CN110943663A - Permanent magnet synchronous motor dynamic finite state set model prediction torque control method - Google Patents

Permanent magnet synchronous motor dynamic finite state set model prediction torque control method Download PDF

Info

Publication number
CN110943663A
CN110943663A CN201911215132.5A CN201911215132A CN110943663A CN 110943663 A CN110943663 A CN 110943663A CN 201911215132 A CN201911215132 A CN 201911215132A CN 110943663 A CN110943663 A CN 110943663A
Authority
CN
China
Prior art keywords
flux linkage
voltage vector
torque
error
amplitude
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201911215132.5A
Other languages
Chinese (zh)
Other versions
CN110943663B (en
Inventor
李耀华
秦辉
苏锦仕
秦玉贵
赵承辉
周逸凡
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Changan University
Original Assignee
Changan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Changan University filed Critical Changan University
Priority to CN201911215132.5A priority Critical patent/CN110943663B/en
Publication of CN110943663A publication Critical patent/CN110943663A/en
Application granted granted Critical
Publication of CN110943663B publication Critical patent/CN110943663B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/20—Estimation of torque
    • 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
    • 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
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/10—Arrangements for controlling torque ripple, e.g. providing reduced torque ripple

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Ac Motors In General (AREA)

Abstract

The invention discloses a permanent magnet synchronous motor dynamic finite state set model prediction torque control method which is based on a surface permanent magnet synchronous motor flux linkage and torque prediction model, analyzes the action rule of a voltage vector angle and an amplitude value on the motor flux linkage and the torque, designs an alternative voltage vector set on line according to the system state by using fuzzy control, and analyzes the influence of adding a zero voltage vector in the alternative voltage vector set on the system control effect. The control requirements of system torque, flux linkage, switching frequency and dynamic performance are comprehensively considered, the flux linkage error absolute value is used as a static and dynamic judgment condition of the system, the alternative voltage vector set of the system is dynamically adjusted, the alternative voltage vector set is simplified, meanwhile, the comprehensive performance of the control system is improved, and torque pulsation is reduced.

