CN112398403A - Motor model prediction control method and device based on continuous control set and controller - Google Patents

Motor model prediction control method and device based on continuous control set and controller Download PDF

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CN112398403A
CN112398403A CN202011379291.1A CN202011379291A CN112398403A CN 112398403 A CN112398403 A CN 112398403A CN 202011379291 A CN202011379291 A CN 202011379291A CN 112398403 A CN112398403 A CN 112398403A
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motor
state equation
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高乐
孙楠楠
陈文淼
孟庆辉
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Weichai Power Co Ltd
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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
    • H02P21/22—Current control, e.g. using a current control loop
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/28—Arrangements for controlling current
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • 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

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Abstract

The embodiment of the invention discloses a motor model predictive control method, a motor model predictive control device and a motor model predictive control controller based on a continuous control set, wherein the control method comprises the following steps: under a synchronous rotating coordinate system, establishing a basic continuous state equation on the basis of a voltage equation of a motor, and obtaining a basic discrete state equation based on a continuous control set by adopting a forward Euler formula; constructing an extended state equation and designing a performance evaluation function of model predictive control; and converting the performance evaluation function into a quadratic programming problem, and designing corresponding constraint conditions. In the embodiment of the invention, the continuous control set model prediction control method and the quadratic programming problem are cooperatively applied in the embedded permanent magnet synchronous motor, so that the control of the motor current is realized, the high-performance control of the motor torque and speed can be ensured, and the dynamic corresponding capacity of the current is improved.

Description

Motor model prediction control method and device based on continuous control set and controller
Technical Field
The embodiment of the invention relates to a motor current control technology, in particular to a motor model predictive control method, a motor model predictive control device and a motor model predictive control controller based on a continuous control set.
Background
An Interior Permanent Magnet Synchronous Motor (IPMSM) has been popularized and applied in various fields, such as the metallurgical industry, the rubber industry, the automobile industry, and the like, because of its advantages of high efficiency, high power density, high power factor, and the like. The starting and running of the permanent magnet synchronous motor are formed by the interaction of magnetic fields generated by the stator winding, the rotor assembly and the permanent magnet.
At present, high-performance permanent magnet synchronous motors are mainly controlled by a current loop based on a proportional-integral (PI) regulator. The current loop control mode based on the PI regulator has the advantages of good steady-state performance, small torque pulsation and the like. However, the problems of slow dynamic response, easy overshoot, integral saturation, coupling influence and the like exist, so that the dynamic response capability of a current loop is limited, and the current loop cannot be further improved.
Disclosure of Invention
The embodiment of the invention provides a motor model predictive control method, a motor model predictive control device and a motor model predictive control controller based on a continuous control set, and aims to solve the problems of low performance and the like of the existing motor current control method.
The embodiment of the invention provides a motor model predictive control method based on a continuous control set, which comprises the following steps:
under a synchronous rotating coordinate system, establishing a basic continuous state equation on the basis of a voltage equation of a motor, and obtaining a basic discrete state equation based on a continuous control set by adopting a forward Euler formula;
constructing an extended state equation according to the basic discrete state equation, and designing a performance evaluation function of model predictive control;
and converting the performance evaluation function into a quadratic programming problem, and designing corresponding constraint conditions.
Further, obtaining the base discrete state equation comprises:
determining a stator voltage equation of the motor under the synchronous rotating coordinate system, wherein the stator voltage comprises an output stator voltage and a feedforward voltage of a continuous control set model predictive control regulator;
setting the angular speed of the motor in the stator voltage equation to be constant so as to determine a basic continuous state equation of a linear time-invariant system;
discretizing the basic continuous state equation by adopting a forward Euler formula to obtain the basic discrete state equation based on a continuous control set.
Further, according to the basic discrete state equation, an extended state equation is constructed, and a performance evaluation function of model predictive control is designed, including:
constructing a new state vector, and obtaining the extended state equation according to the new state vector and the basic discrete state equation;
performing state prediction on the extended state equation to obtain a state space equation and calculating the system output quantity of the state space equation;
and defining a performance evaluation function of the model predictive control according to the state space equation and the system output quantity thereof.
