CN113437912B - Prediction control method and system for strong robustness model of permanent magnet synchronous motor - Google Patents

Prediction control method and system for strong robustness model of permanent magnet synchronous motor Download PDF

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CN113437912B
CN113437912B CN202110775776.0A CN202110775776A CN113437912B CN 113437912 B CN113437912 B CN 113437912B CN 202110775776 A CN202110775776 A CN 202110775776A CN 113437912 B CN113437912 B CN 113437912B
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permanent magnet
magnet synchronous
synchronous motor
axis
control
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CN113437912A (en
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桂卫华
高锦秋
杨超
陶宏伟
彭涛
殷士才
戴柳祥
阳春华
陈志文
樊欣宇
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Central South University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/13—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
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • 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
    • 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

Abstract

The invention relates to the technical field of permanent magnet synchronous motor control, and discloses a strong robustness model prediction control method and a strong robustness model prediction control system for a permanent magnet synchronous motor, so as to realize strong robustness current control of the permanent magnet synchronous motor. The method comprises the following steps: constructing a finite set prediction model of the permanent magnet synchronous motor; constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure; obtaining a disturbance observation value by using a disturbance observer, constructing a disturbance controller, and calculating dq axis compensation voltage; constructing dq axis control voltage containing compensation voltage under each switch state of the inverter according to the dq axis compensation voltage; and constructing a target function of the prediction control of the strong robustness model, and selecting the switch state on the bridge arm of the inverter with the minimum target function value as the switch control output of the current period of the inverter to inhibit the disturbance in the permanent magnet synchronous motor.

Description

Prediction control method and system for strong robustness model of permanent magnet synchronous motor
Technical Field
The invention relates to the technical field of permanent magnet synchronous motor control, in particular to a strong robustness model prediction control method and system of a permanent magnet synchronous motor.
Background
In recent years, an embedded permanent magnet synchronous motor has the advantages of high power factor, high efficiency, high reliability and the like, and is widely applied to the industrial field such as numerical control machine tools, household appliances, subways, high-speed railway traction systems and the like. Compared with the traditional permanent magnet synchronous motor drive control algorithm, such as vector control based on Proportional Integral (PI), direct torque control and the like, the model predictive control developed in the 20 th century and the 70 th century has strong constraint processing capacity and follows the optimal control principle. In addition, the model prediction control method has no risk of integral saturation, is easy to meet the requirements on control performance, safety and reliability, and becomes a competitive motor control scheme. At present, model predictive control can achieve multiple functions in a mechanical transmission system, such as torque control, flux linkage and power control, speed regulation current control, and the like. According to the implementation process of model predictive control, the method can be divided into two types of continuous control set model predictive control and limited control set model predictive control. In contrast, the finite set model predictive control does not use a strategy of generating control pulses by a modulator, and can greatly reduce the computational complexity. In addition, the algorithm is simple to realize, and engineering personnel do not need to master too much deep professional knowledge, so that the method is very suitable for industrial practical application.
A common problem of model predictive control in permanent magnet synchronous motor drives is parameter mismatch, which is unavoidable. In order to improve the robustness of model predictive control on parameter mismatch, two different solutions are available at present. Firstly, a prediction error value caused by parameter mismatching is regarded as disturbance, and then a disturbance observer is used for observation and compensation; secondly, a parameter online identification technology is introduced into model prediction control, and the problem of unmatched motor parameters is directly solved. In contrast, the disturbance observer can detect not only general disturbances caused by parameter mismatch, but also non-linearity and even external disturbances of the system, and the advantages are more obvious, and thus the disturbance observer is receiving more and more attention. Some solutions for directly compensating parameter mismatch based on the sliding mode disturbance observer are only suitable for application occasions with small parameter mismatch due to no disturbance controller.
