CN110289795B - Permanent magnet synchronous motor control system and control method for electric automobile - Google Patents

Permanent magnet synchronous motor control system and control method for electric automobile Download PDF

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CN110289795B
CN110289795B CN201910456729.2A CN201910456729A CN110289795B CN 110289795 B CN110289795 B CN 110289795B CN 201910456729 A CN201910456729 A CN 201910456729A CN 110289795 B CN110289795 B CN 110289795B
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axis
controller
sliding mode
current
permanent magnet
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CN110289795A (en
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余海涛
王尧
夏涛
李东暘
王玉晨
张建文
杨依林
郭蓉
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Nanjing Jinzai New Energy Dynamics Research Institute Co ltd
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Nanjing Jinzai New Energy Dynamics Research Institute Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • 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
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0007—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using sliding mode 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
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/001—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using fuzzy 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
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/13—Observer control, e.g. using Luenberger observers or Kalman filters
    • 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
    • H02P21/22—Current control, e.g. using a current control loop
    • 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
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/022—Synchronous motors
    • 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
    • H02P2205/00—Indexing scheme relating to controlling arrangements characterised by the control loops
    • H02P2205/01—Current loop, i.e. comparison of the motor current with a current reference
    • 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
    • H02P2205/00—Indexing scheme relating to controlling arrangements characterised by the control loops
    • H02P2205/07—Speed loop, i.e. comparison of the motor speed with a speed reference
    • 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
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/64—Electric machine technologies in electromobility
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Fuzzy Systems (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Feedback Control In General (AREA)
  • Control Of Ac Motors In General (AREA)

Abstract

The invention discloses a speed control system and a speed control method of a permanent magnet synchronous motor for an electric automobile, wherein a q-axis expected current is obtained according to a designed fuzzy self-adaptive sliding mode speed controller, and a d-axis reference current is given as 0; according to a disturbance value observed by a designed disturbance extended state observer, feeding back and compensating q-axis current output from a fuzzy self-adaptive sliding mode speed controller, and taking the q-axis current as q-axis input of a dq-axis internal model current controller; the current value fed back by the d axis is different from the given d axis current value 0 and is used as the d axis input of the dq axis internal model current controller; u is obtained after being processed by a dq axis internal model current controllerd，uqAnd finally outputting the current running driving voltage of the permanent magnet synchronous motor through vector control reverse park conversion, SVPWM modulation and an inverter. The invention improves the response speed and the disturbance resistance of the permanent magnet synchronous motor for the electric automobile, avoids the overshoot phenomenon, reduces the buffeting of the system and enhances the robustness of the system.

Description

Permanent magnet synchronous motor control system and control method for electric automobile
Technical Field
The invention relates to a permanent magnet synchronous motor control method, in particular to a permanent magnet synchronous motor control system and a permanent magnet synchronous motor control method for an electric automobile.
Background
The permanent magnet synchronous motor has the advantages of high efficiency, convenience in control and the like, and is the main trend of motors for electric vehicles. The permanent magnet synchronous motor for the electric automobile mainly comprises a torque control part and a speed control part, and the speed and the torque control of the permanent magnet synchronous motor can be efficiently realized by adopting a vector control method. However, there are many problems to realize high-performance control of the permanent magnet synchronous motor for the electric vehicle: due to the variability of the operating environment, the electric automobile needs frequent acceleration and deceleration, torque increase, braking and the like; the resistance, inductance, flux linkage, rotational inertia, friction coefficient and the like of the permanent magnet synchronous motor will change under the influence of the operating environment; these require an improvement in the control performance of the control system to cope with the change in the external environment. By adopting the fuzzy self-adaptive sliding mode speed control method, the overshoot phenomenon in the speed control process can be reduced, the response speed of the system is increased, the feedback compensation of the extended state observer is adopted, the disturbance resistance of the system is enhanced, and the dq axis current response speed is increased by adopting an internal model current control strategy.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a permanent magnet synchronous motor control system and a control method for an electric vehicle, aiming at improving the disturbance resistance and the dynamic response speed of the permanent magnet synchronous motor control system for the electric vehicle.
The technical scheme is as follows: in order to realize the purpose, the invention adopts the following technical scheme:
a permanent magnet synchronous motor control system for an electric automobile comprises a fuzzy self-adaptive sliding mode speed controller, an extended state observer, a dq-axis internal model current controller, an inverse park conversion module, an SVPWM vector pulse width modulation module, an inverter, a permanent magnet synchronous motor, a clark conversion module, a park conversion module, a current sensor and a position and speed sensor;
wherein the desired speed ω of the permanent magnet synchronous machinerefSpeed omega from position and speed sensor outputmAnd the rate of change of the difference is connected with the input of the fuzzy self-adaptive sliding mode speed controller; extending the input of the state observer to the speed ω of the position and speed sensor outputmAnd q-axis current i output by the park conversion moduleq(ii) a Output of a fuzzy adaptive sliding mode speed controller
Figure GDA0002653028030000011
The difference value of the output disturbance of the extended state observer is used as the q-axis current input of the dq-axis internal model current controller; d-axis current i output by park conversion moduledAnd d-axis current desired value id *The difference value which is equal to 0 is used as d-axis current input of the dq-axis internal model current controller; the output of the dq-axis internal model current controller passes through the inverse park conversion module and the SVPWM vector pulse width modulation module, and then the actual output voltage is transmitted to the permanent magnet synchronous motor through the current sensor through the inverter; the abc three-phase current output by the current sensor passes through the click conversion module and the park conversion module to obtain the actual dq axis current which is recorded as id，iq(ii) a The position and speed sensor is used for acquiring the current speed and the current electric angle of the permanent magnet synchronous motor, and the current electric angle of the permanent magnet synchronous motor is respectively transmitted to the park conversion module and the reverse park conversion module, so that the two-phase static coordinate system of the motor vector control system is converted into the two-phase rotating coordinate system, and the two-phase rotating coordinate system is converted into the two-phase static coordinate system.
The fuzzy self-adaptive sliding mode speed controller comprises the following implementation processes: set desired speed ωrefSpeed omega fed back from position and speed sensorsmMake a difference omegaref-ωmAccording to ωref-ωmAnd ωref-ωmConstructing a sliding mode surface S according to the change rate of the sliding mode surface S, and adjusting sliding in real time through a fuzzy self-adaptive controller according to the sliding mode surface S and the change rate of the sliding mode surface SAnd the mode switching function transmits the sliding mode switching function to the sliding mode speed controller.
