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CN110266238B - A Simplified Method for Predicting PMSM Direct Torque Control by Finite State Set Model - Google Patents

A Simplified Method for Predicting PMSM Direct Torque Control by Finite State Set Model Download PDF

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CN110266238B
CN110266238B CN201910610311.2A CN201910610311A CN110266238B CN 110266238 B CN110266238 B CN 110266238B CN 201910610311 A CN201910610311 A CN 201910610311A CN 110266238 B CN110266238 B CN 110266238B
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torque
flux linkage
voltage vector
stator flux
control
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CN110266238A (en
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李耀华
杨启东
任佳越
师浩浩
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Changan University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/24—Vector control not involving the use of rotor position or rotor speed sensors
    • H02P21/28—Stator flux based control
    • H02P21/30—Direct torque control [DTC] or field acceleration method [FAM]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
    • H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
    • H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using DC to AC converters or inverters
    • H02P27/08—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using DC to AC converters or inverters with pulse width modulation
    • H02P27/12—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using DC to AC converters or inverters with pulse width modulation pulsing by guiding the flux vector, current vector or voltage vector on a circle or a closed curve, e.g. for direct torque control

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Abstract

本发明公开了一种有限状态集模型预测PMSM直接转矩控制简化方法，计算成本函数g值并带入七个基本电压矢量集合，加入滞环控制信号，加入一个扇区位置信号以及转矩角信号判断，得出成本函数最小的电压矢量；加入当前转矩角的信号，以不同滞环控制信号、扇区位置信号以及不同转矩角的范围作为限制条件，简化备选电压矢量集合；根据转矩脉动均方根误差、磁链脉动均方根误差、评价函数平均值和平均开关频率，以30、45度为划分界限，采用简化的备选电压矢量集合控制策略减小模型预测控制的计算负担，实现PMSM直接转矩简化控制。本发明在保持良好的控制性能的同时减轻了计算负担，更进一步的减少开关表次数。

Figure 201910610311

The invention discloses a simplified method for predicting PMSM direct torque control by a finite state set model. The g value of the cost function is calculated and seven basic voltage vector sets are added, a hysteresis control signal is added, a sector position signal and a torque angle are added. Signal judgment to obtain the voltage vector with the smallest cost function; adding the signal of the current torque angle, using different hysteresis control signals, sector position signals and the range of different torque angles as constraints, simplify the set of alternative voltage vectors; Torque ripple root mean square error, flux linkage ripple root mean square error, average value of evaluation function and average switching frequency, with 30 and 45 degrees as the dividing boundaries, a simplified alternative voltage vector ensemble control strategy is used to reduce the cost of model predictive control. The calculation burden is reduced, and the PMSM direct torque simplified control is realized. The present invention reduces the computational burden while maintaining good control performance, and further reduces the number of switch tables.

Figure 201910610311

Description

Finite state set model prediction PMSM direct torque control simplification method
Technical Field
The invention belongs to the technical field of motor control, and particularly relates to a finite state set model prediction PMSM direct torque control simplification method.
Background
The direct torque control technology is based on a stator flux linkage coordinate system and directly takes the torque as a control object, so that a large amount of calculation and dependency on motor parameters during rotation coordinate transformation are avoided, the dynamic performance is good, and the torque response time is short.
In the direct torque prediction control system of the surface permanent magnet synchronous motor, six basic voltage vectors and two zero voltage vectors are introduced, an evaluation function is introduced, and the voltage vector with the minimum evaluation function is directly output according to the angular position of a stator flux linkage at a static coordinate in the aspect of comprehensive consideration of a torque error and a stator flux linkage error.
However, along with variables and operation functions, the time and complexity of prediction calculation operation are increased, so that a simplified method for predicting PMSM direct torque control based on a finite state set model of a voltage vector utilization rate rule is provided, and the performance of a control system is optimized.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a simplified method for predicting PMSM direct torque control by using a finite state set model, which can reduce the number of prediction operations while keeping the control performance equivalent, and effectively improve the real-time performance of the system.
The invention adopts the following technical scheme:
a finite state set model prediction PMSM direct torque control simplification method comprises the following steps:
s1, calculating a cost function g value through the current torque, the stator flux linkage, the reference torque, the reference flux linkage, the angular position of the stator flux linkage, the stator flux linkage amplitude value at the next moment and the torque value, and bringing seven basic voltage vector sets into the cost function, wherein the model prediction control principle is to select a voltage vector which enables the cost function to be minimum;
s2, adding a hysteresis control signal, adding a sector position signal and a torque angle signal for judgment, and calculating a cost function g value according to the stator flux amplitude and the torque value at the next moment to obtain a voltage vector with the minimum cost function;
s3, adding a current torque angle signal, taking different hysteresis control signals, sector position signals and different torque angle ranges as limiting conditions, abandoning voltage vectors with low utilization rate, switching different alternative voltage alternative sets, and simplifying alternative voltage vector sets;
and S4, according to the torque ripple root mean square error, the flux linkage ripple root mean square error, the evaluation function average value and the average switching frequency, taking 30 and 45 degrees as dividing boundaries, and reducing the calculation load of model prediction control by adopting a simplified alternative voltage vector set control strategy to realize PMSM direct torque simplified control.
