CN114665766A - Permanent magnet synchronous motor force and position hybrid control system based on load moment estimation - Google Patents

Permanent magnet synchronous motor force and position hybrid control system based on load moment estimation Download PDF

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CN114665766A
CN114665766A CN202210311554.8A CN202210311554A CN114665766A CN 114665766 A CN114665766 A CN 114665766A CN 202210311554 A CN202210311554 A CN 202210311554A CN 114665766 A CN114665766 A CN 114665766A
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CN114665766B (en
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鲁文其
唐波
冯建超
汪健
曾玉程
鄢鹏飞
潘国忠
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Zhejiang Sci Tech University ZSTU
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust 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
    • H02P21/22—Current control, e.g. using a current control loop

Abstract

The invention discloses a force and position hybrid control system of a permanent magnet synchronous motor based on load moment estimation, which comprises a force PI controller, a position control current given calculation module, a multi-target particle swarm algorithm module, a current PI controller, an IPark transformation module, a Space Vector Pulse Width Modulation (SVPWM) module, a three-phase inverter, a Permanent Magnet Synchronous Motor (PMSM), an encoder, a Clarke transformation module, a Park transformation module, a load moment estimation module, a derivative device, a multiplier and a first subtractor, a second subtractor, a third subtractor and a fifth subtractor; compared with the traditional control structure, the invention can simply and effectively realize force control and position control at the same time, and adopts the load moment algorithm to estimate the load moment so as to realize force and position hybrid control with simpler algorithm and lower cost.

Description

Permanent magnet synchronous motor force and position hybrid control system based on load moment estimation
Technical Field
The invention relates to the field of motor control, in particular to a force and position hybrid control system of a permanent magnet synchronous motor based on load moment estimation.
Background
The permanent magnet alternating current servo system taking the permanent magnet synchronous motor as an execution element is widely applied to the fields of production and manufacturing, medical facilities, national defense and military and the like. The control method mainly comprises vector control and direct torque control, and the vector control technology has the advantages of high precision, high dynamic response, wide speed regulation range and the like, and is generally suitable for occasions with higher requirements on control precision. However, the control structure of the current vector control technology of the permanent magnet synchronous motor is a three-ring series structure of position, speed and current. The position ring is the outermost ring, and force control cannot be performed when position control is performed. The force control is realized by a current loop of the inner loop, and when the force control is performed, the position control cannot be performed. A single motor is not adequate for applications requiring both force and position control. Therefore, the existing force and position hybrid control solution usually needs to perform cooperative control on a plurality of permanent magnet synchronous motors at the same time so as to realize force control in a certain plane and position control in another plane, but the algorithm complexity and the application cost are greatly increased, and the timeliness and the accuracy of control are reduced. Moreover, the load torque of the force closed-loop control is usually realized by a torque sensor, which further increases the application cost.
Accordingly, there is a need for improvements in the art.
Disclosure of Invention
The invention aims to solve the technical problem of providing a force and position hybrid control system of a permanent magnet synchronous motor based on load moment estimation, wherein a force and position control structure is changed from a series structure to a parallel structure through a multi-target particle swarm algorithm, so that the integration and optimization of force control and position control of a permanent magnet synchronous motor servo system are realized, and the load moment feedback of a non-moment sensor is realized through load moment estimation.
In order to solve the technical problem, the inventionThe permanent magnet synchronous motor force and position hybrid control system based on load moment estimation comprises a permanent magnet synchronous motor, and two-phase current i of the permanent magnet synchronous motoraAnd ibAs input to the Clarke transformation module, the actual position θ of the permanent magnet synchronous motormRespectively as the input of the derivator, the multiplier and the second subtracter; given position
Figure BDA0003567308720000011
A preset constant is used as the input of the second subtracter, and the torque T is given*A preset constant is used as the input of the first subtracter, and a direct-axis current is given
Figure BDA0003567308720000012
Is an input of a fifth subtractor;
the output of the derivator is respectively connected with the input of the third subtracter and the input of the load moment estimation module;
the output of the Clarke transformation module is connected with the input of the Park transformation module, the output of the multiplier is respectively connected with the inputs of the Park transformation module and the IPark transformation module, and the output of the Park transformation module is respectively connected with the inputs of the load moment estimation module, the fourth subtracter and the fifth subtracter; the output of the load moment estimation module is connected with the input of the first subtracter;
the output of the second subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the position P controller; the output of the position P controller is connected with the input of a third subtracter, the output of the third subtracter is connected with the input of a speed PI controller, and the output of the speed PI controller is connected with the input of a multi-target particle swarm algorithm;
the output of the first subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the force PI controller, and the output of the force PI controller is connected with the input of the multi-target particle swarm algorithm module; the output of the multi-target particle swarm algorithm module is connected with the input of the fourth subtracter;
the outputs of the fourth subtracter and the fifth subtracter are connected with the input of a current PI controller, the output of the current PI controller is connected with the input of a three-phase inverter after sequentially passing through an IPark conversion module and a space vector pulse width modulation module, and the permanent magnet synchronous motor is driven by the three-phase inverter.
