CN116961512A - Model prediction-based current control method, device and storage medium - Google Patents
Model prediction-based current control method, device and storage medium Download PDFInfo
- Publication number
- CN116961512A CN116961512A CN202311221212.8A CN202311221212A CN116961512A CN 116961512 A CN116961512 A CN 116961512A CN 202311221212 A CN202311221212 A CN 202311221212A CN 116961512 A CN116961512 A CN 116961512A
- Authority
- CN
- China
- Prior art keywords
- current
- control period
- voltage vector
- optimal voltage
- current control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 67
- 239000013598 vector Substances 0.000 claims abstract description 259
- 230000001360 synchronised effect Effects 0.000 claims abstract description 116
- 230000008859 change Effects 0.000 claims description 47
- 238000005070 sampling Methods 0.000 claims description 19
- 238000006243 chemical reaction Methods 0.000 claims description 10
- 230000004907 flux Effects 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000013178 mathematical model Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 9
- 230000008901 benefit Effects 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000000295 complement effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/22—Current control, e.g. using a current control loop
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P21/0021—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using different modes of control depending on a parameter, e.g. the speed
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—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
- H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
- H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
- H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
- H02P27/08—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters with pulse width modulation
- H02P27/12—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters with pulse width modulation pulsing by guiding the flux vector, current vector or voltage vector on a circle or a closed curve, e.g. for direct torque 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
- 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 application relates to the field of synchronous motor current control, and provides a current control method, a device and a storage medium based on model prediction, wherein the method comprises the following steps: after the permanent magnet synchronous motor system is started, the following operations are performed after each control period is entered to generate pulse control signals: and determining an optimal voltage vector of the current control period based on the current prediction model and the cost function, and according to the difference between the optimal voltage vector of the previous control period and the optimal voltage vector of the current control period, applying the optimal voltage vector of the previous control period to the first time segment and the optimal voltage vector of the current control period to the second time segment, or applying the optimal voltage vector of the previous control period to all time segments of the current control period. According to the application, the number of voltage vectors in a control period is increased on the premise of not increasing the switching loss of the power device, so that the steady-state performance of the permanent magnet synchronous motor system is improved.
Description
Technical Field
The application relates to the field of synchronous motor current control, in particular to a current control method, a current control device and a storage medium based on model prediction.
Background
The permanent magnet synchronous motor system is widely studied and applied to the fields of aerospace, electric automobiles and the like due to the advantages of high efficiency, high reliability and good control performance.
The permanent magnet synchronous motor system is composed of power electronic devices, has different topological structures, has mutually independent rectifying stages and inverting stages in terms of the topological structures, can modulate all parts by adopting different modulation methods, and has very flexible control strategies. The permanent magnet synchronous motor system is usually subjected to a current control method based on model prediction, so that the optimal switching state of the system is selected through variable prediction and cost function evaluation, and the permanent magnet synchronous motor system is controlled according to the optimal switching state, so that the multivariable control of the system is realized, and the dynamic response speed and the steady-state control precision are improved.
In the conventional current control method based on model prediction, only one voltage vector acts on the inverter in each control period, which also means that a large voltage error exists between the reference voltage and the actual voltage, thereby affecting the operation effect of the motor. Increasing the number of voltage vectors in the control period results in higher switching losses and thus reduces the control performance of the motor system.
Disclosure of Invention
In order to configure a proper voltage vector to improve the problem steady-state performance of a permanent magnet synchronous motor system, the application provides a current control method, a device and a storage medium based on model prediction.
The application provides a current control method, a device and a storage medium based on model prediction, which adopt the following technical scheme:
a model prediction-based current control method for a permanent magnet synchronous motor system, the method comprising:
determining a current prediction model and a cost function according to the topological structure of the permanent magnet synchronous motor system and a preset reference current value;
after the permanent magnet synchronous motor system is started, the following operations are performed after each control period is entered to generate pulse control signals:
determining an optimal voltage vector of a current control period based on the current prediction model and the cost function, and comparing the optimal voltage vector of a previous control period with the optimal voltage vector of the current control period;
under the condition that the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period acts on all time periods of the current control period;
Under the condition that the optimal voltage vector of the previous control period is different from the optimal voltage vector of the current control period, determining a first time section and a second time section in the current control period according to the current change rate corresponding to the optimal voltage vector of the previous control period and the current change rate corresponding to the optimal voltage vector of the current control period; and applying the optimal voltage vector of the previous control period to the first time segment, and applying the optimal voltage vector of the current control period to the second time segment.
By adopting the technical scheme, the current prediction model and the value parameter are determined according to the topological structure and the variable parameter of the permanent magnet synchronous motor system, so that the optimal voltage vector of each control period in a conventional state is obtained, and then fine adjustment is carried out according to the optimal voltage vectors.
Under the condition that the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, the optimal voltage vector in the previous control period is extended to the whole time period of the current control period, the previous control period is directly used for continuing, the operation steps are simplified, and the overall working efficiency is improved.
And under the condition that the optimal voltage vector of the previous control period is different from the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period acts on the first time segment, and the optimal voltage vector of the current control period acts on the second time segment. By extending the optimal voltage vector in the previous control period to the current control period, there are two voltage vectors in the current control period. The method has the advantages that the number of voltage vectors in a control period is increased on the premise of not increasing the switching loss of the power device, so that voltage errors existing between voltage actually acting on a motor system and expected voltage are effectively reduced, the problem of poor steady-state performance in a traditional current control method based on model prediction is solved, and the steady-state performance of a permanent magnet synchronous motor system is improved.
Optionally, the sum of the first time segment and the second time segment is equal to the total time period of the current control period, and the first time segment is located before the second time segment in time series.
Through the technical scheme, the first time segment and the second time segment in the current control period are limited, and the fact that no idle time segment exists in the current control period is guaranteed.
Optionally, in the case that the current control period is an initial control period after the permanent magnet synchronous motor is started, the optimal voltage vector of the previous control period is assigned as the optimal voltage vector of the current control period.
By the technical scheme, the initial voltage vector of the previous control period does not exist in the initial state, so that the optimal voltage vector of the previous control period is assigned as the optimal voltage vector of the current control period, and the operation of the method in the initial state is ensured.