Description

Permanent magnet synchronous motor dynamic finite state set model prediction torque control method
Technical Field
The invention belongs to the technical field of motor control, and particularly relates to a permanent magnet synchronous motor dynamic finite state set model prediction torque control method.
Background
According to the finite state set model prediction control of the traditional permanent magnet synchronous motor, model prediction and cost function calculation are carried out according to 7 basic voltage vectors which can be generated by a three-phase two-level inverter. Although the control idea of the finite state set model prediction control is simple, 7 basic voltage vector angles and amplitudes of the candidate voltage vector set in each control period are fixed, the control effect on the motor torque and the stator flux linkage is limited, and the problems of large torque ripple and flux linkage ripple exist.
The voltage vectors with variable angles and amplitudes can be generated through space vector modulation, the control of a system on torque and flux linkage is optimized, but the problems that the number of alternative voltage vectors is increased, the switching frequency of the system is increased, and the calculation load is increased are also caused, and the performance is limited by simply increasing the number of the alternative voltage vectors.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method for controlling a permanent magnet synchronous motor dynamic finite state set model prediction torque, which determines an optimized candidate voltage vector set according to a system state, wherein the design concept of the candidate voltage vector set is changed from more as better as possible to better as possible, so as to improve the performance of the permanent magnet synchronous motor model prediction torque control system and reduce torque ripple and system calculation burden.
The invention adopts the following technical scheme:
a permanent magnet synchronous motor dynamic finite state set model prediction torque control method comprises the following steps:
s1, establishing a traditional finite state set prediction control model of the surface permanent magnet synchronous motor;
s2, obtaining a simplified calculation model of motor torque and stator flux linkage variable quantity under the action of angles and amplitudes of different voltage vectors based on a surface permanent magnet synchronous motor flux linkage and torque prediction model, and analyzing to obtain action rules of the angles and amplitudes of the voltage vectors on the motor flux linkage and the motor torque;
s3, combining the action rule of voltage vector angle and amplitude on motor flux linkage and torque to makeDetermining a candidate voltage vector set by using fuzzy control, and designing an input variable of the fuzzy controller as a torque error ETAnd flux linkage error EψOutputting a candidate voltage vector angle and a candidate voltage vector amplitude, and designing a fuzzy rule by combining the action rule of the voltage vector angle and the amplitude on the flux linkage and the torque of the motor;
and S4, analyzing the influence of the zero voltage vector added in the candidate voltage vector set determined by the fuzzy control on the system control effect, taking the absolute value of the flux linkage error as a static and dynamic judgment condition of the system, and dynamically adding the zero voltage vector on the basis of the candidate voltage vector output by the fuzzy control.
Specifically, in step S1, the conventional finite state flux linkage and torque prediction model of the surface permanent magnet synchronous motor is as follows:
Figure BDA0002299286130000021
Figure BDA0002299286130000022
wherein the content of the first and second substances,
Figure BDA0002299286130000023
Figure BDA0002299286130000024
Te(k) respectively the stator flux linkage and the torque observed value at the current moment,
Figure BDA0002299286130000025
Te(k +1) are the stator flux linkage and the predicted torque value at the next moment,
Figure BDA0002299286130000026
is the amplitude of the voltage vector, delta t is the action time of the voltage vector, p is the pole pair number of the motor, psifIs the amplitude of flux linkage of permanent magnet of rotor, Ldα is the included angle between the voltage vector applied to the stator winding at the current moment and the stator flux linkage, and delta (k) is whenThe previous moment motor torque angle.
Further, the cost function used by the model predictive control is:
Figure BDA0002299286130000027
wherein, Te *、
Figure BDA0002299286130000028
The motor torque and the stator flux linkage reference value are respectively.
Specifically, in step S2, the motor torque and the stator flux linkage variation are calculated simply as:
Figure BDA0002299286130000031
wherein the content of the first and second substances,
Figure BDA0002299286130000032
Figure BDA0002299286130000033
is the stator flux linkage at the current moment, p is the pole pair number of the motor, psifFor the amplitude of the rotor permanent magnet flux linkage, α is an included angle between a voltage vector acting on the stator winding at the current moment and the stator flux linkage, and delta (k) is a torque angle of the motor at the current moment.
Specifically, in step S2, the law of the action of the voltage vector angle and the amplitude on the motor flux linkage and the torque is as follows: the voltage vector amplitude and the torque change magnitude are in a linear relation; the included angle between the voltage vector and the rotor flux linkage is in a sine relationship with the torque change; the voltage vector amplitude and the flux linkage amplitude change are in a linear relation; the included angle between the voltage vector and the stator flux linkage is in cosine relation with the change of the flux linkage amplitude.
Specifically, in step S3, the input variable of the fuzzy controller is the torque error ETAnd flux linkage error EψError in torque ETDiscourse domain is [ -2N.m, 2N.m]Dividing the fuzzy subset into 5 fuzzy subsets { NB, NS, ZO, PS, PB }; flux linkage error EψHas a domain of [ -0.002Wb，0.002Wb]Dividing the fuzzy subset into 3 fuzzy subsets { NB, ZO, PB }; the output variables of the fuzzy controller in each control period are the angles and the amplitudes of 3 alternative voltage vectors; the angular domain of the alternative voltage vector under the stator flux linkage coordinate is [ -pi, pi [ -pi [ ]]At 13 discrete angle values
Figure BDA0002299286130000034
The output discourse domain of the alternative voltage vector magnitude is [0, 1 ]]Divided into 3 fuzzy subsets { lambda1，λ2，λ3}，λ3Is 0.
Further, by combining the action rule of the voltage vector angle and the amplitude on the flux linkage and the torque of the motor, the designed fuzzy rule is as follows: when flux linkage error EψWhen ZO is the value, priority is given to torque control;
when the torque error is NB, the included angles between the alternative voltage vector and the rotor flux linkage are respectively NB
Figure BDA0002299286130000035
Amplitude of λ1(ii) a When the torque error is NS, the included angles between the alternative voltage vector and the rotor flux linkage are respectively NS
Figure BDA0002299286130000036