Wherein, the input of the extended state observer is the speed omega fed back by the position and speed sensormAnd q-axis current obtained through park conversion is added with error after passing through gain K and then is integrated to obtain the speed omegamObserved value of (2)
Figure GDA0002653028030000021
Observed value
Figure GDA0002653028030000022
Minus the actual speed omega of the motormObtaining a gain value error1 of the rotation speed error through gain beta 1; observed value
Figure GDA0002653028030000023
Minus the actual speed omega of the motormMultiplying the result by-1, performing integration processing after gain beta2 to obtain error2, and subtracting error1 from error2 to obtain an error value; the error2 obtains an observed disturbance value disturbance through a gain of 1/K; wherein the gain K is set to be K3 p psif/(2*J)。
Wherein the dq-axis internal model current controller is connected with the id *Difference 0-i between 0 and the actual d-axis current feedback valuedAs d-axis current input to dq-axis internal model controller, output of fuzzy adaptive sliding mode speed controller
Figure GDA0002653028030000024
And a disturbance feedback compensation value disturbance through an extended state observer are subtracted to be used as q-axis current input of the dq-axis internal model controller,
Figure GDA0002653028030000025
and
Figure GDA0002653028030000026
multiplication, 0-idAnd
Figure GDA0002653028030000027
multiplying to obtain uq，ud。
The invention also provides a control method of the permanent magnet synchronous motor for the electric automobile, which comprises the following steps:
(1) design fuzzy self-adaptive sliding mode speed controller
A mathematical model of the permanent magnet synchronous motor is constructed according to the actual situation of the permanent magnet synchronous motor, a vector-controlled permanent magnet synchronous motor speed loop current loop control system is obtained according to the mathematical model, and a permanent magnet synchronous motor speed loop controller based on sliding mode control is designed based on the sliding mode control principle, namely a sliding mode speed controller;
(2) based on the sliding mode speed controller designed in the step (1), combining a fuzzy self-adaptive control method to obtain a fuzzy self-adaptive sliding mode speed controller;
(3) designing an extended state observer based on the mathematical model of the permanent magnet synchronous motor established in the step (1) for observing the rotating speed and load disturbance of the permanent magnet synchronous motor;
(4) designing a dq-axis internal model current controller based on the fuzzy self-adaptive sliding model speed controller and the extended state observer designed in the steps (2) and (3);
(5) outputting u required by the control motor according to the dq axis internal model current controller designed in the step (4)dAnd uqAnd the speed and current double closed-loop vector control of the motor is realized.
Further, step (1) uses idAnd (3) if the control method is 0, the mathematical model of the following attached permanent magnet synchronous motor in the d-q synchronous rotating coordinate system is as follows:
Figure GDA0002653028030000031
the torque equation is:
Figure GDA0002653028030000032
the equation of motion is:
Figure GDA0002653028030000033
wherein u isd,uqD-axis voltage and q-axis voltage respectively; i.e. id,iqD and q axis currents respectively; psifIs a permanent magnet flux linkage; r and L are respectively a stator winding resistor and an inductor; omegaeIs the electrical angular velocity of the motor; omegamIs the mechanical angular velocity, omega, of the motorm＝ωeP; p is the number of pole pairs; j is moment of inertia; b is a friction coefficient; t isLIs the load torque; l isqIs a q-axis inductor; l isdIs a d-axis inductor; t iseIs the electromagnetic force of the motor;
due to Ld＝LqSo the torque equation for a surface-mounted PMSM is expressed as:
Figure GDA0002653028030000041
further, the design method of the sliding mode speed controller in the step (1) comprises the following steps:
by using idThe rotor magnetic field vector control method which is 0 can be obtained by a permanent magnet synchronous motor mathematical model and a motion equation:
Figure GDA0002653028030000042
the state variables defining the PMSM system are:
Figure GDA0002653028030000043
wherein, ω isrefThe reference rotating speed of the motor can be obtained by the following two formulas:
Figure GDA0002653028030000044
definition of
Figure GDA0002653028030000045
The above equation is written as:
Figure GDA0002653028030000046
defining a sliding mode surface function as:
S＝cx1+x2；
wherein c >0 is a parameter to be designed;
deriving a sliding mode surface function:
Figure GDA0002653028030000047
in order to ensure that the three-phase permanent magnet synchronous motor has good dynamic performance, the sliding mode is made to approach law
Figure GDA0002653028030000048
Figure GDA0002653028030000049
Wherein the content of the first and second substances,
Figure GDA00026530280300000410
Figure GDA00026530280300000411
thus, the reference current of the q axis is:
Figure GDA0002653028030000051
further, the fuzzy self-adaptive sliding mode speed controller design method in the step (2) comprises the following steps:
the inputs for the fuzzy control are: derivative function of sliding mode surface functions S and S
Figure GDA0002653028030000052
The output is: u. offz(ii) a U of outputfzAssigning k to the value;
the fuzzy rule is as follows:
case 1: if it is not
Figure GDA0002653028030000053
Is PB, then ufzTaking a value PB;
case 2: if it is not
Figure GDA0002653028030000054
Is PM, then ufzTaking a value PM;
case 3: if it is not
Figure GDA0002653028030000055
Is PS, then ufzTaking a value PS;
case 4: if it is not
Figure GDA0002653028030000056
Is ZE, then ufzTaking a value ZE;
case 5: if it is not
Figure GDA0002653028030000057
Is NS, then ufzTaking a value NS;
case 6: if it is not
Figure GDA0002653028030000058
Is NM, then ufzTaking NM;
case 7: if it is not
Figure GDA0002653028030000059
Is NB, then ufzTaking a value NB;
the membership function of the fuzzy rule is:
adopting a triangular membership function, wherein the input fuzzy rule membership function is as follows:
if it is not
Figure GDA00026530280300000510
Then it is considered that
Figure GDA00026530280300000511
Is NB;
if it is not
Figure GDA00026530280300000512
Then it is considered that
Figure GDA00026530280300000513
Is NM;
if it is not
Figure GDA00026530280300000514
Then it is considered that
Figure GDA00026530280300000515
Is NS;
if it is not
Figure GDA00026530280300000516
Then it is considered that
Figure GDA00026530280300000517
Is ZE;
if it is not
Figure GDA00026530280300000518
Then it is considered that
Figure GDA00026530280300000519
Is PS;
if it is not
Figure GDA00026530280300000520
Then it is considered that
Figure GDA00026530280300000521
Is PM;
if it is not
Figure GDA00026530280300000522
Then it is considered that
Figure GDA00026530280300000523
Is PB;
the output membership function is:
PB is equal to 3;
PM is equal to 2;
PS is equal to 1;
ZE is equal to 0;
the center of an input function in the membership function is { -6, -4, -2,0, 2, 4, 6}, and the center of a corresponding output function is {3, 2,1, 0,1, 2, 3 };
finally, resolving the ambiguity by adopting a gravity center method:
Figure GDA0002653028030000061
wherein u isiIs the output in the ith region, is uiMembership function of kiIs uiThe weight coefficient of (2) represents the overlapping range of the membership function to be solved and represents all ranges of the membership function;
self-adaptive rule establishment:
the switching control is used for adjusting the control input of the system when the system deviates from the sliding mode surface, so that the system returns to the sliding mode surface, the adjusted threshold value is determined by a switching coefficient, and an adaptive model is constructed to determine the K value:
Figure GDA0002653028030000062
where K is a constant greater than 0, K >0, defining a Lyapunov function:
Figure GDA0002653028030000063
therefore, it is not only easy to use
Figure GDA0002653028030000064
It can be judged that, whether S is greater than 0 or less than 0,
Figure GDA0002653028030000065
when S is equal to 0, the first electrode is,
Figure GDA0002653028030000066
in summary,
Figure GDA0002653028030000067
and using the output u of the fuzzy adaptive algorithmfzAnd adjusting the switching coefficient of the sliding mode speed controller in real time on line.