Optionally, determining an optimal voltage vector for the current control period based on the current prediction model and the cost function includes:
based on the current prediction model, obtaining predicted currents corresponding to each candidate voltage vector in the current control period;
taking the predicted current with the smallest value obtained after substituting the cost function as a target predicted current;
and taking the candidate voltage vector corresponding to the target predicted current as an optimal voltage vector of the current control period.
By adopting the above technical solution, the candidate voltage vector is usually a plurality of preset voltage vectors, and then the candidate voltage vector with the smallest predicted current for the target is found out through the cost function to serve as the optimal voltage vector. The accuracy of the optimal voltage vector is determined in this case depending on the number of candidate voltage vectors and the accuracy of the cost function.
Optionally, determining the first time segment and the second time segment in the current control period according to the current change rate corresponding to the optimal voltage vector in the previous control period and the current change rate corresponding to the optimal voltage vector in the current control period includes:
calculating the current change rate corresponding to the optimal voltage vector in the previous control period and the current change rate corresponding to the optimal voltage vector in the current control period;
and according to the current change rate of the current control period and the current conversion rate of the previous control period, and combining a current dead beat prediction control principle, calculating the first time segment and the second time segment.
By adopting the technical scheme, the first time segment and the second time segment are calculated according to the current transformation rate of the previous control period and the current change rate of the current control period and the current dead beat prediction control principle of the previous control period, so that the static error is shortened.
Optionally, the determining the current prediction model and the cost function according to the topology structure of the permanent magnet synchronous motor system and a preset reference current value includes:
according to the topological structure of the permanent magnet synchronous motor system, a voltage equation of a d axis and a q axis of the permanent magnet synchronous motor system is obtained through a discretization motor mathematical model;
Performing Euler discretization on the voltage equation to obtain a current prediction model;
and constructing a cost function according to the current prediction model.
Optionally, the voltage equations of the d axis and the q axis of the permanent magnet synchronous motor system are that,
；
wherein ,id For d-axis current, i q Q-axis currents, respectively; u (u) d As the d-axis voltage component, u q Is the q-axis voltage component; r is,L、Ψ f The resistance, the inductance and the flux linkage of the permanent magnet synchronous motor system are respectively; omega is the electrical angular speed of the permanent magnet synchronous motor system;
the current prediction model is that,
wherein ,id (k) Sampling current of d-axis of current control period, i q (k) Sampling current of q axis of the current control period; i.e d (k+1) is the d-axis sampling current of the next control period, i q (k) Sampling current of q axis for next control period; u (u) d (k) U is the voltage component of the d-axis of the candidate voltage vector q (k) Voltage components of the candidate voltage vector q-axis respectively; r, L, ψ f The resistance, the inductance and the flux linkage of the permanent magnet synchronous motor system are respectively; omega is the electrical angular velocity of the motor during operation; t is the control period of the motor system;
the cost function is a function of the value of,
；
wherein ,for a preset d-axis current reference value, +.>Is a preset reference value of q-axis current.
Optionally, the calculating the current change rate corresponding to the optimal voltage vector in the previous control period and the current change rate corresponding to the optimal voltage vector in the current control period includes:
the current change rates of the d-axis and the q-axis of the optimal voltage vector in the previous control period are respectively and />The current change rates of the d-axis and q-axis of the optimum voltage vector in the present control period are +.> and />The voltage components of the d-axis and q-axis of the optimum voltage vector in the previous control period are +.> and />The voltage components of the d-axis and q-axis of the optimal voltage vector in the current control period are +.> and />Specifically, the method comprises the steps of,
；
wherein ,id For d-axis current, i q Q-axis currents, R, L, ψ respectively f The resistance, the inductance and the flux linkage of the permanent magnet synchronous motor system are respectively;
the calculating the first time segment and the second time segment according to the current change rate of the current control period and the current conversion rate of the previous control period and combining the current dead beat prediction control principle comprises the following steps:
according to the current change rate of the current control period and the current conversion rate of the previous control period combined with the current error beat prediction control principle, the d-axis and q-axis currents at the end of the current control period are obtained,
；
wherein ,t1 and t2 Respectively represent the previous controlThe time of action of the optimal voltage vector in the period and the optimal voltage vector in the current control period is satisfied and t 1 +t 2 =T，For a preset d-axis current reference value, +.>A preset q-axis current reference value;
solving to obtain a first time segment t 1 And a second time segment t 2 ，
；
wherein ,xd and xq The polynomial of (2) is given by,
；
according to t 1 and t2 And obtaining the first time segment and the second time segment.
The invention provides a current control device based on model prediction, which is connected with a permanent magnet synchronous motor system, wherein the permanent magnet synchronous motor system comprises an inverter and a permanent magnet synchronous motor, and the control device comprises:
the modeling unit is used for determining a current prediction model and a cost function according to the topological structure of the permanent magnet synchronous motor system and a preset reference current value;
a voltage vector calculation unit for determining an optimal voltage vector for a current control period based on the current prediction model and the cost function;
the pulse control generation unit is used for generating pulse control signals after entering each control period after the permanent magnet synchronous motor system is started:
An output unit for inputting the pulse control signal to the inverter;
the inverter is used for switching the power switch states of all bridge arms in the inverter according to the pulse control signals to generate dynamic control voltages, and inputting the dynamic control voltages into the permanent magnet synchronous motor;
wherein, after the permanent magnet synchronous motor system is started, generating the pulse control signal after entering each control period comprises:
determining an optimal voltage vector of a current control period based on the current prediction model and the cost function, and comparing the optimal voltage vector of a previous control period with the optimal voltage vector of the current control period;
under the condition that the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period acts on all time periods of the current control period;
under the condition that the optimal voltage vector of the previous control period is different from the optimal voltage vector of the current control period, determining a first time section and a second time section in the current control period according to the current change rate corresponding to the optimal voltage vector of the previous control period and the current change rate corresponding to the optimal voltage vector of the current control period; and applying the optimal voltage vector of the previous control period to the first time segment, and applying the optimal voltage vector of the current control period to the second time segment.
By adopting the technical scheme, the current prediction model and the value parameter are determined according to the topological structure and the variable parameter of the permanent magnet synchronous motor system, so that the optimal voltage vector of each control period in a conventional state is obtained, and then fine adjustment is carried out according to the optimal voltage vectors.
Under the condition that the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, the optimal voltage vector in the previous control period is extended to the whole time period of the current control period, the previous control period is directly used for continuing, the operation steps are simplified, and the overall working efficiency is improved.