At present, for a permanent magnet synchronous motor control system, a direct compensation parameter mismatch scheme based on a sliding mode disturbance observer with a disturbance controller is not common. Therefore, a strong robustness model prediction control method of the permanent magnet synchronous motor is urgently needed to improve the robustness of a permanent magnet synchronous motor control system, eliminate prediction errors caused by parameter mismatch in real time and achieve strong robustness current control of the permanent magnet synchronous motor, and the strong robustness model prediction control method has important theoretical research value and engineering application potential.
Disclosure of Invention
Under the influence of a working environment, when the model is adopted to predict and control the running of the permanent magnet synchronous motor, the motor parameters are not invariable, the parameter mismatch condition brings errors to a prediction model, the selection of the optimal vector of the motor is influenced, the control cannot reach the expected performance, and the safety of the whole motor control system is influenced in severe cases. The invention provides a strong robustness model prediction control method and system of a permanent magnet synchronous motor, aiming at solving the existing problems, improving the robustness of a control system and eliminating prediction errors caused by parameter mismatch in real time.
In order to achieve the above object, the present invention discloses a strong robustness model prediction control method for a permanent magnet synchronous motor, comprising:
step S1: constructing a finite set prediction model of the permanent magnet synchronous motor;
step S2: constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure;
and step S3: obtaining a disturbance observation value by using an observer; constructing a disturbance controller, and calculating dq axis compensation voltage;
and step S4: constructing dq axis control voltage containing compensation voltage under each switch state of the inverter according to the dq axis compensation voltage;
step S5: and constructing a target function of the prediction control of the strong robustness model, and selecting the switch state on the bridge arm of the inverter with the minimum target function value as the switch control output of the current period of the inverter to inhibit the disturbance in the permanent magnet synchronous motor.
Preferably, in the prediction control method of the strong robustness model of the permanent magnet synchronous motor, the finite set prediction model of the permanent magnet synchronous motor is a motor dq-axis stator current prediction model.
Preferably, the step S1 specifically includes the following steps:
constructing a finite set prediction model of the permanent magnet synchronous motor, wherein the expression is as follows:
Figure BDA0003155183650000021
in the formula, Ld_mea、Lq_meaFor measured d, q-axis inductance values, Rs_meaFor measured stator winding resistance, ΨfIs a permanent magnet flux linkage, p is the number of pole pairs of the motor, id(k)、iq(k) D and q axis current values at time k, ud(k)、uq(k) D, q-axis voltage values at time k, ωm(k) The motor rotation speed at the moment k, T is the sampling time id(k+1)、iq(k + 1) is the estimated d and q axis predicted current values at the time k + 1.
Preferably, the step S2 specifically includes the following steps:
constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure, wherein the expression of the disturbance observer is as follows:
Figure BDA0003155183650000022
Figure BDA0003155183650000031
in the formula (f)d(k)、fq(k) For d and q axis disturbance observed values obtained at the moment k,
Figure BDA0003155183650000032
the estimated d and q axis current values for time k, j =0,1,2, \ 8230, k, sign (x) is a sign function, λ1And λ2Is a sliding mode coefficient, and lambda is used for ensuring the stability of the disturbance observer1And λ2Is selected to satisfy the following formula:
Figure BDA0003155183650000033
wherein max is the maximum value and min is the minimum value.
Preferably, the step S3 specifically includes the following steps:
obtaining a k-moment disturbance observation value f by using a disturbance observerd(k) And fq(k) (ii) a Constructing a disturbance controller (PI controller) on d and q axes, calculating dq axis compensation voltage, and expressing as:
Figure BDA0003155183650000034
Figure BDA0003155183650000035
in the formula ud_com(k)、uq_com(k) Compensation voltage for d and q axes at time k, ud_com(k-1)、uq_com(k-1) is the compensation voltage of d and q axes at the time of k-1, and the initial value is 0; f. ofd_err(k)、fq_err(k) For the tracking error of d and q axes at time k, kp_dAnd ki_dTwo parameters, k, for a disturbance controller on the d-axis (PI controller)p_qAnd ki_qFor two parameters of the disturbance controller (PI controller) on the q-axis, k for the stabilization of the systemp_d、ki_d、kp_qAnd ki_qThe following conditions need to be satisfied:
Figure BDA0003155183650000036
preferably, the step S4 specifically includes the following steps:
s41: the inverter controls the possible switch states at the time K to be K groups, and the switch state of the mth group is recorded as SmThe expression of the control voltage is as follows:
Figure BDA0003155183650000037
in the formula uunm、uvnmAnd uwnmRespectively, the m-th group of switches of the invertermThe three-phase voltage is shown as theta (k) as the rotor position angle at the moment k, udm(k)、uqm(k) For the m group of switching states S of the inverter at the time of kmControlling voltage values of the lower d and q axes; m =1,2.