The invention also provides a method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation, which comprises the following steps:
step 1, obtaining two-phase current i by a method of acquiring current signals through two Hall current sensorsaAnd ibObtaining the actual current i under a static two-phase coordinate system through a Clarke transformation moduleaAnd iβThen the input is used as the input of a Park conversion module;
step 2, acquiring the actual position theta of the permanent magnet synchronous motor through the encodermActual position thetamThe actual electrical angle theta obtained by the multipliereActual current i in stationary two-phase coordinate systemaAnd iβAnd the actual electrical angle thetaeObtaining actual direct axis current i through Park conversion moduledAnd the actual quadrature axis current iqThen the Park conversion module outputs the actual direct axis current idThe fifth subtracter outputs the actual quadrature axis current iqTo a fourth subtracter, while outputting an actual direct axis current idAnd the actual quadrature axis current iqA load moment estimation module;
step 3, actual position thetamObtaining the actual angular velocity omega through a derivatormThen the actual angular velocity ω is determinedmRespectively output to a load moment estimation module and a third subtracter, and the load moment estimation module outputs the estimated load moment
Figure BDA0003567308720000021
As an input to a first subtractor;
step 4, setting torque T*And estimating the load moment
Figure BDA0003567308720000022
Obtained by a first subtracterGiven torque T*And estimating the load moment
Figure BDA0003567308720000023
The error of the power control is used as the input of a force PI controller, and the force PI controller outputs the error as the force control given current
Figure BDA0003567308720000024
Then the torque T is given*And estimating the load moment
Figure BDA0003567308720000031
Error of (2), force control of the given current
Figure BDA0003567308720000032
All input into a multi-target particle swarm algorithm module;
step 5, giving position
Figure BDA0003567308720000033
From the actual position thetamObtaining the given position after passing through a second subtracter
Figure BDA0003567308720000034
From the actual position thetamIs input to a position P controller, and a given rotation speed is obtained through the position P controller
Figure BDA0003567308720000035
Given rotational speed
Figure BDA0003567308720000036
With actual angular velocity omegamObtaining a given rotation speed by a third subtracter
Figure BDA0003567308720000037
With actual angular velocity omegamIs input as a speed PI controller, the output of which is a position control given current
Figure BDA0003567308720000038
Then a given position is set
Figure BDA0003567308720000039
From the actual position thetamError of (2), position control of a given current
Figure BDA00035673087200000310
Inputting the data into a multi-target particle swarm algorithm module together;
step 6, the multi-target particle swarm algorithm module gives positions according to input
Figure BDA00035673087200000311
From the actual position thetamError of (1), given torque T*With actual load moment TLThe method comprises the steps of constructing a target function for the error, evaluating the current force control and position control conditions of the motor, obtaining an optimal integrated optimization factor a through a multi-target particle swarm algorithm, and controlling the given current through the optimal integrated optimization factor a
Figure BDA00035673087200000312
And position controlling the given current
Figure BDA00035673087200000313
Integrating output as force-position hybrid control current setting
Figure BDA00035673087200000314
And input to the fourth subtractor;
step 7, the current PI controller module comprises a quadrature axis current PI controller and a direct axis current PI controller, and the force position hybrid control current is given
Figure BDA00035673087200000315
Current i intersecting the actual axisqObtaining force position mixed control current setting after passing through a fourth subtracter
Figure BDA00035673087200000316
Current i intersecting the actual axisqError of (2), then force-location mixingControlling current setting
Figure BDA00035673087200000317
Current i intersecting the actual axisqThe error of the voltage is calculated by a quadrature axis current PI controller to output a given quadrature axis voltage
Figure BDA00035673087200000318
Given direct axis current
Figure BDA00035673087200000319
With actual direct axis current idObtaining a given direct axis current through a fifth subtracter
Figure BDA00035673087200000320
With actual direct axis current idThen a direct axis current is given
Figure BDA00035673087200000321
With actual direct axis current idThe error of the direct-axis current PI controller is calculated and then a given direct-axis voltage is output
Figure BDA00035673087200000322
Given quadrature axis voltage
Figure BDA00035673087200000323
And a given direct axis voltage
Figure BDA00035673087200000324
Inputting the data into an IPark transformation module together;
step 8, giving quadrature axis voltage
Figure BDA00035673087200000325
Given direct axis voltage
Figure BDA00035673087200000326
And the actual electrical angle thetaeConverted into given voltage under a static two-phase coordinate system through an IPark conversion module
Figure BDA00035673087200000327
And
Figure BDA00035673087200000328
and input to the space vector pulse width modulation module;
step 9, voltage set value under static two-phase coordinate system
Figure BDA00035673087200000329
And
Figure BDA00035673087200000330
six paths of PWM signals are obtained through a space vector pulse width modulation module and are used as input for controlling the three-phase inverter;
step 10, the three-phase inverter performs switching action on six switching tubes according to the six input PWM signals to control the bus voltage UdcThe output voltage is input into the permanent magnet synchronous motor to realize the driving of the permanent magnet synchronous motor.
The improvement of the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation is as follows:
in step 4, the first subtracter has the following formula:
Figure BDA0003567308720000041
the force PI controller is calculated as shown in equation (8):
Figure BDA0003567308720000042
wherein, KPTIs the proportionality coefficient of the force PI controller; kITIs the integral coefficient of the force PI controller; equation (8) is in the form of a computer discrete system:
Figure BDA0003567308720000043
where k is the sampling instant, TsIs the sampling time.
The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation is further improved as follows:
in step 5, the second subtracter has the following formula:
Figure BDA0003567308720000044
the third subtracter has the following formula:
Figure BDA0003567308720000045
the calculation of the speed PI controller is shown in equation (10):
Figure BDA0003567308720000046
wherein, KPPIs the scaling factor of the position P controller; kPSIs the proportionality coefficient of the speed PI controller; k isISIs an integral coefficient of a speed PI controller and has
Figure BDA0003567308720000047
The form of equation (10) in a computer discrete system is:
Figure BDA0003567308720000048
the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation is further improved as follows:
the step 6 is realized by the multi-target particle swarm algorithm module and comprises the following specific steps:
(1) initializing a particle swarm with a swarm size of N;
vi∈[vmin,vmax],xi∈[xmin,xmax],i＝1,2,…,N-1,N (12)
wherein v isiIs the velocity, v, of each particleminAnd vmaxRespectively, the range of the velocityiIs the position of each particle, xminAnd xmaxRespectively defining domain ranges of positions, and N is the total number of particles;
(2) constructing the objective function as shown in formula (6);
Figure BDA0003567308720000051
wherein f isliIs an index for evaluating force control, fweiIs an index for controlling position control;
(3) constructing an overall evaluation function:
Figure BDA0003567308720000052
wherein r (0. ltoreq. r. ltoreq.1) is a force control side weight coefficient;
calculating the fitness value of each particle through a formula (14), and obtaining a global extreme value as shown in a formula (15);
gBest＝min[fi],i＝1,2,…,N-1,N (15)
(4) calculating to obtain individual extreme value pBesti；
pBesti＝min[f(n)],n＝1,2,…,D-1,D (16)
Wherein D is the iteration number;
(5) updating the velocity and position of each particle;
vi(n)＝vi(n-1)+c1×rand()×(pBesti(n-1)-xi(n-1)) (17)
+c2×rand()×(gBest(n-1)-xi(n-1))
xi(n)＝xi(n-1)+vi(n)
wherein v isi(n) and xi(n) represents the velocity and position of each particle of the current iteration, vi(n-1) and xi(n-1) represents the velocity and position of each particle of the last iteration, rand () is a random number between 0 and 1, c1And c2Respectively representing learning factors for the individual and the global whole;
(6) evaluating the fitness value according to the termination condition formula (18), and outputting the optimal optimization factor a as x if the termination condition is satisfiediAnd outputs a force-position hybrid control current setting for integrated force control and position control according to equation (19)
Figure BDA0003567308720000061
f≤Thr (18)
Wherein Thr is a set overall evaluation function threshold;
Figure BDA0003567308720000062
wherein a (a is more than or equal to 0 and less than or equal to 1) is an integrated optimization factor of force control and position control;
(7) returning to the step (2) when the termination is not met, and entering the iteration process again until the termination condition is met or the maximum iteration number is reached;
(8) after training, an off-line table is generated, an optimal integrated optimization factor a can be found for each given torque and position, and the force control and position control of the motor are optimal.