Specifically, the simplified alternative voltage vector set control strategy is as follows:
stator flux linkage sector theta1When flux linkage is added and torque is added, the torque angle is less than 45 degrees, and the candidate voltage vector set is { V0，V1，V2，V3Torque angle greater than 45 °, set of candidate voltage vectors as { V }0，V2，V3，V6}；
When the flux linkage is increased and the torque is reduced, the torque angle is less than 30 degrees, and the candidate voltage vector set is { V0，V1，V4，V5，V6Torque angle greater than 30 deg., and set of candidate voltage vectors as { V }0，V5，V6}；
When the flux linkage is reduced and the torque is increased, the torque angle is less than 30 degrees, and the candidate voltage vector set is { V0，V1，V4，V2，V3Torque angle greater than 30 deg., and set of candidate voltage vectors as { V }0，V2，V3}；
When the flux linkage is reduced and the torque is reduced, the torque angle is less than 45 degrees, and the candidate voltage vector set is { V0，V4，V5，V6Torque angle greater than 45 °, set of candidate voltage vectors as { V }0，V3，V5，V6}。
Specifically, in step S1, six basic voltage vectors V from the origin to six vertices of a hexagon are determined from the pm synchronous motor voltage vector diagram1～V6And 1 zero voltage vector, determining the voltage vector with the minimum cost function value according to the torque and the stator flux linkage, and outputting the switching state of the voltage vector, wherein the voltage vector candidate set comprises the following components:
Figure BDA0002122144940000031
the amplitude of 6 non-zero voltage vectors is 2Udc/3，UdcThe zero voltage vector magnitude is zero for the dc bus voltage.
Further, six basic voltage vectors V1～V6Angle of (2)Degree set alpha1-6The calculation is as follows:
α1-6∈{-θs(k),60°-θs(k),120°-θs(k),180°-θs(k),240°-θs(k),300°-θs(k)}
wherein, thetas(k) The stator flux angular position under the static coordinate system.
Specifically, in step S1, the cost function value g and the cost function average value gaveThe calculation is as follows:
Figure BDA0002122144940000032
Figure BDA0002122144940000033
wherein, Te *For reference torque, Te(k +1) is the torque at the next time,
Figure BDA0002122144940000034
for reference to the stator flux linkage,
Figure BDA0002122144940000035
is the stator flux linkage at the next moment.
Further, the flux linkage and torque changes are as follows:
Figure BDA0002122144940000036
Figure BDA0002122144940000037
Figure BDA0002122144940000038
where Δ t is a voltage vectorThe time of action of (a) is,
Figure BDA0002122144940000039
as a vector of voltage, #fIs the rotor flux, delta is the torque angle, and alpha is the angle between the voltage vector and the stator flux.
Specifically, in step S4, the torque ripple root mean square error Trip_RMSEThe calculation is as follows:
Figure BDA0002122144940000041
wherein, TeIs the torque at the present moment in time,
Figure BDA0002122144940000042
for reference torque, n is the number of samples.
Specifically, in step S4, the stator flux linkage ripple root mean square error ψrip_RMSEThe calculation is as follows:
Figure BDA0002122144940000043
wherein psisIs the stator flux linkage at the current moment,
Figure BDA0002122144940000044
for reference stator flux linkage, n is the number of samples.
Specifically, in step S4, the average value m of the evaluation function isaveThe calculation is as follows:
Figure BDA0002122144940000045
wherein n is the number of samples,
Figure BDA0002122144940000046
for reference stator flux linkage, Te *For reference torque, TeFor the rotation of the current timeMoment.
Specifically, in step S4, the average switching frequency faveThe calculation is as follows:
Figure BDA0002122144940000047
where N is the number of samples, NswitchingThe total number of times of switching the inverter, and t is the simulation duration.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention discloses a simplified method for predicting PMSM direct torque control by a finite state set model, which determines a basic voltage vector at the next moment through the angular position of a stator flux linkage, the torque ripple and the stator flux linkage ripple, analyzes from seven basic voltage vectors, calculates a cost function g value through the current torque, the stator flux linkage, a reference torque, the flux linkage and the angular position of the stator flux linkage, and the stator flux linkage amplitude and the torque value at the next moment, and obtains a voltage vector with the minimum cost function.
Furthermore, under the constraint of increasing flux sector, flux and torque hysteresis control and torque angle signal judgment, the cost function g value can be calculated again according to the stator flux amplitude and the torque value at the next moment, and the voltage vector with the minimum cost function can be obtained.
Furthermore, under the additional conditions of adding flux sector, flux and torque hysteresis control and torque angle range, different torque angle intervals are divided according to the trend of the voltage vector utilization rate, different voltage vector alternative sets are switched, and the requirement of simplifying the sets and keeping the good control performance of the system is met.
Furthermore, a series of evaluation indexes are provided for the model prediction system, the provided simplified voltage vector alternative set is compared with seven basic voltage vector sets on the aspect of control performance, and the control system based on the simplified voltage vector alternative set is verified to sacrifice a small amount of control performance so as to reduce the calculation burden of model prediction control.
