CN111313781A - Model-free prediction current control method based on super-local model - Google Patents

Model-free prediction current control method based on super-local model Download PDF

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CN111313781A
CN111313781A CN201911092519.6A CN201911092519A CN111313781A CN 111313781 A CN111313781 A CN 111313781A CN 201911092519 A CN201911092519 A CN 201911092519A CN 111313781 A CN111313781 A CN 111313781A
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super
local model
asynchronous motor
current control
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张永昌
黄朋
张博越
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North China University of Technology
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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

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Abstract

The invention provides a model-free prediction current control method based on a super-local model, which comprises the following steps: step A, according to an asynchronous motor super-local model, adopting indirect magnetic field directional control, and simplifying the current control of the asynchronous motor super-local model into a first-order system by utilizing complex vector description; b, combining the first-order system with the intelligent PI according to the simplified first-order system in the step A to obtain the input of a closed-loop system; and C: estimating F in a short time by adopting a differential algebra method, wherein the F is a variable which contains the structural information of the system and contains an unknown part and interference of the system; step D: and combining the super-local model-free control with the indirect magnetic field directional prediction current control of the asynchronous motor, and solving the estimated value of F according to a complex vector mathematical model and a super-local model under a two-phase static coordinate system of the asynchronous motor. The method of the invention greatly improves the robustness of Model Predictive Control (MPC) to the motor parameters.