The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation is further improved as follows:
in step 7, the fourth subtractor has the following formula:
Figure BDA0003567308720000063
the fifth subtractor has the following formula:
Figure BDA0003567308720000064
the calculation formula of the current PI controller module is as follows:
Figure BDA0003567308720000065
wherein, KPiqIs the proportionality coefficient of the quadrature axis current PI controller; kIiqIs the integral coefficient of the quadrature axis current PI controller; kPidIs the proportionality coefficient of the direct axis current PI controller; kIidIs the integral coefficient of the direct axis current PI controller;
the formula (20) is in the form of a computer discrete system:
Figure BDA0003567308720000066
the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation is further improved as follows:
the load moment estimation module in the step 3 is realized by the following steps:
the electromagnetic torque equation of the permanent magnet synchronous motor is as follows:
Figure BDA0003567308720000071
wherein, TeIs an electromagnetic torque, LdIs a direct-axis inductance of the motor, LqIs a quadrature axis inductor of the motor,
Figure BDA0003567308720000072
is a permanent magnet flux linkage, PnThe number of pole pairs of the permanent magnet synchronous motor is;
the mechanical equation of motion of a permanent magnet synchronous motor is as follows:
Figure BDA0003567308720000073
wherein J is the rotational inertia of the motor, TLIs the actual load moment, B is the viscosity coefficient;
selecting the state variables as follows:
x＝[ωm TL]T (5)
wherein, TLFor the actual load moment, it is estimated by the moment observer equation (7):
from equations (3) and (4), the state space equation is written as follows:
Figure BDA0003567308720000074
wherein the content of the first and second substances,
Figure BDA0003567308720000075
is the first derivative of the mechanical angular velocity,
Figure BDA0003567308720000076
is the first derivative of the actual load moment;
then, a load moment observer is designed as follows:
Figure BDA0003567308720000077
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003567308720000078
to estimate the first derivative of the mechanical angular velocity,
Figure BDA0003567308720000079
to estimate the first derivative of the load moment, L1And L2Respectively, are feedback coefficients.
The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation is further improved as follows:
the Clarke transformation module has the transformation formula as follows:
Figure BDA0003567308720000081
wherein the phase current icThe method accords with the following steps: i.e. ia+ib+ic＝0；
The multiplier has the following formula: thetae＝Pn·θm，PnThe number of pole pairs of the permanent magnet synchronous motor is;
the derivation device has the following formula: omegam＝dθm/dt；
The Park transformation formula is as follows:
Figure BDA0003567308720000082
wherein, thetaeIs the actual electrical angle;
the IPark transformation formula is:
Figure BDA0003567308720000083
the invention has the following beneficial effects:
compared with the traditional three-ring series control structure of position, speed and current, the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation can simply and effectively realize force control and position control at the same time, adopts a load moment algorithm to estimate load moment, realizes force and position hybrid control with a simpler algorithm and lower cost, and is particularly suitable for occasions with higher requirements on force and position control, such as industrial robots and the like.
Drawings
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 is a block diagram of a PMSM force-position hybrid control system based on load torque estimation according to the present invention;
FIG. 2 is a block diagram of the calculation of force control given current in FIG. 1;
FIG. 3 is a block diagram of the calculation of position control set current of FIG. 1;
FIG. 4 is a flow chart of a method for implementing the multi-target particle swarm algorithm in FIG. 1;
FIG. 5 is a schematic block diagram of the Clarke transform module of FIG. 1;
FIG. 6 is a schematic block diagram of the Park transformation module of FIG. 1;
FIG. 7 is a functional block diagram of a current PI controller block of FIG. 1;
FIG. 8 is a schematic block diagram of the IPark transform module of FIG. 1;
FIG. 9 is a schematic block diagram of the space vector pulse width modulation module of FIG. 1;
fig. 10 is a structural block diagram of a dSPACE-based semi-physical motor test platform in experiment 1.
Detailed Description
The invention will be further described with reference to specific examples, but the scope of protection of the invention is not limited thereto:
examples 1,
A force and position hybrid control system of a permanent magnet synchronous motor based on load moment estimation, as shown in fig. 1, includes a force PI controller, a position control current given calculation module (including a position P controller and a speed PI controller), a multi-target particle swarm algorithm module, a current PI controller, an IPark transformation module, a Space Vector Pulse Width Modulation (SVPWM) module, a three-phase inverter, a Permanent Magnet Synchronous Motor (PMSM), an encoder, a Clarke transformation module, a Park transformation module, a load moment estimation module, a derivation device, a multiplier, and first to fifth subtractors;
method for collecting two-phase current (i) of Permanent Magnet Synchronous Motor (PMSM) by adopting two-phase Hall current sensor sampling methodaAnd ib) As input of the Clarke transformation module, an encoder is adopted to acquire the actual position theta of a Permanent Magnet Synchronous Motor (PMSM)mRespectively as the input of the derivator, the multiplier and the second subtracter; permanent Magnet Synchronous Motor (PMSM) is obtained by adopting load moment estimation module to estimateEstimated load moment
Figure BDA0003567308720000091
Is an input of a first subtractor, and the other input of the first subtractor is a given torque T*The other input of the second subtractor is a given position
Figure BDA0003567308720000092
Given torque T*And given position
Figure BDA0003567308720000093
Is a preset constant.
The output of the first subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the force PI controller, and the output of the force PI controller is connected with the input of the multi-target particle swarm algorithm module; the output of the multi-target particle swarm algorithm module is connected with the input of the fourth subtracter;
the output of the second subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the position P controller; actual position thetamThe output of the position P controller is connected with the input of the third subtracter, the output of the third subtracter is connected with the input of a speed PI controller, and the output of the speed PI controller is connected with the input of a multi-target particle swarm algorithm;
the output of the Clarke transformation module is connected with the input of the Park transformation module, the output of the multiplier is respectively connected with the inputs of the Park transformation module and the IPark transformation module, the output of the Park transformation module is respectively connected with the inputs of the load moment estimation module, the fourth subtracter and the fifth subtracter, and the output of the load moment estimation module is connected with one input of the first subtracter; the other input of the fifth subtractor is given
Figure BDA0003567308720000101
Due to the adoption of the system
Figure BDA0003567308720000102
FOC of (1) thus given
Figure BDA0003567308720000103
The outputs of the fourth subtracter and the fifth subtracter are connected with the input of a current PI controller, the output of the current PI controller is connected with a three-phase inverter through an IPark conversion module and a Space Vector Pulse Width Modulation (SVPWM) module in sequence, and a Permanent Magnet Synchronous Motor (PMSM) is driven through the three-phase inverter.