In conclusion, the invention reduces the calculation burden and further reduces the times of switching the meter while maintaining good control performance.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a diagram of a PMSM model predictive control system using 7 sets of fundamental voltage vectors;
FIG. 2 is a flow chart of a PMSM model predictive control using 7 sets of fundamental voltage vectors;
FIG. 3 is a diagram of a PMSM model predictive control system using a reduced set of fundamental voltage vectors;
FIG. 4 is a flow chart of a PMSM model predictive control using a reduced set of basis voltage vectors;
FIG. 5 is a voltage vector diagram of a permanent magnet synchronous motor;
FIG. 6 is a diagram of a PMSM model predictive control speed waveform using 7 sets of fundamental voltage vectors;
FIG. 7 is a graph of a PMSM model predicted control torque waveform using 7 sets of basis voltage vectors;
FIG. 8 is a graph of a PMSM model predictive control stator flux linkage amplitude waveform using 7 sets of fundamental voltage vectors;
FIG. 9 is a stator flux linkage trajectory diagram under a permanent magnet synchronous motor model predictive control static coordinate system using 7 basic voltage vector sets;
fig. 10 is a graph of a model predictive control a-phase stator current for a permanent magnet synchronous machine using 7 sets of fundamental voltage vectors;
FIG. 11 is a graph of a PMSM model predicted control torque angle waveform using 7 sets of basis voltage vectors;
FIG. 12 is a schematic diagram of a PMSM model predictive control stator flux linkage sector and voltage vectors using 7 sets of fundamental voltage vectors;
FIG. 13 is a graph of a PMSM model predictive control speed waveform using a reduced set of fundamental voltage vectors;
FIG. 14 is a graph of a PMSM model predicted control torque waveform using a reduced set of basis voltage vectors;
FIG. 15 is a graph of a PMSM model predictive control stator flux linkage amplitude waveform using a reduced set of basis voltage vectors;
FIG. 16 is a stator flux linkage trajectory diagram under a PMSM model predictive control stationary coordinate system using a simplified set of fundamental voltage vectors;
fig. 17 is a graph of a permanent magnet synchronous machine model predictive control of a-phase stator current using a reduced set of basis voltage vectors.
Detailed Description
Referring to fig. 1 and 2, the present invention provides a simplified method for predicting PMSM direct torque control based on a finite state set model of voltage vector utilization rule, which first calculates a cost function g value according to a current torque and stator flux linkage, a reference torque and reference flux linkage, and an angular position of the stator flux linkage, and a stator flux linkage amplitude value and a torque value at the next moment, and selects a basic voltage vector with the minimum g value.
Referring to fig. 3 and 4, a hysteresis signal, a sector position signal, and a torque angle are added to simplify a candidate voltage set, and a cost function g value is calculated according to a stator flux amplitude and a torque value at the next time, so as to select a basic voltage vector with the minimum g value.
The invention discloses a finite state set model prediction PMSM direct torque control simplification method, which comprises the following steps:
s1, calculating a cost function g value through the current torque and stator flux linkage, the reference torque and flux linkage and the angular position of the stator flux linkage, and the stator flux linkage amplitude and torque value at the next moment, and bringing seven basic voltage vector sets into the cost function, wherein the model prediction control principle is to select a voltage vector which enables the cost function to be minimum;
referring to fig. 5, six basic voltage vectors V from the origin to six vertices of a hexagon are determined according to the voltage vector diagram of the pm synchronous motor1～V6And 1 zero potentialAnd the voltage vector is used for determining the voltage vector with the minimum cost function value according to the torque and the stator flux linkage, and outputting the switching state of the voltage vector. Wherein the amplitude of 6 non-zero voltage vectors is 2Udc/3，UdcThe zero voltage vector magnitude is zero for the dc bus voltage. The alternative set of voltage vectors is as follows:
Figure BDA0002122144940000071
six basic voltage vectors V1～V6Angle set alpha of1-6The calculation is as follows in equation (2):
α1-6∈{-θs(k),60°-θs(k),120°-θs(k),180°-θs(k),240°-θs(k),300°-θs(k)} (2)
wherein, thetas(k) The stator flux angular position under the static coordinate system.
And according to the torque and the stator flux linkage, determining a voltage vector with the minimum cost function value, and outputting the switching state of the voltage vector.
After the voltage vector is applied, the flux linkage and the torque change as shown in formulas (3) and (4).
Figure BDA0002122144940000081
Figure BDA0002122144940000082
The model prediction cost function is shown in equation (5):
Figure BDA0002122144940000083
the mean value of the model prediction cost function is shown in formula (6):
Figure BDA0002122144940000084
the torque ripple root mean square error is shown in equation (7):
Figure BDA0002122144940000085
the stator flux linkage pulsation root mean square error is shown as formula (8):
Figure BDA0002122144940000086
the average evaluation function is shown in formula (9):
Figure BDA0002122144940000087
the average switching frequency is shown in equation (10):
Figure BDA0002122144940000088
and S2, judging by adding a hysteresis control signal and a sector position signal and a torque angle signal, and calculating the cost function g value again by the stator flux amplitude and the torque value at the next moment to obtain the voltage vector with the minimum cost function.