And under the condition that the optimal voltage vector of the previous control period is different from the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period acts on the first time segment, and the optimal voltage vector of the current control period acts on the second time segment. By extending the optimal voltage vector in the previous control period to the current control period, there are two voltage vectors in the current control period. The method has the advantages that the number of voltage vectors in a control period is increased on the premise of not increasing the switching loss of the power device, so that voltage errors existing between voltage actually acting on a motor system and expected voltage are effectively reduced, the problem of poor steady-state performance in a traditional current control method based on model prediction is solved, and the steady-state performance of a permanent magnet synchronous motor system is improved.
The present application also provides a storage medium storing at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by a processor to implement the model-prediction-based current control method as set forth in any one of the above.
In summary, the application has the following beneficial technical effects:
(1) Under the condition that the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period is extended to the whole time period of the current control period, the previous control period is directly used for continuing, and a pulse control signal of the current control period is rapidly obtained so as to be rapidly transmitted to the permanent magnet synchronous motor system, so that the operation steps are simplified, and the overall working efficiency is improved.
(2) And under the condition that the optimal voltage vector of the previous control period is different from the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period acts on the first time segment, and the optimal voltage vector of the current control period acts on the second time segment. By extending the optimal voltage vector in the previous control period to the current control period, there are two voltage vectors in the current control period. The method has the advantages that the number of voltage vectors in a control period is increased on the premise of not increasing the switching loss of the power device, so that voltage errors existing between voltage actually acting on a motor system and expected voltage are effectively reduced, the problem of poor steady-state performance in a traditional current control method based on model prediction is solved, and the steady-state performance of a permanent magnet synchronous motor system is improved.
Drawings
FIG. 1 is a schematic flow chart of a current control method based on model prediction according to a first embodiment of the present invention;
fig. 2 is a schematic topology diagram of a permanent magnet synchronous motor system according to a first embodiment of the present invention;
FIG. 3 is a vector distribution diagram of the base voltage vector generated by the two-level voltage source inverter of FIG. 2;
FIG. 4 is an equivalent circuit of the d-axis and q-axis of the permanent magnet synchronous motor system of FIG. 2;
FIG. 5 is a schematic diagram of switching of a conventional single vector model-based predictive current control method;
FIG. 6 is a schematic diagram illustrating switching of a current control method based on model prediction according to a first embodiment of the present invention;
FIG. 7 is a graph of current THD results of a motor operating at 1000rpm and rated load in a conventional single vector model-based predictive current control method;
fig. 8 is a graph of current THD results of the current control method based on model prediction according to the first embodiment of the present invention when the rotation speed is 1000rpm and the load is the rated load;
fig. 9 is a graph comparing current THD results of a motor operating at rated load and different rotational speeds according to a current control method based on model prediction provided in a first embodiment of the present invention and a current control method based on model prediction of a conventional single vector;
Fig. 10 is a comparison chart of current THD results of a motor operating at rated rotational speed and different load torques according to a current control method based on model prediction provided in a first embodiment of the present application and a conventional single vector current control method based on model prediction;
FIG. 11 is a schematic block diagram of a model predictive current control apparatus according to a second embodiment of the present application;
fig. 12 is a control block diagram of a model predictive current control apparatus according to a second embodiment of the present application when applied.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the inventive concepts. As part of this specification, some of the drawings of the present disclosure represent structures and devices in block diagram form in order to avoid obscuring the principles of the disclosure. In the interest of clarity, not all features of an actual implementation are necessarily described. Reference in the present disclosure to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment, and multiple references to "one embodiment" or "an embodiment" should not be understood as necessarily all referring to the same embodiment.
The terms "a," "an," and "the" are not intended to refer to a singular entity, but rather include the general class of which a particular example may be used for illustration, unless clearly defined. Thus, the use of the terms "a" or "an" may mean any number of at least one, including "one", "one or more", "at least one", and "one or more than one". The term "or" means any of the alternatives and any combination of alternatives, including all alternatives, unless alternatives are explicitly indicated as mutually exclusive. The phrase "at least one of" when combined with a list of items refers to a single item in the list or any combination of items in the list. The phrase does not require all of the listed items unless specifically so defined.
First embodiment:
a first embodiment of the present invention provides a current control method based on model prediction for a permanent magnet synchronous motor system, the method comprising:
determining a current prediction model and a cost function according to the topological structure of the permanent magnet synchronous motor system and a preset reference current value;
after the permanent magnet synchronous motor system is started, the following operations are performed after each control period is entered to generate pulse control signals:
Determining an optimal voltage vector of a current control period based on the current prediction model and the cost function, and comparing the optimal voltage vector of a previous control period with the optimal voltage vector of the current control period;
under the condition that the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period acts on all time periods of the current control period;
under the condition that the optimal voltage vector of the previous control period is different from the optimal voltage vector of the current control period, determining a first time section and a second time section in the current control period according to the current change rate corresponding to the optimal voltage vector of the previous control period and the current change rate corresponding to the optimal voltage vector of the current control period; and applying the optimal voltage vector of the previous control period to the first time segment, and applying the optimal voltage vector of the current control period to the second time segment.
By adopting the technical scheme, the current prediction model and the value parameter are determined according to the topological structure and the variable parameter of the permanent magnet synchronous motor system, so that the optimal voltage vector of each control period in a conventional state is obtained, and then fine adjustment is carried out according to the optimal voltage vectors.
Under the condition that the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, the optimal voltage vector in the previous control period is extended to the whole time period of the current control period, the previous control period is directly used for continuing, the operation steps are simplified, and the overall working efficiency is improved.
And under the condition that the optimal voltage vector of the previous control period is different from the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period acts on the first time segment, and the optimal voltage vector of the current control period acts on the second time segment. By extending the optimal voltage vector in the previous control period to the current control period, there are two voltage vectors in the current control period. The method has the advantages that the number of voltage vectors in a control period is increased on the premise of not increasing the switching loss of the power device, so that voltage errors existing between voltage actually acting on a motor system and expected voltage are effectively reduced, the problem of poor steady-state performance in a traditional current control method based on model prediction is solved, and the steady-state performance of a permanent magnet synchronous motor system is improved.
The implementation details of the current control method based on model prediction in this embodiment are specifically described below, but the following details are provided only for easy understanding, and are not essential to this embodiment, and a specific flow in this embodiment is shown in fig. 1, and includes the following steps:
and step 101, determining a current prediction model and a cost function according to the topological structure of the permanent magnet synchronous motor system and a preset reference current value.