Amplitude of λ2(ii) a When the torque error is ZO, the candidate voltage vector magnitude is lambda3Namely, the vector is a zero voltage vector; when the torque error is PS, the included angles between the alternative voltage vector and the rotor flux linkage are respectively
Figure BDA0002299286130000041
The amplitude is lambda; when the torque error is PB, the included angles between the alternative voltage vector and the rotor flux linkage are respectively
Figure BDA0002299286130000042
Amplitude of λ1。
Further, by combining the action rule of the voltage vector angle and the amplitude on the flux linkage and the torque of the motor, the designed fuzzy rule is as follows: when flux linkage error EψWhen the number is NB or PB, the flux linkage control is preferred;
when it is magneticWhen the chain error is NB and the torque error is NB, the included angles between the alternative voltage vector and the stator flux linkage are respectively NB
Figure BDA0002299286130000043
Amplitude of λ1(ii) a When flux linkage error is NB and torque error is NS, the included angles between the alternative voltage vector and the stator flux linkage are respectively NB and NS
Figure BDA0002299286130000044
Amplitude of λ2(ii) a When flux linkage error is NB and torque error is ZO, the included angles between the alternative voltage vector and the stator flux linkage are respectively NB and ZO
Figure BDA0002299286130000045
Amplitude of λ3(ii) a When flux linkage error is NB and torque error is PS, the included angles between the alternative voltage vector and the stator flux linkage are respectively NB and PS
Figure BDA0002299286130000046
Amplitude of λ2(ii) a When flux linkage error is NB and torque error is PB, included angles between the alternative voltage vector and the stator flux linkage are respectively NB and PB
Figure BDA0002299286130000047
Amplitude of λ1(ii) a When flux linkage error is PB and torque error is NB, included angles between the alternative voltage vector and the stator flux linkage are respectively PB
Figure BDA0002299286130000048
Amplitude of λ1(ii) a When the flux linkage error is PB and the torque error is NS, the included angles between the alternative voltage vector and the stator flux linkage are respectively PB and NS
Figure BDA0002299286130000049
Amplitude of λ2(ii) a When the flux linkage error is PB and the torque error is ZO, included angles between the alternative voltage vector and the stator flux linkage are respectively PB and ZO
Figure BDA00022992861300000410
Amplitude of λ3(ii) a When the flux linkage error is PB, the torque error isIn PS, the included angles between the alternative voltage vector and the stator flux linkage are respectively
Figure BDA00022992861300000411
Amplitude of λ2(ii) a When the flux linkage error is PB and the torque error is PB, included angles between the alternative voltage vector and the stator flux linkage are respectively PB
Figure BDA00022992861300000412
Amplitude of λ1。
Specifically, in step S4, a zero voltage vector is added to the candidate voltage vector set determined by the fuzzy control, and the zero voltage vector is dynamically added on the basis of the candidate voltage vector output by the fuzzy control in consideration of the control requirements of the system torque, flux linkage, switching frequency and dynamic performance, and the method includes:
when the absolute value of the flux linkage error is larger than 0.001Wb, the system is dynamic, and the alternative voltage vector set consists of 3 alternative voltage vectors output by the fuzzy controller; otherwise, the system is static, and the candidate voltage vector set consists of 3 candidate voltage vectors output by the fuzzy controller and a zero voltage vector.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention discloses a permanent magnet synchronous motor dynamic finite state set model prediction torque control method, which analyzes the action rule of voltage vector angle and amplitude on motor flux linkage and torque based on a surface permanent magnet synchronous motor flux linkage and torque prediction model, provides a permanent magnet synchronous motor dynamic finite state set model prediction torque control method, realizes the online design of alternative voltage vector sets according to the system state, reduces the number of the alternative voltage vectors from the traditional 7 to no more than 4, reduces the subsequent prediction model and cost function calculation workload, improves the performance of a permanent magnet synchronous motor model prediction torque control system, and reduces torque pulsation.
Furthermore, the motor torque and the stator flux linkage variation are analyzed in a simplified calculation formula, and the voltage vector amplitude and the torque variation are in a linear relation; the included angle between the voltage vector and the rotor flux linkage is in a sine relationship with the torque change; the voltage vector amplitude and the flux linkage amplitude change are in a linear relation; the included angle between the voltage vector and the stator flux linkage is in cosine relation with the change of the flux linkage amplitude.
In conclusion, the fuzzy control-based permanent magnet synchronous motor dynamic finite state set model prediction torque control method determines an optimized candidate voltage vector set according to the system state, and the design concept of the candidate voltage vector set is changed from more traditional to better. The performance of a permanent magnet synchronous motor model prediction torque control system is improved, and torque pulsation and system calculation burden are reduced.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a block diagram of a conventional finite state set model predictive control system;
FIG. 2 is a diagram of simulation results of a conventional MPC;
FIG. 3 is a graph of a torque error membership function;
FIG. 4 is a graph of flux linkage error membership function;
FIG. 5 is a diagram of alternative voltage vector angle membership function;
FIG. 6 is a graph of alternative voltage vector magnitude membership functions;
FIG. 7 is a diagram of the voltage vector angle versus torque action;
FIG. 8 is a diagram showing the effect of voltage vector angle on flux linkage amplitude;
FIG. 9 is a block diagram of a fuzzy control based dynamic finite state set model predictive control system;
FIG. 10 is a graph of fuzzy MPC simulation results;
FIG. 11 is a block diagram of a fuzzy control-based dynamic finite state set model predictive control system with zero voltage vector addition;
FIG. 12 is a graph of the simulation results of a fuzzy MPC with zero voltage vector added;
FIG. 13 is a block diagram of a fuzzy control-based dynamic finite state set model predictive control system that dynamically adds a zero voltage vector;
FIG. 14 is a graph of the simulation results of a fuzzy MPC with dynamically added zero voltage vectors.
Detailed Description
The invention provides a permanent magnet synchronous motor dynamic finite state set model prediction torque control method, which is based on a surface permanent magnet synchronous motor flux linkage and torque prediction model, analyzes the action rule of a voltage vector angle and an amplitude value on the motor flux linkage and the torque, designs an alternative voltage vector set on line according to the system state by using fuzzy control, and analyzes the influence of adding a zero voltage vector in the alternative voltage vector set on the system control effect. The control requirements of system torque, flux linkage, switching frequency and dynamic performance are comprehensively considered, the flux linkage error absolute value is used as a static and dynamic judgment condition of the system, the alternative voltage vector set of the system is dynamically adjusted, the alternative voltage vector set is simplified, meanwhile, the comprehensive performance of the control system is improved, and torque pulsation is reduced.