And S3, adding a current torque angle signal, analyzing that the voltage vector is selected unevenly under different hysteresis control signals and sector position signals, presenting a certain height rule trend along with the voltage utilization rate of the torque angle signal, simplifying an alternative voltage vector set under different torque angle ranges, abandoning part of voltage vectors with lower selection rates, dividing different torque angle intervals, switching different voltage vector sets, and sacrificing a small amount of control performance to reduce the calculation burden of model prediction control.
And S4, comparing the proposed simplified voltage vector alternative set with seven basic voltage vector sets on the aspect of control performance, wherein the proposed simplified voltage vector alternative set comprises a cost function mean value, a torque root mean square error and a stator flux linkage root mean square error, and evaluating the function mean value and the average switching frequency. The verification can achieve the purpose of reducing the operation burden while maintaining good performance.
The simplified alternative voltage vector set control strategy is as follows:
stator flux linkage sector theta1When flux linkage is added and torque is added, the torque angle is less than 45 degrees, and the candidate voltage vector set is { V0，V1，V2，V3Torque angle greater than 45 °, set of candidate voltage vectors as { V }0，V2，V3，V6}；
When the flux linkage is increased and the torque is reduced, the torque angle is less than 30 degrees, and the candidate voltage vector set is { V0，V1，V4，V5，V6Torque angle greater than 30 deg., and set of candidate voltage vectors as { V }0，V5，V6}；
When the flux linkage is reduced and the torque is increased, the torque angle is less than 30 degrees, and the candidate voltage vector set is { V0，V1，V4，V2，V3Torque angle greater than 30 deg., and set of candidate voltage vectors as { V }0，V2，V3}；
When the flux linkage is reduced and the torque is reduced, the torque angle is less than 45 degrees, and the candidate voltage vector set is { V0，V4，V5，V6Torque angle greater than 45 °, set of candidate voltage vectors as { V }0，V3，V5，V6}。
From this alternative voltage vector sets for other stator flux linkage sectors can be derived recursively.
In order to make the objects, 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 with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The simulation parameters of the surface permanent magnet synchronous motor system are as follows:
a surface permanent magnet synchronous motor model prediction torque control simulation model is established based on MATLAB/Simulink.
The simulation model is a discrete model, and the sampling period is 5 multiplied by 10 < -5 > s.
The dc bus voltage is 312V.
The parameters of the rotating speed PI regulator are as follows: KP is 5, KI is 10, and the PI regulator output upper and lower limits are [ -35, 35 ].
The reference speed was 60rpm, the load torque was 18n.m, and the corresponding torque angle was 29 °.
The reference stator flux linkage amplitude is 0.3 Wb.
The simulation total duration is 2 s.
The parameters of the surface permanent magnet synchronous motor for simulation are shown in table 1.1.
TABLE 1.1 simulation surface-mounted PMSM parameters
Figure BDA0002122144940000101
Figure BDA0002122144940000111
The simulation results are shown in fig. 6 to 11, and the simulation results indicate that the model predicted torque control performance is good. The steady state torque angles averaged about 29 degrees and varied over a range of about (25 °, 33 °). The model prediction torque control based on the candidate voltage vector set expressed by the formula (1) requires 7 prediction calculations each time, and the calculation amount is large.
Meanwhile, simulation results show that the use of the 7 voltage vectors by the model predictive torque control is not balanced. Defining the voltage vector utilization rate as shown in formula (11), wherein N is the total number of times of voltage vectors applied by model predicted torque control in a certain time period, and Ni is the voltage vector V0～V6Total number of applications.
Figure BDA0002122144940000112
7 voltage vectors V0～V6The utilization over the simulation time 2s is shown in table 1.2.
TABLE 1.2 Voltage vector utilization
Figure BDA0002122144940000113
The stator flux linkage position has some effect on the flux linkage and torque effects of the applied voltage vector. Under the judgment of the stator flux linkage sectors, 7 voltage vectors V under different stator flux linkage sectors0～V6The utilization within the simulation time 2s is shown in table 1.3.
TABLE 1.3 Voltage vector utilization
Figure BDA0002122144940000114
Figure BDA0002122144940000121
The flux and torque increase and decrease control signals output by the flux and torque hysteresis comparators also have certain influence on the voltage utilization rate. Under the judgment of increase and decrease control signals of flux linkage and torque, 7 voltage vectors V under different stator flux linkage sectors0～V6The utilization within the simulation time 2s is shown in tables 1.4 to 1.7, in which the torque hysteresis width is 0.02n.m and the flux linkage hysteresis width is 0.002 Wb.
TABLE 1.4 Voltage vector utilization (increase flux linkage, increase torque)
Figure BDA0002122144940000122
Figure BDA0002122144940000131
TABLE 1.5 Voltage vector utilization (increase flux linkage, decrease torque)
Figure BDA0002122144940000132
TABLE 1.6 Voltage vector utilization (decreasing flux linkage, increasing torque)
Figure BDA0002122144940000133
Figure BDA0002122144940000141
TABLE 1.7 Voltage vector utilization (reduced flux, reduced torque)
θ1 θ2 θ3 θ4 θ5 θ6
V0 39.10％ 44.76％ 41.41％ 47.04％ 43.18％ 41.66％
V1 0.00％ 16.36％ 31.96％ 8.18％ 0.82％ 0.00％
V2 0.00％ 0.00％ 17.05％ 27.44％ 7.83％ 1.54％
V3 0.60％ 0.00％ 0.00％ 16.71％ 32.01％ 8.52％
V4 8.01％ 0.57％ 0.00％ 0.00％ 16.16％ 31.30％
V5 34.83％ 8.57％ 0.71％ 0.00％ 0.00％ 16.97％
V6 17.45％ 29.75％ 8.88％ 0.62％ 0.00％ 0.00％
Studies have shown that changes in torque angle also affect voltage vector utilization. There are 10 different load torques set for different torque angles as shown in table 1.8.