Further, the design method of the extended state observer in the step (3) is as follows:
for a first order system:
Figure GDA0002653028030000068
wherein the content of the first and second substances,
Figure GDA0002653028030000069
is the system output, f (x, t) is the unknown nonlinear time-varying function, ω (t) is the external disturbance, u (t) is the control input, b is the model parameter0Is an estimate of b;
let the total perturbation term a (t) of the system be f (y, t) + ω (t) + (b-b)0) u (t), the perturbation term comprising an internal perturbation f (y, t) + (b-b)0) u (t), also including external disturbances ω (t); taking a (t) as an expanded state, let x11＝y，x12A (t), the dynamic system can be written as the following equation of state:
Figure GDA0002653028030000071
order to
Figure GDA0002653028030000072
Then construct linear ESO (extended state observer):
Figure GDA0002653028030000073
wherein, -p is a double pole of the extended state observer, p > 0;
the corresponding linear control rates are:
Figure GDA0002653028030000074
in the formula, y*Is a reference input of the system;
let e1＝z1-y，e2＝z2-a (t), available:
Figure GDA0002653028030000075
according to a first order differential equation model of the permanent magnet synchronous motor and a design method of an extended state observer, the following formula can be obtained:
Figure GDA0002653028030000076
let perturbation a (T) be-B omega/J-TL/J+(b-b0)iq，b＝pψf/J，b0Is an estimate of b; obtaining:
Figure GDA0002653028030000077
an expression for a proportional controller based on an extended state observer:
(1) ESO expression:
Figure GDA0002653028030000081
(2) the control law expression:
Figure GDA0002653028030000082
wherein, -p (p)>0) Is a pole, kpIs a proportional gain.
Further, the design method of the dq-axis internal model current controller in the step (4) is as follows:
the transfer function of the internal model current controller in the dq axis is represented by c(s), then:
Figure GDA0002653028030000083
wherein G(s) is a controlled object model,
Figure GDA0002653028030000084
is the internal model of the controlled object, Q(s) is the internal controller of the dq axis internal model current controller;
because the current loop of the permanent magnet synchronous motor is a first-order system, the current loop is taken according to the internal model control principle
Figure GDA0002653028030000085
Wherein f(s) is a filter, λ is a time constant of the filter;
when the system model is designed accurately, i.e.
Figure GDA0002653028030000086
Then, the feedback controller obtained by the above two equations is:
Figure GDA0002653028030000087
wherein the content of the first and second substances,
Figure GDA0002653028030000088
setting the parameter lambda by adopting a maximum sensitivity internal model positive setting method:
Figure GDA0002653028030000089
wherein M isSHas a value range of [1.2, 2]]。
Has the advantages that: compared with the prior art, the fuzzy self-adaptive sliding mode speed controller is designed, and the dynamic response speed of the system is improved. Input to a fuzzy adaptive algorithm in a sliding mode speed controller
Figure GDA0002653028030000092
And s, output is ufzAdjusting the output u of the controller in dependence on the fuzzy inputfzApproaching the desired control ueqAnd constructing a self-adaptive model to determine the K value of the switching function, and dynamically ensuring the stability of the sliding mode. The extended state observer enhances the disturbance resistance of the system, improves the robustness of the system, and can still stably operate when the permanent magnet synchronous motor control system causes the motor parameters to change and the load to suddenly change due to the change of the operating environment. The permanent magnet synchronous motor current control system based on the internal model control has the characteristics of high response speed, small steady-state error and high robustness.
Drawings
FIG. 1 is a block diagram of a PMSM control system for an electric vehicle according to the present invention;
FIG. 2 is a block diagram of a permanent magnet synchronous motor adaptive fuzzy sliding mode speed controller of the present invention;
FIG. 3 is a functional block diagram of an extended state observer;
FIG. 4 is a functional block diagram of an internal model current controller for the dq axis;
FIG. 5 is a flow chart of a control method of the present invention;
FIG. 6 is a distribution diagram of a membership function curve of a fuzzy control triangle, wherein (a) is a membership function curve of an input function of a fuzzy adaptive controller, and (b) is a fuzzy domain curve of an output function of the fuzzy adaptive controller;
FIG. 7 is a block diagram of a feedforward compensation controller.
Detailed Description
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and the detailed description.
As shown in fig. 1, a Permanent Magnet Synchronous Motor (PMSM) control system for an electric vehicle includes: the system comprises a fuzzy self-adaptive sliding mode speed controller, an Extended State Observer (ESO), a dq-axis internal model current controller, a reverse park conversion module, an SVPWM vector pulse width modulation module, a three-phase inverter, a permanent magnet synchronous motor, a clark conversion module, a park conversion module, a current sensor and a position and speed sensor.
The position and speed sensors are used for obtaining a position theta and a speed omegamThen, the speed ω will be givenrefWith the output speed signal omegamMake a difference, and ωrefAnd omegamThe differential of the difference is used as the input of a fuzzy self-adaptive sliding mode speed controller; q-axis current iqAnd ωmIs an input to an extended state observer; output of a fuzzy adaptive sliding mode speed controller
Figure GDA0002653028030000091
Making a difference with a disturbance feedback compensation value disturbance through an extended state observer, and using the difference as q-axis current input of the dq-axis internal model controller; i.e. id *0 and d-axis collection current idMaking a difference as d-axis current input of the dq-axis internal model controller; the expected dq-axis currents are respectively denoted as id *，iq *(ii) a The output of the dq axis internal model controller is subjected to inverse park conversion and SVPWM pulse width modulation respectively, three-phase current is collected by a three-phase inverter through a current sensor, the actually output voltage is transmitted to a permanent magnet synchronous motor, the permanent magnet synchronous motor is connected with a position and speed sensor, abc three-phase current output by the current sensor is subjected to park conversion and park conversion to obtain actual dq axis current, and the actual dq axis current is recorded as idAnd iq. The position and speed sensor is connected with the park transformation and the reverse park transformation, and transmits the current electric angle theta of the permanent magnet synchronous motor to the park transformation and the reverse park transformation functions, so that the conversion from a two-phase static coordinate system of the motor vector control system to a two-phase rotating coordinate system and the conversion from the two-phase rotating coordinate system to the two-phase static coordinate system are respectively realized.
Fig. 2 is a schematic block diagram of a fuzzy adaptive sliding mode speed controller designed by the invention, and the implementation process of the fuzzy adaptive sliding mode speed controller is as follows: set desired speed ωrefSpeed omega fed back from position and speed sensorsmMake a difference omegaref-ωmAccording to ωref-ωmAnd ωref-ωmOf (2) aAnd (3) establishing a sliding mode surface S according to the conversion rate, adjusting a sliding mode switching function in real time through a fuzzy self-adaptive controller according to the sliding mode surface S and the change rate of the sliding mode surface S, and transmitting the sliding mode switching function to a sliding mode speed controller.
FIG. 3 shows an extended state observer designed according to the invention, whose input is the velocity ω fed back by the position and velocity sensorsmAnd q-axis current obtained by park conversion. The q-axis current is added with error after passing through a gain K, and then is integrated to obtain the relative speed omegamObserved value of (2)
Figure GDA0002653028030000101
Observed value
Figure GDA0002653028030000102
Minus the actual speed omega of the motormObtaining a gain value error1 of the rotation speed error through gain beta 1; observed value
Figure GDA0002653028030000103
Minus the actual speed omega of the motormMultiplying the result by-1, performing integration processing after gain beta2 to obtain error2, and subtracting error1 from error2 to obtain an error value; error2 obtains the observed disturbance value disturbance through a gain of 1/K. Wherein the gain K is set to be K3 p psifAnd/(2 × J), the value of beta1 is beta 1-200, and the value of beta2 is beta 2-200.
The dq-axis internal model current controller shown in FIG. 4, id *Difference 0-i between 0 and the actual d-axis current feedback valuedAs d-axis current input to dq-axis internal model controller, output of fuzzy adaptive sliding mode speed controller
Figure GDA0002653028030000104
And a disturbance feedback compensation value disturbance through an extended state observer are subtracted to be used as q-axis current input of the dq-axis internal model controller,
Figure GDA0002653028030000105
and
Figure GDA0002653028030000106
multiplication, 0-idAnd
Figure GDA0002653028030000107
multiplying to obtain uq,ud。
FIG. 5 is a flow chart of a control method of the present invention, which includes the steps of:
(1) analyzing PMSM (permanent magnet synchronous motor) mathematical model
Suppose that: neglecting iron core saturation; eliminating eddy current and magnetic hysteresis loss; the rotor is not provided with damping windings, and the damping effect of the permanent magnet is not counted; and fourthly, the waveform of the induced electromotive force in the phase winding is a sine wave. By using idIn the vector control strategy of 0, under a d-q rotating coordinate system, the voltage equation of the PMSM is as follows:
Figure GDA0002653028030000111
the torque equation is:
Figure GDA0002653028030000112
the equation of motion is:
Figure GDA0002653028030000113
wherein u isd,uq-d, q-axis voltages;
id,iq-d, q axis currents;
ψf-a permanent magnet flux linkage;
r, L-stator winding resistance and inductance;
ωe-the electrical angular velocity of the electrical machine;
ωmmechanical angular velocity of the electric machine, ωm＝ωe/p；
p is the number of pole pairs;
j-moment of inertia;
b-coefficient of friction;
TL-a load torque;
Lq-a q-axis inductance;
Ld-a d-axis inductance;
Te-the electromagnetic force of the motor;
due to Ld＝LqSo the torque equation for a surface-mounted PMSM is expressed as:
Figure GDA0002653028030000121
(2) according to the PMSM mathematical model, a vector control system is constructed
By using idA vector control method of 0, which can be obtained from equations (1) and (3):
Figure GDA0002653028030000122
(3) sliding mode speed controller as speed ring controller
Designing a sliding mode speed controller:
defining a state variable of a PMSM system as x1,x2：
Figure GDA0002653028030000123
Wherein, ω isrefIs a reference rotational speed of the motor, and is set to a constant value. From formulas (5) and (6):
Figure GDA0002653028030000124
definition of
Figure GDA0002653028030000125
Equation (7) can be written as:
Figure GDA0002653028030000126
defining a sliding mode surface function as:
S＝cx1+x2(9)；
wherein c >0 is a parameter to be designed.
Derivation of equation (9):
Figure GDA0002653028030000131
in order to ensure that the three-phase permanent magnet synchronous motor has good dynamic performance, a novel sliding mode approach law is provided
Figure GDA0002653028030000132
Figure GDA0002653028030000133
Wherein the content of the first and second substances,
Figure GDA0002653028030000134
Figure GDA0002653028030000135
the reference current for the q-axis can thus be found as:
Figure GDA0002653028030000136
the approximation law means that the system is composed of a variable speed term and an exponential term from the beginning state, when the system approaches a sliding mode, the | e | term plays a main role, and when the error | e | approaches 0, | e | sgn (S) is continuously reduced, and the system is finally stabilized at the origin.
(4) Obtaining the fuzzy self-adaptive sliding mode speed controller after the fuzzy self-adaptive design of the sliding mode speed controller
Performing fuzzification control on the basis of the sliding mode speed controller designed in the step (3) and performing adaptive design on the fuzzification sliding mode controller to form a fuzzy adaptive sliding mode speed controller with an adaptive function;
designing a fuzzy self-adaptive speed sliding mode controller:
error e of speed and its rate of change in sliding mode control
Figure GDA0002653028030000137
The sliding mode surface function S is formed, so after the sliding mode speed controller is subjected to fuzzy self-adaptive processing, the input of the fuzzy self-adaptive controller is
Figure GDA0002653028030000138
And S, output is ufz(ii) a Based on input by fuzzy adaptive controller
Figure GDA00026530280300001312
And S, adjusting the switching coefficient of the sliding mode speed controller in real time on line. The fuzzy rule table is built as shown in table 1.
PB refers to the maximum forward direction, PM refers to the middle forward direction value, and PM refers to the smaller forward direction value; ZE means 0;
NB means the largest negative direction, NM means the middle negative direction, and NM means the smaller negative direction.
Table 1 is expressed in fuzzy language:
the fuzzy rule is as follows:
case 1: if it is not
Figure GDA0002653028030000139
Is PB, then ufzTaking a value PB;
case 2: if it is not
Figure GDA00026530280300001310
Is PM, then ufzTaking a value PM;
case 3: if it is not
Figure GDA00026530280300001311
Is PS, then ufzTaking a value PS;
case 4: if it is not
Figure GDA0002653028030000141
Is ZE, then ufzTaking a value ZE;
case 5: if it is not
Figure GDA0002653028030000142
Is NS, then ufzTaking a value NS;
case 6: if it is not
Figure GDA0002653028030000143
Is NM, then ufzTaking NM;
case 7: if it is not
Figure GDA0002653028030000144
Is NB, then ufzTaking a value NB;
the membership function of the fuzzy rule is:
adopting a triangular membership function, wherein the input fuzzy rule membership function is as follows:
if it is not
Figure GDA0002653028030000145
Then it is considered that
Figure GDA0002653028030000146
Is NB;
if it is not
Figure GDA0002653028030000147
Then it is considered that
Figure GDA0002653028030000148
Is NM;
if it is not
Figure GDA0002653028030000149
Then it is considered that
Figure GDA00026530280300001410
Is NS;
if it is not
Figure GDA00026530280300001411
Then it is considered that
Figure GDA00026530280300001412
Is ZE;
if it is not
Figure GDA00026530280300001413
Then it is considered that
Figure GDA00026530280300001414
Is PS;
if it is not
Figure GDA00026530280300001415
Then it is considered that
Figure GDA00026530280300001416
Is PM;
if it is not
Figure GDA00026530280300001417
Then it is considered that
Figure GDA00026530280300001418
Is PB;
the output membership function is:
PB is equal to 3;
PM is equal to 2;
PS is equal to 1;
ZE is equal to 0;
TABLE 1 fuzzy rule Table
Figure GDA00026530280300001419
Figure GDA00026530280300001420
The input of the fuzzy adaptive controller is the function center corresponding to the membership function of { -6, -4, -2,0, 2, 4, 6}, and the distribution diagram of the membership curve is shown in (a) of FIG. 6; the center of the output function is {3, 2,1, 0,1, 2, 3}, and the distribution diagram of the membership function is shown in (b) of FIG. 6. Finally, resolving the ambiguity by adopting a gravity center method:
Figure GDA0002653028030000151
wherein u isiIs the output in the ith region, is uiMembership function of kiIs uiThe weight coefficient of (2) represents the overlapping range of the membership function to be solved and represents all ranges of the membership function;
self-adaptive rule establishment:
the switching control is used for adjusting the system control input when the system deviates from the sliding mode surface, so that the system returns to the sliding mode surface, the adjusted threshold value is determined by the switching coefficient of the formula (15), and the adaptive model is constructed to determine the K value:
Figure GDA0002653028030000152
where K is a constant greater than 0, K >0, defining a Lyapunov function:
Figure GDA0002653028030000153
therefore, it is not only easy to use
Figure GDA0002653028030000154
It can be judged that, whether S is greater than 0 or less than 0,
Figure GDA0002653028030000155
when S is equal to 0, the first electrode is,
Figure GDA0002653028030000156
in summary,
Figure GDA0002653028030000157
the invention adopts the output u of the fuzzy self-adaptive algorithmfzAnd adjusting the switching coefficient of the sliding mode controller in real time on line.
(5) Establishing extended state observer according to PMSM mathematical model
In order to improve the anti-interference capability of a permanent magnet synchronous motor for an electric vehicle, an extended state observer is adopted to observe a system disturbance variable, a disturbance value is fed back and compensated to a fuzzy self-adaptive sliding mode speed controller, and the anti-interference capability of the system is enhanced. Velocity value omega calculated by position and velocity sensormAnd the q-axis current value obtained through park conversion is used as the input of an extended state observer, the output of the extended state observer is a disturbance observed value disturbance, and the disturbance observed value is fed back and compensated to the output value of the fuzzy self-adaptive sliding mode speed controller and is used as the q-axis reference current input of the dq-axis internal model controller.
For a first order system:
Figure GDA0002653028030000161
wherein the content of the first and second substances,
Figure GDA0002653028030000162
is the system output, f (x, t) is the unknown nonlinear time-varying function, ω (t) is the external disturbance, u (t) is the control input, b is the model parameter0Is an estimate of b.
Let the total perturbation term a (t) of the system be f (y, t) + ω (t) + (b-b)0) u (t), the perturbation term including an internal perturbation f (y, t) + (b-b)0) u (t), also including external perturbations ω (t). Taking a (t) as an expanded state, let x11＝y，x12The dynamic system of equation (6) can be written as the following equation of state:
Figure GDA0002653028030000163
in formula (19), the
Figure GDA0002653028030000164
Then a linear eso (extended state observer) can be constructed:
Figure GDA0002653028030000165
where-p is the dual pole of the extended state observer, p > 0.
The corresponding linear control rates are:
Figure GDA0002653028030000166
in the formula, y*Is the reference input of the system.
Let e1＝z1-y，e2＝z2-a (t), available:
Figure GDA0002653028030000167
this is a second order continuous system that stabilizes with the essential condition that the dual pole p > 0. The observed effect of ESO can be guaranteed only by choosing the appropriate desired closed-loop pole-p (p > 0).
According to a first order differential equation model of the permanent magnet synchronous motor and a design method of an extended state observer, the following formula can be obtained:
Figure GDA0002653028030000168
let perturbation a (T) be-B omega/J-TL/J+(b-b0)iq，b＝pψf/J，b0Is an estimate of b. The following results were obtained:
Figure GDA0002653028030000171
from equations (23) and (24), the load torque, friction system, disturbance of inertia and b0The perturbation due to the estimation error can be reflected in a (t). If a (t) can be observed and compensated, the disturbance capability of the system can be obviously improved. The structure of the feedforward compensation controller based on the extended state observer is shown in fig. 7.
An extended state observer based expression:
(1) ESO expression:
Figure GDA0002653028030000172
(2) the control law expression:
Figure GDA0002653028030000173
according to theoretical analysis, the observed effect of ESO depends on the pole-p (p)>0) Relative to the tracking speed of the ESO, the greater p, the faster the ESO tracks the output signal response, i.e., z1The faster the response to speed ω. Proportional gain kpThis is usually large, but too large will cause the speed response to oscillate, making the system unstable.
(6) dq axis internal model current controller as current controller
Output of fuzzy adaptive sliding mode speed controller
Figure GDA0002653028030000174
Making a difference with a feedback compensation value disturbance of the extended state observer, using the difference as a q-axis current input value of the dq-axis internal model controller, and adding i to the feedback compensation value disturbanced *Taking the difference between 0 and d-axis current feedback value as d-axis current input value of the dq-axis internal model controller, and respectively outputting u after passing through the dq-axis internal model controllerd，uqThen u isd，uqAnd the motor is used as the input of inverse park conversion, and is controlled through SVPWM conversion and an inverter. The transfer function of the dq-axis internal model controller is represented by c(s), then:
Figure GDA0002653028030000175
wherein G(s) is a controlled object model,
Figure GDA0002653028030000176
is an internal model of the controlled object, and q(s) is an internal controller of the dq-axis internal model controller.
Because the current loop of the permanent magnet synchronous motor is a first-order system, the current loop is taken according to the internal model control principle
Figure GDA0002653028030000181
Where f(s) is the filter and λ is the time constant of the filter.
When the system model is designed accurately, i.e.
Figure GDA0002653028030000182
Then, the feedback controller obtained by bringing the formula (28) into the formula (27) is:
Figure GDA0002653028030000183
wherein the content of the first and second substances,
Figure GDA0002653028030000184
the internal model control is similar to a PI controller, but only one control parameter lambda is provided, the control is simple, and the parameter lambda is set by adopting a maximum sensitivity internal model positive setting method:
Figure GDA0002653028030000185
wherein M isSHas a value range of [1.2, 2]]. The structure of the internal model current controller of dq axis is shown in fig. 4.
(7) And (3) adjusting the PMSM control system based on the fuzzy adaptive sliding mode speed control, the extended state observer and the dq-axis internal model current control according to the fuzzy adaptive sliding mode controller, the extended state observer and the dq-axis internal model current controller which are designed in the steps (1), (2), (3), (4), (5) and (6).
As shown in figure 6(a) is the membership function of the input function of the fuzzy adaptive controller,
Figure GDA0002653028030000186
the membership function is at-6, -4 for the input of the fuzzy adaptive controller]In intervals, it is defined as fuzzyNB of input values of the adaptive controller adopts Z function, namely when input data is divided into intervals of [ -6, -4 [ -6 [ -4 ]]Taking a Z-type membership function, wherein the maximum height is 1, and the coordinate point is (-6, 1); when the membership function is at [ -6, -2 [)]In the interval, the input value NM of the fuzzy self-adaptive controller is defined, a triangular membership function is adopted, and the vertex coordinate of the triangular membership function is (-4, 1); when the membership function is at [ -4,0 []In the interval, NS of the input value of the fuzzy self-adaptive controller is defined, a triangular membership function is adopted, and the vertex coordinate of the triangular membership function is (-2, 1); when the membership function is at [ -2,2 [)]In the interval, the constant value is defined as ZO of the input value of the fuzzy self-adaptive controller, a triangular membership function is adopted, and the vertex coordinate of the triangular membership function is (0, 1); when the membership function is at [0,4 ]]In the interval, defining the interval as PS of the input value of the fuzzy adaptive controller, adopting a triangular membership function, wherein the vertex coordinate of the triangular membership function is (2, 1); when the membership function is at [2,6 ]]In the interval, the PM defined as the input value of the fuzzy adaptive controller adopts a triangular membership function, and the vertex coordinate of the triangular membership function is (4, 1); when the membership function is in [4,6 ]]In interval, PB defined as input value of fuzzy adaptive controller adopts Z function, i.e. when input data is divided into intervals [4,6 ]]And (3) taking a Z-type membership function, wherein the maximum height of the Z-type membership function is 1, and the coordinate point is (6, 1).
As shown in fig. 6(b), the ambiguity domain of the output function of the fuzzy adaptive controller is defined as NB of the output value of the fuzzy adaptive controller when the ambiguity domain of the output value is smaller than-2, that is, when the output data is smaller than-2, a trigonometric function is adopted, and the maximum height is 1, and the coordinate point is (-3, 1); when the fuzzy domain of the output value is in the range of (-3, -1), the output value is defined as NM of the output value of the fuzzy self-adaptive controller, a trigonometric function is adopted, and the vertex coordinate of the triangular membership function is (-2, 1); when the fuzzy domain of the output value is in [ -2,0], defining the output value as NS of the output value of the fuzzy self-adaptive controller, adopting a trigonometric function, and setting the vertex coordinate of the triangular membership function as (-1, 1); when the fuzzy domain of the output value is [ -1,1], defining the output value as ZO of the output value of the fuzzy self-adaptive controller, and adopting a trigonometric function, wherein the vertex coordinate of the triangular membership function is (0, 1); when the fuzzy domain of the output value is in [0,2], defining the fuzzy domain as PS of the output value of the fuzzy self-adaptive controller, adopting a trigonometric function, and setting the vertex coordinates of a triangular membership function as (1, 1); when the fuzzy domain of the output value is in [1,3], defining the output value as PM of the fuzzy self-adaptive controller, adopting a trigonometric function, and setting the vertex coordinates of the triangular membership function as (2, 1); when the fuzzy domain of the output value is more than 2, the output value is defined as PB of the output value of the fuzzy adaptive controller, namely when the output data is more than 2, a trigonometric function is adopted, the maximum height of the output value is 1, and the coordinate point is (3, 1).
FIG. 7 shows a schematic diagram of the feedforward compensation of an extended state observer system with a target value y*Is subtracted from the first output feedback value of the ESO and then multiplied by kpTo obtain u0Second output value of ESO and 1/b0Negative feedback of the product of u0U, u are obtained as control inputs of the controlled object, ωtThe output of the controlled object is y as the input disturbance of the controlled object; wherein u is multiplied by b0The value of (c) and y are input as ESO.

Claims (9)

1. The utility model provides a PMSM control system for electric automobile which characterized in that: the system comprises a fuzzy self-adaptive sliding mode speed controller, an extended state observer, a dq-axis internal model current controller, a reverse park conversion module, an SVPWM vector pulse width modulation module, an inverter, a permanent magnet synchronous motor, a clark conversion module, a park conversion module, a current sensor and a position and speed sensor;
wherein the desired speed ω of the permanent magnet synchronous machinerefSpeed omega from position and speed sensor outputmAnd the rate of change of the difference is connected with the input of the fuzzy self-adaptive sliding mode speed controller; extending the input of the state observer to the speed ω of the position and speed sensor outputmAnd q-axis current i output by the park conversion moduleq(ii) a Output of a fuzzy adaptive sliding mode speed controller
Figure FDA0002653028020000011
And enlargingThe difference value of output disturbance of the extended state observer is used as q-axis current input of a dq-axis internal model current controller; d-axis current i output by park conversion moduledAnd d-axis current desired value id *The difference value which is equal to 0 is used as d-axis current input of the dq-axis internal model current controller; the output of the dq-axis internal model current controller passes through the inverse park conversion module and the SVPWM vector pulse width modulation module, and then the actual output voltage is transmitted to the permanent magnet synchronous motor through the current sensor through the inverter; the abc three-phase current output by the current sensor passes through the click conversion module and the park conversion module to obtain the actual dq axis current which is recorded as id，iq(ii) a The position and speed sensor is used for acquiring the current speed and the current electric angle of the permanent magnet synchronous motor, and the current electric angle of the permanent magnet synchronous motor is respectively transmitted to the park conversion module and the reverse park conversion module, so that the two-phase static coordinate system of the motor vector control system is converted into the two-phase rotating coordinate system, and the two-phase rotating coordinate system is converted into the two-phase static coordinate system;
extending the input of the state observer to the velocity ω fed back by the position and velocity sensorsmAnd q-axis current obtained through park conversion is added with error after passing through gain K and then is integrated to obtain the speed omegamObserved value of (2)
Figure FDA0002653028020000012
Observed value
Figure FDA0002653028020000013
Minus the actual speed omega of the motormObtaining a gain value error1 of the rotation speed error through gain beta 1; observed value
Figure FDA0002653028020000014
Minus the actual speed omega of the motormMultiplying the result by-1, performing integration processing after gain beta2 to obtain error2, and subtracting error1 from error2 to obtain an error value; the error2 obtains an observed disturbance value disturbance through a gain of 1/K; wherein the gain K is set to be K3 p psifV (2 × J); wherein p is the logarithm of the pole, #fIs a permanent magnetChain, J is moment of inertia.
2. The permanent magnet synchronous motor control system for the electric vehicle according to claim 1, characterized in that: the implementation process of the fuzzy self-adaptive sliding mode speed controller is as follows: set desired speed ωrefSpeed omega fed back from position and speed sensorsmMake a difference omegaref-ωmAccording to ωref-ωmAnd ωref-ωmThe sliding mode surface S is constructed according to the change rate of the sliding mode surface S, the sliding mode switching function is adjusted in real time through the fuzzy self-adaptive controller according to the change rate of the sliding mode surface S and the change rate of the sliding mode surface S, and the sliding mode switching function is transmitted to the sliding mode speed controller.
3. The permanent magnet synchronous motor control system for the electric vehicle according to claim 1, characterized in that: d q axis internal model current controller will id *Difference 0-i between 0 and the actual d-axis current feedback valuedAs d-axis current input to dq-axis internal model controller, output of fuzzy adaptive sliding mode speed controller
Figure FDA0002653028020000021
And a disturbance feedback compensation value disturbance through an extended state observer are subtracted to be used as q-axis current input of the dq-axis internal model controller,
Figure FDA0002653028020000022
and
Figure FDA0002653028020000023
multiplication, 0-idAnd
Figure FDA0002653028020000024
multiplying to obtain uq，udWherein u isd,uqThe d-axis voltage and the q-axis voltage are respectively.
4. A control method of the permanent magnet synchronous motor control system for the electric vehicle according to any one of claims 1 to 3, characterized by comprising the steps of:
(1) design fuzzy self-adaptive sliding mode speed controller
A mathematical model of the permanent magnet synchronous motor is constructed according to the actual situation of the permanent magnet synchronous motor, a vector-controlled permanent magnet synchronous motor speed loop current loop control system is obtained according to the mathematical model, and a permanent magnet synchronous motor speed loop controller based on sliding mode control is designed based on the sliding mode control principle, namely a sliding mode speed controller;
(2) based on the sliding mode speed controller designed in the step (1), combining a fuzzy self-adaptive control method to obtain a fuzzy self-adaptive sliding mode speed controller;
(3) designing an extended state observer based on the mathematical model of the permanent magnet synchronous motor established in the step (1) for observing the rotating speed and load disturbance of the permanent magnet synchronous motor;
(4) designing a dq-axis internal model current controller based on the fuzzy self-adaptive sliding model speed controller and the extended state observer designed in the steps (2) and (3);
(5) outputting u required by the control motor according to the dq axis internal model current controller designed in the step (4)dAnd uqAnd the speed and current double closed-loop vector control of the motor is realized.