The invention discloses a permanent magnet synchronous motor dynamic finite state set model prediction torque control method which comprises the following specific steps:
s1, establishing a traditional finite state set prediction control model of the surface permanent magnet synchronous motor;
referring to FIG. 1, the set of alternative voltage vectors in each control cycle
Figure BDA0002299286130000071
Since the zero voltage vector can be generated by two switch states (111 or 000), the principle of minimum switching times is specifically selected;
set of voltage vectors
Figure BDA0002299286130000072
Comprises the following steps:
Figure BDA0002299286130000073
the traditional finite state flux linkage and torque prediction model of the surface permanent magnet synchronous motor is as follows:
Figure BDA0002299286130000074
Figure BDA0002299286130000075
Figure BDA0002299286130000076
wherein the content of the first and second substances,
Figure BDA0002299286130000077
Te(k) respectively the stator flux linkage and the torque observed value at the current moment,
Figure BDA0002299286130000078
Te(k +1) are the stator flux linkage and the predicted torque value at the next moment,
Figure BDA0002299286130000079
is the amplitude of the voltage vector, delta t is the action time of the voltage vector, p is the pole pair number of the motor, psifIs the amplitude of flux linkage of permanent magnet of rotor, LdThe stator winding direct axis inductance component α is the included angle between the stator flux and the voltage vector acting on the stator winding at the current moment, and the motor torque angle delta (k) at the current moment.
The cost function used for model predictive control is
Figure BDA00022992861300000710
Wherein, Te *、
Figure BDA00022992861300000711
The motor torque and the stator flux linkage reference value are respectively.
S2, obtaining a simplified calculation model of motor torque and stator flux linkage variable quantity under the action of angles and amplitudes of different voltage vectors based on the surface permanent magnet synchronous motor flux linkage and torque prediction model;
and establishing a surface permanent magnet synchronous motor model prediction torque control simulation model based on MATLAB/Simulink.
The simulation model is a discrete model with a sampling period of 5 × 10-5s。
The dc bus voltage is 312V.
The parameters of the rotating speed PI regulator are as follows: KP is 5, KI is 100, and the PI regulator output upper and lower limits are [ -35, 35 ].
The reference speed was initially 60rpm, stepped to 30rpm at 1s, and the load torque was initially 10N · m, stepped to 30N · m at 0.5 s. The reference stator flux linkage amplitude is 0.3 Wb.
The total simulation duration is 1.5 s.
The parameters of the surface permanent magnet synchronous motor for simulation are shown in table 1.
TABLE 1 simulation surface-mounted PMSM parameters
Figure BDA0002299286130000081
Defining the Mean switching frequency, the Root Mean Square Error (RMSE) of the torque ripple and the Root Mean square Error of the flux ripple of the system as shown in formulas (5) to (7), wherein N isswitchingThe total number of times of switching the inverter, t is simulation duration, and n is the number of samples. And evaluating the control effect of the system in a steady state (0.1-1 s).
Figure BDA0002299286130000082
Figure BDA0002299286130000091
Figure BDA0002299286130000092
The rotating speed, the motor torque, the stator flux linkage amplitude and the a-phase stator current of the permanent magnet synchronous motor are shown in figure 2. The simulation evaluation results are shown in table 2.
Table 2 results of simulation evaluation of conventional MPC
Figure BDA0002299286130000093
Because the traditional finite state set model prediction control only carries out model prediction and cost function calculation according to 7 basic voltage vectors which can be generated by the three-phase two-level inverter. Although the control idea of the finite state set model prediction control is simple, 7 basic voltage vector angles and amplitudes of the candidate voltage vector set in each control period are fixed, the control effect on the motor torque and the stator flux linkage is limited, and the problems of large torque ripple and flux linkage ripple exist. The voltage vectors with variable angles and amplitudes can be generated through space vector modulation, the control of a system on torque and flux linkage is optimized, but the problems that the number of alternative voltage vectors is increased, the switching frequency and the calculation burden of the system are increased are also caused, and the performance improvement is limited by simply increasing the number of the alternative voltage vectors.
Based on a surface permanent magnet synchronous motor flux linkage and torque prediction model, under the action of angles and amplitudes of different voltage vectors, motor torque and stator flux linkage variable quantity calculation is simplified to
Figure BDA0002299286130000094
As can be seen from the equation (8), the magnitude of the voltage vector and the magnitude of the torque change are approximately linear; the included angle between the voltage vector and the rotor flux linkage is approximately in a sine relationship with the torque change; the voltage vector amplitude and the flux linkage amplitude change are approximately in a linear relation; the included angle between the voltage vector and the stator flux linkage is approximately in cosine relation with the change of the flux linkage amplitude.
S3, determining a candidate voltage vector set by using fuzzy control according to the action rule of the voltage vector angle and the amplitude on the flux linkage and the torque of the motor, wherein the input variable of the fuzzy controller is a torque error ETAnd flux linkage error EψOutputting a candidate voltage vector angle and a candidate voltage vector amplitude;
torque error ETDiscourse domain is [ -2N.m, 2N.m]The method is divided into 5 fuzzy subsets { NB, NS, ZO, PS, PB }, and the membership function is shown in FIG. 3; magnetic linkageError EψThe universe of discourse is [ -0.002Wb,0.002Wb]And the fuzzy subsets are divided into 3 fuzzy subsets (NB, ZO, PB), and the membership function is shown in FIG. 4. The output variables of the fuzzy controller are the angle and the amplitude of 3 candidate voltage vectors in each control period. The angular domain of the alternative voltage vector under the stator flux linkage coordinate is [ -pi, pi [ -pi [ ]]At 13 discrete angle values
Figure BDA0002299286130000101
The membership function is shown in FIG. 5; the output discourse domain of the alternative voltage vector magnitude is [0, 1 ]]Divided into 3 fuzzy subsets { lambda1，λ2，λ3Membership function see FIG. 6, λ3Is 0.