S42: constructing a compensated switching state S of the mth group of the invertermLower dq-axis control voltage, i.e. udm_com(k) And uqm_com(k) The expression is as follows:
Figure BDA0003155183650000041
preferably, the step S5 specifically includes the following steps:
s51: obtaining a predicted current value i of the inverter in the m group of switch states at the moment of k +1dm(k + 1) and iqm(k + 1), expressed as:
Figure BDA0003155183650000042
s52: constructing an objective function J of the model predictive control of the permanent magnet synchronous motor, wherein the expression is as follows:
Figure BDA0003155183650000043
in the formula id *、iq *Reference currents of d and q axes of a permanent magnet synchronous motor control system are provided.
S53: establishing a one-step optimized calculation function by taking the minimum objective function value which enables current tracking to be the optimal control as an optimization target, wherein the expression is as follows:
J(Sl)＝min{J(Sm)}
in the formula, J (S)l) Indicating the state S of the first group of switches of the inverterlMinimum value of lower objective function, Sl∈[S1,S2,...,SK]；
S54: and taking the switching state of the first group of the inverter as a control instruction of a k-time permanent magnet synchronous motor control system, and controlling the switching state of each bridge arm of the inverter, thereby realizing the strong robustness model predictive control method of the permanent magnet synchronous motor.
In order to achieve the above object, the present invention further discloses a model predictive control system for a permanent magnet synchronous motor, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps corresponding to the above method when executing the computer program.
The invention has the following beneficial effects:
the method has the advantages that disturbance observation is achieved by using a second-order sliding-mode observer, disturbance rejection control is achieved by using a disturbance controller, the problem of parameter mismatch in model prediction control of the permanent magnet synchronous motor is solved, the method can enable a permanent magnet synchronous motor model prediction control system to have strong robustness, and the limitation that a traditional algorithm is only suitable for occasions with small-degree parameter mismatch is broken through.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification. In the drawings:
FIG. 1 is a schematic diagram of the overall control principle of a permanent magnet traction motor model predictive control system according to an embodiment of the invention;
FIG. 2 is a flowchart of a finite set model prediction method of a permanent magnet synchronous motor based on a disturbance observer according to an embodiment of the present invention;
fig. 3 is an experimental result of strong robustness model predictive control of a permanent magnet synchronous motor according to an embodiment of the present invention; the states of the motor voltage and the motor current before and after the parameter mismatch are respectively shown in (a) and (c), and the states of the motor voltage and the motor current before and after the compensation by the finite set model prediction method based on the disturbance observer are respectively shown in (b) and (d).
Detailed Description
Embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example 1
In this embodiment, the "strong robustness model prediction control method of the permanent magnet synchronous motor" of the present invention is also referred to as: 'permanent magnet synchronous motor finite set model prediction method based on disturbance observer'. The subsequent system is the same and will not be described in detail.