Specifically, the topology of the permanent magnet synchronous motor system is shown in fig. 2, and includes a transmission voltage U dc The DC bus, the two-level voltage source type inverter and the permanent magnet synchronous motor. The setting of the variable parameters is shown in table 1, and the sampling frequency is set to 10kHz.
Table 1 variable parameter table of permanent magnet synchronous motor system
Analyzing the topological structure of the permanent magnet synchronous motor system, wherein the two-level voltage source type inverter can be divided into three groups of ABC phases, and the upper power switch and the lower power switch of the same bridge arm form complementary conduction, specifically: s is S a1 and Sa2 The power switch outputs V to the permanent magnet synchronous motor through the a node a ，S b1 and Sb2 The power switch outputs V to the permanent magnet synchronous motor through the b node b ，S c1 and Sc2 The power switch outputs V to the permanent magnet synchronous motor through the c node c . Because the upper power switch and the lower power switch of the same bridge arm are complementary, the output voltage of each bridge arm is only two, and is respectively represented as 0 and 1, for example, when the A phase is 0, the upper bridge arm switch S a1 Switch S for switching off and switching down bridge arm a2 When the conduction phase A is 1, the upper bridge arm switch S a1 Switch S of conducting and lower bridge arm a2 And (5) disconnecting.
Thus, by switching three groups of bridge arms, 2 can be obtained 3 Combination of 8 switches of different states, i.e. producing different output voltages in 8, thus forming the basic voltage vector V shown in fig. 3 0 （100）、V 1 （100）、V 2 （110）、V 3 （010）、V 4 （011）、V 5 （001）、V 6 (101) And V 7 （111）。
The inverter of the two-level voltage source type in the permanent magnet synchronous motor system adopts the 8 basic voltage vectors to represent a regular hexagonal rotating magnetic field in space. All reference voltages in the permanent magnet synchronous motor system set by staff are synthesized through the 8 basic voltage vectors.
In step 101, according to the topology structure and variable parameters of the permanent magnet synchronous motor system, a current prediction model and a cost function are calculated, including:
s1-1, obtaining a voltage equation of a d axis and a q axis of the permanent magnet synchronous motor system through a discretization motor mathematical model according to the topological structure of the permanent magnet synchronous motor system.
Specifically, based on the topology structure of the permanent magnet synchronous motor system shown in fig. 2, the voltages of the permanent magnet synchronous motor system on the d axis and the q axis are subjected to circuit equivalence, and an equivalent circuit diagram of the d axis and the q axis shown in fig. 4 is obtained.
The voltage equation of the permanent magnet synchronous motor system on the d axis and the q axis is obtained by the method:
；
wherein ,id For d-axis current, i q Q-axis currents, respectively; u (u) d As the d-axis voltage component, u q Is the q-axis voltage component; r, L, ψ f The resistance, the inductance and the flux linkage of the permanent magnet synchronous motor system are respectively; omega is the electrical angular velocity of the permanent magnet synchronous motor system when in operation.
S1-2, performing Euler discretization on the voltage equation to obtain a current prediction model.
Specifically, the current prediction model obtained after Euler discretization is that,
；
wherein ,id (k) Sampling current of d-axis of current control period, i q (k) Sampling current of q axis of the current control period; i.e d (k+1) is the d-axis sampling current of the next control period, i q (k) Sampling current of q axis for next control period; u (u) d (k) U is the voltage component of the d-axis of the candidate voltage vector q (k) Voltage components of the candidate voltage vector q-axis respectively; r, L, ψ f The resistance, the inductance and the flux linkage of the permanent magnet synchronous motor system are respectively; omega is the electrical angular velocity of the motor during operation; t is the control period of the motor system.
S1-3, constructing a cost function according to the current prediction model.
In particular, the cost function is that,
；
wherein ,for a preset d-axis current reference value, +.>K represents the current control period, and k+1 represents the predicted next control period, which is a preset reference value of q-axis current.
Step 102, detecting the start of the permanent magnet synchronous motor system, detecting whether the current time enters a new control period, and if yes, executing step 103.
Specifically, the permanent magnet synchronous motor system sends a feedback signal to the execution main body of the method during operation, so as to inform the execution main body of the working state of the permanent magnet synchronous motor system, when the working state shows that the permanent magnet synchronous motor is in the working state, if the current time is detected to enter a new control period, step 103 is performed, and if the current time is still in one control period, the work in the current control period is continuously performed.
Step 103, determining an optimal voltage vector of the current control period based on the current prediction model and the cost function.
Specifically, the implementation of step 103 includes:
s3-1, obtaining predicted currents corresponding to each candidate voltage vector in the current control period based on the current prediction model;
S3-2, taking the predicted current with the smallest value obtained after substituting the cost function as a target predicted current;
and S3-3, taking the candidate voltage vector corresponding to the target predicted current as an optimal voltage vector of the current control period.
In particular, the candidate voltage vectors are typically a plurality of voltage vectors set by the user himself, each candidate voltage vector being required to be defined by the 8 basic voltage vectors V 0 ~V 7 And (5) synthesizing. Substituting the candidate voltage vector into the current prediction model in S3-1 to obtain the corresponding predicted current in the current control period. S3-2 takes the minimum value of all the predicted currents in S3-1 as a target predicted current, and the candidate voltage vector corresponding to the target predicted current in S3-3 is taken as the optimal voltage vector of the current control period. The accuracy of the optimal voltage vector is determined by the number of candidate voltage vectors, the cost function and the accuracy of the current prediction model.
Step 104, determining whether the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, if so, executing step 105, and if not, executing step 106.
Specifically, this step is performed after the operation of step 103, and is used to generate a pulse control signal for inputting to the permanent magnet synchronous motor system.
Step 105, applying the optimal voltage vector of the previous control period to the entire time period of the current control period.
Specifically, if two optimal voltage vectors of adjacent control periods are the same, the previous control period is directly used for continuing the whole current control period to obtain a pulse control signal of the current control period, so that operation steps are simplified, and overall working efficiency is improved.
It is noted that, in the case that the current control period is the initial control period after the permanent magnet synchronous motor is started, the optimal voltage vector of the previous control period is assigned as the optimal voltage vector of the current control period.