Description

Model-free prediction current control method based on super-local model
Technical Field
The invention relates to a super-local model design strategy of an asynchronous motor, in particular to a model-free prediction current control method based on a super-local model.
Background
Model Predictive Control (MPC) is a kind of computer control algorithm appearing in the industrial engineering control field in the later period of the 20 th century 70 s, and is widely applied to the process control industries such as chemical engineering and the like. Compared with vector control (VOC), MPC (multi-control) which is an emerging control strategy in recent years does not need current inner loop and parameter setting, directly generates inverter driving signals without pulse modulation, is easy to process system constraints or increase other control targets, and has the advantages of simple structure, quick dynamic response, easy expansion and the like. Compared with Direct Torque Control (DTC), the MPC optimizes and selects the optimal voltage vector by predicting the state of the motor, is more accurate and effective in vector selection, is easier to consider various nonlinear constraints including switching frequency reduction, and has the advantages of good steady-state performance, flexible control and the like.
However, the MPC uses a large number of motor parameters to perform calculation in the prediction process and the control process, and when the motor is subjected to external disturbance and the motor parameters change, the optimal voltage vector generated by prediction will deviate, thereby affecting the overall control performance of the motor.
Disclosure of Invention
In order to improve the parameter robustness of the MPC, the invention provides a control method combining the concept of super-local model-free control and the control of the indirect magnetic field directional prediction current of the asynchronous motor, and the control method is applied to the current control of the asynchronous motor.
The invention provides a model-free prediction current control method based on a super-local model, which comprises the following steps:
step A, according to an asynchronous motor super-local model, adopting indirect magnetic field directional control, and simplifying the current control of the asynchronous motor super-local model into a first-order system by utilizing complex vector description;
b, combining the first-order system with the intelligent PI according to the simplified first-order system in the step A to obtain the input of a closed-loop system;
and C: estimating F in a short time by adopting a differential algebra method, wherein the F is a variable which contains the structural information of the system and contains an unknown part and interference of the system;
step D: and combining the super-local model-free control with the indirect magnetic field directional prediction current control of the asynchronous motor, and solving the estimated value of F according to a complex vector mathematical model and a super-local model under a two-phase static coordinate system of the asynchronous motor.
In the above model-free predictive current control method based on the super-local model, the step a includes: according to the super-local model of the asynchronous motor y(n)F + α u, where y is system output variable, system order n is not less than 1, input variable weight coefficient α is constant value, obtained by debugging, F contains structure information of system and unknown part and interference of system, u is system input variable, indirect magnetic field orientation control is adopted, and complex vector description is used to simplify current control into a first-order system, n is 1, the super-local model of asynchronous motor can be rewritten as:
y(1)＝F+αu (1)。
in the above model-free predictive current control method based on the super-local model, the step B includes:
according to the simplified first-order system in the step A, combining the first-order system with the intelligent PI to obtain the input of a closed-loop system:
Figure BDA0002267182200000021
in the formula y*Outputting the reference value, e-y*For tracking errors, KPAnd KIRespectively proportional coefficient and integral coefficient, by adjusting KPAnd KIObtaining a pair reference value y*The tracking performance of (2).
In the above model-free predictive current control method based on the super-local model, the step C includes:
estimating F in a short time by using a differential algebra according to the formula (2) in the step B, and then using the estimated value of F
Figure BDA0002267182200000031
Instead, equation (1) may be rewritten as:
Figure BDA0002267182200000032
the transformation calculation is carried out on the formula (3) to obtain:
Figure BDA0002267182200000033
wherein s is the complex frequency of pull-type transformation;
wherein y is0Is a corresponding time interval [ t-L, t]Where L is a sufficiently small time constant, the specific value depending on the sampling period and the noise intensity, typically not exceeding 0.1 times the sampling period;
multiplication on both sides of equation (4) simultaneously
Figure BDA0002267182200000034
To eliminate y0Obtaining:
Figure BDA0002267182200000035
multiplying both sides of formula (5) by s-2And converted into the time domain to obtain:
Figure BDA0002267182200000036
where σ is a derivative argument.
In the above model-free predictive current control method based on the super-local model, the step D includes:
according to the formula in the step C, combining the concept of ultra-local model-free control with the control of the indirect magnetic field directional prediction current of the asynchronous motor, and solving the estimation value of F according to a complex vector mathematical model and an ultra-local model under a two-phase static coordinate system of the asynchronous motor;
the complex vector mathematical model under the two-phase static coordinate system of the asynchronous motor is as follows: u. ofs＝Rsis+pψs+jωkψs(7)
In the formula usIs stator voltage vector, Rs、isAnd psisRespectively stator resistance, stator current and stator magnetism of asynchronous motorChain, p ψsIs shown to psisPerforming a differential operation, ωkIs any angular velocity;
the estimated value of F is calculated as follows:
Figure BDA0002267182200000041
wherein n isFTo control the number of cycles, TscIs the time of one control cycle and is,
Figure BDA0002267182200000042
represents the pair isAnd carrying out differential operation.
The invention provides a model-free prediction current control algorithm based on a super-local model, and realizes current control without any motor parameter through a model-free idea, thereby greatly improving the robustness of MPC to the motor parameter.
The invention combines the super-local model with the model-free prediction current control and obtains stable control effect, thereby further widening the application range of the super-local model.
Drawings
FIG. 1 is a block diagram of model-free predictive current control.
Figure 2 is a waveform from a stationary start to 1500rpm with the motor unloaded.
Fig. 3 is a waveform of steady state operation at each speed when the asynchronous motor is in no-load operation: (a)150rpm, (b)900rpm, and (c)1500 rpm.
Detailed Description
The following examples are presented to enable those skilled in the art to more fully understand the present invention, but are not intended to limit the invention in any way. The experimental procedures in the following examples are conventional unless otherwise specified.
Fig. 1 is a model-free predictive current control block diagram, which is used for establishing a super-local model of a controlled system according to input and output of the controlled system, estimating the super-local model through differential algebra, and only using input and output data of the controlled system without any parameter information in the process, so that the model-free current control of an asynchronous motor is realized by combining with indirect magnetic field directional control, and the robustness of an MPC to motor parameters is improved.
Step 1: the asynchronous motor is simplified into a single-input single-output (SISO) system, and is approximately described by a common differential equation, which is as follows:
E(t,y,y(1),…,y(n),u,u(1),…,u(m))＝0 (1)
where u is the system input variable, y is the system output variable, t is time, m and n are integers greater than 1, and E is an unknown but assumed to be a sufficiently smooth function.