TABLE 1.8 load Torque and Torque Angle
Figure BDA0002122144940000142
Figure BDA0002122144940000151
Fig. 12 shows inverter voltage vectors V0 to V6 and stator flux sectors θ 1 to θ 6. As can be seen from fig. 12, inverter voltage vectors V0-V6 and stator flux linkage sector θ1～θ6In a periodic relationship. Tables 1.4 to 1.7 show that the voltage vector utilization varies substantially periodically in different sectors. Therefore, voltage vectors V0-V6 are used hereinafter in stator flux sector θ1The voltage utilization in the case is an example for detailed analysis. Under different torque angles, voltage vectors V0-V6 are in stator flux sector theta1The voltage vector utilization within is shown in tables 1.9-1.13, respectively.
TABLE 1.9 Voltage vector utilization (increase flux linkage, increase torque)
Figure BDA0002122144940000152
Figure BDA0002122144940000161
TABLE 1.10 Voltage vector utilization (increase flux linkage, decrease torque)
Figure BDA0002122144940000162
Figure BDA0002122144940000171
TABLE 1.11 Voltage vector utilization (decreasing flux linkage, increasing torque)
Figure BDA0002122144940000172
TABLE 1.12 Voltage vector utilization (reduced flux, reduced torque)
Figure BDA0002122144940000173
Figure BDA0002122144940000181
From table 1.9 to table 1.12, it can be seen that: stator flux linkage sector theta1When a flux linkage is added and torque is increased, a torque angle is smaller than 45 degrees, voltage vectors with higher voltage vector utilization rate are { V0, V1, V2 and V3}, a torque angle is larger than 45 degrees, and voltage vectors with higher voltage vector utilization rate are { V0, V2, V3 and V6 };
when the flux linkage is increased and the torque is reduced, the torque angle is smaller than 30 degrees, the voltage vector with higher voltage vector utilization rate is { V0, V1, V4, V5 and V6}, the torque angle is larger than 30 degrees, and the voltage vector with higher voltage vector utilization rate is { V0, V5 and V6 };
when the flux linkage is reduced and the torque is increased, the torque angle is smaller than 30 degrees, the voltage vector with higher voltage vector utilization rate is { V0, V1, V4, V2 and V3}, the torque angle is larger than 30 degrees, and the voltage vector with higher voltage vector utilization rate is { V0, V2 and V3 };
when the flux linkage is reduced and the torque is reduced, the torque angle is smaller than 45 degrees, the voltage vectors with higher voltage vector utilization rate are { V0, V4, V5 and V6}, the torque angle is larger than 45 degrees, and the voltage vectors with higher voltage vector utilization rate are { V0, V3, V5 and V6 }.
From this alternative voltage vector sets for other stator flux linkage sectors can be derived recursively. The simplified alternative voltage set is also formed by taking the voltage vector set with higher voltage utilization rate as the simplified set.
And (3) comparing a series of performance indexes of the simplified alternative voltage set, the 7 basic voltage vector set model predictive control system and the traditional switch table control.
A surface permanent magnet synchronous motor model prediction torque control simulation model is established based on MATLAB/Simulink.
The simulation model is a discrete model, and the sampling period is 5 multiplied by 10 < -5 > s.
The dc bus voltage is 312V.
The parameters of the rotating speed PI regulator are as follows: KP is 5, KI is 10, and the PI regulator output upper and lower limits are [ -35, 35 ].
The reference speed was 60rpm, the load torque was initially 5n.m, stepped to 10n.m at 2s, 15n.m at 4s, 20n.m at 6s, 25n.m at 8s, and 30n.m at 10 s.
The reference stator flux linkage amplitude is 0.3 Wb.
The simulation total duration is 12 s.
The motor parameters of the surface permanent magnet synchronous motor for simulation are the same as those shown in the table 1.1 above, and are not described again here. The simulation waveforms under the three strategies are stable, the control effect is stable and good, and because the waveform diagrams are basically similar, the rotating speed, the torque, the stator flux linkage amplitude, the stator flux linkage track under the static coordinate system and the a-phase stator current under the prediction control of the simplified basic voltage vector set model are only shown as the graphs in fig. 12 to 16.
The performance indexes include: torque ripple root mean square error, flux linkage ripple root mean square error, cost function average, evaluation function average. The simulation evaluation results are shown in Table 1.13
TABLE 1.13 results of simulation evaluation
Figure BDA0002122144940000201
Table 1.13 simulation evaluation results show that a series of evaluation indexes are compared. The control performance of the simplified basic voltage vector set control strategy is extremely close to and slightly inferior to that of the traditional switch table control strategy, and all items are superior to the traditional switch table control strategy, so that the simplified voltage vector set meets the requirement of sacrificing the control performance a little, and the operation burden of reducing the calculation cost function in each period is met.