By the technical scheme, the initial voltage vector of the previous control period does not exist in the initial state, so that the optimal voltage vector of the previous control period is assigned as the optimal voltage vector of the current control period, and the operation of the method in the initial state is ensured.
And 106, determining a first time section and a second time section in the current control period according to the current change rate corresponding to the optimal voltage vector in the previous control period and the current change rate corresponding to the optimal voltage vector in the current control period, applying the optimal voltage vector in the previous control period to the first time section, and applying the optimal voltage vector in the current control period to the second time section.
Specifically, the implementation of step 106 includes:
s6-1, determining a first time segment and a second time segment in the current control period according to the current change rate corresponding to the optimal voltage vector in the previous control period and the current change rate corresponding to the optimal voltage vector in the current control period;
s6-2, enabling the optimal voltage vector of the previous control period to act on the first time segment, and enabling the optimal voltage vector of the current control period to act on the second time segment.
By extending the optimal voltage vector in the previous control period to the current control period, at least two voltage vectors are provided in the current control period. The number of voltage vectors in a control period is increased on the premise of not increasing the switching loss of the power device, so that voltage errors existing between the voltage actually applied to the motor system and the expected voltage are effectively reduced, the problem of poor steady-state performance in the traditional current control method based on model prediction is solved, and the steady-state performance of the permanent magnet synchronous motor system is improved.
In some examples, the sum of the first time segment and the second time segment is equal to the entire time period of the current control period, and the first time segment is located before the second time segment in a time sequence.
Through the technical scheme, the first time segment and the second time segment in the current control period are limited, and the fact that no idle time segment exists in the current control period is guaranteed.
Further, the implementation of step S6-1 includes the following steps:
s6-1-1, calculating the current change rate of the optimal voltage vector in the previous control period and the current change rate of the optimal voltage vector in the current control period;
s6-1-2, calculating the first time segment and the second time segment according to the current change rate of the current control period and the current conversion rate of the previous control period and combining the current dead beat prediction control principle.
In specific implementation, step S3-1-1 includes: the current change rates of the d-axis and the q-axis of the optimal voltage vector in the previous control period are respectively expressed as and />The current change rates of the d-axis and q-axis of the optimum voltage vector in the present control period are expressed as +.> and />The voltage components of the d-axis and q-axis of the optimum voltage vector in the previous control period are respectively expressed as +.> and />The voltage components of the d-axis and q-axis of the optimal voltage vector in the current control period are expressed as +.> and />Thus, the following equation can be constructed, +.>
；
wherein ,id For d-axis current, i q Q-axis currents, R, L, ψ respectively f The resistance, the inductance and the flux linkage of the permanent magnet synchronous motor system are respectively.
In step S3-1-2: current change rate according to current control period and />) Current conversion rate (+_for previous control period)> and />) By combining the current dead beat prediction control principle, the current of d axis and q axis is obtained when the current control period is finished,
；
wherein ,t1 and t2 Respectively representing the acting time of the optimal voltage vector in the last control period and the optimal voltage vector in the current control period and meeting t 1 +t 2 =T，For a preset d-axis current reference value, +.>A preset q-axis current reference value;
solving to obtain t 1 and t2 The two kinds of the materials are respectively that,
；
wherein ,xd and xq The polynomial of (2) is given by,
；
according to t 1 and t2 Deriving the first time segment (initial time, initial time+t 1 ]And a second time segment (initial time +t 1 Initial time +t 1 +t 2 ]。
The implementation of the further step S6-2 comprises:
extending the optimal voltage vector of the previous control period to last t in the current control period 1 The optimal voltage vector for realizing the previous control period acts on the first time segment (initial time, initial time + t 1 ]The method comprises the steps of carrying out a first treatment on the surface of the The optimal voltage vector for the current control period is then continued for t 2 Realizing the application of the optimal voltage vector of the current control period to the second time segment (initial time + t 1 Initial time +t 1 +t 2 ]And obtaining a pulse control signal of the current control period.
Specifically, in the conventional current control method based on model prediction, a pulse control signal is input to an inverter so that a switch in the inverter changes, as shown in fig. 5, k (th) represents a current control period, k-1 (th) represents a previous control period, and k+1 (th) represents a next idle period, so that in the existing scheme, the switch of each bridge arm in the topology structure of the permanent magnet synchronous motor system does not change (i.e., the switch state is switched) in one control period.
In this scheme, because the optimal voltage vector in the previous control period is extended to the current control period, as shown in fig. 6, in one control period, the switch of each bridge arm in the topology structure of the permanent magnet synchronous motor system shown in fig. 2 will change (i.e. switch state is switched), so as to ensure that the switching times of the whole power device are unchanged, increase the number of voltage vectors in the control period, and improve the steady-state performance of the permanent magnet synchronous motor system.
In some examples, t is as described in S3-1 1 and t2 May be manually specified for the user.
Step 107, obtaining a pulse control signal of the current control period according to step 105 and step 106, and transmitting the pulse control signal to the permanent magnet synchronous motor system to realize predictive control of the permanent magnet synchronous motor system.
Specifically, the permanent magnet synchronous motor system includes an inverter, preferably a two-level voltage source inverter, and a permanent magnet synchronous motor. And (3) inputting the dynamic pulse control signals corresponding to the current control period obtained in the step (105) and the step (106) into a two-level voltage source type inverter of a permanent magnet synchronous motor system, switching the power switch state of each bridge arm by the two-level voltage source type inverter according to the pulse control signals to generate dynamic control voltages, and inputting the dynamic control voltages into the permanent magnet synchronous motor, thereby realizing predictive control compensation of the permanent magnet synchronous motor.
In addition, in order to verify the practical application effect of the method: a simulation model of the control system is built by using a MATLAB/Simulink tool, parameters of a control motor are shown in a table 1, and sampling frequency in simulation is set to be 10kHz.
The current THD result of the permanent magnet synchronous motor running at 1000rpm and the load at the rated load in the current control method based on model prediction by adopting the traditional single vector is shown in figure 7, and the current THD result of the motor running at the working condition of 1000rpm and the load at the rated load by adopting the scheme is shown in figure 8. As can be seen from fig. 7 and fig. 8, the current THD in the conventional method is 17.60%, while the current THD in the method provided by the present invention is 13.33%, which effectively reduces the current THD in the conventional single vector model-based prediction current control method.