Step 2: according to the model-free control concept, in a very small sampling time, equation (1) can be defined as a super-local model as follows:
y(n)＝F+αu (2)
wherein the system order n is greater than or equal to 1, the input variable weight coefficient α is a constant value, obtained by simulation debugging.
And step 3: and (3) according to the super-local model of the asynchronous motor in the step (2), adopting indirect magnetic field directional control, and simplifying the current control into a first-order system by using complex vector description. Taking n as 1, the hyper-local model of equation (2) can be rewritten as:
y(1)＝F+αu (3)
and 4, step 4: and (4) combining the first-order system simplified in the step (3) with the intelligent PI to obtain the input of the closed-loop system.
Figure BDA0002267182200000051
In the formula y*Outputting the reference value, e-y*For tracking errors, KPAnd KIRespectively a proportionality coefficient and an integration coefficient. By adjusting KPAnd KIA reference value y can be obtained*Better tracking performance.
And 5: according to the equation in step 4F is estimated in a short time by using a differential algebra method, and then the F can be used as an estimated value
Figure BDA0002267182200000052
Instead, equation (3) may be rewritten as:
Figure BDA0002267182200000053
the transformation calculation is carried out on the formula (5) to obtain:
Figure BDA0002267182200000054
where s is the complex frequency of the pull transform.
Wherein y is0Is a corresponding time interval [ t-L, t]Wherein L is a sufficiently small time constant. Multiplication on both sides of equation (6) simultaneously
Figure BDA0002267182200000055
To eliminate y0Obtaining:
Figure BDA0002267182200000061
multiplying both sides of equation (7) by s-2And converted into the time domain to obtain:
Figure BDA0002267182200000062
where σ is a sufficiently small time constant.
Step 6: and (5) combining the control idea of the super-local model-free control with the indirect magnetic field directional prediction current control of the asynchronous motor according to the equation in the step 5, and solving the estimation value of F according to a complex vector mathematical model and a super-local model under a two-phase static coordinate system of the asynchronous motor.
The complex vector mathematical model under the two-phase static coordinate system of the asynchronous motor is as follows:
us＝Rsis+pψs+jωkψs(9)
in the formula usIs stator voltage vector, Rs、isAnd psisRespectively asynchronous motor stator resistance, stator current and stator flux, p psisIs shown to psisPerforming a differential operation, ωkIs any angular velocity.
The estimated value of F is calculated as follows:
Figure BDA0002267182200000063
wherein n isFTo control the number of cycles, TscIs the time of one control cycle and is,
Figure BDA0002267182200000064
represents the pair isAnd carrying out differential operation.
And 7: according to the above steps, as shown in FIG. 1, the d-axis stator current reference value to be input is predicted based on the ultra-local model-free prediction current control
Figure BDA0002267182200000065
And q-axis stator current reference
Figure BDA0002267182200000066
Synthesized to a voltage reference
Figure BDA0002267182200000067
And then, the switching value of the two-level inverter is obtained through space vector pulse width modulation, so that the on-off of a three-phase bridge arm of the inverter is controlled, and the control of the asynchronous motor is realized.
Wherein q-axis stator current reference value
Figure BDA0002267182200000068
Is the motor speed omegarWith reference value of rotational speed
Figure BDA0002267182200000069
The difference is obtained by a PI controller.
The effectiveness of the proposed method can be derived from the experimental waveforms of fig. 2 and 3. Fig. 2 is an experimental waveform for starting the asynchronous motor from rest to 1500rpm under no load. The channels are respectively as follows from top to bottom: motor speed omegarQ-axis current IqD-axis current IdAnd a-phase current ia. It can be clearly found that the control strategy provided by the invention can realize the quick and stable starting of the motor.
Fig. 3 is a waveform of steady state operation at each speed when the asynchronous motor is in no-load operation: (a)150rpm, (b)900rpm, and (c)1500 rpm. Because the tested motor platform is provided with the coupler, the magnetic powder brake and the like, the motor is not completely unloaded. As shown in the figure, the asynchronous motor has good control effect at low speed of 150rpm, and the dq axis current and the stator current are smooth. When the motor operates in a medium-high speed region, the dq-axis current is slightly pulsed, and the stator current contains certain harmonic waves. On the whole, under the condition that the motor parameters are accurate, the model-free prediction current control method based on the super-local model can realize good steady-state performance.
Finally, the invention compares the proposed model-free predictive current control method based on the super-local model with DB _ PCC under the condition of motor parameter change. As shown, in order to clearly show the influence of the motor parameters on the operation of the motor, dotted lines are used to distinguish the various stages of the motor parameter variation. The changes of the motor parameters in (a) of the graph are respectively: all the parameters All of the motor are accurate values (All is 1pu, pu is an accurate value base value), and the stator resistance R issTo 0.5 times the exact value: rs0.5pu, rotor resistance RrTo 0.5 times the exact value: rr0.5pu, mutual inductance LmTo 0.5 times the exact value: l ism0.5pu and All motor parameters All become 0.5 times the exact value: all ═ 0.5 pu. The changes of the motor parameters in (b) of the graph are respectively: all 1pu, Rs＝3pu、Rr＝3pu、Lm3pu and All 3 pu. In (c) of the figure, the K ═ 1 stage is All ═ 1 pu; k is 2 stage Rs＝3pu、Rr＝0.5pu、L m3 pu; k is 3 stage Rs＝3pu、Rr＝3pu、Lm0.5 pu; k is 4 stage Rs＝0.5pu、Rr＝0.5pu、 L m3 pu; k is 5 stage Rs＝0.5pu、Rr＝3pu、Lm0.5 pu. As shown in the figure, when the motor parameter becomes small, the change of the mutual inductance parameter causes a more obvious deviation to the dq-axis current, and the motor has a remarkable stalling phenomenon. Similarly, when all the parameters are simultaneously reduced, the same phenomenon occurs because the mutual inductance parameter becomes smaller. And the change of the stator resistance and the rotor resistance has little influence on the operation of the motor. When the motor parameters are increased, the d-axis current is slightly deviated due to the increase of the stator resistance parameters, the dq-axis current pulsation is obviously increased when the mutual inductance parameters are increased, the stator current is obviously copied and has larger harmonic waves, and the rotating speed is also fluctuated. And when all parameters are simultaneously increased, the mutual inductance is mainly increased, and the motor is in a relatively unstable operation state. In the process of random variation of motor parameters, the motor is in an extremely unstable state in the stages of K-3 and K-5 under the influence of the reduced mutual inductance parameters, an obvious stall phenomenon occurs, dq-axis current has obvious deviation, and the load carrying capacity of the motor is reduced. And in the K-2 stage and the K-4 stage, the motor operation is also in an unstable state under the influence of the increased mutual inductance parameter, and the fluctuation of the motor rotating speed is obvious. The super-local method is influenced by differential noise and has low F measurement accuracy, the dq axis current has large pulsation, the stator current has certain distortion phenomenon, and the stable on-load operation capability of the motor can still be ensured.
In summary, as can be seen from the waveform analysis of fig. 2 and 3, the model-free predictive current control method based on the super-local model provided by the invention can achieve good dynamic performance and steady-state performance in a full-speed domain without any motor parameter information.
Those skilled in the art will appreciate that the above embodiments are merely exemplary embodiments and that various changes, substitutions, and alterations can be made without departing from the spirit and scope of the invention.