In summary, the following conclusions are drawn:
the prediction strategy control performance is optimal based on seven basic voltage vector set models, the simplified basic voltage vector set model prediction strategy control is slightly inferior, and the traditional switch table (DTC) is the worst.
The basic voltage vector set model prediction strategy is simplified, the alternative voltage set is reduced, and although the hysteresis signal and the torque angle signal are added, the alternative voltage set is relatively easy to obtain, so that the system basically keeps the original control performance, and the calculation burden is reduced.
The simplified alternative voltage vector set control strategy is as follows:
stator flux linkage sector theta1When flux linkage is added and torque is added, a torque angle is smaller than 45 degrees, a set of candidate voltage vectors is { V0, V1, V2 and V3}, a torque angle is larger than 45 degrees, and a set of candidate voltage vectors is { V0, V2, V3 and V6 };
when flux linkage is increased and torque is reduced, the torque angle is smaller than 30 degrees, the set of candidate voltage vectors is { V0, V1, V4, V5 and V6}, the torque angle is larger than 30 degrees, and the set of candidate voltage vectors is { V0, V5 and V6 };
when the flux linkage is reduced and the torque is increased, the torque angle is smaller than 30 degrees, the set of candidate voltage vectors is { V0, V1, V4, V2 and V3}, the torque angle is larger than 30 degrees, and the set of candidate voltage vectors is { V0, V2 and V3 };
when the flux linkage is reduced and the torque is reduced, the torque angle is smaller than 45 degrees, the set of candidate voltage vectors is { V0, V4, V5 and V6}, the torque angle is larger than 45 degrees, and the set of candidate voltage vectors is { V0, V3, V5 and V6 }. From this alternative voltage vector sets for other stator flux linkage sectors can be derived recursively.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (9)

1.一种有限状态集模型预测PMSM直接转矩控制简化方法，其特征在于，包括以下步骤：1. a finite state set model prediction PMSM direct torque control simplified method, is characterized in that, comprises the following steps: S1、通过当前转矩和定子磁链、参考转矩、参考定子磁链、定子磁链的角位置、下一时刻的定子磁链幅值和转矩值计算成本函数g值，将七个基本电压矢量集合{V0,V1,V2,V3,V4,V5,V6}带入成本函数中，模型预测控制原则是选择令成本函数最小的电压矢量；S1. Calculate the cost function g value through the current torque and stator flux linkage, reference torque, reference stator flux linkage, angular position of stator flux linkage, stator flux linkage amplitude and torque value at the next moment, and put the seven basic The voltage vector set {V 0 , V 1 , V 2 , V 3 , V 4 , V 5 , V 6 } is brought into the cost function, and the model predictive control principle is to select the voltage vector that minimizes the cost function; S2、加入滞环控制信号，加入一个扇区位置信号以及转矩角信号判断，通过下一时刻的定子磁链幅值和转矩值计算成本函数g值，得出成本函数最小的电压矢量；S2. Add a hysteresis control signal, add a sector position signal and a torque angle signal to judge, calculate the cost function g value through the stator flux linkage amplitude and torque value at the next moment, and obtain the voltage vector with the smallest cost function; S3、加入当前转矩角的信号，以不同滞环控制信号、扇区位置信号以及不同转矩角的范围作为限制条件，舍弃利用率低的电压矢量，切换不同的备选电压备选集合，简化备选电压矢量集合；S3, adding the signal of the current torque angle, using different hysteresis control signals, sector position signals and the range of different torque angles as constraints, discarding the voltage vector with low utilization rate, and switching between different candidate voltage candidate sets, Simplified alternative voltage vector set; S4、根据转矩脉动均方根误差、定子磁链脉动均方根误差、评价函数平均值和平均开关频率，以转矩角30度和45度为划分界限，采用简化的备选电压矢量集合控制策略减小模型预测控制的计算负担，实现PMSM直接转矩简化控制，简化的备选电压矢量集合控制策略如下：S4. According to the torque ripple root mean square error, the stator flux link ripple root mean square error, the average value of the evaluation function and the average switching frequency, the torque angle is 30 degrees and 45 degrees as the dividing boundaries, and a simplified set of alternative voltage vectors is used. The control strategy reduces the computational burden of model predictive control and realizes the simplified control of PMSM direct torque. The simplified alternative voltage vector set control strategy is as follows: 定子磁链扇区θ1内，当增加磁链，增加转矩时，转矩角小于45°，备选电压矢量集合为{V0，V1，V2，V3}，转矩角大于45°，备选电压矢量集合为{V0，V2，V3，V6}；In the stator flux linkage sector θ 1 , when the flux linkage