Further, the extended state equation is an augmented state equation.
Further, converting the performance evaluation function into a quadratic programming problem, and designing corresponding constraint conditions, including:
defining a reference output vector or converting the performance evaluation function into a quadratic programming problem according to the relation between the control quantity and the control increment;
and solving through quadratic programming to obtain a control input increment in a control time domain, and designing a hexagonal inequality constraint condition.
Further, still include: and effectively compensating the one-beat delay of the quadratic programming problem by adopting delay compensation.
Based on the same inventive concept, the embodiment of the present invention further provides a motor model predictive control apparatus based on a continuous control set, including:
the state equation establishing module is used for establishing a basic continuous state equation on the basis of a voltage equation of the motor under a synchronous rotating coordinate system and then obtaining a basic discrete state equation based on the continuous control set by adopting a forward Euler formula;
the state equation expansion module is used for constructing an expansion state equation according to the basic discrete state equation and designing a performance evaluation function of model predictive control;
and the quadratic form conversion module is used for converting the performance evaluation function into a quadratic form planning problem and designing a corresponding constraint condition.
Further, still include: and the compensation module is used for effectively compensating the one-beat delay of the quadratic programming problem by adopting delay compensation.
Based on the same inventive concept, an embodiment of the present invention further provides a motor controller, including: the motor model predictive control apparatus based on the continuous control set as described above.
In the embodiment of the invention, the current of the motor is controlled by combining the continuous control set model prediction control method and the quadratic programming problem in the embedded permanent magnet synchronous motor, so that the application of the continuous control set model prediction control method and the quadratic programming problem in the embedded permanent magnet synchronous motor is realized. The CCS-MPC is an optimal Control method based on a Model in a time domain, has the advantages of good dynamic characteristic, small overshoot, easy processing of multivariable Control problems including constraint conditions and the like, and has the advantages of higher steady-state precision, smaller current ripple, determined switching frequency and the like by adopting the CCS-MPC to carry out current Control, thereby ensuring the high-performance Control of the torque and the speed of a motor.
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To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief description will be given below of the drawings required for the embodiments or the technical solutions in the prior art, and it is obvious that the drawings in the following description, although being some specific embodiments of the present invention, can be extended and extended to other structures and drawings by those skilled in the art according to the basic concepts of the device structure, the driving method and the manufacturing method disclosed and suggested by the various embodiments of the present invention, without making sure that these should be within the scope of the claims of the present invention.
FIG. 1 is a schematic diagram of a motor model predictive control method based on a continuous control set according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an electric motor system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of step S1;
FIG. 4 is a schematic diagram of step S2;
FIG. 5 is a schematic diagram of step S3;
FIG. 6 is a schematic diagram of a motor model predictive control method based on a continuous control set according to an embodiment of the present invention;
fig. 7 is a schematic diagram of step S3 according to another embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described through embodiments with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the basic idea disclosed and suggested by the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1, a schematic diagram of a motor model predictive control method based on a continuous control set according to an embodiment of the present invention is provided, where the control method is executable by a control device, the control device is implemented in a software and/or hardware manner and is configured in a motor, the motor is an embedded permanent magnet synchronous motor, and the embedded permanent magnet synchronous motor is applicable to various industries, such as a new energy vehicle. Fig. 2 is a control diagram of the motor system.
In this embodiment, the motor model predictive control method based on the continuous control set includes:
s1, under a synchronous rotation coordinate system, establishing a basic continuous state equation on the basis of a voltage equation of the motor, and then obtaining a basic discrete state equation based on a continuous control set by adopting a forward Euler formula;
s2, constructing an extended state equation according to the basic discrete state equation, and designing a performance evaluation function of model predictive control;
and S3, converting the performance evaluation function into a quadratic programming problem, and designing corresponding constraint conditions.
In this embodiment, the current of the motor is controlled in the embedded permanent magnet synchronous motor by combining the continuous control set model prediction control method and the quadratic programming problem, and the application of the continuous control set model prediction control method and the quadratic programming problem to the embedded permanent magnet synchronous motor is realized. The CCS-MPC is an optimal Control method based on a Model in a time domain, has the advantages of good dynamic characteristic, small overshoot, easy processing of multivariable Control problems including constraint conditions and the like, and has the advantages of higher steady-state precision, smaller current ripple, determined switching frequency and the like by adopting the CCS-MPC to carry out current Control, thereby ensuring the high-performance Control of the torque and the speed of a motor.
The operation of optional step S1 shown in fig. 3 includes:
s11, determining a stator voltage equation of the motor under a synchronous rotation coordinate system, wherein the stator voltage comprises an output stator voltage and a feedforward voltage of the continuous control set model predictive control regulator;
s12, setting the angular speed of the motor in the stator voltage equation to be constant, and determining a basic continuous state equation of the linear time-invariant system;
and S13, discretizing the basic continuous state equation by adopting a forward Euler formula to obtain a basic discrete state equation based on the continuous control set.
The operation of optional step S2 shown in fig. 4 includes:
s21, constructing a new state vector, and obtaining an extended state equation according to the new state vector and the basic discrete state equation;
s22, performing state prediction on the expanded state equation to obtain a state space equation and calculating the system output quantity of the state space equation;
and S23, defining a performance evaluation function of model predictive control according to the state space equation and the system output quantity thereof.
The operation of optional step S3 shown in fig. 5 includes:
s31, defining a reference output vector, and converting the performance evaluation function into a quadratic programming problem;
and S32, solving through quadratic programming to obtain control input increment in a control time domain, and designing a hexagonal inequality constraint condition.
Optional step S3 shown in fig. 6 is followed by: and S4, effectively compensating the one-beat delay of the quadratic programming problem by adopting delay compensation.
Based on the above technical solution, the current control method will be described below through a specific derivation process.
The operation of step S1 is essentially mathematical modeling, which includes the steps of:
1) under a synchronous rotating coordinate system, the stator voltage equation of the embedded permanent magnet synchronous motor is shown as the following formula (1),
usdq＝(Ldqp+Rs)isdq+jωe(Ldqisdq+ψr) (1)。
the permanent magnet synchronous motor comprises a stator winding, a rotor assembly and a permanent magnet, wherein Usdq is a stator voltage, isdqIs the stator current, Rs is the stator resistance, Ldq is the stator quadrature-direct axis reactance, Ψ r is the rotor flux linkage, ω iseFor rotor flux linkage angular velocity, p is a differential operator, and p is expressed as p ═ d/dt. It is understood that U is U.
2) The stator voltage Usdq can be divided into two parts, including an output stator voltage and a feed-forward voltage, respectively, represented by the following formula (2),
Figure BDA0002808031780000071
wherein, Usdq_mpcIs the output stator voltage, U, of the CCS-MPC regulatorsdq_ffIs the feed forward voltage.
Considering that the mechanical time constant of the permanent magnet synchronous motor is far larger than the electrical time constant, the angular speed of the motor, namely the rotor flux linkage angular speed omega, is assumed to be within one sampling periodeNot changing, then ωeConsidered as a constant.
3) The state equation of a Linear-Time-Invariant (LTI) system is defined according to equations (1) and (2), the Linear-Time-Invariant system is defined according to whether the input and the output of the permanent magnet synchronous motor system have a Linear relationship, and the permanent magnet synchronous motor system satisfying the superposition principle has a Linear characteristic. The state equation is expressed as the following formula (3),
Figure BDA0002808031780000081
wherein x ═ isd isq]TIs a system state vector of the permanent magnet synchronous motor, U ═ Usd_mpc Usq_mpc]TIs the system input of the permanent magnet synchronous motor, y ═ isd isq]TIs the system output of the permanent magnet synchronous motor.
4) Discretizing the formula (3) by adopting a forward Euler formula to obtain a basic discrete state equation (4),
Figure BDA0002808031780000082
wherein k e Ν is the current sampling time, k +1 is the next sampling time, and Ts is the sampling period.
The operation of step S2 includes the steps of:
1) construction of a New State vector ξ (k | t) ═ x (k) u (k-1)]TBased on this, a new extended state equation can be obtained in conjunction with the operation of step S1, which is expressed as the following equation (5),
Figure BDA0002808031780000083
wherein n is 2.
2) The state prediction is carried out according to the formula (5), a state space equation can be obtained, which is expressed as the formula (6),
Figure BDA0002808031780000091
3) calculating the system output quantity according to the state space equation (6) as the following equation (7),
Figure BDA0002808031780000092
4) converting into matrix form, the matrix expression of equation (7) is as following equation (8),
Y＝ψξ(k)+ΘΔU (8)，
wherein,
Figure BDA0002808031780000101
according to the formula (8), both the state variable and the output quantity in the prediction time domain can be obtained by calculating the current state quantity xi (k) of the system and the control increment delta u in the control time domain, namely the realization of the 'prediction' function in the model prediction control algorithm.
5) Defining a performance evaluation function, wherein the expression of the performance evaluation function is shown as the following formula (9),
Figure BDA0002808031780000102
where t-1 is the last sampling time, Np is the prediction step, Nc is the control step, yp(k + i | k) is the control output prediction value, yref(k + i | k) is a control output reference value, and k + i | k represents a value for predicting the k + i time from the information of the k sampling times, where i is 1,2, …, Np, and u (k + i) is the k + i timeΔ u (k + i) is the control input increment at time k + i, where Q is the system output and R is the control output weight system matrix. And the first item on the right side of the equal sign of the equation (9) reflects the tracking capability of the system on the reference trajectory, and the second item reflects the requirement on the smooth change of the control quantity. According to expression (9), the system can be enabled to track the desired trajectory as quickly and smoothly as possible.
The operation of step S3 includes the steps of:
1) conversion to a quadratic programming problem:
defining a reference output vector Yref(k)＝[ηref,...,ηref(k+NP)]TThe performance evaluation function shown in formula (10) can be obtained by substituting formula (8) into target formula (9) with E ═ ξ (k),
Figure BDA0002808031780000111
wherein,
Figure BDA0002808031780000112
the equation (10) relates to a matrix, in which some data are constants and can be optimized and ignored in the optimization solution, the performance evaluation function is transformed into the following equation (11),
Figure BDA0002808031780000113
let H ═ ΘTQQΘ+RR,g＝ΘTQQ(E-Yref) Then, the formula (11) can be rewritten as the following formula (12),
Figure BDA0002808031780000114
considering that the following relationship (equations 13 to 15) exists between the control amount and the control increment,
u(k+i)＝u(k+i-1)+Δu(k) (13)，
Umin≤AuΔU+Ut≤Umax (14)，
ΔUmin≤ΔUt≤ΔUmax (15)，
wherein,
Figure BDA0002808031780000115
the control quantity is a column vector of a determinant Ne, u (k-1) is an actual control quantity at the last moment, Umin and Umax are respectively a minimum value and a maximum value set of the control quantity in a control time domain, and delta Umin and delta Umax are respectively a minimum value and a maximum value set of a control increment in the control time domain.
And the expression A is shown in the following formula (16),
Figure BDA0002808031780000116
according to the above operation steps of S3, the optimization solution problem of model predictive control is converted into a standard quadratic programming problem.
2) Solving a quadratic form:
through quadratic solution, a control input increment Δ U in the control time domain can be obtained, and the expression (17) is as follows:
ΔU＝[Δu(k)Δu(k+1)...Δu(k+Nc-1)]T (17)，
the operation of step S4 includes the steps of:
in practical application, because digital control has a beat delay, the voltage vector selected at the current sampling moment is not acted on the motor system until the next moment, and the control performance of the motor system is influenced. When the frequency of the application is low, the negative effect of the one-beat delay problem is more serious. Therefore, it is necessary to effectively compensate for the one-beat delay, which can be compensated to obtain a given control input urefThe expression (18) is as follows:
uref＝u(k+1)＝u(k-1)+Δu(k)+Δu(k+1) (18)。
control structure diagram of motor system shown in combination with fig. 2Wherein
Figure BDA0002808031780000121
namely, the angle and amplitude compensation is performed on the one-beat delay, specifically, the expression (19) is as follows:
Figure BDA0002808031780000122
in the embodiment, the embedded permanent magnet synchronous motor adopts a continuous control set model prediction method, the continuous control set model prediction control method and the quadratic programming problem are creatively combined and applied to the embedded permanent magnet synchronous motor, and the advantages of higher steady-state precision, smaller current ripple, determined switching frequency and the like of CCS-MPC prediction control based on the continuous control set model are ensured, so that the high performance of the torque and the speed of the motor system is ensured.
The embodiment of the present invention further provides another motor model predictive control method based on a continuous control set, which is different from the previous embodiment in that the extended state equation constructed in step S2 may be selected as an extended state equation.
The operation of optional step S3 shown in fig. 7 includes:
s331, converting the performance evaluation function into a quadratic programming problem according to the relation between the control quantity and the control increment;
and S332, solving through quadratic programming to obtain a control input increment in a control time domain, and designing a hexagonal inequality constraint condition.
In this embodiment, the current of the motor is controlled by combining the augmentation model and the quadratic programming problem in the embedded permanent magnet synchronous motor, so that the application of the augmentation model in the embedded permanent magnet synchronous motor is realized. The method has the advantages that the non-static-error control of model prediction of the continuous control set is realized by adopting the augmentation model, the augmentation state equation is constructed based on state increment, the static error can be effectively inhibited, the steady-state performance of the motor control system is improved, the non-static-error control is ensured, the deviation of identification parameters and actual parameters is reduced, the static error is reduced, the steady-state performance of the motor control system is further improved, and the problems of oscillation or divergence of the motor control system and the like are avoided.
Based on the above technical solution, the current control method will be described below through a specific derivation process.
In the operation of step S1, equations (1) and (2) are the same as in the previous embodiment.
Wherein, the state equation of the Linear-Time-Invariant (LTI) system is defined according to the equations (1) and (2), and the basic continuous state equation is expressed as the following equation (3),
Figure BDA0002808031780000131
discretizing the formula (3) by adopting a forward Euler formula to obtain a formula (4),
Figure BDA0002808031780000141
the operation of step S2 includes the steps of:
1) construction of a New State vector ξ (k | t) ═ x (k) u (k-1)]TWherein Δ x (k) is x (k) -x (k-1).
Based on this, a new augmented state equation can be obtained in conjunction with the operation of step S1, which is expressed as the following equation (5),
Figure BDA0002808031780000142
Figure BDA0002808031780000143
Figure BDA0002808031780000144
where Δ u (k) -u (k-1), I is an identity matrix, 0 is a zero matrix, and n is 2.
2) The output of the system at the future time is expressed in a matrix form, and the matrix expression is as the following formula (6),
Y＝ψξ(k)+ΘΔU (6)，
wherein,
Figure BDA0002808031780000151
Figure BDA0002808031780000152
Figure BDA0002808031780000153
ΔU＝[Δu(k)Δu(k+1)...Δu(k+Nc-1)]T
3) defining a performance evaluation function, wherein the expression of the performance evaluation function is shown as the following formula (7),
Figure BDA0002808031780000154
where t-1 is the last sampling time, Np is the prediction step, Nc is the control step, yp(k + i | k) is the control output prediction value, yref(k + i | k) is a control output reference value, k + i | k denotes a value at the time k + i predicted from information at the time k sampling, where i is 1,2, …, Np, u (k + i) is a control input quantity at the time k + i, and Δ u (k + i) is a control input increment at the time k + i, where Q is a system output quantity, and R is a control weight system matrix.
The operation of step S3 includes the steps of:
1) conversion to a quadratic programming problem:
let H ═ ΘTQQΘ+RR,g＝ΘTQQ(E-Yref) Then, the formula (7) can be rewritten as the following formula (8),
Figure BDA0002808031780000155
considering that the following relationship (equations 9 to 11) exists between the control amount and the control increment,
u(k+i)＝u(k+i-1)+Δu(k) (9)，
Umin≤AuΔU+Ut≤Umax (10)，
ΔUmin≤ΔUt≤ΔUmax (11)，
wherein,
Figure BDA0002808031780000161
the control quantity is a column vector of a determinant Ne, u (k-1) is an actual control quantity at the last moment, Umin and Umax are respectively a minimum value and a maximum value set of the control quantity in a control time domain, and delta Umin and delta Umax are respectively a minimum value and a maximum value set of a control increment in the control time domain.
And the expression A is shown in the following formula (12),
Figure BDA0002808031780000162
according to the above operation steps of S3, the optimization solution problem of model predictive control is converted into a standard quadratic programming problem.
2) Solving a quadratic form:
through quadratic solution, a control input increment Δ U in the control time domain can be obtained, and the expression (13) is as follows:
ΔU＝[Δu(k)Δu(k+1)...Δu(k+Nc-1)]T (13)，
it is understood that the same steps or contents of this embodiment and the previous embodiment are not repeated.
In the embodiment, the embedded permanent magnet synchronous motor adopts a continuous control set model prediction method, an augmentation model is also adopted to realize the static-error-free control of the CCS-MPC, an augmentation state equation is constructed based on state increment, even if the deviation between an identification parameter and an actual parameter is large, the static error can be effectively restrained, and the steady-state performance of a control system is improved.
Based on the same inventive concept, the embodiment of the present invention further provides a motor model predictive control apparatus based on a continuous control set, including: the state equation establishing module is used for establishing a basic continuous state equation on the basis of a voltage equation of the motor under a synchronous rotating coordinate system and then obtaining a basic discrete state equation based on the continuous control set by adopting a forward Euler formula; the state equation expansion module is used for constructing an expansion state equation according to the basic discrete state equation and designing a performance evaluation function of model predictive control; and the quadratic form conversion module is used for converting the performance evaluation function into a quadratic form planning problem and designing a corresponding constraint condition.
Optionally, the method further comprises: and the compensation module is used for effectively compensating the one-beat delay of the quadratic programming problem by adopting delay compensation.
The optional equation of state establishment module comprises: the voltage equation establishing unit is used for determining a stator voltage equation of the motor under a synchronous rotating coordinate system, wherein the stator voltage comprises an output stator voltage and a feedforward voltage of the continuous control set model predictive control regulator; the linear equation determining unit is used for setting the angular speed of the motor in the stator voltage equation to be constant so as to determine a basic continuous state equation of a linear time-invariant system; and the discretization unit is used for discretizing the basic continuous state equation by adopting a forward Euler formula to obtain the basic discrete state equation based on the continuous control set.
The optional state equation extension module comprises: the state equation expansion unit is used for constructing a new state vector and obtaining an expanded state equation according to the new state vector and the basic discrete state equation; the state prediction unit is used for performing state prediction on the extended state equation to obtain a state space equation and calculating the system output quantity of the state space equation; and the performance function definition unit is used for defining a performance evaluation function of model predictive control according to the state space equation and the system output quantity thereof.
The optional extended state equation is an augmented state equation.
An optional secondary form conversion module comprises: the quadratic form conversion unit is used for converting the performance evaluation function into a quadratic form planning problem; and the quadratic form solving unit is used for solving through quadratic form planning to obtain a control input increment in a control time domain and then designing a hexagon inequality constraint condition.
In the embodiment, a continuous control set model prediction control method and a quadratic programming problem are combined in the embedded permanent magnet synchronous motor, an augmentation model is adopted to realize the static-error-free control of the CCS-MPC, an augmentation state equation is constructed based on state increment, even if the deviation between an identification parameter and an actual parameter is large, the static error can be effectively restrained, and the steady-state performance of a control system is improved.
An embodiment of the present invention further provides a motor controller, including: the continuous control set-based motor model predictive control apparatus according to any of the above embodiments.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious modifications, rearrangements, combinations and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (9)

1. A motor model prediction control method based on a continuous control set is characterized by comprising the following steps:
under a synchronous rotating coordinate system, establishing a basic continuous state equation on the basis of a voltage equation of a motor, and obtaining a basic discrete state equation based on a continuous control set by adopting a forward Euler formula;
constructing an extended state equation according to the basic discrete state equation, and designing a performance evaluation function of model predictive control;
and converting the performance evaluation function into a quadratic programming problem, and designing corresponding constraint conditions.