Claims (5)

1. A model-free prediction current control method based on a super-local model comprises the following steps:
step A, according to an asynchronous motor super-local model, adopting indirect magnetic field directional control, and simplifying the current control of the asynchronous motor super-local model into a first-order system by utilizing complex vector description;
b, combining the first-order system with the intelligent PI according to the simplified first-order system in the step A to obtain the input of a closed-loop system;
and C: estimating F in a short time by adopting a differential algebra method, wherein the F is a variable which contains the structural information of the system and contains an unknown part and interference of the system;
step D: and combining the super-local model-free control with the indirect magnetic field directional prediction current control of the asynchronous motor, and solving the estimated value of F according to a complex vector mathematical model and a super-local model under a two-phase static coordinate system of the asynchronous motor.
2. The model-free predictive current control method based on a hyper-local model according to claim 1, wherein the step a comprises:
according to the super-local model of the asynchronous motor y(n)F + α u, where y is system output variable, system order n is not less than 1, input variable weight coefficient α is constant value, obtained by debugging, F contains structure information of system and unknown part and interference of system, u is system input variable, indirect magnetic field orientation control is adopted, and complex vector description is used to simplify current control into a first-order system, n is 1, the super-local model of asynchronous motor can be rewritten as:
y(1)＝F+αu (1)。
3. the model-free predictive current control method based on a hyper-local model as claimed in claim 1, wherein said step B comprises:
according to the simplified first-order system in the step A, combining the first-order system with the intelligent PI to obtain the input of a closed-loop system:
Figure FDA0002267182190000021
in the formula y*Outputting the reference value, e-y*For tracking errors, KPAnd KIProportional and integral coefficients, respectively, by adjusting KPAnd KIObtaining a pair reference value y*The tracking performance of (2).
4. The model-free predictive current control method based on a hyper-local model as claimed in claim 3, wherein said step C comprises:
estimating F in a short time by using a differential algebra according to the formula (2) in the step B, and then using the estimated value of F
Figure FDA0002267182190000022
Instead, equation (1) may be rewritten as:
Figure FDA0002267182190000023
the transformation calculation is carried out on the formula (3) to obtain:
Figure FDA0002267182190000024
wherein s is the complex frequency of pull-type transformation;
wherein y is0Is a corresponding time interval [ t-L, t]Wherein L is a time constant of no more than 0.1 times the sampling period; multiplication on both sides of equation (4) simultaneously
Figure FDA0002267182190000025
To eliminate y0Obtaining:
Figure FDA0002267182190000026
multiplying both sides of formula (5) by s-2And converted into the time domain to obtain:
Figure FDA0002267182190000027
where σ is a derivative argument.
5. The model-free predictive current control method based on a hyper-local model as claimed in claim 4, wherein said step D comprises:
according to the formula in the step C, combining the control idea of the super-local model-free control with the indirect magnetic field directional prediction current control of the asynchronous motor, and solving the estimation value of F according to a complex vector mathematical model and a super-local model under a two-phase static coordinate system of the asynchronous motor;
the complex vector mathematical model under the two-phase static coordinate system of the asynchronous motor is as follows: u. ofs＝Rsis+pψs+jωkψs(7)
In the formula usIs stator voltage vector, Rs、isAnd psisRespectively asynchronous motor stator resistance, stator current and stator flux linkage, p psisIs shown to psisPerforming a differential operation, ωkIs any angular velocity;
the estimated value of F is calculated as follows:
Figure FDA0002267182190000031
wherein n isFTo control the number of cycles, TscIs the time of one control cycle and is,
Figure FDA0002267182190000032
represents the pair isAnd carrying out differential operation.
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