is increased and the torque is increased, the torque angle is less than 45°, the set of alternative voltage vectors is {V 0 , V 1 , V 2 , V 3 }, and the torque angle is greater than 45°, the set of candidate voltage vectors is {V 0 , V 2 , V 3 , V 6 }; 当增加磁链，减小转矩时，转矩角小于30°，备选电压矢量集合为{V0，V1，V4，V5，V6}，转矩角大于30°，备选电压矢量集合为{V0，V5，V6}；When the flux linkage is increased and the torque is decreased, the torque angle is less than 30°, the alternative voltage vector set is {V 0 , V 1 , V 4 , V 5 , V 6 }, the torque angle is greater than 30°, the alternative The set of voltage vectors is {V 0 , V 5 , V 6 }; 当减小磁链，增加转矩时，转矩角小于30°，备选电压矢量集合为{V0，V1，V4，V2，V3}，转矩角大于30°，备选电压矢量集合为{V0，V2，V3}；When reducing the flux linkage and increasing the torque, the torque angle is less than 30°, the set of alternative voltage vectors is {V 0 , V 1 , V 4 , V 2 , V 3 }, the torque angle is greater than 30°, the alternative The voltage vector set is {V 0 , V 2 , V 3 }; 当减小磁链，减小转矩时，转矩角小于45°，备选电压矢量集合为{V0，V4，V5，V6}，转矩角大于45°，备选电压矢量集合为{V0，V3，V5，V6}。When the flux linkage is reduced and the torque is reduced, the torque angle is less than 45°, the set of alternative voltage vectors is {V 0 , V 4 , V 5 , V 6 }, the torque angle is greater than 45°, the alternative voltage vector The set is {V 0 , V 3 , V 5 , V 6 }. 2.根据权利要求1所述的有限状态集模型预测PMSM直接转矩控制简化方法，其特征在于，步骤S1中，根据永磁同步电机电压矢量图确定从原点到六边形六个顶点的六个基本电压矢量V1～V6和1个零电压矢量V0，根据转矩和定子磁链确定出最小成本函数值的电压矢量，输出该电压矢量的开关状态，电压矢量备选集合如下：2. finite state set model prediction PMSM direct torque control simplification method according to claim 1, is characterized in that, in step S1, according to permanent magnet synchronous motor voltage vector diagram to determine from the origin to the six vertices of the six hexagons. A basic voltage vector V 1 ～ V 6 and a zero-voltage vector V 0 , the voltage vector with the minimum cost function value is determined according to the torque and stator flux linkage, and the switching state of the voltage vector is output. The voltage vector candidate set is as follows:
Figure FDA0002922169680000021
Figure FDA0002922169680000021
 6个非零电压矢量幅值为2Udc/3，Udc为直流母线电压，零电压矢量幅值为零。The magnitude of the six non-zero voltage vectors is 2U dc /3, U dc is the DC bus voltage, and the magnitude of the zero-voltage vector is zero. 3.根据权利要求2所述的有限状态集模型预测PMSM直接转矩控制简化方法，其特征在于，六个基本电压矢量V1～V6的角度集合α1-6计算如下：3. The simplified method for predicting PMSM direct torque control by finite state set model according to claim 2, wherein the angle set α 1-6 of the six basic voltage vectors V 1 to V 6 is calculated as follows: α1-6∈{-θs(k),60°-θs(k),120°-θs(k),180°-θs(k),240°-θs(k),300°-θs(k)}α 1-6 ∈ {-θ s (k), 60°-θ s (k), 120°-θ s (k), 180°-θ s (k), 240°-θ s (k), 300 °-θ s (k)} 其中，θs(k)为静止坐标系下定子磁链角位置。Among them, θ s (k) is the angular position of the stator flux linkage in the static coordinate system. 4.根据权利要求1所述的有限状态集模型预测PMSM直接转矩控制简化方法，其特征在于，步骤S1中，成本函数值g和成本函数均值gave计算如下：4. finite state set model prediction PMSM direct torque control simplification method according to claim 1 is characterized in that, in step S1, cost function value g and cost function mean value g ave are calculated as follows:
Figure FDA0002922169680000022
Figure FDA0002922169680000022
Figure FDA0002922169680000023
Figure FDA0002922169680000023
 其中，
Figure FDA0002922169680000024
为参考转矩，Te(k+1)为下一时刻的转矩，
Figure FDA0002922169680000025
为参考定子磁链，
Figure FDA0002922169680000026
为下一时刻的定子磁链，n为样本数量。in,
Figure FDA0002922169680000024
is the reference torque, T e (k+1) is the torque at the next moment,
Figure FDA0002922169680000025
For the reference stator flux linkage,
Figure FDA0002922169680000026
is the stator flux linkage at the next moment, and n is the number of samples. 5.根据权利要求4所述的有限状态集模型预测PMSM直接转矩控制简化方法，其特征在于，定子磁链和转矩变化如下：5. finite state set model prediction PMSM direct torque control simplification method according to claim 4 is characterized in that, stator flux linkage and torque change are as follows:
Figure FDA0002922169680000027
Figure FDA0002922169680000027
Figure FDA0002922169680000028
Figure FDA0002922169680000028
Figure FDA0002922169680000029
Figure FDA0002922169680000029
 其中，Δt为电压矢量的作用时间，
Figure FDA00029221696800000210
为当前时刻的定子磁链，
Figure FDA00029221696800000211
为当前k时刻要施加的电压矢量，ψf为转子磁链，δ为转矩角，α为电压矢量与定子磁链的夹角，Ld为电机直轴同步电感，p是指电机的极对数。Among them, Δt is the action time of the voltage vector,
Figure FDA00029221696800000210
is the stator flux linkage at the current moment,
