Methods, equipment, and media for predictive direct torque control of dual three-phase permanent magnet synchronous motors

Abstract

This application discloses a model predictive direct torque control method, device, and medium for a dual three-phase permanent magnet synchronous motor, relating to the field of motor control. The method includes: determining a control set based on a set of virtual voltage vectors according to a preset screening rule; the preset screening rule is: selecting four virtual voltage vectors from 12 virtual voltage vectors based on the stator flux linkage position and torque deviation in the dual three-phase permanent magnet synchronous motor; determining four candidate groups based on the four virtual voltage vectors, each candidate group including three vectors, including at least one vector for increasing stator flux linkage and one vector for decreasing stator flux linkage; determining the duty cycle of each vector in the candidate group; determining the optimized switching sequence corresponding to the candidate group; and applying the candidate group corresponding to the minimum cost function to the next control cycle for control. This invention can achieve precise control of torque and stator flux linkage while reducing computational burden.

Description

Technical Field

[0001] This application relates to the field of motor control, and in particular to a method, device and medium for predictive direct torque control of a dual three-phase permanent magnet synchronous motor. Background Technology

[0002] In switch-meter-based direct torque control (DTC), a suitable voltage vector is selected by designing a switch-meter. However, dual three-phase permanent magnet synchronous motors (PMSMs) have a large number of space voltage vectors, meaning that the voltage vector selected based on the switch-meter is not necessarily optimal. Model predictive direct torque control (MMDC) can select the optimal voltage vector for the next control cycle through rolling optimization. However, traditional MMDC strategies for dual three-phase PMSMs face three challenges: 1. Similar to switch-meter-based DTC, MMDC for dual three-phase PMSMs also needs to consider current harmonic suppression in the xy subplane. 2. The number of space voltage vectors in a multiphase motor increases exponentially with the number of phases. A dual three-phase PMSM driven by a two-level voltage source inverter can generate 2 6 = There are 64 voltage vectors, and evaluating all voltage vectors using a cost function would impose a huge computational burden. 3. Due to the discrete nature of voltage vectors, the modulation region is discontinuous. Therefore, there will still be an error between the selected optimal vector and the reference vector, resulting in additional torque and stator flux pulsation. Summary of the Invention

[0003] The purpose of this application is to provide a model predictive direct torque control method, device, and medium for dual three-phase permanent magnet synchronous motors, which can achieve precise control of torque and stator flux linkage while reducing computational burden.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] In a first aspect, this application provides a model predictive direct torque control method for a dual three-phase permanent magnet synchronous motor, comprising:

[0006] Based on the voltage vector distribution of the dual three-phase permanent magnet synchronous motor in the fundamental α-β subplane and the harmonic xy subplane, voltage vector synthesis is performed to obtain a virtual voltage vector set;

[0007] Based on the set of virtual voltage vectors, a control set is determined according to a preset screening rule. The preset screening rule is as follows: based on the stator flux linkage position and torque deviation in the dual three-phase permanent magnet synchronous motor, four virtual voltage vectors are selected from the twelve virtual voltage vectors. Then, four candidate groups are determined based on the four selected virtual voltage vectors. Each candidate group includes three vectors, and at least one vector for increasing the stator flux linkage and one vector for decreasing the stator flux linkage.

[0008] Based on the deadbeat control principle of torque and stator flux linkage, the duty cycle of each vector in the candidate group is determined;

[0009] For any candidate group, based on the principles of symmetry, minimum number of switching operations, and duty cycles of the three vectors in the candidate group, the three vectors are sorted to determine the optimal switching sequence corresponding to the candidate group.

[0010] Based on the optimized switching sequence corresponding to each candidate group, the value of the cost function corresponding to each candidate group is calculated; the cost function is used to characterize the torque and stator flux performance of any candidate group during the control cycle.

[0011] The candidate group corresponding to the minimum cost function value is marked as the preferred control quantity and applied to the next control cycle.

[0012] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the model predictive direct torque control method for a dual three-phase permanent magnet synchronous motor as described above.

[0013] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the model predictive direct torque control method for dual three-phase permanent magnet synchronous motors described above.