And S4, analyzing the influence of the zero voltage vector added in the candidate voltage vector set on the control effect of the system, comprehensively considering the control requirements of the system torque, flux linkage, switching frequency and dynamic performance, and dynamically adjusting the candidate voltage vector set of the system by taking the absolute value of the flux linkage error as a static and dynamic judgment condition of the system.
In order to optimize the control performance, a zero voltage vector is added in the candidate voltage vector set, the average switching frequency of the system is reduced to a certain extent, but the torque response is slowed down in a dynamic state, and the dynamic performance of the system is deteriorated. Comprehensively considering control requirements of system torque, flux linkage, switching frequency and dynamic performance, taking an absolute value of flux linkage error as a static and dynamic judgment condition of the system, wherein when the absolute value of flux linkage error exceeds a limit, the system is dynamic, and a candidate voltage vector set consists of 3 candidate voltage vectors output by a fuzzy controller; otherwise, the system is static, and a zero voltage vector is added on the basis of outputting the alternative voltage vector by fuzzy control.
When flux linkage error EψWhen the value is smaller, namely ZO, the flux linkage control is more ideal, and the torque control is prioritized. As can be seen from equation (8), the voltage vector amplitude and the torque variation magnitude are approximately linear, and the voltage vector angle and the torque variation magnitude are approximately sinusoidal, see fig. 7.
According to the action rule of the voltage vector angle and the amplitude on the torque change of the surface permanent magnet synchronous motor, a fuzzy control rule can be defined as follows:
when the torque error is NB, the included angles between the alternative voltage vector and the rotor flux linkage are respectively NB
Figure BDA0002299286130000111
Amplitude of λ1。
When the torque error is NS, the included angles between the alternative voltage vector and the rotor flux linkage are respectively NS
Figure BDA0002299286130000112
Amplitude of λ2。
When the torque error is ZO, the candidate voltage vector magnitude is lambda3I.e. zero voltage vector. For the convenience of fuzzy controller output, the alternative voltage vector and the rotor flux linkage angle are both set to 0 degrees.
When the torque error is PS, the included angles between the alternative voltage vector and the rotor flux linkage are respectively
Figure BDA0002299286130000113
Amplitude of λ2。
When the torque error is PB, the included angles between the alternative voltage vector and the rotor flux linkage are respectively
Figure BDA0002299286130000114
Amplitude of λ1。
When flux linkage error EψWhen the value is larger, namely NB or PB, the flux linkage control is preferred; as can be seen from equation (8), the voltage vector amplitude and the flux linkage amplitude change are approximately in a linear relationship, and the voltage vector angle and the flux linkage amplitude change are approximately in a cosine relationship, see fig. 8. According to the action rule of the voltage vector angle and the amplitude on the change of the flux linkage amplitude of the surface permanent magnet synchronous motor, a fuzzy control rule can be defined as follows:
when flux linkage error is NB and torque error is NB, included angles between the alternative voltage vector and the stator flux linkage are respectively NB
Figure BDA0002299286130000115
Amplitude of λ1(ii) a At this time, the included angle range between the alternative voltage vector and the rotor flux linkage is
Figure BDA0002299286130000116
The control requirement of reducing the torque can be considered.
When flux linkage error is NB and torque error is NS, the included angles between the alternative voltage vector and the stator flux linkage are respectively NB and NS
Figure BDA0002299286130000121
Amplitude of λ2(ii) a At this time, the included angle range between the alternative voltage vector and the rotor flux linkage is
Figure BDA0002299286130000122
The control requirement of reducing the torque can be considered.
When flux linkage error is NB and torque error is ZO, the included angles between the alternative voltage vector and the stator flux linkage are respectively NB and ZO
Figure BDA0002299286130000123
Amplitude of λ3。
When flux linkage error is NB and torque error is PS, the included angles between the alternative voltage vector and the stator flux linkage are respectively NB and PS
Figure BDA0002299286130000124
Amplitude of λ2(ii) a At this time, the included angle range between the alternative voltage vector and the rotor flux linkage is
Figure BDA0002299286130000125
The control requirement of increasing the torque can be considered to a certain extent.
When flux linkage error is NB and torque error is PB, included angles between the alternative voltage vector and the stator flux linkage are respectively NB and PB
Figure BDA0002299286130000126
Amplitude of λ1(ii) a At this time, the included angle range between the alternative voltage vector and the rotor flux linkage is
Figure BDA0002299286130000127
The control requirement of increasing the torque can be considered to a certain extent.
When flux linkage error is PB and torque error is NB, included angles between the alternative voltage vector and the stator flux linkage are respectively PB
Figure BDA0002299286130000128
Amplitude of λ1(ii) a At this time, the included angle range between the alternative voltage vector and the rotor flux linkage is
Figure BDA0002299286130000129
The control requirement of reducing the torque can be considered to a certain extent.
When the flux linkage error is PB and the torque error is NS, the included angles between the alternative voltage vector and the stator flux linkage are respectively PB and NS
Figure BDA00022992861300001210
Amplitude of λ2(ii) a At this time, the included angle range between the alternative voltage vector and the rotor flux linkage is
Figure BDA00022992861300001211
The control requirement of reducing the torque can be considered to a certain extent.
When the flux linkage error is PB and the torque error is ZO, included angles between the alternative voltage vector and the stator flux linkage are respectively PB and ZO
Figure BDA00022992861300001212
Amplitude of λ3。
When the flux linkage error is PB and the torque error is PS, included angles between the alternative voltage vector and the stator flux linkage are respectively PB and PS
Figure BDA0002299286130000131
Amplitude of λ2(ii) a At this time, the included angle range between the alternative voltage vector and the rotor flux linkage is
Figure BDA0002299286130000132
The control requirement of increasing the torque can be considered.
When the flux linkage error is PB and the torque error is PB, included angles between the alternative voltage vector and the stator flux linkage are respectively PB
Figure BDA0002299286130000133
Amplitude of λ1(ii) a At this time, the included angle range between the alternative voltage vector and the rotor flux linkage is
Figure BDA0002299286130000134
The control requirement of increasing the torque can be considered.
By combining the fuzzy control rules, a fuzzy control rule table can be established, as shown in table 3, wherein the angle is the included angle between the alternative voltage vector and the stator flux linkage.
TABLE 3 fuzzy control rules Table
Figure BDA0002299286130000135
