claimed in claim 5, characterized in that: The fuzzy rules are represented by fuzzy linguistic variables of input variables and output variables. The fuzzy rules are expressed as in is the fuzzy subset of torque, is the fuzzy subset of the speed, is the fuzzy subset of dynamic weighting factors, For the The fuzzy level corresponding to the torque in the fuzzy rules, For the The fuzzy level corresponding to the speed in the fuzzy rules, For the The fuzzy level of the dynamic weighting factor output by the fuzzy rule.

7. A three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method as claimed in claim 1, characterized in that: The cost function is defined as: in is the torque reference value, and is the flux linkage reference value The constant reference component in the coordinate system, Indicates The torque prediction value at time t, and express The predicted value of magnetic flux at the moment The component below the coordinate axis, is the dynamic weighting factor.

8. A three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method as claimed in claim 7, characterized in that: The process of selecting the optimal voltage vector includes: Traverse the six non-zero voltage vectors of the three-phase inverter and calculate the The moment-by-moment flux linkage prediction value and torque prediction value are iteratively optimized through the cost function, and the voltage vector that minimizes the cost function value is selected as the optimal voltage vector.

9. A three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method as claimed in claim 1, characterized in that: The optimal voltage vector satisfies: in is the optimal voltage vector action time, limited to Within the interval, is the torque change slope when zero vector is applied, is the torque change slope when the optimal voltage vector acts, is the torque reference value, Indicates The torque prediction value at time t, is the sampling period, and the action time of the optimal voltage vector and the zero vector is finally allocated through PWM modulation.

10. A three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method as claimed in claim 9, characterized in that: The torque change slope is calculated as: in is the stator equivalent inductance, is the number of motor pole pairs, is the motor angular velocity, is the reference amplitude of the magnetic flux, is the imaginary unit, is the magnetic flux of the permanent magnet, is the stator resistance, is the motor inductance, For the selected non-zero voltage vector The axis's components.

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