The method for controlling the permanent magnet synchronous motor by using the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation comprises the following steps: the input of the force PI controller is the output of the first subtracter
Figure BDA0003567308720000104
Output as force control given current
Figure BDA0003567308720000105
The input of a position control current given calculation module (comprising a position P controller and a speed PI controller) is the output of a second subtracter
Figure BDA0003567308720000106
Output for position control given current
Figure BDA0003567308720000107
According to input, multi-target particle swarm algorithm module
Figure BDA0003567308720000108
And
Figure BDA0003567308720000109
constructing an objective function, evaluating the current force control and position control conditions of the motor, obtaining an optimal optimization factor through a multi-objective particle swarm algorithm, and controlling the given current through the force by the optimal optimization factor
Figure BDA00035673087200001010
And position controlling the given current
Figure BDA00035673087200001011
Integrating output as force-position hybrid control current setting
Figure BDA00035673087200001012
Meanwhile, two-phase current i is acquired through a two-phase Hall current sensoraAnd ibAs the input of the Clarke transformation module, the actual current i under the static two-phase coordinate system is obtainedaAnd iβAs input to the Park transformation module. Acquisition of the actual position θ by an encodermInput into a second subtracter and a multiplier, and processed by the formula of the multipliere＝Pn·θm(PnPole pair number of the motor), the obtained actual electrical angle thetaeEntering into a Park transformation module and an IPark transformation module. The Park conversion module outputs actual direct axis current idThe fifth subtracter outputs the actual quadrature axis current iqTo a fourth subtracter, while outputting an actual direct axis current idAnd the actual quadrature axis current iqAll output to a load moment estimation module; then, the current PI controller outputs a given quadrature axis voltage
Figure BDA00035673087200001013
And a given direct axis voltage
Figure BDA00035673087200001014
As input to the IPark transform module; the IPark conversion module outputs given voltage under a static two-phase coordinate system
Figure BDA00035673087200001015
And
Figure BDA00035673087200001016
as an input to a Space Vector Pulse Width Modulation (SVPWM) module; the three-phase inverter is controlled to output voltage through 6 paths of PWM signals output by a Space Vector Pulse Width Modulation (SVPWM) module, so that a Permanent Magnet Synchronous Motor (PMSM) is driven). The specific process is as follows:
step 1, Clarke transformation module principle, as shown in FIG. 5.
Two-phase current i of permanent magnet synchronous motor under three-phase coordinate system is obtained by using method of collecting current signals by two Hall current sensorsaAnd ibAnd inputting the data into a Clarke transformation module, wherein the transformation formula of the Clarke transformation module is as follows:
Figure BDA0003567308720000111
wherein the phase current icBy the formula ia+ib+icCalculating as 0;
the output of the Clarke transformation module is the actual current i under a static two-phase coordinate systemaAnd iβAs input to the Park transformation module.
Step 2, Park transformation module principle, as shown in fig. 6. Step 1 input current i under static two-phase coordinate systemaAnd iβWith actual position theta of the permanent-magnet synchronous machinemThe actual electrical angle theta obtained by the multipliereThe actual direct axis current i under the synchronous rotation coordinate system is obtained through a Park conversion moduledAnd the actual quadrature axis current iqThe Park transformation formula is:
Figure BDA0003567308720000112
wherein, thetaeActual position θ of the PMSM for the actual electrical anglemThrough a multiplier thetae＝Pn·θmTo obtain thetae，PnThe number of pole pairs of the permanent magnet synchronous motor is.
Actual direct axis current i under synchronous rotation coordinate system output by Park conversion moduledThe fifth subtracter outputs the actual quadrature axis current iqTo a fourth subtracter, while outputting an actual direct axis current idAnd the actual quadrature axis current iqLoad moment estimation module。
Step 3, load moment estimation module
Acquiring the actual position theta of a Permanent Magnet Synchronous Motor (PMSM) by adopting an encodermThe actual angular velocity omega is obtained through calculation of a derivatorm＝dθmDt, then the actual angular velocity ωmRespectively outputting the signals to a load moment estimation module and a third subtracter;
step 2 of outputting actual direct axis current idAnd the actual quadrature axis current iqAnd the actual angular velocity ω of the output of the derivatormAs input to the load torque estimation module, the output is the estimated load torque
Figure BDA0003567308720000113
As an input to the first subtractor. The method comprises the following specific steps:
the electromagnetic torque equation of the permanent magnet synchronous motor is as follows:
Figure BDA0003567308720000121
wherein, TeIs an electromagnetic torque, LdIs a direct-axis inductance of the motor, LqIs the cross-axis inductance of the motor,
Figure BDA0003567308720000122
is a permanent magnet flux linkage.
The mechanical equation of motion of a permanent magnet synchronous motor is as follows:
Figure BDA0003567308720000123
wherein J is the rotational inertia of the motor, TLIs the actual load moment and B is the viscosity coefficient.
Selecting the state variables as follows:
x＝[ωm TL]T (5)
actual angular velocity ωmMeasurable, actual load forceMoment TLNot measurable but estimated by a designed load moment observer.
From equations (3) and (4), the state space equation is written as follows:
Figure BDA0003567308720000124
wherein the content of the first and second substances,
Figure BDA0003567308720000125
is the first derivative of the mechanical angular velocity,
Figure BDA0003567308720000126
the first derivative of the actual load moment.
Then, according to a general design method of a lunberger observer, a load moment observer is designed as in formula (7):
Figure BDA0003567308720000127
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003567308720000128
to estimate the first derivative of the mechanical angular velocity,
Figure BDA0003567308720000129
to estimate the first derivative of the load moment, L1And L2Respectively, are feedback coefficients.
Therefore, a closed-loop feedback algorithm of the dSPACE semi-physical motor test platform based on Descemet electromechanical control technology company shown in the figure 10 is constructed, and the actual load torque T can be measuredLThe observation is made to replace the torque sensor, further reducing the cost of the force-position hybrid control system.