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.
A block diagram of a fuzzy control-based permanent magnet synchronous motor dynamic finite state set model predictive torque control system is shown in fig. 9. Under the same simulation conditions, the rotating speed of the permanent magnet synchronous motor, the torque of the motor, the amplitude of the stator flux linkage and the a-phase stator current are shown in fig. 10. The simulation evaluation results are shown in table 4.
TABLE 4 fuzzy MPC simulation evaluation results
Figure BDA0002299286130000141
The zero voltage vector has the effects of reducing torque pulsation and flux linkage pulsation in steady-state operation in a permanent magnet synchronous motor system and reducing the switching frequency of an inverter, and is favorable for optimizing the comprehensive control effect of the system. So 1 zero voltage vector is added on the basis of 3 voltage vectors output by the fuzzy control. At this time, the number of voltage vectors in the candidate voltage vector set still does not exceed 4 per control cycle. A block diagram of a fuzzy control-based permanent magnet synchronous motor dynamic finite state set model predictive torque control system with zero voltage vector added is shown in fig. 11. Under the same simulation conditions, the rotating speed of the permanent magnet synchronous motor, the torque of the motor, the amplitude of the stator flux linkage and the a-phase stator current are shown in fig. 12. The simulation evaluation results are shown in table 5.
TABLE 5 fuzzy MPC simulation evaluation results with zero voltage vector added
Figure BDA0002299286130000142
Figure BDA0002299286130000151
Simulation results show that: after the zero voltage vector is added in the candidate voltage vector set, the average switching frequency of the system is reduced to a certain extent, but the torque response under the dynamic condition is slow, and the dynamic performance of the system is poor. This is because the system uses too much of the zero voltage vector during the dynamic response, weakening the torque tracking effect.
In order to take the steady-state control effect and the dynamic response of the system into consideration, whether the zero voltage vector is added in the alternative voltage vector set or not is dynamically determined according to the state of the system. The invention judges the current state through the flux linkage error of the system, when the absolute value of the flux linkage error is more than 0.001Wb, the system is dynamic, otherwise, the system is static. Under the dynamic state, the candidate voltage vector set is composed of 3 candidate voltage vectors output by the fuzzy controller, and under the static state, a zero voltage vector is added on the basis of the candidate voltage vectors output by the fuzzy controller. A block diagram of a fuzzy control-based permanent magnet synchronous motor dynamic finite state set model predictive torque control system for dynamically adding a zero voltage vector is shown in fig. 13. Under the same simulation conditions, the rotating speed of the permanent magnet synchronous motor, the torque of the motor, the amplitude of the stator flux linkage and the a-phase stator current are shown in fig. 14. The simulation evaluation results are shown in table 6.
Table 6 simulation results of conventional MPC
Figure BDA0002299286130000152
In summary, the control performance of the permanent magnet synchronous motor system under different control strategies is shown in table 7.
TABLE 7 simulation results of different control strategies
Figure BDA0002299286130000153
Figure BDA0002299286130000161
As can be seen from Table 7:
1. the fuzzy MPC is adopted to output 3 alternative voltage vectors in real time through fuzzy control, then the optimal voltage vector is determined through model prediction control, the alternative voltage vector set is simplified, the calculation cycle number of system model prediction control in each control period is reduced from 7 times of the traditional MPC to 3 times, the torque and flux linkage pulsation of the system are obviously reduced, and the average switching frequency is increased.
2. And the fuzzy MPC of the zero voltage vector is added, the number of the alternative voltage vectors in each control period is not more than 4, the calculation load of model prediction control is also reduced, and compared with the fuzzy MPC, the average switching frequency of the system is reduced, but the dynamic response speed of the system is slowed down.
3. The fuzzy MPC dynamically adding the zero voltage vector judges the system state through the flux linkage error, dynamically adds the zero voltage vector on the basis of 3 voltage vectors of the fuzzy MPC, gives consideration to the steady-state control effect of the system and the control requirement of dynamic response, and improves the comprehensive control performance of the system.
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. A permanent magnet synchronous motor dynamic finite state set model prediction torque control method is characterized by comprising the following steps:
s1, establishing a traditional finite state set prediction control model of the surface permanent magnet synchronous motor;
s2, obtaining a simplified calculation model of motor torque and stator flux linkage variable quantity under the action of angles and amplitudes of different voltage vectors based on a surface permanent magnet synchronous motor flux linkage and torque prediction model, and analyzing to obtain action rules of the angles and amplitudes of the voltage vectors on the motor flux linkage and the motor torque;
s3, determining a candidate voltage vector set by using fuzzy control according to the action rule of the voltage vector angle and the amplitude on the flux linkage and the torque of the motor, and designing an input variable of the fuzzy controller as a torque error ETAnd flux linkage error EψOutputting a candidate voltage vector angle and a candidate voltage vector amplitude, and designing a fuzzy rule by combining the action rule of the voltage vector angle and the amplitude on the flux linkage and the torque of the motor;
and S4, analyzing the influence of the zero voltage vector added in the candidate voltage vector set determined by the fuzzy control on the system control effect, taking the absolute value of the flux linkage error as a static and dynamic judgment condition of the system, and dynamically adding the zero voltage vector on the basis of the candidate voltage vector output by the fuzzy control.
2. The method of claim 1, wherein in step S1, the conventional finite-state flux-concentration and torque prediction model of the surface permanent magnet synchronous motor is as follows:
Figure FDA0002299286120000011
Figure FDA0002299286120000012
wherein the content of the first and second substances,
Figure FDA0002299286120000013
Te(k) respectively the stator flux linkage and the torque observed value at the current moment,
Figure FDA0002299286120000014
Te(k +1) are the stator flux linkage and the predicted torque value at the next moment,
Figure FDA0002299286120000015
is the amplitude of the voltage vector, delta t is the action time of the voltage vector, p is the pole pair number of the motor, psifIs the amplitude of flux linkage of permanent magnet of rotor, LdFor the stator winding direct axis inductance component, α is the angle between the voltage vector acting on the stator winding at the current moment and the stator flux linkage, and δ (k) is the motor torque angle at the current moment.