[0014] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0015] This application provides a model predictive direct torque control method, device, and medium for a dual three-phase permanent magnet synchronous motor. It uses virtual voltage vectors to suppress current harmonics. Based on the set of virtual voltage vectors, a control set is determined according to a preset screening rule. The preset screening rule is as follows: based on the stator flux position and torque deviation in the dual three-phase permanent magnet synchronous motor, four virtual voltage vectors are selected from 12 virtual voltage vectors. Then, to optimize the stator flux, each candidate group must include one virtual voltage vector for increasing the stator flux and one virtual voltage vector for decreasing the stator flux. Based on this principle, four candidate groups are determined from the obtained four virtual voltage vectors. Due to the screening, the number of subsequent evaluations in a control cycle is reduced, thereby reducing the computational burden. Simultaneously, since each candidate group includes three vectors, the modulation region is expanded. Based on the deadbeat control principle of torque and stator flux, the duty cycle of each voltage vector in the control set is determined, achieving precise control of torque and stator flux. Finally, the average torque and stator flux pulsation of each candidate group in the control cycle are evaluated through a cost function, and the candidate group corresponding to the minimum value of the cost function is applied to the next control cycle. In summary, this application achieves precise control of torque and stator flux linkage, reduces torque and stator flux linkage pulsation in dual three-phase permanent magnet synchronous motor drive systems, and avoids a huge computational burden. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an application environment diagram of a model predictive direct torque control method for a dual three-phase permanent magnet synchronous motor according to an embodiment of this application;

[0018] Figure 2 A flowchart illustrating a model predictive direct torque control method for a dual three-phase permanent magnet synchronous motor provided in an embodiment of this application;

[0019] Figure 3 This is a schematic diagram of the spatial voltage vector distribution of a dual three-phase permanent magnet synchronous motor in the fundamental α-β subplane.

[0020] Figure 4 This is a schematic diagram of the spatial voltage vector distribution of a dual three-phase permanent magnet synchronous motor in the harmonic xy subplane.

[0021] Figure 5 This is a schematic diagram showing the distribution of the virtual voltage vector in the α-β subplane;

[0022] Figure 6 This is a schematic diagram illustrating the process of optimizing torque and stator flux using a duty cycle modulation strategy.

[0023] Figure 7 shows a schematic diagram of the switching sequence of three vectors V0, VV3, and VV4; where Figure 7(a) is a schematic diagram of the traditional switching sequence of three vectors V0, VV3, and VV4, and Figure 7(b) is a schematic diagram of the optimized switching sequence of three vectors V0, VV3, and VV4.

[0024] Figure 8 A schematic diagram of the torque and stator flux optimization curves of the three vectors V0, VV3, and VV4 during the control cycle;

[0025] Figure 9 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] To make the objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] The model predictive direct torque control method for dual three-phase permanent magnet synchronous motors provided in this application can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send relevant data of the dual three-phase permanent magnet synchronous motor to server 104. Server 104 receives the relevant data of the dual three-phase permanent magnet synchronous motor, and then performs voltage vector synthesis, determination of the control set, duty cycle calculation, cost function calculation and comparison to finally obtain the optimal control quantity for application in the next control cycle. Server 104 can feed back the obtained optimal control quantity to terminal 102. Furthermore, in some embodiments, the model predictive direct torque control method for the dual three-phase permanent magnet synchronous motor can also be implemented independently by server 104 or terminal 102. For example, terminal 102 can directly process the relevant data of the dual three-phase permanent magnet synchronous motor, or server 104 can obtain the relevant data of the dual three-phase permanent magnet synchronous motor from the data storage system and process it accordingly.

[0029] The terminal 102 can be, but is not limited to, various desktop computers and IoT devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0030] In one exemplary embodiment, such as Figure 2 As shown, a model predictive direct torque control method for a dual three-phase permanent magnet synchronous motor is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 206. Wherein:

[0031] Step 201: Based on the voltage vector distribution of the dual three-phase permanent magnet synchronous motor in the fundamental α-β subplane and the harmonic xy subplane, voltage vector synthesis is performed to obtain a virtual voltage vector set.

[0032] like Figure 3 and Figure 4The figures show the spatial voltage vector distribution of a dual three-phase permanent magnet synchronous motor in the fundamental α-β subplane and the harmonic xy subplane, respectively. The motor is powered by two three-phase two-level voltage source inverters. The amplitudes of different vector groups in the two subplanes are shown in Table 1, all of which are fixed values. Since the harmonic xy subplane is only related to motor losses, to maximize the utilization of the DC bus voltage and suppress current harmonics in the xy subplane, voltage vectors L3 and L4 with larger amplitudes in the fundamental α-β subplane are selected to synthesize a virtual voltage vector. The voltage vectors L3 and L4 are in the same direction in the fundamental α-β subplane and opposite in the harmonic xy subplane. Therefore, by reasonably allocating the action time of the L3 and L4 voltage vectors within one control cycle, the amplitude of the virtual vector synthesized from the L3 and L4 vectors in the harmonic xy subplane can be reduced to zero, thereby suppressing current harmonics. The action time of the L3 and L4 vectors can be calculated using the following formula:

[0033]

[0034] Right now:

[0035]

[0036] Among them, L 3_xy and L 4_xy T3 and T4 represent the amplitudes of voltage vectors L3 and L4 in the harmonic xy subplane, respectively; T3 and T4 represent the duration of action of voltage vectors L3 and L4 within the control period, respectively; T s This is the control cycle. The distribution of all obtained virtual voltage vectors in the α-β subplane is as follows: Figure 5 As shown, the set of virtual voltage vectors includes 12 virtual voltage vectors.