Specifically, in this embodiment, referring to a certain type of permanent magnet traction motor control system, a schematic diagram of the overall control principle of a medium-point clamped three-level inverter and a permanent magnet traction motor model prediction control system is shown in fig. 1. In this embodiment, a finite set model prediction method of a permanent magnet synchronous motor based on a disturbance observer is taken as an example for explanation, a permanent magnet traction motor control system adopts a double closed-loop control structure, a control outer loop is a rotation speed loop, and a control inner loop is a current loop. According to the rotating speed feedback, obtaining a reference current value of a dq axis of the motor by a lookup table and adopting a maximum torque current ratio (MTPA) motor control strategy; and finally, selecting the inverter switching state combination which minimizes the inverter switching state combination as the control instruction output of the motor control system by the target function of the permanent magnet synchronous motor finite set model predictive control based on the disturbance observer, and realizing the tracking of the system dq axis reference current value and the regulation of the disturbance existing in the permanent magnet synchronous motor.
In this embodiment, the main parameters of a permanent magnet traction motor of a certain type are shown in table 1.
TABLE 1 main parameters of permanent magnet synchronous motor system
Figure BDA0003155183650000051
Figure BDA0003155183650000061
As shown in fig. 2, the method for predicting the finite set model of the permanent magnet synchronous motor based on disturbance suppression control in the embodiment specifically includes the following steps:
step S1: and constructing a finite set prediction model of the permanent magnet synchronous motor.
It should be noted that, as shown in fig. 2, the model predictive control of the permanent magnet synchronous motor in this embodiment is in an inner loop current control link in a motor double closed-loop control structure, and the system control quantity in this embodiment is voltage.
Step 11: and sampling the permanent magnet synchronous motor by using sensors such as voltage and current, and obtaining the motor state of the permanent magnet synchronous motor in real time. In the kth sampling period, obtaining the three-phase current i in the current perioda(k)，ib(k)，ic(k) The rotor position angle is theta (k), and the motor rotating speed is omegam(k) .1. The Obtaining the current value i of the k moment under the dq axis by using a coordinate transformation formulad(k)、iq(k)：
Figure BDA0003155183650000062
Step 12: establishing a mathematical model of the permanent magnet synchronous motor, wherein the mathematical model is expressed as follows:
Figure BDA0003155183650000063
in the formula, Ld_mea、Lq_meaFor measured d, q-axis inductance values, Rs_meaFor measured stator winding resistance, psifIs a permanent magnet flux linkage, p is the number of pole pairs of the motor, id(k)、iq(k) D and q axis current values at time k, ud(k)、uq(k) The d-axis and q-axis voltage values at the time k are shown.
In this example, L isd_mea＝0.0030H，Lq_mea＝0.0480H，Rs_mea＝0.07Ω。
Step 13: according to a mathematical model of the permanent magnet synchronous motor, a finite set prediction model of the permanent magnet synchronous motor is constructed by using a first-order Euler formula, and the expression is as follows:
Figure BDA0003155183650000071
where T is the sampling time, id(k+1)、iq(k + 1) is the estimated d and q axis predicted current values at the time k + 1.
Step S2: and constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure.
It should be noted that, in this embodiment, the disturbance of the motor model predictive control system is caused by the parameter mismatch of the permanent magnet traction motor, and only this disturbance is considered in the following steps. However, based on the teaching of the present embodiment and in combination with the common sense, if the gist of the present embodiment can be obviously applied to the disturbance caused by other situations of the permanent magnet synchronous motor, such variations all fall into the protection scope of the present invention, and the protection scope of the claims is specifically subject to the protection scope.
Step 21: establishing a disturbed permanent magnet synchronous motor mathematical model, wherein the expression is as follows:
Figure BDA0003155183650000072
in the formula (f)d(k)、fq(k) The disturbance values caused by parameter mismatch on the d and q axes.
Step 22: constructing a disturbance observer based on a second-order sliding mode structure, wherein the expression of the disturbance observer is as follows:
Figure BDA0003155183650000073
Figure BDA0003155183650000074
in the formula (I), the compound is shown in the specification,
Figure BDA0003155183650000075
d and q axis current values estimated for time k, j =0,1,2, \8230, k, sign (x) is a sign function, lambda1And λ2Is the sliding mode coefficient.