Step 4, the force PI controller inputs the given torque T as shown in figure 2*And estimated load moment obtained by the load moment estimation module
Figure BDA00035673087200001210
After a first subtractor, i.e. given a torque T*And estimating the load moment
Figure BDA00035673087200001211
Error of (2)
Figure BDA00035673087200001212
As input to the force PI controller, the force of the force PI controller controls a given current
Figure BDA00035673087200001213
Is calculated as shown in equation (8):
Figure BDA0003567308720000131
wherein, KPTIs the proportionality coefficient of the force PI controller; kITIs the integral coefficient of the force PI controller.
Equation (8) is in the form of a computer discrete system:
Figure BDA0003567308720000132
where k is a certain sampling instant, TsIs the sampling time.
Will give a given torque T*And estimating the load moment
Figure BDA0003567308720000133
Error of (i.e. the
Figure BDA0003567308720000134
) Sum force control of given current
Figure BDA0003567308720000135
And inputting the data into the multi-target particle swarm algorithm module together. The function of the force PI controller is to make the motor output a given torque T*And force control is completed.
And step 5, the position control current setting calculation module comprises a position P controller and a speed PI controller, as shown in figure 3,
acquiring the actual position theta of a Permanent Magnet Synchronous Motor (PMSM) by adopting an encodermThe actual angular velocity omega is obtained through calculation of a derivatorm＝dθm(dt); given position of input
Figure BDA0003567308720000136
From the actual position thetamAfter passing through a second subtracter, the position control current is used as the input of a position control current setting calculation module, namely, the position is set
Figure BDA0003567308720000137
From the actual position thetamError of (2)
Figure BDA0003567308720000138
As input to the position P controller, a given rotational speed is output via the position P controller
Figure BDA0003567308720000139
Then the rotating speed is set
Figure BDA00035673087200001310
From the actual angular velocity omegamGiven rotation speed obtained by a third subtracter
Figure BDA00035673087200001311
With actual angular velocity omegamError (i.e. error)
Figure BDA00035673087200001312
) The output of the speed PI controller is used as the input of the speed PI controller for controlling the given current for the position
Figure BDA00035673087200001313
As shown in equation (10):
Figure BDA00035673087200001314
wherein, KPPIs the scaling factor of the position P controller; kPSIs the proportionality coefficient of the speed PI controller; k isISIs an integral coefficient of a speed PI controller and has
Figure BDA00035673087200001315
The form of equation (10) in a computer discrete system is:
Figure BDA00035673087200001316
will give a position
Figure BDA00035673087200001317
From the actual position thetamError of (i.e. the
Figure BDA00035673087200001318
) And position controlling the given current
Figure BDA00035673087200001319
And inputting the data into the multi-target particle swarm algorithm module together. The position control current setting calculation module is used for enabling the motor to output a set position thetamAnd completing the position control.
And 6, a flow of a method for realizing the multi-target particle swarm algorithm is shown in FIG. 4. The multi-target particle swarm algorithm is widely applied to the application fields of function optimization, neural network training, fuzzy system control and other genetic algorithms at present. The method is based on a particle swarm algorithm, birds in a bird swarm are simulated by designing a particle without mass, and the particle only has two attributes: speed and position, the speed representing the speed of movement and the position representing the direction of movement. And each particle independently searches an optimal solution in the search space, records the optimal solution as a current individual extremum, shares the individual extremum with other particles in the whole particle swarm, finds the optimal individual extremum as a current global extremum of the whole particle swarm, and adjusts the speed and the position of each particle in the particle swarm according to the found current individual extremum and the shared current global extremum of the whole particle swarm.
The method comprises the following specific steps:
(1) initializing a particle swarm with the swarm size N, namely randomly setting the initial speed and the position of each particle in a defined domain, as shown in formula (12);
vi∈[vmin,vmax],xi∈[xmin,xmax],i＝1,2,…,N-1,N (12)
wherein v isiIs the velocity, v, of each particleminAnd vmaxRespectively, the range of the velocityiIs the position of each particle, xminAnd xmaxRespectively, the domain ranges of the positions, and N is the total number of particles.
(2) According to a given torque T*And estimating the load moment
Figure BDA0003567308720000141
Error of (i.e. the
Figure BDA0003567308720000142
) And given position
Figure BDA0003567308720000143
From the actual position thetamError of (i.e. the
Figure BDA0003567308720000144
) Respectively constructing target functions, which are respectively shown in formula (13);
Figure BDA0003567308720000145
wherein f isliIs an index for evaluating force control, fweiIs an index for controlling the position control.
(3) According to different application requirements, an overall evaluation function is constructed, as shown in formula (14):
Figure BDA0003567308720000146
wherein r (0 ≦ r ≦ 1) is a force control side weight coefficient, and the larger it is, the more the system focuses on force control, and the more excellent the force control effect is.
Calculating the fitness value of each particle through the overall evaluation function, and obtaining a global extreme value gBest, namely the minimum fitness value of all the current particles, as shown in formula (15);
gBest＝min[fi],i＝1,2,…,N-1,N (15)
(4) according to the formula (16), calculating to obtain an individual extreme value pBestiI.e., the minimum value of each individual in the past updates;
pBesti＝min[f(n)],n＝1,2,…,D-1,D (16)
wherein D is the number of iterations.
(5) After the global extreme value and the individual extreme value are obtained through the steps (3) and (4), updating the speed and the position of each particle according to a formula (17);
vi(n)＝vi(n-1)+c1×rand()×(pBesti(n-1)-xi(n-1)) (17)
+c2×rand()×(gBest(n-1)-xi(n-1))
xi(n)＝xi(n-1)+vi(n)
wherein v isi(n) and xi(n) represents the velocity and position of each particle of the current iteration, vi(n-1) and xi(n-1) represents the velocity and position of each particle of the last iteration, rand () is a random number between 0 and 1, c1And c2Representing the learning factors for the individual itself and for the global whole, respectively.
(6) Evaluating the fitness value according to the termination condition formula (18), and outputting the optimal optimization factor a as x if the termination condition is satisfiediAnd outputs force-position hybrid control integrating force control and position control according to the formula (19)System current setting
Figure BDA0003567308720000151
f ≦ Thr (18) where Thr is the set overall merit function threshold.
Figure BDA0003567308720000152
The method automatically adjusts the integrated optimization factor a through the calculation of a multi-target particle swarm algorithm, has the advantages of simple calculation, high convergence speed, flexible parameter adjustment and the like, and can simultaneously realize the integrated optimization of the position control and the force control.
(7) And (4) returning to the step (2) when the termination condition is not met, and entering the iteration process again until the termination condition is met or the maximum iteration number is reached.
(8) After a large amount of training, an off-line table is generated, so that an optimal integrated optimization factor a can be found for each given torque and position, and the force control and position control of the motor are optimal.