Under the current control method based on model prediction by adopting the traditional single vector and the current THD comparison result of the motor running at rated rotation speed and different load torques is shown in figure 10, under the same working condition, the current THD in the current control method based on model prediction by adopting the traditional single vector can be effectively reduced.
The reduction of the current THD shows the improvement of the steady-state performance, and the simulation result strongly proves that the current control method based on the model prediction provided by the invention can effectively reduce the current THD in the traditional current control method based on the model prediction, and improves the steady-state performance of the permanent magnet synchronous motor system.
The above steps of the various methods are divided, for clarity of description, and may be combined into one step or split into multiple steps when implemented, so long as they include the same logic relationship, and all the steps are within the scope of protection of this patent, and adding insignificant modifications or introducing insignificant designs to the algorithm or the process, but not changing the core designs of the algorithm and the process are within the scope of protection of this patent.
Second embodiment:
a second embodiment of the present invention provides a model prediction-based current control device 70, as shown in fig. 11, connected to the permanent magnet synchronous motor system 80, wherein the permanent magnet synchronous motor system 80 includes an inverter 81 and a permanent magnet synchronous motor 82.
The model predictive current control apparatus 70 includes:
the modeling unit 701 is configured to determine a current prediction model and a cost function according to a topology structure of the permanent magnet synchronous motor system and a preset reference current value;
a voltage vector calculation unit 702 for determining an optimal voltage vector for a current control period based on the current prediction model and the cost function;
a pulse control generating unit 703, configured to generate a pulse control signal after entering each control period after the permanent magnet synchronous motor system is started;
An output unit 704 for inputting the pulse control signal to the inverter 801;
an inverter 801, configured to switch power switch states of each bridge arm in the inverter 801 according to the pulse control signal to generate a dynamic control voltage, and input the dynamic control voltage to the permanent magnet synchronous motor 802;
wherein, after the permanent magnet synchronous motor system is started, generating the pulse control signal after entering each control period comprises:
determining an optimal voltage vector of a current control period based on the current prediction model and the cost function, and comparing the optimal voltage vector of a previous control period with the optimal voltage vector of the current control period;
under the condition that the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period acts on all time periods of the current control period;
under the condition that the optimal voltage vector of the previous control period is different from the optimal voltage vector of the current control period, determining a first time section and a second time section in the current control period according to the current change rate corresponding to the optimal voltage vector of the previous control period and the current change rate corresponding to the optimal voltage vector of the current control period; and applying the optimal voltage vector of the previous control period to the first time segment, and applying the optimal voltage vector of the current control period to the second time segment.
The specific embodiment can refer to a control block diagram formed by the cooperation between the model predictive current control device and the permanent magnet synchronous motor system shown in fig. 12. The inverter 801 is preferably a two-level voltage source type inverter.
Firstly, collecting current i currently input into a permanent magnet synchronous motor abc (k) Then converting the i by a coordinate system abc (k) Conversion into dq-axis coordinate system to obtain i dq (k) Then input into the modeling unit 701; at the same time, the electrical angular velocity omega of the permanent magnet synchronous motor in operation is also collected e (k) And is input into the modeling unit 701. The modeling unit 701 comprises a first modeling subunit for establishing a current prediction model and a second modeling subunit for establishing a cost function, as can be seen from fig. 12, the current prediction model of the first modeling subunit is according to i dq （k）、ω e (k) From V 0 ~V 7 Candidate voltage vectors composed of basic voltage vectors to form a current prediction model；
wherein ,id (k) Sampling current of d-axis of current control period, i q (k) Sampling current of q axis of the current control period; i.e d (k+1) is the d-axis sampling current of the next control period, i q (k) Sampling current of q axis for next control period; u (u) d (k) U is the voltage component of the d-axis of the candidate voltage vector q (k) Voltage components of the candidate voltage vector q-axis respectively; r, L, ψ f The resistance, the inductance and the flux linkage of the permanent magnet synchronous motor system are respectively; omega is the electrical angular velocity of the permanent magnet synchronous motor during operation; t is the control period of the motor system.
At the same time, refer to the electrical angular velocity omega e * And the electrical angular velocity omega of the currently collected permanent magnet electromagnetic synchronous motor e The error of the electric angular velocity sampling value is obtained through the controller PI, and then the reference value i of the q-axis current is obtained q * While the reference value for the q-axis current is set to i d * =0, the cost function of the second modeling subunit is according to idq (k+1), ω e * 、i d * Obtaining a cost function；/>
wherein ,for a preset d-axis current reference value, +.>K represents the current control period, and k+1 represents the predicted next control period, which is a preset reference value of q-axis current.
Then the voltage vector calculation unit 702 obtains the optimal voltage vector according to the current prediction model and the cost function. The pulse configuration unit 703 performs the following ∈ -> and />The comparison between them makes a mode decision when +.>When the first algorithm is performed, namely, the optimal voltage vector of the previous control period is prolonged to be in the whole current control period, when +.>A second algorithm is performed to extend the optimal voltage vector of the previous control period to a first time segment in the current control period, and the remaining time of the current control period is a second time segment in which the optimal voltage vector of the current control period is used, so as to obtain a pulse control signal (dq axis) output by the pulse configuration unit 703, and then coordinate system conversion of the dq axis and abc axis is performed to obtain a pulse control signal (abc axis) S a S b S c Then pulse control signal (abc axis) S a S b S c The two-level voltage source type inverter 801 inputted to the permanent magnet synchronous motor system 80 is further inputted to the permanent magnet synchronous motor 802.
By adopting the technical scheme, the current prediction model and the value parameter are determined according to the topological structure and the variable parameter of the permanent magnet synchronous motor system, so that the optimal voltage vector of each control period in a conventional state is obtained, and the optimal voltage vector corresponding to each control period is finely adjusted. By extending the optimal voltage vector in the previous control period to the current control period, two voltage vectors are arranged in the current control period, the switching times of the whole power device are ensured to be unchanged, and the number of the voltage vectors in the control period is increased on the premise of not increasing the switching loss of the power device, so that the voltage error between the voltage actually applied to the motor system and the expected voltage is effectively reduced, the problem of poor steady-state performance in the traditional current control method based on model prediction is solved, and the steady-state performance of the permanent magnet synchronous motor system is improved.
Other implementation details and operation manners of the model predictive current control apparatus 70 disclosed in the present application are the same as or similar to those of the model predictive current control method described above, and are not described herein.