3. The method of claim 2, wherein the cost function used by the model predictive control is:
Figure FDA0002299286120000021
wherein, Te *、
Figure FDA0002299286120000022
The motor torque and the stator flux linkage reference value are respectively.
4. The method of claim 1, wherein in step S2, the motor torque and stator flux linkage variation calculation is simplified to:
Figure FDA0002299286120000023
wherein the content of the first and second substances,
Figure FDA0002299286120000024
Figure FDA0002299286120000025
is the stator flux linkage at the current moment, p is the pole pair number of the motor, psifFor the amplitude of the rotor permanent magnet flux linkage, α is an included angle between a voltage vector acting on the stator winding at the current moment and the stator flux linkage, and delta (k) is a torque angle of the motor at the current moment.
5. The method of claim 1, wherein in step S2, the action rule of the voltage vector angle and amplitude on the motor flux linkage and torque is: the voltage vector amplitude and the torque change magnitude are in a linear relation; the included angle between the voltage vector and the rotor flux linkage is in a sine relationship with the torque change; the voltage vector amplitude and the flux linkage amplitude change are in a linear relation; the included angle between the voltage vector and the stator flux linkage is in cosine relation with the change of the flux linkage amplitude.
6. The method of claim 1, wherein in step S3, the input variable of the fuzzy controller is a torque error ETAnd flux linkage error EψError in torque ETDiscourse domain is [ -2N.m, 2N.m]Dividing the fuzzy subset into 5 fuzzy subsets { NB, NS, ZO, PS, PB }; flux linkage error EψThe universe of discourse is [ -0.002Wb,0.002Wb]Dividing the fuzzy subset into 3 fuzzy subsets { NB, ZO, PB }; the output variables of the fuzzy controller in each control period are the angles and the amplitudes of 3 alternative voltage vectors; the angular domain of the alternative voltage vector under the stator flux linkage coordinate is [ -pi, pi [ -pi [ ]]At 13 discrete angle values
Figure FDA0002299286120000031
The output discourse domain of the alternative voltage vector magnitude is [0, 1 ]]Divided into 3 fuzzy subsets { lambda1，λ2，λ3}，λ3Is 0.
7. According toThe method of claim 6, wherein in combination with the law of the effect of voltage vector angle and magnitude on motor flux linkage and torque, the fuzzy rule is designed as follows: when flux linkage error EψWhen ZO is the value, priority is given to torque control;
when the torque error is NB, the included angles between the alternative voltage vector and the rotor flux linkage are respectively NB
Figure FDA0002299286120000032
Amplitude of λ1(ii) a When the torque error is NS, the included angles between the alternative voltage vector and the rotor flux linkage are respectively NS
Figure FDA0002299286120000033
Amplitude of λ2(ii) a When the torque error is ZO, the candidate voltage vector magnitude is lambda3Namely, the vector is a zero voltage vector; when the torque error is PS, the included angles between the alternative voltage vector and the rotor flux linkage are respectively
Figure FDA0002299286120000034
The amplitude is lambda; when the torque error is PB, the included angles between the alternative voltage vector and the rotor flux linkage are respectively
Figure FDA0002299286120000035
Amplitude of λ1。
8. The method of claim 6, wherein in combination with the law of the effect of voltage vector angle and magnitude on motor flux linkage and torque, the fuzzy rule is designed as follows: when flux linkage error EψWhen the number is NB or PB, the flux linkage control is preferred;
when flux linkage error is NB and torque error is NB, included angles between the alternative voltage vector and the stator flux linkage are respectively NB
Figure FDA0002299286120000036
Amplitude of λ1(ii) a When flux linkage error is NB and torque error is NS, the included angles between the alternative voltage vector and the stator flux linkage are respectively NB and NS
Figure FDA0002299286120000037
Amplitude of λ2(ii) a When flux linkage error is NB and torque error is ZO, the included angles between the alternative voltage vector and the stator flux linkage are respectively NB and ZO
Figure FDA0002299286120000038
Amplitude of λ3(ii) a When flux linkage error is NB and torque error is PS, the included angles between the alternative voltage vector and the stator flux linkage are respectively NB and PS
Figure FDA0002299286120000039
Amplitude of λ2(ii) a When flux linkage error is NB and torque error is PB, included angles between the alternative voltage vector and the stator flux linkage are respectively NB and PB
Figure FDA00022992861200000310
Amplitude of λ1(ii) a When flux linkage error is PB and torque error is NB, included angles between the alternative voltage vector and the stator flux linkage are respectively PB
Figure FDA00022992861200000311
Amplitude of λ1(ii) a When the flux linkage error is PB and the torque error is NS, the included angles between the alternative voltage vector and the stator flux linkage are respectively PB and NS
Figure FDA0002299286120000041
Amplitude of λ2(ii) a When the flux linkage error is PB and the torque error is ZO, included angles between the alternative voltage vector and the stator flux linkage are respectively PB and ZO
Figure FDA0002299286120000042
Amplitude of λ3(ii) a When the flux linkage error is PB and the torque error is PS, included angles between the alternative voltage vector and the stator flux linkage are respectively PB and PS
Figure FDA0002299286120000043
Amplitude of λ2(ii) a When the flux linkage error is PB and the torque error is PB, the alternative is carried outThe included angles of the voltage vector and the stator flux linkage are respectively
Figure FDA0002299286120000044
Amplitude of λ1。
9. The method of claim 1, wherein in step S4, a zero voltage vector is added to the candidate voltage vector set determined by the fuzzy control, and the control requirements of the system torque, flux linkage, switching frequency and dynamic performance are comprehensively considered, and the zero voltage vector is dynamically added on the basis of the candidate voltage vector output by the fuzzy control, and the method comprises:
when the absolute value of the flux linkage error is larger than 0.001Wb, the system is dynamic, and the alternative voltage vector set consists of 3 alternative voltage vectors output by the fuzzy controller; otherwise, the system is static, and the candidate voltage vector set consists of 3 candidate voltage vectors output by the fuzzy controller and a zero voltage vector.
CN201911215132.5A 2019-12-02 2019-12-02 Permanent magnet synchronous motor dynamic finite state set model prediction torque control method Expired - Fee Related CN110943663B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911215132.5A CN110943663B (en) 2019-12-02 2019-12-02 Permanent magnet synchronous motor dynamic finite state set model prediction torque control method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911215132.5A CN110943663B (en) 2019-12-02 2019-12-02 Permanent magnet synchronous motor dynamic finite state set model prediction torque control method