Output force position mixed control current setting
Figure BDA0003567308720000153
And input to a fourth subtractor.
Step 7, the principle of the current PI controller module, as shown in fig. 7, includes a quadrature axis current PI controller and a direct axis current PI controller, where the input of the quadrature axis current PI controller is force-level hybrid control current setting
Figure BDA0003567308720000161
Current i intersecting the actual axisqForce-position mixed control current setting obtained by a fourth subtracter
Figure BDA0003567308720000162
Current i intersecting the actual axisqError (i.e. error)
Figure BDA0003567308720000163
) Outputting given quadrature axis voltage after calculation of quadrature axis current PI controller
Figure BDA0003567308720000164
Since the system adopts idFOC basic control method of 0, therefore
Figure BDA0003567308720000165
The input of the direct-axis current PI controller is given direct-axis current
Figure BDA0003567308720000166
With actual direct axis current idGiven direct-axis current obtained by a fifth subtracter
Figure BDA0003567308720000167
With actual direct axis current idError of (i.e. the
Figure BDA0003567308720000168
) Outputting given direct axis voltage after calculation of the direct axis current PI controller
Figure BDA0003567308720000169
As shown in equation (20).
Figure BDA00035673087200001610
Wherein, KPiqIs the proportionality coefficient of the quadrature axis current PI controller; kIiqIs the integral coefficient of the quadrature axis current PI controller; kPidIs the proportionality coefficient of the direct axis current PI controller; k isIidIs the integral coefficient of the direct axis current PI controller.
The formula (20) is in the form of a computer discrete system:
Figure BDA00035673087200001611
given quadrature voltage of output
Figure BDA00035673087200001612
And a given direct axis voltage
Figure BDA00035673087200001613
And are input into the IPark transform module together.
And 8, transforming the module principle by the IPArk, as shown in FIG. 8. The function of the device is to realize the given quadrature axis voltage under the synchronous rotating coordinate system
Figure BDA00035673087200001614
And a given direct axis voltage
Figure BDA00035673087200001615
Conversion to a given voltage in a stationary two-phase coordinate system
Figure BDA00035673087200001616
And
Figure BDA00035673087200001617
the IPark transform formula is:
Figure BDA00035673087200001618
given voltage of output two-phase static coordinate system
Figure BDA00035673087200001619
And
Figure BDA00035673087200001620
inputting the signals into a space vector pulse width modulation module (SVPWM); thetaeFor actual electrical angle, from the actual position θ of the PMSMmObtained through a multiplier.
And 9, a Space Vector Pulse Width Modulation (SVPWM) principle, as shown in fig. 9. Step 7 input static two-phase coordinate systemGiven value of voltage
Figure BDA00035673087200001621
And
Figure BDA00035673087200001622
and obtaining six paths of PWM signals through a space vector pulse width modulation module (SVPWM) as input for controlling the three-phase inverter.
And a space vector pulse width modulation module (SVPWM) controls the three-phase inverter to output a three-phase sine wave phase voltage waveform with an electrical angle difference of 120 degrees according to space voltage vector switching, so that a motor stator winding obtains a sine wave phase current waveform with an electrical angle difference of 120 degrees. The position of the current rotor is obtained by resolving through an input voltage given value under a static two-phase coordinate system, and six PWM signals are correspondingly output according to a seven-segment output method, so that a three-phase inverter and a motor are controlled.
Step 10, the three-phase inverter performs switching action on six switching tubes according to the six PWM signals input in the step 9 and controls the bus voltage UdcThe output voltage is input into the permanent magnet synchronous motor to realize the driving of the permanent magnet synchronous motor.
The three-phase inverter is a typical two-level three-phase voltage source inverter and consists of three upper and lower bridge arms (six switching devices), six PWM signals output by a Space Vector Pulse Width Modulation (SVPWM) algorithm are respectively applied to the six switching devices, and when one upper bridge arm switching device is switched on, bus voltage is applied to a motor stator winding from the upper bridge arm switching device. Through a series of on-off combinations, the bus voltage is regularly applied to the motor, thereby driving the motor.
Experiment 1:
semi-physical simulation was performed on the PMSM force-position hybrid control system based on load torque estimation as in example 1. In order to verify the effectiveness of the method, a dSPACE semi-physical motor test platform based on Desbys electromechanical control technology company is designed, which is a set of semi-physical simulation system capable of rapidly carrying out algorithm research, as shown in FIG. 10. The platform is mainly composed of permanent magnetsThe device comprises a synchronous motor (PMSM), an encoder, a coupler, a magnetic powder brake, a torque and rotation speed sensor, a base, a driver (SD800), an adapter plate, a dSPACE (DS1202), a PC upper computer and the like. In the experiment, a given position can be set in a PC upper computer by self
Figure BDA0003567308720000171
And a given torque T*After the algorithm steps 1-10 of the embodiment 1, a control signal is output to a driver, the driver drives a motor to run, and an encoder and a torque and rotation speed sensor obtain an actual position thetamActual angular velocity ωmAnd the actual load moment TLAnd the signals are equalized and fed back to dSPACE for closed-loop control.
The permanent magnet synchronous motor force and position hybrid control system based on load torque estimation of the embodiment 1 is subjected to given torque T when the given positions are 5000 pulses, 10000 pulses and 40000 pulses respectively*Semi-physical simulation was performed under no-load (0n.m), half-load (3n.m) and full-load (6n.m), respectively, and the experimental results are shown in table 1 below.
TABLE 1 Experimental data sheet of PMSM force-position hybrid control system based on load moment estimation
Figure BDA0003567308720000172
Figure BDA0003567308720000181
Experimental data show that the force and position hybrid control system of the permanent magnet synchronous motor based on load moment estimation in the embodiment 1 can automatically select the optimal integrated optimization factor under the algorithm under different given positions and given torques, so that the integration and optimization of force and position control are realized. Under the condition that the side weight position control is more desirable (the force side weight coefficient r is 0.1), the position error under the steady state is the minimum; under the condition of considering both force control and position control (the force-side weight coefficient r is 0.5), the torque error and the position error are moderate in steady state; under the condition of side gravity control (the force side gravity coefficient r is 0.9), the steady-state torque error is minimum, so that the algorithm can be applied under different requirements, and the aim of the invention is achieved.
Finally, it is also noted that the above-mentioned list is only a few specific embodiments of the present invention. It is obvious that the invention is not limited to the above embodiments, but that many variations are possible. All modifications which can be derived or suggested by a person skilled in the art from the disclosure of the present invention are to be considered within the scope of the invention.

Claims (8)

1. The force and position hybrid control system of the permanent magnet synchronous motor based on load moment estimation comprises the permanent magnet synchronous motor and is characterized in that two-phase current i of the permanent magnet synchronous motoraAnd ibAs input to the Clarke transformation module, the actual position θ of the permanent magnet synchronous motormRespectively as the input of the derivator, the multiplier and the second subtracter; given position
Figure FDA0003567308710000011
A predetermined constant is applied as an input to the second subtractor, and a torque T is given*A preset constant is used as the input of the first subtracter, and a direct-axis current is given
Figure FDA0003567308710000012
Is an input of a fifth subtractor;
the output of the derivator is respectively connected with the input of the third subtracter and the input of the load moment estimation module;
the output of the Clarke transformation module is connected with the input of the Park transformation module, the output of the multiplier is respectively connected with the inputs of the Park transformation module and the IPark transformation module, and the output of the Park transformation module is respectively connected with the inputs of the load moment estimation module, the fourth subtracter and the fifth subtracter; the output of the load moment estimation module is connected with the input of the first subtracter;
the output of the second subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the position P controller; the output of the position P controller is connected with the input of a third subtracter, the output of the third subtracter is connected with the input of a speed PI controller, and the output of the speed PI controller is connected with the input of a multi-target particle swarm algorithm;
the output of the first subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the force PI controller, and the output of the force PI controller is connected with the input of the multi-target particle swarm algorithm module; the output of the multi-target particle swarm algorithm module is connected with the input of the fourth subtracter;
the outputs of the fourth subtracter and the fifth subtracter are connected with the input of a current PI controller, the output of the current PI controller is connected with the input of a three-phase inverter after sequentially passing through an IPark conversion module and a space vector pulse width modulation module, and the permanent magnet synchronous motor is driven by the three-phase inverter.
2. The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation according to claim 1, wherein the method comprises the following steps:
the method comprises the following specific steps:
step 1, obtaining two-phase current i by a method of acquiring current signals through two Hall current sensorsaAnd ibObtaining the actual current i under a static two-phase coordinate system through a Clarke transformation moduleαAnd iβThen the input is used as the input of a Park conversion module;
step 2, acquiring the actual position theta of the permanent magnet synchronous motor through the encodermActual position thetamThe actual electrical angle theta obtained by the multipliereActual current i in stationary two-phase coordinate systemαAnd iβAnd the actual electrical angle thetaeObtaining actual direct axis current i through Park conversion moduledAnd the actual quadrature axis current iqThen the Park conversion module outputs the actual direct-axis current idThe fifth subtracter outputs the actual quadrature axis current iqTo a fourth subtracter, while outputting an actual direct axis current idAnd the actual quadrature axis current iqA load moment estimation module;
step 3, actual position thetamObtaining the actual angular velocity omega through a derivatormThen the actual angular velocity ω is determinedmRespectively output to a load moment estimation module and a third subtracter, and the load moment estimation module outputs the estimated load moment
Figure FDA0003567308710000021
As an input to a first subtractor;
step 4, setting torque T*And estimating the load moment
Figure FDA0003567308710000022
Obtaining the given torque T after passing through a first subtracter*And estimating the load moment
Figure FDA0003567308710000023
The error of the power control is used as the input of a force PI controller, and the force PI controller outputs the error as the force control given current
Figure FDA0003567308710000024
Then the torque T is given*And estimating the load moment
Figure FDA0003567308710000025
Error of (2), force control of the given current
Figure FDA0003567308710000026
All input into a multi-target particle swarm algorithm module;
step 5, giving position
Figure FDA0003567308710000027
From the actual position thetamObtaining the given position after passing through a second subtracter
Figure FDA0003567308710000028
From the actual position thetamIs input to the position P controller, and a given rotation speed is obtained through the position P controller
Figure FDA0003567308710000029
Given rotational speed
Figure FDA00035673087100000210
With actual angular velocity omegamObtaining a given rotation speed by a third subtracter
Figure FDA00035673087100000211
With actual angular velocity omegamIs input as a speed PI controller, the output of which is a position control given current
Figure FDA00035673087100000212
Then a given position is set
Figure FDA00035673087100000213
From the actual position thetamError of (2), position control of a given current
Figure FDA00035673087100000214
Inputting the data into a multi-target particle swarm algorithm module together;
step 6, the multi-target particle swarm algorithm module gives positions according to input
Figure FDA00035673087100000215
From the actual position thetamError of (1), given torque T*With actual load moment TLThe method comprises the steps of constructing a target function for the error, evaluating the current force control and position control conditions of the motor, obtaining an optimal integrated optimization factor a through a multi-target particle swarm algorithm, and controlling the given current through the optimal integrated optimization factor a
Figure FDA00035673087100000216
Sum positionControlling a given current
Figure FDA00035673087100000217
Integrating output as force-position hybrid control current setting
Figure FDA00035673087100000218
And input to the fourth subtractor;
step 7, the current PI controller module comprises a quadrature axis current PI controller and a direct axis current PI controller, and the force position hybrid control current is given
Figure FDA00035673087100000219
Current i intersecting the actual axisqObtaining force position mixed control current setting after passing through a fourth subtracter
Figure FDA00035673087100000220
Current i intersecting the actual axisqThen force-bit hybrid control current is given
Figure FDA00035673087100000221
Current i intersecting the actual axisqThe error of the voltage is calculated by a quadrature axis current PI controller to output a given quadrature axis voltage
Figure FDA00035673087100000222
Given direct axis current
Figure FDA00035673087100000223
With actual direct axis current idObtaining a given direct axis current through a fifth subtracter
Figure FDA00035673087100000224
With actual direct axis current idThen a direct axis current is given
Figure FDA0003567308710000031
And realityDirect axis current idThe error of the direct-axis current PI controller is calculated and then a given direct-axis voltage is output
Figure FDA0003567308710000032
Given quadrature axis voltage
Figure FDA0003567308710000033
And a given direct axis voltage
Figure FDA0003567308710000034
Inputting the data into an IPark transformation module together;
step 8, giving quadrature axis voltage
Figure FDA0003567308710000035
Given direct axis voltage
Figure FDA0003567308710000036
And the actual electrical angle thetaeThe voltage is converted into given voltage under a static two-phase coordinate system through an IPark conversion module
Figure FDA0003567308710000037
And
Figure FDA0003567308710000038
and input to the space vector pulse width modulation module;
step 9, voltage set value under static two-phase coordinate system
Figure FDA0003567308710000039
And
Figure FDA00035673087100000310
six paths of PWM signals are obtained through a space vector pulse width modulation module and are used as input for controlling the three-phase inverter;
step 10, the three-phase inverter performs switching action on six switching tubes according to the six input PWM signals to control the bus voltage UdcThe output voltage is input into the permanent magnet synchronous motor to realize the driving of the permanent magnet synchronous motor.
3. The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation according to claim 2, is characterized in that:
in step 4, the first subtracter has the following formula:
Figure FDA00035673087100000311
the force PI controller is calculated as shown in equation (8):
Figure FDA00035673087100000312
wherein, KPTIs the proportionality coefficient of the force PI controller; kITIs the integral coefficient of the force PI controller, TLIs the actual load moment; equation (8) is in the form of a computer discrete system:
Figure FDA00035673087100000313
where k is the sampling instant, TsIs the sampling time.
4. The method for controlling the PMSM according to the PMSM force-position hybrid control system based on load torque estimation of claim 3, wherein:
in step 5, the second subtracter has the following formula:
Figure FDA00035673087100000314
the third subtracter has the following formula:
Figure FDA00035673087100000315
the calculation of the speed PI controller is shown in equation (10):
Figure FDA00035673087100000316
wherein, KPPIs the scaling factor of the position P controller; k isPSIs the proportionality coefficient of the speed PI controller; kISIs an integral coefficient of a speed PI controller and has
Figure FDA0003567308710000041
The form of equation (10) in a computer discrete system is:
Figure FDA0003567308710000042
5. the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation according to claim 4, wherein the method comprises the following steps:
the step 6 is realized by the multi-target particle swarm algorithm module, and comprises the following specific steps:
(1) initializing a particle swarm with a swarm size of N;
vi∈[vmin,vmax],xi∈[xmin,xmax],i＝1,2,…,N-1,N (12)
wherein v isiIs the velocity, v, of each particleminAnd vmaxRespectively, the range of the velocityiIs the position of each particle, xminAnd xmaxRespectively defining domain ranges of positions, and N is the total number of particles;
(2) constructing the objective function as shown in formula (6);
Figure FDA0003567308710000043
wherein f isliIs an index for evaluating force control, fweiIs an index for controlling position control;
(3) constructing an overall evaluation function:
Figure FDA0003567308710000044
wherein r (0. ltoreq. r. ltoreq.1) is a force control side weight coefficient;
calculating the fitness value of each particle through a formula (14), and obtaining a global extreme value as shown in a formula (15);
gBest＝min[fi],i＝1,2,…,N-1,N (15)
(4) calculating to obtain individual extreme value pBesti；
pBesti＝min[f(n)],n＝1,2,…,D-1,D (16)
Wherein D is the number of iterations;
(5) updating the velocity and position of each particle;
Figure FDA0003567308710000051
xi(n)＝xi(n-1)+vi(n)
wherein v isi(n) and xi(n) represents the velocity and position of each particle of the current iteration, vi(n-1) and xi(n-1) represents the velocity and position of each particle of the last iteration, rand () is a random number between 0 and 1, c1And c2Respectively representing learning factors for the individual and the global whole;
(6) the fitness value is evaluated according to the termination condition formula (18), and if the termination condition is satisfied, the optimal optimization factor a is output as xiAnd outputs a force-position hybrid control current setting for integrated force control and position control according to equation (19)
Figure FDA0003567308710000052
f≤Thr (18)
Wherein Thr is a set overall evaluation function threshold;
Figure FDA0003567308710000053
wherein a (a is more than or equal to 0 and less than or equal to 1) is an integrated optimization factor of force control and position control;
(7) returning to the step (2) when the termination is not met, and entering the iteration process again until the termination condition is met or the maximum iteration number is reached;
(8) after training, an off-line table is generated, an optimal integrated optimization factor a can be found for each given torque and position, and the force control and position control of the motor are optimal.
6. The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation according to claim 5, wherein the method comprises the following steps:
in step 7, the fourth subtractor has the following formula:
Figure FDA0003567308710000054
the fifth subtractor has the following formula:
Figure FDA0003567308710000055
the calculation formula of the current PI controller module is as follows:
Figure FDA0003567308710000056
wherein, KPiqIs the proportionality coefficient of the quadrature axis current PI controller; kIiqIs a crossIntegral coefficient of the shaft current PI controller; kPidIs the proportionality coefficient of the direct axis current PI controller; kIidIs the integral coefficient of the direct axis current PI controller;
the formula (20) is in the form of a computer discrete system:
Figure FDA0003567308710000061
7. the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation according to claim 6, wherein the method comprises the following steps:
the load moment estimation module in the step 3 is realized by the following steps:
the electromagnetic torque equation of the permanent magnet synchronous motor is as follows:
Figure FDA0003567308710000062
wherein, TeIs an electromagnetic torque, LdIs a direct-axis inductance of the motor, LqIs a quadrature axis inductor of the motor,
Figure FDA0003567308710000063
is a permanent magnet flux linkage, PnThe number of pole pairs of the permanent magnet synchronous motor is;
the mechanical equation of motion of a permanent magnet synchronous motor is as follows:
Figure FDA0003567308710000064
wherein J is the rotational inertia of the motor, and B is the viscosity coefficient;
selecting the state variables as follows:
x＝[ωm TL]T (5)
wherein, TLFor the actual load moment, it is estimated by the moment observer equation (7):
from equations (3) and (4), the state space equation is written as follows:
Figure FDA0003567308710000065
wherein the content of the first and second substances,
Figure FDA0003567308710000066
is the first derivative of the mechanical angular velocity,
Figure FDA0003567308710000067
is the first derivative of the actual load moment;
then, a load moment observer is designed as follows:
Figure FDA0003567308710000068
wherein the content of the first and second substances,
Figure FDA0003567308710000069
to estimate the first derivative of the mechanical angular velocity,
Figure FDA00035673087100000610
to estimate the first derivative of the load moment, L1And L2Respectively, are feedback coefficients.
8. The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force and position hybrid control system based on load moment estimation according to claim 7, wherein the method comprises the following steps:
the Clarke transformation module has the transformation formula as follows:
Figure FDA0003567308710000071
wherein the phase current icThe method accords with the following steps: i.e. ia+ib+ic＝0；
The multiplier has the following formula: thetae＝Pn·θm；
The derivation device has the following formula: omegam＝dθm/dt；
The Park transformation formula is as follows:
Figure FDA0003567308710000072
wherein, thetaeIs the actual electrical angle;
the IPark transform formula is:
Figure FDA0003567308710000073
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