5. The method for controlling the PMSM for the electric automobile according to claim 4, wherein i is adopted in the step (1)dAnd (3) if the control method is 0, the mathematical model of the following attached permanent magnet synchronous motor in the d-q synchronous rotating coordinate system is as follows:
Figure FDA0002653028020000031
the torque equation is:
Figure FDA0002653028020000032
the equation of motion is:
Figure FDA0002653028020000033
wherein u isd,uqD-axis voltage and q-axis voltage respectively; i.e. id,iqD and q axis currents respectively; psifIs a permanent magnet flux linkage; r and L are respectively a stator winding resistor and an inductor; omegaeIs the electrical angular velocity of the motor; omegamIs the mechanical angular velocity, omega, of the motorm＝ωeP; p is the number of pole pairs; j is moment of inertia; b is a friction coefficient; t isLIs the load torque; l isqIs a q-axis inductor; l isdIs a d-axis inductor; t iseIs the electromagnetic force of the motor;
due to Ld＝LqSo the torque equation for a surface-mounted PMSM is expressed as:
Figure FDA0002653028020000034
6. the method for controlling the permanent magnet synchronous motor for the electric automobile according to claim 4, wherein the method for designing the sliding mode speed controller in the step (1) comprises the following steps:
by using idThe rotor magnetic field vector control method which is 0 can be obtained by a permanent magnet synchronous motor mathematical model and a motion equation:
Figure FDA0002653028020000035
the state variables defining the PMSM system are:
Figure FDA0002653028020000036
wherein, ω isrefThe reference rotating speed of the motor can be obtained by the following two formulas:
Figure FDA0002653028020000041
definition of
Figure FDA0002653028020000042
The above equation is written as:
Figure FDA0002653028020000043
defining a sliding mode surface function as:
S＝cx1+x2；
wherein c >0 is a parameter to be designed;
deriving a sliding mode surface function:
Figure FDA0002653028020000044
in order to ensure that the three-phase permanent magnet synchronous motor has good dynamic performance, the sliding mode is made to approach law
Figure FDA0002653028020000045
Figure FDA0002653028020000046
Wherein the content of the first and second substances,
Figure FDA0002653028020000047
Figure FDA0002653028020000048
thus, the reference current of the q axis is:
Figure FDA0002653028020000049
7. the method for controlling the PMSM for the electric vehicle according to claim 4, wherein the fuzzy adaptive sliding mode speed controller design method in step (2) is as follows:
the inputs for the fuzzy control are: derivative function of sliding mode surface functions S and S
Figure FDA00026530280200000415
The output is: u. offz(ii) a U of outputfzAssigning k to the value;
the fuzzy rule is as follows:
case 1: if it is not
Figure FDA00026530280200000410
Is PB, then ufzTaking a value PB;
case 2: if it is not
Figure FDA00026530280200000411
Is PM, then ufzTaking a value PM;
case 3: if it is not
Figure FDA00026530280200000412
Is PS, then ufzTaking a value PS;
case 4: if it is not
Figure FDA00026530280200000413
Is ZE, then ufzTaking a value ZE;
case 5: if it is not
Figure FDA00026530280200000414
Is NS, then ufzTaking a value NS;
case 6: if it is not
Figure FDA0002653028020000051
Is NM, then ufzTaking NM;
case 7: if it is not
Figure FDA0002653028020000052
Is NB, then ufzTaking a value NB;
the membership function of the fuzzy rule is:
adopting a triangular membership function, wherein the input fuzzy rule membership function is as follows:
if it is not
Figure FDA0002653028020000053
Then it is considered that
Figure FDA0002653028020000054
Is NB;
if it is not
Figure FDA0002653028020000055
Then it is considered that
Figure FDA0002653028020000056
Is NM;
if it is not
Figure FDA0002653028020000057
Then it is considered that
Figure FDA0002653028020000058
Is NS;
if it is not
Figure FDA0002653028020000059
Then it is considered that
Figure FDA00026530280200000510
Is ZE;
if it is not
Figure FDA00026530280200000511
Then it is considered that
Figure FDA00026530280200000512
Is PS;
if it is not
Figure FDA00026530280200000513
Then it is considered that
Figure FDA00026530280200000514
Is PM;
if it is not
Figure FDA00026530280200000515
Then it is considered that
Figure FDA00026530280200000516
Is PB;
the output membership function is:
PB is equal to 3;
PM is equal to 2;
PS is equal to 1;
ZE is equal to 0;
the center of an input function in the membership function is { -6, -4, -2,0, 2, 4, 6}, and the center of a corresponding output function is {3, 2,1, 0,1, 2, 3 };
finally, resolving the ambiguity by adopting a gravity center method:
Figure FDA00026530280200000517
wherein u isiFor output in the ith region, μ (u)i) Is uiMembership function of kiIs uiThe weight coefficient of (a) is,
Figure FDA00026530280200000518
representing the overlapping range of the function of the membership degree to be solved,
Figure FDA00026530280200000519
all ranges representing membership functions;
self-adaptive rule establishment:
the switching control is used for adjusting the control input of the system when the system deviates from the sliding mode surface, so that the system returns to the sliding mode surface, the adjusted threshold value is determined by a switching coefficient, and an adaptive model is constructed to determine the K value:
Figure FDA0002653028020000061
where K is a constant greater than 0, K >0, defining a Lyapunov function:
Figure FDA0002653028020000062
therefore, it is not only easy to use
Figure FDA0002653028020000063
It can be judged that, whether S is greater than 0 or less than 0,
Figure FDA0002653028020000064
when S is equal to 0, the first electrode is,
Figure FDA0002653028020000065
in summary,
Figure FDA0002653028020000066
and using the output u of the fuzzy adaptive algorithmfzAnd adjusting the switching coefficient of the sliding mode speed controller in real time on line.
8. The method for controlling the PMSM for the electric vehicle according to claim 4, wherein the extended state observer in the step (3) is designed by:
for a first order system:
Figure FDA0002653028020000067
wherein the content of the first and second substances,
Figure FDA0002653028020000068
is the system output, f (x, t) is unknownω (t) is the external disturbance, u (t) is the control input, b is the model parameter0Is an estimate of b;
let the total perturbation term a (t) of the system be f (y, t) + ω (t) + (b-b)0) u (t), the perturbation term comprising an internal perturbation f (y, t) + (b-b)0) u (t), also including external disturbances ω (t); taking a (t) as an expanded state, let x11＝y，x12A (t), the dynamic system can be written as the following equation of state:
Figure FDA0002653028020000069
order to
Figure FDA00026530280200000610
Then construct linear ESO (extended state observer):
Figure FDA00026530280200000611
wherein, -p is a double pole of the extended state observer, p > 0;
the corresponding linear control rates are:
Figure FDA0002653028020000071
in the formula, y*Is a reference input of the system;
let e1＝z1-y，e2＝z2-a (t), available:
Figure FDA0002653028020000072
according to a first order differential equation model of the permanent magnet synchronous motor and a design method of an extended state observer, the following formula can be obtained:
Figure FDA0002653028020000073
let perturbation a (T) be-B omega/J-TL/J+(b-b0)iq，b＝pψf/J，b0Is an estimate of b; obtaining:
Figure FDA0002653028020000074
an expression for a proportional controller based on an extended state observer:
(1) ESO expression:
Figure FDA0002653028020000075
(2) the control law expression:
Figure FDA0002653028020000076
wherein, -p (p)>0) Is a pole, kpIs a proportional gain.
9. The method for controlling the permanent magnet synchronous motor for the electric vehicle according to claim 4, wherein the design method of the dq-axis internal model current controller in the step (4) comprises the following steps:
the transfer function of the internal model current controller in the dq axis is represented by c(s), then:
Figure FDA0002653028020000081
wherein G(s) is a controlled object model,
Figure FDA0002653028020000082
is the internal model of the controlled object, Q(s) is the internal controller of the dq axis internal model current controller;
because the current loop of the permanent magnet synchronous motor is a first-order system, the current loop is taken according to the internal model control principle
Figure FDA0002653028020000083
Wherein f(s) is a filter, λ is a time constant of the filter;
when the system model is designed accurately, i.e.
Figure FDA0002653028020000084
Then, the feedback controller obtained by the above two equations is:
Figure FDA0002653028020000085
wherein the content of the first and second substances,
Figure FDA0002653028020000086
setting the parameter lambda by adopting a maximum sensitivity internal model positive setting method:
Figure FDA0002653028020000087
wherein M isSHas a value range of [1.2, 2]]。
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