[0037] Table 1

[0038]

[0039] Step 202: Based on the set of virtual voltage vectors, determine the control set according to a preset screening rule. The preset screening rule is as follows: select 4 virtual voltage vectors from 12 virtual voltage vectors based on the stator flux position and torque deviation in the dual three-phase permanent magnet synchronous motor, and then determine 4 candidate groups based on the 4 selected virtual voltage vectors. Each candidate group includes three vectors, and includes at least one vector for increasing stator flux and one vector for decreasing stator flux.

[0040] In another exemplary embodiment of this application, in order to achieve precise control of the stator flux linkage and determine the three vectors in each candidate group, step 202 is replaced by steps 301 to 304:

[0041] Step 301: The stator current model of the dual three-phase permanent magnet synchronous motor is discretized using the first-order Euler formula to determine the stator flux linkage and torque at time k+1. The corresponding function formulas are as follows:

[0042]

[0043] in, This represents the stator flux linkage at time k+1. and These represent the stator flux linkages on the d-axis and q-axis at time k+1, respectively. and L represents the stator current on the d-axis and q-axis at time k+1, respectively; d and L q These are the inductances on the d-axis and q-axis, respectively; ψ represents the torque at time k+1; f n is the amplitude of the magnetic flux linkage of the permanent magnet; p It is an extreme logarithm.

[0044] Step 302: Based on the stator flux linkage at time k+1, calculate the position of the stator flux linkage at time k+1. The corresponding function formula is as follows:

[0045]

[0046] in, This indicates the position of the stator flux linkage at time k+1. and ω represents the rotor electrical angle at time k and time k+1, respectively; e It represents the electric angular velocity.

[0047] Step 303: Calculate the torque deviation based on the preset torque value and the torque at time k+1. The corresponding function formula is: Where, ΔT e The torque deviation can be taken as ΔT. e ≥0 and ΔT e <0.

[0048] Step 304: Determine the sector based on the stator flux linkage position at time k+1 (e.g., if it is in the...). Figure 5 The system selects sector I, sector II, or sector III from the virtual voltage vector set and then filters the virtual voltage vector set according to the sector and the torque deviation, based on a preset filtering rule, to determine the control set.

[0049] For example, when in sector I and ΔT eWhen ≥0, it means that the torque at time k+1 is less than the preset torque value, so the virtual vectors VV2, VV3, VV4, and VV5 that can increase the torque are selected; when in sector I and ΔT e When <0, the torque at time k+1 is greater than the preset torque value, thus the virtual vectors VV8, VV9, and VV can reduce the torque. 10 and VV 11 Selected. Based on Figure 5 The different colors used in the markers indicate that this method reduces the number of selectable virtual vectors from 12 to 4.

[0050] Furthermore, to achieve precise control of the stator flux linkage, the three vectors in the candidate group need to include one vector that can increase the stator flux linkage and one vector that can decrease it. The vector that increases the stator flux linkage generally has an angle of less than 90° with the stator flux linkage, while the voltage vector that decreases the stator flux linkage generally has an angle of greater than 90° with the stator flux linkage.

[0051] Taking VV2, VV3, VV4, and VV5 as examples, the influence of these four vectors on the stator flux linkage can be divided into two groups (D1 and D2). Group D1 includes VV2 and VV3, which can increase the stator flux linkage; group D2 includes VV4 and VV5, which can decrease the stator flux linkage. Finally, based on the vector selection requirements, four candidate groups can be determined: VV2, VV4, and V0; VV2, VV5, and V0; VV3, VV4, and V0; VV3, VV5, and V0. When the stator flux linkage is located in other sectors, the control set can also be determined using the above method, and the specific control sets are presented in Table 2.

[0052] Table 2

[0053]

[0054]

[0055] Step 203: Based on the deadbeat control principle of torque and stator flux linkage, determine the duty cycle of each vector in the candidate group.