Publications (2)

Publication Number Publication Date
CN110943663A true CN110943663A (en) 2020-03-31
CN110943663B CN110943663B (en) 2021-07-02

Family

ID=69908659

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911215132.5A Expired - Fee Related CN110943663B (en) 2019-12-02 2019-12-02 Permanent magnet synchronous motor dynamic finite state set model prediction torque control method

Country Status (1)

Country Link
CN (1) CN110943663B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111555684A (en) * 2020-04-03 2020-08-18 浙江工业大学 Variable-switching-point multi-step model prediction torque control method for weight-factor-free permanent magnet synchronous motor finite set
CN113098348A (en) * 2021-04-09 2021-07-09 沈阳工业大学 Double three-phase permanent magnet synchronous motor predicted torque control method

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008228419A (en) * 2007-03-12 2008-09-25 Mie Univ Torque control method of motor based on model prediction control
CN104092422A (en) * 2014-07-28 2014-10-08 北方工业大学 Finite state set asynchronous motor model prediction flux linkage control method and device
US20180212541A1 (en) * 2017-01-26 2018-07-26 The Board Of Trustees Of The University Of Alabama Systems, methods and devices for approximate dynamic programming vector controllers for operation of ipm motors in linear and over modulation regions
CN109379013A (en) * 2018-11-30 2019-02-22 北京理工大学 A kind of permanent magnet synchronous motor method for suppressing torque ripple
CN109560726A (en) * 2019-01-02 2019-04-02 长安大学 A kind of voltage vector regional selection method based on varied angle PREDICTIVE CONTROL

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008228419A (en) * 2007-03-12 2008-09-25 Mie Univ Torque control method of motor based on model prediction control
CN104092422A (en) * 2014-07-28 2014-10-08 北方工业大学 Finite state set asynchronous motor model prediction flux linkage control method and device
US20180212541A1 (en) * 2017-01-26 2018-07-26 The Board Of Trustees Of The University Of Alabama Systems, methods and devices for approximate dynamic programming vector controllers for operation of ipm motors in linear and over modulation regions
CN109379013A (en) * 2018-11-30 2019-02-22 北京理工大学 A kind of permanent magnet synchronous motor method for suppressing torque ripple
CN109560726A (en) * 2019-01-02 2019-04-02 长安大学 A kind of voltage vector regional selection method based on varied angle PREDICTIVE CONTROL

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
JACKSON JOHN JUSTO等: "Fuzzy Model Predictive Direct Torque Control of IPMSMs for Electric Vehicle Applications", 《IEEE/ASME TRANSACTIONS ON MECHATRONICS》 *
余现飞等: "基于模糊在线调整权重因子的三相六开关容错逆变器驱动异步电机FCS-MPTC", 《电机与控制应用》 *
李耀华等: "表贴式永磁同步电机有限集模型预测转矩控制成本函数研究", 《电机与控制应用》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111555684A (en) * 2020-04-03 2020-08-18 浙江工业大学 Variable-switching-point multi-step model prediction torque control method for weight-factor-free permanent magnet synchronous motor finite set
CN111555684B (en) * 2020-04-03 2021-10-29 浙江工业大学 Permanent magnet synchronous motor multi-step model prediction torque control method with variable switching points
CN113098348A (en) * 2021-04-09 2021-07-09 沈阳工业大学 Double three-phase permanent magnet synchronous motor predicted torque control method
CN113098348B (en) * 2021-04-09 2023-10-31 沈阳工业大学 Predictive torque control method for double three-phase permanent magnet synchronous motor

Also Published As

Publication number Publication date
CN110943663B (en) 2021-07-02

Similar Documents

Publication Publication Date Title
Ren et al. Direct torque control of permanent-magnet synchronous machine drives with a simple duty ratio regulator
CN106936356A (en) Vector is screened and dutycycle is combined motor model Predictive Control System and method
CN110943663B (en) Permanent magnet synchronous motor dynamic finite state set model prediction torque control method
Uddin et al. Fuzzy logic-based efficiency optimization and high dynamic performance of IPMSM drive system in both transient and steady-state conditions
CN110808701B (en) Switching frequency reduction method based on fuzzy control self-adaptive variable weight coefficient
CN109687789B (en) Method for vector prediction direct torque control based on inverter basic voltage
Zhang et al. A duty ratio control strategy to reduce both torque and flux ripples of DTC for permanent magnet synchronous machines
Mahmoudi et al. Online fuzzy tuning of weighting factor in model predictive control of PMSM
Otkun Scalar speed control of induction motors with difference frequency
Uma et al. Implementation of real-coded GA-based fuzzy controller for sensorless SR motor drive
CN110350837B (en) Method for controlling direct torque through model prediction of simplified candidate finite state set
Li et al. A comparative study of model predictive current control and FOC for PMSM
Dang et al. Model Predictive Direct Torque Control for PMSM Drives in M–T Frame
CN114614719B (en) Control method and system for predicting power factor of motor driving system
Alik et al. Online Tuning Weighting Factor for A Predictive Torque Control of Induction Motor Drive
Xu et al. Research on permanent magnet synchronous motor drive system of electric vehicle based on fuzzy control
Adam et al. Adaptive steering‐based HDTC algorithm for PMSM
Qin et al. Fuzzy direct torque control of surface permanent magnet synchronous motor
Hafeez et al. A new torque hysteresis control algorithm for direct torque control of an IM drive
Sulaiman et al. Effects of parameters variation in fuzzy based induction motor drives
Sahridayan et al. Modeling and analysis of field-oriented control based permanent magnet synchronous motor drive system using fuzzy logic controller with speed response improvement
Talavat et al. Direct predictive control of asynchronous machine torque using matrix converter
CN111464094A (en) Brushless direct current motor model prediction controller and construction method thereof
CN113965123B (en) Model prediction direct torque control method based on three-level virtual vector
CN110266239B (en) Direct torque control method for simplified finite state set model prediction

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20210702

Termination date: 20211202