Step 23: in order to ensure the stability of the disturbance observer, a Lyapunov function is constructed, and the expression of the Lyapunov function is as follows:
Figure BDA0003155183650000076
wherein, the sliding mode coefficient lambda1And λ2Is selected to satisfy the following formula:
Figure BDA0003155183650000081
it should be noted that in the present embodiment, λ1＝-300，λ2And-200, o is any one of 5s to 6 s.
And step S3: and obtaining a disturbance observation value by using an observer, constructing a disturbance controller, and calculating the dq axis compensation voltage.
Step 31: interference value fdAnd fqControlled to 0, the current estimated by the measured motor parameters will be equal to the accurate result. To achieve this goal, d-axis and q-axis disturbances f are detected with the proposed disturbance observerd(k) And fq(k) Then, a tracking error f with a disturbance reference of zero is calculatedd_err(k) And fq_err(k) And then a PI controller is adopted to adjust the disturbance in the motor so as to track the required compensation electricityPressing:
Figure BDA0003155183650000082
Figure BDA0003155183650000083
in the formula ud_com(k)、uq_com(k) Compensation voltage for d and q axes at time k, ud_com(k-1)、uq_com(k-1) is the compensation voltage of d and q axes at the time of k-1, and the initial value is 0; k is a radical of formulap_dAnd ki_dTwo parameters, k, for a disturbance controller on the d-axis (usually a PI controller)p_qAnd ki_qTwo parameters for a disturbance controller (typically a PI controller) on the q-axis.
Step 32: according to the Routh stability criterion, in order to stabilize the system, the following conditions need to be satisfied:
Figure BDA0003155183650000084
it should be noted that, in the present embodiment, k isp_d＝2，ki_d＝20，kp_q＝2，ki_q＝10。
And step S4: and constructing a dq axis control voltage containing the compensation voltage under each switching state of the inverter according to the compensation voltage of the dq axis.
Step 41: the switching states of the neutral point clamping type three-level inverter are 27, the switching states of K time control operations are K, and the switching state of the mth group is recorded as SmThe expression is as follows:
Sm＝[SU SV SW]
in the formula, SUIndicating the switching state of the U-bridge arm, SVIndicating the switching state of the V-arm, SWThe switching state of the W arm is shown.
Establishing a model between the output phase voltage and the switching state of the inverter bridge arm, wherein the expression is as follows:
Figure BDA0003155183650000085
in the formula uunm、uvnmAnd uwnmRespectively inverter in switched state SmThree phase voltage of lower, UdcIs the dc side voltage.
Step 42: and obtaining the dq axis control voltage corresponding to the m group of switching states of the inverter through coordinate transformation, wherein the expression of the dq axis control voltage is as follows:
Figure BDA0003155183650000091
in the formula udm(k)、uqm(k) And theta (k) is a rotor position angle at the moment k.
Step 43: controlling the dq axis control voltage value, i.e. u, of the m group of switching states of an inverter in a permanent magnet synchronous motor control systemdm(k) And uqm(k) Adding the dq axis compensation voltage at the k moment when the disturbance exists to obtain a dq axis control voltage value, namely u, of the compensated inverter in the m group of switching statesd_com(k) And uq_com(k) The expression is as follows:
Figure BDA0003155183650000092
step S5: and constructing a target function of the prediction control of the strong robustness model, and selecting the switch state on the bridge arm of the inverter with the minimum target function value as the switch control output of the current period of the inverter to regulate the disturbance existing in the permanent magnet synchronous motor.
Step 51: d-axis and q-axis voltage values u corresponding to the compensated switching vectors of all three-level invertersd_com(k)、uq_com(k) Substituting the prediction model into the finite set prediction model of the permanent magnet synchronous motor constructed in S31 to obtain the prediction of the m group of the inverters at the k +1 moment in the switching stateCurrent value idm(k + 1) and iqm(k + 1), expressed as:
Figure BDA0003155183650000093
step 52: constructing an objective function J of the model predictive control of the permanent magnet synchronous motor, wherein the expression is as follows:
Figure BDA0003155183650000094
in the formula id *、iq *Reference currents of d and q axes of a permanent magnet synchronous motor control system are provided.
In this embodiment, the dq-axis reference current i of the permanent magnet synchronous motor control systemd *And iq *And the calculation is given by the system control outer loop.
Step 53: establishing a one-step optimized calculation function by taking the minimum objective function value which enables current tracking to be the optimal control as an optimization target, wherein the expression is as follows:
J(Sl)＝min{J(Sm)}
in the formula, J (S)l) Shows the combination S of the switching states of the first group of the inverterlMinimum value of lower objective function, Sl∈[S1,S2,...,SK]；
Step 54: and combining the switching states of the first group of the inverters to be used as a control instruction of a k-time permanent magnet synchronous motor control system, and controlling the switching states of each bridge arm of the inverters, thereby realizing the high robustness model predictive control method of the permanent magnet synchronous motor.
Specifically, in this embodiment, parameter mismatch occurs at the 5 th second during the operation of the motor system, that is, parameters in the model predictive controller are changed, the states of the motor before and after the parameter mismatch are shown in fig. 3 (a) (c), and the states of the motor before and after compensation by using the finite set model predictive method based on the disturbance observer are shown in fig. 3 (b) (d). Therefore, the motor dq axis current tracking effect of the finite set model prediction method based on the disturbance observer is more accurate, and the anti-interference capability is stronger. Compared with a permanent magnet traction motor system based on a finite set model prediction method of a disturbance observer, the control method of the embodiment can enable the permanent magnet synchronous motor to correctly select the optimal vector of the motor under the condition of parameter mismatch and other disturbances, so that the control can achieve the expected performance, the robustness of the motor control system is improved, and the prediction error caused by parameter mismatch is eliminated in real time.
Example 2
In correspondence with the above method embodiments, the present embodiment provides a finite set model predictive control system of a permanent magnet synchronous motor based on a disturbance observer, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
In summary, the methods and systems disclosed in the two embodiments of the present invention observe the motor disturbance in real time through the disturbance observer, compensate the motor model prediction control system, and achieve strong robustness control of the motor, so as to suppress the disturbance existing in the permanent magnet synchronous motor. The method is implemented without additional hardware equipment, improves the reliability level of train operation, reduces the equipment maintenance cost and the like, and has important significance.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (5)

1. A strong robustness model prediction control method of a permanent magnet synchronous motor is characterized by comprising the following steps:
step S1: constructing a finite set prediction model of the permanent magnet synchronous motor;
step S2: constructing a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure;
and step S3: obtaining a disturbance observation value by using an observer; constructing a disturbance controller, and calculating dq axis compensation voltage;
and step S4: constructing dq axis control voltage containing compensation voltage under each switch state of the inverter according to the dq axis compensation voltage;
step S5: constructing a target function of the prediction control of the strong robustness model, and selecting the switch state on the bridge arm of the inverter with the minimum target function value as the switch control output of the current period of the inverter so as to inhibit the disturbance in the permanent magnet synchronous motor;
the finite set prediction model of the permanent magnet synchronous motor is a motor dq axis stator current prediction model;
the step S1 specifically includes the steps of:
constructing a finite set prediction model of the permanent magnet synchronous motor, wherein the expression is as follows:
Figure FDA0003842540350000011
in the formula, Ld_mea、Lq_meaFor measured d, q-axis inductance values, Rs_meaFor measured stator winding resistance, psifIs a permanent magnet flux linkage, p is the number of pole pairs of the motor, id(k)、iq(k) D and q axis current values at time k, ud(k)、uq(k) D and q axis voltage values at time k, ωm(k) The motor rotation speed at the moment k, T is the sampling time id(k+1)、iq(k + 1) estimated d and q axis predicted current values at the time of k + 1;
the step S2 specifically includes the following steps:
a permanent magnet synchronous motor disturbance observer based on a second-order sliding mode variable structure is constructed, and the expression of the disturbance observer is as follows:
Figure FDA0003842540350000012
Figure FDA0003842540350000013
in the formula, fd(k)、fq(k) For d and q axis disturbance observed values obtained at the moment k,
Figure FDA0003842540350000014
Figure FDA0003842540350000015
the estimated d and q axis current values for time k, j =0,1,2, \ 8230, k, sign (x) is a sign function, λ1And λ2Is a sliding mode coefficient, and λ1And λ2Is selected to satisfy the following formula:
Figure FDA0003842540350000021
wherein max is the maximum value, and min is the minimum value.
2. A strong robustness model predictive control method of a permanent magnet synchronous motor according to claim 1, characterized in that said step S3 specifically comprises the steps of:
obtaining a k-moment disturbance observation value f by using a disturbance observerd(k) And fq(k) (ii) a Constructing a disturbance controller on d and q axes, and calculating dq axis compensation voltage, wherein the calculation is represented as:
Figure FDA0003842540350000022
Figure FDA0003842540350000023
in the formula ud_com(k)、uq_com(k) Compensation voltage for d and q axes at time k, ud_com(k-1)、uq_com(k-1) is the compensation voltage of d and q axes at the k-1 momentThe initial values are all 0; f. ofd_err(k)、fq_err(k) For the tracking error of d and q axes at time k, kp_dAnd ki_dTwo parameters for the disturbance controller on the d-axis, kp_qAnd ki_qTwo parameters for the disturbance controller on the q-axis, kp_d、ki_d、kp_qAnd ki_qThe following conditions are satisfied:
Figure FDA0003842540350000024
3. a method for strong robustness model predictive control of a permanent magnet synchronous motor according to claim 2, wherein said step S4 specifically includes the steps of:
s41: the switching states of the inverter at the time K are controlled and operated to be K groups, and the switching state of the mth group is recorded as SmThe expression of the control voltage is as follows:
Figure FDA0003842540350000025
in the formula uunm、uvnmAnd uwnmRespectively, the m-th group of switches of the invertermThe three-phase voltage is shown as theta (k) as the rotor position angle at the moment k, udm(k)、uqm(k) For the m group of switching states S of the inverter at the moment of kmControlling voltage values of the lower d and q axes; m =1,2, \ 8230;, K;
s42: constructing a compensated mth group of switching states S of the invertermThe value of the dq-axis control voltage, i.e. udm_com(k) And uqm_com(k) The expression is as follows:
Figure FDA0003842540350000031
4. a method for strong robustness model predictive control of a permanent magnet synchronous motor as recited in claim 3, wherein said step S5 specifically comprises the steps of:
s51: obtaining the m group switch state S of the inverter at the moment of k +1mPredicted current value ofdm(k + 1) and iqm(k + 1), which is expressed as:
Figure FDA0003842540350000032
s52: constructing a target function J of the permanent magnet synchronous motor model predictive control, wherein the expression is as follows:
Figure FDA0003842540350000033
in the formula id *、iq *Reference currents of d and q axes of a permanent magnet synchronous motor control system are adopted;
s53: establishing a one-step optimized calculation function by taking the minimum objective function value which enables current tracking to be the optimal control as an optimization target, wherein the expression is as follows:
J(Sl)＝min{J(Sm)}
in the formula, J (S)l) Indicating the state S of the first group of switches of the inverterlMinimum value of lower objective function, Sl∈[S1,S2,...,SK]；
S54: and taking the switching state of the first group of the inverter as a control instruction of a k-time permanent magnet synchronous motor control system, and controlling the switching state of each bridge arm of the inverter, thereby realizing the strong robustness model prediction control method of the permanent magnet synchronous motor.
5. A strong robustness model predictive control system of a permanent magnet synchronous machine comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of the preceding claims 1 to 4 are implemented when the computer program is executed by the processor.
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