[0056] In another exemplary embodiment of this application, in order to achieve precise control of torque and stator flux linkage, the duration of action of each vector is adjusted, and step 203 above can be replaced by steps 401-402 as follows:

[0057] Step 401, calculate the torque and stator flux linkage slope at time k+1 for different voltage vectors, which can be expressed as follows:

[0058]

[0059] in, and Represents the stator voltage on the d-axis and q-axis at time k+1, respectively, where p denotes the differential sign, and R... s This indicates the stator resistance.

[0060] Step 402, based on the deadbeat principle of torque and stator flux linkage, that is, the torque and stator flux linkage at time k+2 are equal to their reference values. Therefore, the process of optimizing torque and stator flux linkage using a duty cycle modulation strategy is as follows: Figure 6 As shown. The duty cycle of each vector can be determined according to the following constraints:

[0061]

[0062] Further derivation yields the following formula for calculating the duty cycle of the three vectors within the candidate group:

[0063]

[0064] d0 = 1 - d i -d j .

[0065]

[0066] Among them, d0, d i and d j These are three vectors V0, VV, and VV within a candidate group, respectively. i and VV j Duty cycle, T s Indicates the control period. and These are three vectors V0, VV, and VV respectively. i and VV j The torque slope; and These are three vectors V0, VV, and VV respectively. i and VV j The stator flux linkage slope, Indicates the preset torque value. This indicates the preset sub-magnetic linkage.

[0067] Step 204: For any candidate group, based on the principles of symmetry, minimum number of switching operations, and duty cycles of the three vectors in the candidate group, sort the three vectors to determine the optimal switching sequence corresponding to the candidate group.

[0068] To facilitate the implementation of the digital controller, this application redesigned the switching sequence and obtained optimized torque and stator flux linkage curves within the control cycle through the newly designed switching sequence. Specifically, after calculating the duty cycle of each vector, each vector is arranged according to a certain principle: that is, the action sequence and time of each voltage vector, where the time is related to the duty cycle mentioned above.

[0069] For example, first determine the switching sequence of the three vectors in each candidate group within the control cycle, using V0, VV3 (derived from V 20 and V 54 (synthetic) and VV4 (from V 22 and V 50 Taking synthesis as an example. Traditional switching sequence designs are usually asymmetrical, but asymmetrical switching sequences increase current harmonics and are difficult to implement in digital controllers. The traditional switching sequence is shown in Figure 7(a). The newly designed optimized switching sequence must meet the symmetry principle and should minimize the number of switching operations of the voltage source inverter in one control cycle. Therefore, the optimized switching sequence is shown in Figure 7(b).

[0070] Step 205: Based on the optimized switching sequence corresponding to each candidate group, calculate the value of the cost function corresponding to each candidate group; the cost function is used to characterize the torque and stator flux linkage performance of any candidate group during the control cycle. Specifically, it includes the following steps:

[0071] (1) Based on the optimized switching sequence, determine the torque optimization curve and stator flux linkage optimization curve of the corresponding candidate group. This corresponds to the above-mentioned V0, VV3 (from V... 20 and V 54 (synthetic) and VV4 (from V 22 and V 50 (Synthetic) example, the obtained torque and stator flux optimization curves within the control cycle are as follows: Figure 8 As shown.

[0072] (2) Based on the torque optimization curve and stator flux optimization curve of the candidate group, the average torque and stator flux pulsation within a control cycle are determined by the cost function, and the corresponding cost function value is obtained.

[0073] The function formulas corresponding to the average torque and stator flux pulsation within one control cycle are as follows:

[0074]

[0075] Among them, T ei ψ is the current peak torque ripple determined based on the torque optimization curve. siThe current stator flux pulsation peak value, determined based on the stator flux optimization curve, can be specifically calculated based on the vector torque, stator flux slope, and duration of action. Indicates the preset torque value. Indicates the preset sub-magnetic flux; T e_ripple ψ represents the average torque ripple. s_ripple This represents the average stator flux linkage pulsation, n = (1, 2, ..., 11).

[0076] The cost function is: g = T e_ripple +λψ s_ripple .

[0077] Where λ is the weighting coefficient and g is the value of the cost function.

[0078] Step 206: The candidate group corresponding to the minimum cost function value is marked as the preferred control quantity for application in the next control cycle. As shown in Table 2 above, in each application, the candidate group with the minimum cost function value is selected from the four candidate groups as the optimal vector group for application in the next control cycle.

[0079] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 9 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a model predictive direct torque control method for a dual three-phase permanent magnet synchronous motor.

[0080] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0081] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0082] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0083] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0084] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0085] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0086] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0087] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A model predictive direct torque control method for a dual three-phase permanent magnet synchronous motor, characterized in that, The model-predicted direct torque control method for dual three-phase permanent magnet synchronous motors includes: Based on the voltage vector distribution of the dual three-phase permanent magnet synchronous motor in the fundamental α-β subplane and the harmonic xy subplane, voltage vector synthesis is performed to obtain a virtual voltage vector set; Based on the set of virtual voltage vectors, a control set is determined according to a preset screening rule. The preset screening rule is as follows: based on the stator flux linkage position and torque deviation in the dual three-phase permanent magnet synchronous motor, four virtual voltage vectors are selected from the twelve virtual voltage vectors. Then, four candidate groups are determined based on the four selected virtual voltage vectors. Each candidate group includes three vectors, and at least one vector for increasing the stator flux linkage and one vector for decreasing the stator flux linkage. Based on the deadbeat control principle of torque and stator flux linkage, the duty cycle of each vector in the candidate group is determined; For any candidate group, based on the principles of symmetry, minimum number of switching operations, and duty cycles of the three vectors in the candidate group, the three vectors are sorted to determine the optimal switching sequence corresponding to the candidate group. Based on the optimized switching sequence corresponding to each candidate group, the value of the cost function corresponding to each candidate group is calculated; the cost function is used to characterize the torque and stator flux performance of any candidate group during the control cycle. Based on the optimized switching sequence, the torque optimization curve and stator flux optimization curve of the corresponding candidate group are determined; based on the torque optimization curve and stator flux optimization curve of the candidate group, the average torque and stator flux pulsation within a control cycle are determined by a cost function, and the value of the corresponding cost function is obtained; The functional formulas corresponding to the average torque and stator flux pulsation within one control cycle are: Among them, T en ψ is the current peak torque ripple determined based on the torque optimization curve. sn The current stator flux pulsation peak value is determined based on the stator flux optimization curve. Indicates the preset torque value. Indicates the preset sub-magnetic flux; T e_ripple ψ represents the average torque ripple. s_ripple This represents the average stator flux pulsation, n = (1, 2, ..., 11); The cost function is: g = T e_ripple +λψ s_ripple Where λ is the weighting coefficient and g is the value of the cost function; The candidate group corresponding to the minimum value of the cost function is marked as the preferred control quantity and applied to the next control cycle.

2. The direct torque control method for a dual three-phase permanent magnet synchronous motor model prediction according to claim 1, characterized in that, Based on the virtual voltage vector set, a control set is determined according to a preset filtering rule, specifically including: The stator current model of the dual three-phase permanent magnet synchronous motor is discretized using the first-order Euler formula to determine the stator flux linkage and torque at time k+1. Calculate the position of the stator flux linkage at time k+1 based on the stator flux linkage at time k+1. Calculate the torque deviation based on the preset torque value and the torque at time k+1; The sector is determined based on the stator flux linkage position at time k+1. Then, according to the sector and the torque deviation, the virtual voltage vector set is filtered according to a preset filtering rule to determine the control set.

3. The direct torque control method for a dual three-phase permanent magnet synchronous motor model prediction according to claim 2, characterized in that, The function formulas corresponding to the stator flux linkage and torque at time k+1 are as follows: in, This represents the stator flux linkage at time k+1. and These represent the stator flux linkages on the d-axis and q-axis at time k+1, respectively. and L represents the stator current on the d-axis and q-axis at time k+1, respectively; d and L q These are the inductances on the d-axis and q-axis, respectively; ψ represents the torque at time k+1; f n is the amplitude of the magnetic flux linkage of the permanent magnet; p It is the extreme logarithm; The function formula corresponding to the stator flux linkage position at time k+1 is as follows: in, This indicates the position of the stator flux linkage at time k+1. and ω represents the rotor electrical angle at time k and time k+1, respectively; e It represents the electric angular velocity.

4. The model predictive direct torque control method for a dual three-phase permanent magnet synchronous motor according to claim 3, characterized in that, The formula for calculating the duty cycle of the three vectors within a candidate group in the control set is as follows: d0=1-d i -d j ; Among them, d0, d i and d j These are three vectors V0, VV, and VV within a candidate group, respectively. i and VV j Duty cycle, T s Indicates the control period. and These are three vectors V0, VV, and VV respectively. i and VV j The torque slope; and These are three vectors V0, VV, and VV respectively. i and VV j The stator flux linkage slope, Indicates the preset torque value. This indicates the preset sub-magnetic linkage.

5. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the model predictive direct torque control method for a dual three-phase permanent magnet synchronous motor as described in any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the direct torque control method for predicting a model of a dual three-phase permanent magnet synchronous motor as described in any one of claims 1-4.

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