Third embodiment:
a third embodiment of the present application provides a storage medium storing at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by a processor to implement a model prediction-based current control method as described above.
The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (10)
1. A model prediction-based current control method for a permanent magnet synchronous motor system, the method comprising:
determining a current prediction model and a cost function according to the topological structure of the permanent magnet synchronous motor system and a preset reference current value;
after the permanent magnet synchronous motor system is started, the following operations are performed after each control period is entered to generate pulse control signals:
determining an optimal voltage vector of a current control period based on the current prediction model and the cost function, and comparing the optimal voltage vector of a previous control period with the optimal voltage vector of the current control period;
Under the condition that the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period acts on all time periods of the current control period;
under the condition that the optimal voltage vector of the previous control period is different from the optimal voltage vector of the current control period, determining a first time section and a second time section in the current control period according to the current change rate corresponding to the optimal voltage vector of the previous control period and the current change rate corresponding to the optimal voltage vector of the current control period; and applying the optimal voltage vector of the previous control period to the first time segment, and applying the optimal voltage vector of the current control period to the second time segment.
2. The model prediction based current control method of claim 1, wherein the sum of the first time segment and the second time segment is equal to the entire time period of the current control period, and the first time segment is located before the second time segment in time series.
3. The model prediction-based current control method according to claim 1, wherein in the case where the current control period is an initial control period after the permanent magnet synchronous motor is started, an optimal voltage vector of the previous control period is assigned as an optimal voltage vector of the current control period.
4. The model prediction based current control method of claim 1, wherein determining an optimal voltage vector for a current control period based on the current prediction model and the cost function comprises:
based on the current prediction model, obtaining predicted currents corresponding to each candidate voltage vector in the current control period;
taking the predicted current with the smallest value obtained after substituting the cost function as a target predicted current;
and taking the candidate voltage vector corresponding to the target predicted current as an optimal voltage vector of the current control period.
5. The model prediction-based current control method according to claim 2, wherein determining the first time segment and the second time segment in the current control period according to the current change rate corresponding to the optimal voltage vector of the previous control period and the current change rate corresponding to the optimal voltage vector of the current control period comprises:
calculating the current change rate corresponding to the optimal voltage vector in the previous control period and the current change rate corresponding to the optimal voltage vector in the current control period;
and according to the current change rate of the current control period and the current conversion rate of the previous control period, and combining a current dead beat prediction control principle, calculating the first time segment and the second time segment.
6. The model prediction-based current control method according to claim 1, wherein the determining a current prediction model and a cost function according to a topology of the permanent magnet synchronous motor system and a preset reference current value comprises:
according to the topological structure of the permanent magnet synchronous motor system, a voltage equation of a d axis and a q axis of the permanent magnet synchronous motor system is obtained through a discretization motor mathematical model;
performing Euler discretization on the voltage equation to obtain a current prediction model;
and constructing a cost function according to the current prediction model.
7. The model prediction based current control method according to claim 6, wherein the voltage equations of the d-axis and q-axis of the permanent magnet synchronous motor system are,
；
wherein ,id For d-axis current, i q Q-axis currents, respectively; u (u) d As the d-axis voltage component, u q Is the q-axis voltage component; r, L, ψ f The resistance, the inductance and the flux linkage of the permanent magnet synchronous motor system are respectively; omega is the electrical angular speed of the permanent magnet synchronous motor system;
the current prediction model is that,
；
wherein ,id (k) Sampling current of d-axis of current control period, i q (k) Sampling current of q axis of the current control period; i.e d (k+1) is the d-axis sampling current of the next control period, i q (k) Sampling current of q axis for next control period; u (u) d (k) U is the voltage component of the d-axis of the candidate voltage vector q (k) Voltage components of the candidate voltage vector q-axis respectively; r, L, ψ f The resistance, the inductance and the flux linkage of the permanent magnet synchronous motor system are respectively; omega is the electrical angular velocity of the motor during operation; t is the control period of the motor system;
the cost function is a function of the value of,
；
wherein ,for a preset d-axis current reference value, +.>Is a preset reference value of q-axis current.
8. The model prediction-based current control method according to claim 5, wherein the calculating the current change rate corresponding to the optimal voltage vector in the previous control period and the current change rate corresponding to the optimal voltage vector in the current control period includes:
the current change rates of the d-axis and the q-axis of the optimal voltage vector in the previous control period are respectively and />The current change rates of the d-axis and q-axis of the optimum voltage vector in the present control period are +.> and />The voltage components of the d-axis and q-axis of the optimum voltage vector in the previous control period are +.> and />The voltage components of the d-axis and q-axis of the optimal voltage vector in the current control period are +. > and />Specifically, the method comprises the steps of,
；
wherein ,id For d-axis current, i q Q-axis currents, R, L, ψ respectively f The resistance, the inductance and the flux linkage of the permanent magnet synchronous motor system are respectively;
according to the current change rate of the current control period and the current conversion rate of the previous control period and in combination with a current dead beat prediction control principle, the first time segment and the second time segment are calculated, and the method comprises the following steps:
according to the current change rate of the current control period and the current conversion rate of the previous control period combined with the current error beat prediction control principle, the d-axis and q-axis currents at the end of the current control period are obtained,
；
wherein ,t1 and t2 Respectively representing the acting time of the optimal voltage vector in the last control period and the optimal voltage vector in the current control period and meeting t 1 +t 2 =T，For a preset d-axis current reference value, +.>A preset q-axis current reference value;
solving to obtain t 1 and t2 ，
；
wherein ,xd and xq The polynomial of (2) is given by,
；
according to t 1 and t2 And obtaining the first time segment and the second time segment.