2. The control method of claim 1, wherein obtaining a base discrete state equation comprises:
determining a stator voltage equation of the motor under the synchronous rotating coordinate system, wherein the stator voltage comprises an output stator voltage and a feedforward voltage of a continuous control set model predictive control regulator;
setting the angular speed of the motor in the stator voltage equation to be constant so as to determine a basic continuous state equation of a linear time-invariant system;
discretizing the basic continuous state equation by adopting a forward Euler formula to obtain the basic discrete state equation based on a continuous control set.
3. The control method according to claim 1, wherein constructing an extended state equation from the base discrete state equation and designing a performance evaluation function for model predictive control comprises:
constructing a new state vector, and obtaining the extended state equation according to the new state vector and the basic discrete state equation;
performing state prediction on the extended state equation to obtain a state space equation and calculating the system output quantity of the state space equation;
and defining a performance evaluation function of the model predictive control according to the state space equation and the system output quantity thereof.
4. The control method according to claim 1, characterized in that the extended equation of state is an augmented equation of state.
5. The control method of claim 1, wherein converting the performance evaluation function into a quadratic programming problem and designing corresponding constraints comprises:
defining a reference output vector or converting the performance evaluation function into a quadratic programming problem according to the relation between the control quantity and the control increment;
and solving through quadratic programming to obtain a control input increment in a control time domain, and designing a hexagonal inequality constraint condition.
6. The control method according to claim 1, characterized by further comprising: and effectively compensating the one-beat delay of the quadratic programming problem by adopting delay compensation.
7. A motor model predictive control apparatus based on a continuous control set, comprising:
the state equation establishing module is used for establishing a basic continuous state equation on the basis of a voltage equation of the motor under a synchronous rotating coordinate system and then obtaining a basic discrete state equation based on the continuous control set by adopting a forward Euler formula;
the state equation expansion module is used for constructing an expansion state equation according to the basic discrete state equation and designing a performance evaluation function of model predictive control;
and the quadratic form conversion module is used for converting the performance evaluation function into a quadratic form planning problem and designing a corresponding constraint condition.
8. The control device according to claim 7, characterized by further comprising: and the compensation module is used for effectively compensating the one-beat delay of the quadratic programming problem by adopting delay compensation.
9. A motor controller, comprising: the continuous control set-based motor model predictive control apparatus according to any one of claims 7 to 8.
CN202011379291.1A 2020-11-30 2020-11-30 Motor model prediction control method and device based on continuous control set and controller Pending CN112398403A (en)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW201340584A (en) * 2012-03-16 2013-10-01 cheng-kai Lin A predictive current control method for interior permanent magnet synchronous motors and synchronous reluctance motors based on extended back-EMF estimation
CN110445438A (en) * 2019-06-28 2019-11-12 天津大学 A kind of permanent magnet synchronous motor prediction flux linkage control method based on extension domination set
CN111478637A (en) * 2020-04-16 2020-07-31 广东美的智能科技有限公司 Motor control method and motor control system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW201340584A (en) * 2012-03-16 2013-10-01 cheng-kai Lin A predictive current control method for interior permanent magnet synchronous motors and synchronous reluctance motors based on extended back-EMF estimation
CN110445438A (en) * 2019-06-28 2019-11-12 天津大学 A kind of permanent magnet synchronous motor prediction flux linkage control method based on extension domination set
CN111478637A (en) * 2020-04-16 2020-07-31 广东美的智能科技有限公司 Motor control method and motor control system

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
JUNG-SIK YIM等: "Modified Current Control Schemes for High-Performance Permanent-Magnet AC Drives With Low Sampling to Operating Frequency Ratio", 《IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS》 *
吴为等: "基于复矢量的电流环解耦控制方法研究", 《中国电机工程学报》 *
王治国等: "三相异步电机电流多步预测控制方法", 《电工技术学报》 *
龚建伟 等: "《无人驾驶车辆模型预测控制》", 30 April 2014 *

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