Figure FDA00029221696800000211
is the voltage vector to be applied at the current k moment, ψ f is the rotor flux linkage, δ is the torque angle, α is the angle between the voltage vector and the stator flux linkage, L d is the direct-axis synchronous inductance of the motor, and p refers to the pole of the motor logarithm. 6.根据权利要求1所述的有限状态集模型预测PMSM直接转矩控制简化方法，其特征在于，步骤S4中，转矩脉动均方根误差Trip_RMSE计算如下：6. finite state set model prediction PMSM direct torque control simplification method according to claim 1 is characterized in that, in step S4, torque ripple root mean square error Trip_RMSE is calculated as follows:
Figure FDA0002922169680000031
Figure FDA0002922169680000031
 其中，Te为当前时刻的转矩，
Figure FDA0002922169680000032
为参考转矩，n为样本数量。Among them, T e is the torque at the current moment,
Figure FDA0002922169680000032
is the reference torque, and n is the number of samples. 7.根据权利要求1所述的有限状态集模型预测PMSM直接转矩控制简化方法，其特征在于，步骤S4中，定子磁链脉动均方根误差ψrip_RMSE计算如下：7. finite state set model prediction PMSM direct torque control simplification method according to claim 1 is characterized in that, in step S4, stator flux linkage pulsation root mean square error ψ rip_RMSE is calculated as follows:
Figure FDA0002922169680000033
Figure FDA0002922169680000033
 其中，ψs为当前时刻的定子磁链，
Figure FDA0002922169680000034
为参考定子磁链，n为样本数量。Among them, ψ s is the stator flux linkage at the current moment,
Figure FDA0002922169680000034
For the reference stator flux linkage, n is the number of samples. 8.根据权利要求1所述的有限状态集模型预测PMSM直接转矩控制简化方法，其特征在于，步骤S4中，评价函数平均值mave计算如下：8. The finite state set model prediction PMSM direct torque control simplification method according to claim 1, is characterized in that, in step S4, evaluation function mean value m ave is calculated as follows:
Figure FDA0002922169680000035
Figure FDA0002922169680000035
 其中，n为样本数量，
Figure FDA0002922169680000036
为当前时刻定子磁链，
Figure FDA0002922169680000037
为参考定子磁链，Te *为参考转矩，Te为当前时刻的转矩。where n is the number of samples,
Figure FDA0002922169680000036
is the stator flux linkage at the current moment,
Figure FDA0002922169680000037
is the reference stator flux linkage, T e * is the reference torque, and T e is the torque at the current moment. 9.根据权利要求1所述的有限状态集模型预测PMSM直接转矩控制简化方法，其特征在于，步骤S4中，平均开关频率fave计算如下：9. The finite state set model prediction PMSM direct torque control simplification method according to claim 1 is characterized in that, in step S4, the average switching frequency f ave is calculated as follows:
Figure FDA0002922169680000038
Figure FDA0002922169680000038
 其中，Nswitching为逆变器开关总次数，t为仿真时长。Among them, N switching is the total number of inverter switching times, and t is the simulation time.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2015532586A (en) * 2012-10-23 2015-11-09 アーベーベー・テクノロジー・アーゲー Model predictive control by reference tracking
CN106788073A (en) * 2016-11-24 2017-05-31 天津津航计算技术研究所 Without the prediction method for controlling torque that weight coefficient is adjusted
CN106936356A (en) * 2017-04-24 2017-07-07 东南大学盐城新能源汽车研究院 Vector is screened and dutycycle is combined motor model Predictive Control System and method
CN108649854A (en) * 2018-05-09 2018-10-12 上海电机学院 PMSM Direct Torque Controls based on novel duty ratio modulation
CN108667389A (en) * 2018-04-02 2018-10-16 江苏大学 A Predictive Fault Tolerant Control Method Based on Virtual Voltage Vector Model

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2015532586A (en) * 2012-10-23 2015-11-09 アーベーベー・テクノロジー・アーゲー Model predictive control by reference tracking
CN106788073A (en) * 2016-11-24 2017-05-31 天津津航计算技术研究所 Without the prediction method for controlling torque that weight coefficient is adjusted
CN106936356A (en) * 2017-04-24 2017-07-07 东南大学盐城新能源汽车研究院 Vector is screened and dutycycle is combined motor model Predictive Control System and method
CN108667389A (en) * 2018-04-02 2018-10-16 江苏大学 A Predictive Fault Tolerant Control Method Based on Virtual Voltage Vector Model
CN108649854A (en) * 2018-05-09 2018-10-12 上海电机学院 PMSM Direct Torque Controls based on novel duty ratio modulation

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Model Predictive Direct Torque Control of Surface PMSM System Based on Basic Voltage Vector of VSI;Yaohua Li et al.;《 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE)》;20190704;第1-6页 *
电动汽车用永磁同步电机直接转矩控制电压矢量选择策略;李耀华 等;《电机与控制学报》;20120430;第16卷(第4期);第43-49页 *

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