9. A model prediction-based current control device, connected to a permanent magnet synchronous motor system, the permanent magnet synchronous motor system comprising an inverter and a permanent magnet synchronous motor, the control device comprising:
The modeling unit is used for determining a current prediction model and a cost function according to the topological structure of the permanent magnet synchronous motor system and a preset reference current value;
a voltage vector calculation unit for determining an optimal voltage vector for a current control period based on the current prediction model and the cost function;
the pulse control generation unit is used for generating pulse control signals after entering each control period after the permanent magnet synchronous motor system is started:
an output unit for inputting the pulse control signal to the inverter;
the inverter is used for switching the power switch states of all bridge arms in the inverter according to the pulse control signals to generate dynamic control voltages, and inputting the dynamic control voltages into the permanent magnet synchronous motor;
wherein, after the permanent magnet synchronous motor system is started, generating the pulse control signal after entering each control period comprises:
determining an optimal voltage vector of a current control period based on the current prediction model and the cost function, and comparing the optimal voltage vector of a previous control period with the optimal voltage vector of the current control period;
under the condition that the optimal voltage vector of the previous control period is the same as the optimal voltage vector of the current control period, the optimal voltage vector of the previous control period acts on all time periods of the current control period;
Under the condition that the optimal voltage vector of the previous control period is different from the optimal voltage vector of the current control period, determining a first time section and a second time section in the current control period according to the current change rate corresponding to the optimal voltage vector of the previous control period and the current change rate corresponding to the optimal voltage vector of the current control period; and applying the optimal voltage vector of the previous control period to the first time segment, and applying the optimal voltage vector of the current control period to the second time segment.
10. A storage medium storing at least one instruction, at least one program, code set, or instruction set, the at least one instruction, the at least one program, the code set, or instruction set being loaded and executed by a processor to implement the model predictive based current control method of any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311221212.8A CN116961512B (en) | 2023-09-21 | 2023-09-21 | Model prediction-based current control method, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311221212.8A CN116961512B (en) | 2023-09-21 | 2023-09-21 | Model prediction-based current control method, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116961512A true CN116961512A (en) | 2023-10-27 |
| CN116961512B CN116961512B (en) | 2023-12-15 |
Family
ID=88462438
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311221212.8A Active CN116961512B (en) | 2023-09-21 | 2023-09-21 | Model prediction-based current control method, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116961512B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117240118A (en) * | 2023-11-09 | 2023-12-15 | 国网浙江省电力有限公司丽水供电公司 | Inverter control method and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104967377A (en) * | 2015-07-07 | 2015-10-07 | 河南师范大学 | Doubly-fed wind generator rotor linkage constant-frequency model prediction control method |
| CN109039167A (en) * | 2018-08-20 | 2018-12-18 | 中南大学 | A kind of internal permanent magnet synchronous motor control method and system |
| CN109347390A (en) * | 2018-11-28 | 2019-02-15 | 大连海事大学 | Vector based on model prediction magnetic linkage control is double to choose method for suppressing torque ripple |
| CN111555684A (en) * | 2020-04-03 | 2020-08-18 | 浙江工业大学 | Variable-switching-point multi-step model prediction torque control method for weight-factor-free permanent magnet synchronous motor finite set |
| CN116388634A (en) * | 2022-11-15 | 2023-07-04 | 河海大学 | Dual-vector model prediction current control method for sinusoidal electro-magnetic doubly-salient motor |
-
2023
- 2023-09-21 CN CN202311221212.8A patent/CN116961512B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104967377A (en) * | 2015-07-07 | 2015-10-07 | 河南师范大学 | Doubly-fed wind generator rotor linkage constant-frequency model prediction control method |
| CN109039167A (en) * | 2018-08-20 | 2018-12-18 | 中南大学 | A kind of internal permanent magnet synchronous motor control method and system |
| CN109347390A (en) * | 2018-11-28 | 2019-02-15 | 大连海事大学 | Vector based on model prediction magnetic linkage control is double to choose method for suppressing torque ripple |
| CN111555684A (en) * | 2020-04-03 | 2020-08-18 | 浙江工业大学 | Variable-switching-point multi-step model prediction torque control method for weight-factor-free permanent magnet synchronous motor finite set |
| CN116388634A (en) * | 2022-11-15 | 2023-07-04 | 河海大学 | Dual-vector model prediction current control method for sinusoidal electro-magnetic doubly-salient motor |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117240118A (en) * | 2023-11-09 | 2023-12-15 | 国网浙江省电力有限公司丽水供电公司 | Inverter control method and electronic equipment |
| CN117240118B (en) * | 2023-11-09 | 2024-02-23 | 国网浙江省电力有限公司丽水供电公司 | Inverter control method and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116961512B (en) | 2023-12-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Overview of advanced control strategies for electric machines | |
| CA2889100C (en) | Model predictive control with reference tracking | |
| CN109660170B (en) | High-reliability current prediction control method and system for permanent magnet synchronous motor | |
| CN209844868U (en) | Dead beat current prediction control system of permanent magnet synchronous motor | |
| CN116961512B (en) | Model prediction-based current control method, device and storage medium | |
| CN108923468B (en) | Seamless smooth switching method and system for virtual synchronous motor | |
| He et al. | Linear active disturbance rejection control for three-phase voltage-source PWM rectifier | |
| CN109728756B (en) | Double-reference-voltage single-vector open winding permanent magnet motor prediction control method and equipment | |
| CN110299882B (en) | Three-vector model prediction control method for hybrid power supply type open winding permanent magnet synchronous motor | |
| CN112398401B (en) | Low switching frequency multi-step model prediction control method based on parameter mismatch | |
| JP4775168B2 (en) | Control device for three-phase rotating machine | |
| CN113708688B (en) | Permanent magnet motor vector-reduction model predictive control method | |
| CN114172412A (en) | Non-parameter model prediction current control method for double three-phase permanent magnet motor | |
| Al-Kaf et al. | Hybrid current controller for permanent-magnet synchronous motors using robust switching techniques | |
| Pati et al. | Comparative performance analysis of fuzzy PI, PD and PID controllers used in a scalar controlled induction motor drive | |
| CN112751513B (en) | Motor control method and device, motor, storage medium and processor | |
| Korkmaz | Performance improvement of induction motor drives with model-based predictive torque control | |
| CN110165954B (en) | Model prediction control method for machine side converter of double-fed wind power generation system | |
| Swami et al. | Reducing dependency on rotor time constant in a rotor flux oriented vector controlled induction motor drive based on its static model | |
| CN111934585A (en) | Permanent magnet synchronous motor servo control system based on fuzzy PI control | |
| CN114301361B (en) | Control method of electrolytic capacitor-free permanent magnet synchronous motor driving system based on bus current control | |
| CN113949320B (en) | Induction motor prediction control driving method and system | |
| CN115833690A (en) | Six-phase permanent magnet synchronous motor parameter-free model prediction current control system and method | |
| CN113809967A (en) | Permanent magnet synchronous motor modulation model prediction control method based on matrix converter | |
| González et al. | Predictive current control with kalman filter observer for a five-phase induction machine operating at fixed switching frequency |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |