Motor single vector model-free predictive control method and system based on SOGI filtering

Abstract

The present invention discloses a method and system for single-vector model-free predictive control of a motor with SOGI filtering. The method of the present invention comprises performing Clark transformation according to the three-phase current sampling signal of the motor stator, converting it into a stationary coordinate system, and then using a SOGI filter to filter out high-frequency noise signals; using the current signal after filtering out the noise signal for the update mechanism of the current differential table in the differential method model-free predictive current control to obtain the voltage table and current differential table of eight basic voltage vectors; using the voltage table and current differential table of the eight basic voltage vectors to respectively predict the stator current of the shaft and the shaft at the k+1 moment, and selecting the optimal voltage vector according to a preset cost equation to generate a single vector control signal to act on a voltage-type inverter. The present invention aims to reduce the current stagnation phenomenon of the differential method model-free predictive current control method, improve the current accuracy, and enhance the anti-interference performance of the motor control system to noise.

Description

Technical Field

[0001] The present invention relates to the technical field of synchronous motors, and in particular to a single-vector model-free predictive control method and system for a motor using SOGI filtering. Background Art

[0002] With the advancement of digital technology, model predictive control (MPC) has emerged as a control strategy for advanced motors due to its advantages of multi-objective, multi-variable control, simple implementation, and fast response. The application of MPC in PMSM control strategy can be divided into two categories according to the different control actions: finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). It can also be classified into predictive current control (MPCC) and predictive torque control (MPTC) according to the control behavior. However, the model predictive control method is overly dependent on the parameter accuracy of the motor model. In actual operation, changes in model parameters will lead to model mismatch, affect the selection of the optimal control vector, and cause system performance to deteriorate. In order to deal with this problem, many scholars use online observers to compensate for parameter errors, but online observers will increase the complexity of the control system and increase the computational burden.

[0003] Model-free predictive control (MFPCC) has the advantages of simple principle and small amount of calculation, and has better robustness than model predictive control. It includes recursive minimum (RLS) estimation method, super local model, and method based on current differential table. The model-free predictive current control method based on current differential method is simple and has a small system calculation burden. The current differential value is obtained from the current sampling information of each control cycle and stored in the corresponding current differential table. The control current is predicted using the current differential table, and the optimal control voltage vector for each control cycle is selected in combination with the cost equation, and then acts on the inverter to finally control the permanent magnet synchronous motor. However, in the traditional model-free predictive control based on current differential method, only one differential value in the current differential table corresponding to the selected voltage vector can be updated in each control cycle. When a voltage vector is not used in multiple control cycles, there will be an error between its corresponding current differential value and the value in the differential table. As a result, the control effect of model-free predictive current control is deteriorated.

[0004] The noise signal in the sampled current is mainly generated by factors such as the power supply, temperature and external electromagnetic interference in the circuit. Since the sampling frequency of the model-free predictive current control is high, inaccurate sampled current signals will affect the selection of the optimal control voltage vector in each sampling cycle, thereby having a significant impact on the control effect. The traditional method is to use a low-pass filter to filter out the noise in the current signal. The output of the low-pass filter is inversely proportional to the frequency, that is, it has an attenuation effect on high-frequency signals and can easily filter out the high-frequency part of the sampled signal. However, the output signal will produce a phase shift as the frequency of the input signal increases, causing the output signal to lag behind the input signal. Summary of the invention

[0005] Technical problem to be solved by the present invention: In view of the above-mentioned problems in the prior art, a single-vector model-free predictive control method and system for a motor with SOGI filtering are provided. The present invention aims to reduce the current stagnation phenomenon of the differential model-free predictive current control method, improve the current accuracy, and enhance the anti-interference performance of the motor control system against noise.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A motor single vector model-free predictive control method with SOGI filtering comprises the following steps:

[0008] S101, performing Clark transformation on the three-phase current sampling signal of the motor stator, converting it into a stationary coordinate system, and then using a SOGI filter to filter out high-frequency noise signals;

[0009] S102, using the current signal after filtering out the noise signal for the updating mechanism of the current differential table in the differential method model-free predictive current control to obtain the voltage table and current differential table of eight basic voltage vectors;

[0010] S103, using the voltage table and current differential table of the eight basic voltage vectors to respectively predict the stator currents of the α-axis and the β-axis at time k+1, and selecting the optimal voltage vector V according to the preset cost equation opt A single vector control signal is generated to act on the voltage source inverter.

[0011] Optionally, the function expression of the SOGI filter in step S101 is:

[0012]

[0013] or:

[0014]

[0015] In the above formula, iα(k) and iα(k-2) are the α-axis currents input to the SOGI filter at time k and k-2 respectively, iα′(k), iβ′(k), iα′(k-1), iβ′(k-1) and iα′(k-2), iβ′(k-2) are the α-axis and β-axis currents output by the SOGI filter at time k, k-1 and k-2 respectively, b 0 , b 2 ,qb 0 ,qb 1 ,qb 2 、a 1 and a 2 is the coefficient, z is the complex frequency parameter in the z transform, and:

[0016]

[0017]

[0018] In the above formula, k 1 is the filter gain coefficient, ω is the angular frequency of the tracking signal, T s is the control period, and s is the complex frequency parameter in Laplace transform.

[0019] Optionally, the transfer function of the SOGI filter in step S101 is:

[0020]

[0021] In the above formula, H d (z) and H q (z) are the transfer functions of the two orthogonal output signals of the SOGI filter on the d-axis and q-axis respectively.

[0022] Optionally, in step S102, the function expression of the updating mechanism of the current differential table in the differential method model-free predictive current control using the current signal after filtering out the noise signal is:

[0023]

[0024] In the above formula, Δi αx (k) and Δi βx (k) are the current difference values ​​of the α-axis and β-axis at the updated time k, Δi αx (k-1) and Δi βx (k-1) are the current difference values ​​of the α-axis and β-axis at the k-1 moment before the update, Δi αo (k) and Δi βo (k) are the α-axis and β-axis current differential values ​​at time k corresponding to the basic voltage vector selected in the previous control cycle, Δi αo (k-1) and Δi βo(k-1) are the current differential values ​​of the α-axis and β-axis at the k-1 moment corresponding to the basic voltage vector selected in the previous control cycle, U αo (k-1) and U βp (k-1) are the α-axis and β-axis components of the basic voltage vector acting at time k-1, U αo (k-2) and U βo (k-2) are the α-axis and β-axis components of the basic voltage vector acting at time k-2, U αx (k-1) and U βx (k-1) are the α-axis and β-axis components of the x-axis basic voltage vector that is not acting at time k-2, respectively. αx (k-2) and U βx (k-2) are the α-axis and β-axis components of the x-numbered basic voltage vector that is not in effect at time k-2, and the value range of x is 0 to 7.

[0025] Optionally, the function expression for predicting the stator current of the α-axis and the β-axis at time k+1 in step S103 is:

[0026] iα(k+1)=iα(k)+Δi αx (k)

[0027] iβ(k+1)=iβ(k)+Δi βx (k)

[0028] In the above formula, iα(k+1) and iβ(k+1) are the predicted stator currents of the α-axis and β-axis at the k+1 moment, iα(k) and iβ(k) are the predicted stator currents of the α-axis and β-axis at the k moment, respectively. Δi αx (k) and Δi βx (k) are the updated current difference values ​​of the α-axis and β-axis respectively.

[0029] Optionally, the function expression of the cost equation in step S103 is:

[0030] g = (iref - iα(k+1)) 2 +(iref-iβ(k+1)) 2 ,

[0031] In the above formula, g is the cost equation, iref is the reference current, iα(k+1) and iβ(k+1) are the predicted stator currents of the α-axis and β-axis at the time k+1, respectively.

[0032] Optionally, in step S103, the optimal voltage vector V is selected according to a preset cost equation. opt It means selecting the basic voltage vector with the smallest cost equation as the optimal voltage vector V from the voltage table and current difference table of the eight basic voltage vectors.opt A single vector control signal is generated to act on the voltage source inverter.

[0033] In addition, the present invention also provides a SOGI filtered motor single vector model-free predictive control system, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the SOGI filtered motor single vector model-free predictive control method.

[0034] In addition, the present invention also provides a computer-readable storage medium, in which a computer program / instruction is stored, and the computer program / instruction is programmed or configured to execute the SOGI filtered motor single vector model-free predictive control method through a processor.

[0035] In addition, the present invention also provides a computer program product, including a computer program / instruction, which is programmed or configured to execute the SOGI filtered motor single vector model-free predictive control method through a processor.

[0036] Compared with the prior art, the present invention mainly has the following advantages: the present invention uses the current differential value and voltage amplitude of adjacent cycles to replace the variable parameters in the model predictive control, eliminating the adverse effects of model parameter mismatch on the control. In addition, the entire current differential table can be updated once in each control cycle, thereby improving the update frequency of the current differential value of the model-free predictive control, thereby improving the control accuracy of the permanent magnet synchronous motor and improving the dynamic response performance of the control system. The present invention proposes to use a SOGI filter to filter out high-frequency noise in the current sampling signal. Compared with the traditional low-pass filter, the SOGI filter can track the current signal in a stationary coordinate system without phase delay. The computational burden is much smaller than that of a complex online observer. The harmonics in the current signal can be reduced, thereby reducing the error caused by the noise in the control system in the current control loop, providing more accurate current sampling information for the model-free predictive current control, and improving the robustness of the control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the basic flow of the method of the embodiment of the present invention.

[0038] Figure 2 These are eight basic voltage vectors corresponding to the voltage source inverter in the embodiment of the present invention.

[0039] Figure 3 Schematic diagram of the control principle in an embodiment of the present invention.

[0040] Figure 4 FIG. 4 is a schematic diagram of a transfer function of a SOGI filter in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] like Figure 1 As shown, the motor single vector model-free predictive control method with SOGI filtering in this embodiment includes the following steps:

[0042] S101, performing Clark transformation on the three-phase current sampling signal of the motor stator, converting it into a stationary coordinate system, and then using a SOGI filter to filter out high-frequency noise signals;

[0043] S102, using the current signal after filtering out the noise signal for the updating mechanism of the current differential table in the differential method model-free predictive current control to obtain the voltage table and current differential table of eight basic voltage vectors; Figure 2 The V0 (coded as 000) to V7 (coded as 111) shown are schematic diagrams of eight basic voltage vectors of the motor;

[0044] S103, using the voltage table and current differential table of the eight basic voltage vectors to respectively predict the stator currents of the α-axis and the β-axis at time k+1, and selecting the optimal voltage vector V according to the preset cost equation opt Generate a single vector control signal to act on the voltage source inverter. Figure 3 As shown, the voltage-type inverter used in this embodiment is composed of six IGBTs. When the voltage amplitude of the motor is low, the power electronic switch generally uses a power MOSFET. This device has a fast switching speed and a high operating frequency, and can provide better working performance. However, the carrying power is small. When the input voltage of the motor is high, an IGBT switching device is often used. The switching speed of the IGBT is also relatively fast, but its capacity is greater than that of the power MOSFET, and it is used in places with high voltage and high power. The upper and lower switching devices in the inverter bridge can be regarded as a group. In each cycle, since only one switching element passes in each of the two groups of common cathode and common anode power electronic switches, the switching state of the power electronic devices on the three groups of bridge arms can be described by the three parameters Sa, Sb and Sc. When a group of upper tubes passes and the lower tubes are blocked, we set its parameter value to 1; and when a group of middle and lower tubes passes and the upper tubes are blocked, we set its parameter value to 0. According to the different working modes of the three groups of switches, the inverter can present 2 to the power of 3 = 8 working forms, corresponding to eight voltage vectors. The eight voltage vectors play a vital role in controlling torque and flux. The inverter used in this embodiment can adjust the output voltage through 8 different switch states to achieve the best control effect. Because the two states of 000 and 111 are meaningless, the output voltage state can also be divided into two types: one is 6 working states, and the other is two zero states.

[0045] In step S101, Clark transformation is performed according to the motor stator three-phase current sampling signal, which is a well-known method. The function expression is:

[0046]

[0047] [i α i β i 0 ] T =T 3s / 2s [i A i B i C ] T ,

[0048] In the above formula, A(t), B(t) and C(t) are the expressions of the stator three-phase current in the time domain, t is time, i is α 、i β and i 0 are the current and zero-sequence current components of the α and β axes respectively, T 3s / 2s is the transformation matrix, so the transformation matrix of Clark coordinates can be obtained:

[0049]

[0050] In step S101 of this embodiment, a SOGI filter (second-order generalized integrator filter) is used to achieve error-free tracking of a specific frequency signal and filter out high-frequency noise signals in the current signal. Two orthogonal signals are output, one without phase difference and the other with a 90-degree phase shift. The function expression of the SOGI filter in step S101 of this embodiment is:

[0051]

[0052] In the above formula, iα(k) and iα(k-2) are the α-axis currents input to the SOGI filter at time k and k-2 respectively, iα′(k), iβ′(k), iα′(k-1), iβ′(k-1) and iα′(k-2), iβ′(k-2) are the α-axis and β-axis currents output by the SOGI filter at time k, k-1 and k-2 respectively, b 0 , b 2 ,qb 0 ,qb 1 ,qb 2 、a 1 and a 2 is the coefficient, z is the complex frequency parameter in the z transform, and:

[0053]

[0054] In the above formula, k 1 is the filter gain coefficient, ω is the angular frequency of the tracking signal, T s is the control period, and s is the complex frequency parameter in Laplace transform.

[0055] The transfer function of the SOGI filter in step S101 of this embodiment is:

[0056]

[0057] In the above formula, H d (z) and H q (z) are the transfer functions of the two orthogonal output signals of the SOGI filter, d-axis and q-axis respectively. The derivation process of the transfer function of the SOGI filter in step S101 of this embodiment is as follows:

[0058] The transfer function can reflect the transfer characteristics. In this embodiment, the transfer functions of the two signals are as follows:

[0059]

[0060] The transfer function is discretized using a bilinear transform (Tustin transform):

[0061]

[0062] Substituting the above formula into the continuous transfer function, we can get the discretized transfer function of the SOGI filter:

[0063]

[0064] Transform it:

[0065]

[0066] Thus, the functional expression of the transfer function of the SOGI filter in step S101 of this embodiment can be obtained.

[0067]

[0068] Where k is the filter gain coefficient and ω is the angular frequency of the tracking signal.

[0069] like Figure 4 As shown, the final output of SOGI Boqi is:

[0070] v′=vH d (z),qv′=vH q (z)

[0071] Right now:

[0072]

[0073] It can also be expressed as:

[0074] iα′(k)=b 0 .iα(k)+b 2.iα(k-2)+a 1 .iα′(k-1)+a 2 .iα′(k-2),

[0075]

[0076] It can also be expressed as:

[0077] iβ′(k)=qb 0 .iα(k)+qb 1 .ia(k-1)+qb 2 .iα(k-2)+a 1 .iβ′(k-1)+a 2 .iβ′(k-2),

[0078] In the above formula, iα(k) and iα(k-2) are the α-axis currents input to the SOGI filter at time k and k-2 respectively, iα′(k), iβ′(k), iα′(k-1), iβ′(k-1) and iα′(k-2), iβ′(k-2) are the α-axis and β-axis currents output by the SOGI filter at time k, k-1 and k-2 respectively, b 0 , b 2 ,qb 0 ,qb 1 ,qb 2 、a 1 and a 2 is the coefficient, and z is the complex frequency parameter in the z transform. iα′(k) and iβ′(k) are the results of SOGI filter filtering out the high-frequency noise signal in the motor stator sampling current iα. Using them to update the current differential table in model-free predictive current control will improve the accuracy of the current differential value and thus enhance the robustness of the control method.

[0079] In step S102 of this embodiment, the function expression of the updating mechanism of the current differential table in the differential method model-free predictive current control using the current signal after filtering out the noise signal is:

[0080]

[0081] In the above formula, Δiαx(k) and Δi β x(k) is the current difference value of the α-axis and β-axis at the updated time k, Δiαx(k-1) and Δi β x( k -1) respectively before the update k The current difference between the α-axis and the β-axis at time -1, Δiαo( k ) and Δi β o( k) are the α-axis and β-axis current differential values ​​at time k corresponding to the basic voltage vector selected in the previous control cycle, Δi αo (k-1) and Δi β o(k-1) are the α-axis and β-axis current differential values ​​at time k-1 corresponding to the basic voltage vector selected in the previous control cycle, uαo(k-1) and U β o(k-1) are the α-axis and β-axis components of the basic voltage vector acting at time k-1, uαo(k-2) and u β o(k-2) are the α-axis and β-axis components of the basic voltage vector acting at time k-2, Uαx(k-1) and U β x(k-1) are the α-axis and β-axis components of the x-axis basic voltage vector that is not acting at time k-2, Uαx(k-2) and u β x(k-2) are the α-axis and β-axis components of the x-numbered basic voltage vector that is not acting at time k-2, and the value of x ranges from 0 to 7. αp (k-2)-U αx When (k-2)=0, that is, when the voltage vector corresponding to the current differential value that needs to be updated is the same as the voltage vector selected in the previous cycle, the current differential value will stagnate for one sampling cycle, but because the current differential value of the previous cycle is the current differential value of the voltage vector, stagnation for one cycle will not affect the control effect.

[0082] In step S103 of this embodiment, the function expression for predicting the stator current of the α-axis and the β-axis at time k+1 is:

[0083] iα(k+1)=iα(k)+Δi αx (k)

[0084] iβ(k+1)=iβ(k)+Δi βx (k)

[0085] In the above formula, iα(k+1) and iβ(k+1) are the predicted stator currents of the α-axis and β-axis at the k+1 moment, iα(k) and iβ(k) are the predicted stator currents of the α-axis and β-axis at the k moment, respectively. Δi αx (k) and Δi βx (k) are the updated current difference values ​​of the α-axis and β-axis respectively.

[0086] The function expression of the cost equation in step S103 of this embodiment is:

[0087] g = (iref - iα(k+1)) 2 +(iref-iβ(k+1)) 2 ,

[0088] In the above formula, g is the cost equation, iref is the reference current, iα(k+1) and iβ(k+1) are the predicted stator currents of the α-axis and β-axis at the time k+1, respectively.

[0089] The updating mechanism of the current differential table in the single vector model-free predictive current control of the permanent magnet synchronous motor based on the current differential method used in this embodiment is applicable to the surface-mounted permanent magnet synchronous motor. The basic equation of the permanent magnet synchronous motor is as follows:

[0090]

[0091] In the above formula, u α and u β are the voltages of the α-axis and β-axis respectively, R is the resistance of the motor, L α is the equivalent inductance of the α axis, t is the time, L αβ is the equivalent mutual inductance of the α and β axes, i α and i β are the currents of the α-axis and β-axis, ω e is the electrical angular velocity, ψ f is the permanent magnet flux, θ e is the rotor electrical angle, and where:

[0092]

[0093] In the above formula, L d and L q They are the d-axis and q-axis inductances respectively. The motor with a surface-mounted rotor can optimize the main magnetic flux distribution in the air gap by changing the distribution structure of the permanent magnets. For example, the air gap flux linkage can be set to the required sinusoidal distribution to make the air gap flux density more ideal, reduce the harmonic components to a greater extent, and thereby achieve the purpose of improving and enhancing the various performance of the motor. Not only that, the surface-mounted structure is easier to manufacture and can be modified according to production needs. Since the direct-axis inductance and quadrature-axis inductance of the surface-mounted permanent magnet synchronous motor are equal (L q =L d ), so the corresponding state equation in the stationary coordinate system can be expressed as:

[0094]

[0095] In the above formula, U α and U β are the voltages of the α-axis and β-axis respectively, and iα and iβ are the currents of the α-axis and β-axis respectively.

[0096] Transforming the above equation, we get the current change rate:

[0097]

[0098] In the above formula, diα / dt and diβ / dt are the current change rates of the α-axis and β-axis respectively.

[0099] Therefore, the current differential value corresponding to each cycle can be expressed by the following formula:

[0100]

[0101] Since the sampling frequency of model-free predictive current control is high, the current differential value can be directly expressed as the product of the current change rate and the period:

[0102]

[0103] In the above formula, Δi α (k) represents the current value of the current sampling cycle minus the current value of the previous sampling cycle, that is, i α (k)-i α (k-1), where Δi α0 (k) represents the voltage vector selected in the previous control cycle The corresponding current differential value, Δi αx (k) represents the voltage vector not used in the previous control cycle The corresponding current differential value (where x = 0, 1, 2, ..., 7). Since the mechanical cycle of the motor operation is much longer than the sampling cycle of the model-free predictive current control, the Ri of adjacent sampling cycles α and sinθ e ·w e ψ f The change in can be ignored. Therefore, by subtracting the above two formulas, we can get:

[0104]

[0105] The current difference value corresponding to the voltage vector selected and the unselected voltage vector in the first two control cycles can be expressed as:

[0106]

[0107] Dividing the above two formulas can eliminate the parameter T that changes during motor operation. s / L 0 :

[0108]

[0109] The new current gradient update formula is transformed from the above formula:

[0110]

[0111] When U αo (k-2)-U αxWhen (k-2)=0, that is, when the voltage vector corresponding to the current differential value that needs to be updated is the same as the voltage vector selected in the previous cycle, the current differential value will stagnate for one sampling cycle, but because the current differential value of the previous cycle is the current differential value of the voltage vector, stagnation for one cycle will not affect the control effect.

[0112] Then the current value of the next cycle is predicted:

[0113] iα(k+1)=iα(k)+Δi αx (k)

[0114] iβ(k+1)=iβ(k)+Δi βx (k)

[0115] (where x = 0, 1, 2 ... 7), the eight prediction results are substituted into the cost equation, and the voltage vector corresponding to the minimum cost value, i.e., the optimal voltage vector, is used to act on the voltage-type inverter. In step S103 of this embodiment, the optimal voltage vector V is selected according to the preset cost equation. opt It means selecting the basic voltage vector with the smallest cost equation as the optimal voltage vector V from the voltage table and current difference table of the eight basic voltage vectors. opt A single vector control signal is generated to act on the voltage source inverter.

[0116] In summary, the method of this embodiment discloses a single-vector model-free predictive control method for a permanent magnet synchronous motor based on a current differential method using a SOGI filter. The method of this embodiment includes an updating method for a current differential table in a differential method model-free predictive current control and a SOGI filter (a second-order generalized integrator filter) for dealing with current sampling noise. The updating method for the current differential table is to replace the variable inductance part in the traditional update mechanism (MPCC) with the current differential value and the voltage value at the current moment and the previous sampling moment, and the entire current differential table is refurbished once in each sampling cycle. SOGI The filter is to use the characteristics of the second-order generalized integrator to track the given frequency without difference to filter out the high-frequency noise signal in the current signal. The SOGI filter can track the AC signal of a specific frequency without difference. One of the two output signals has no phase delay with the input signal, and the other lags 90 degrees, which can achieve phase-delay-free filtering of the sampled current. The control system performs Clark transformation on the three-phase current sampling signal of the motor stator, converts it into a stationary coordinate system, and uses a second-order generalized integrator filter to filter out the noise signal. The filtered current signal is used to update the model-free predictive current control. The voltage table and current differential table of the eight basic voltage vectors are used to select the optimal voltage vector in each control cycle to generate a control signal to act on the voltage inverter, and the current differential table is updated in real time. The method of this embodiment has a current differential table update mechanism with a higher update frequency and noise suppression based on a SOGI filter. The new current differential table update mechanism uses the current differential value between the current sampling moment and the previous sampling moment and the selected voltage vector amplitude to update the differential table. The SOGI filter can perform differential tracking on the current signal of a specific frequency and filter out the noise component in the current sampling, thereby reducing the current stagnation phenomenon of the differential method model-free predictive current control method, improving the current accuracy, and enhancing the anti-noise performance of the motor control system, which is particularly suitable for surface-mounted permanent magnet synchronous motors.

[0117] In addition, this embodiment also provides a SOGI filtered motor single vector model-free predictive control system, including a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the SOGI filtered motor single vector model-free predictive control method.

[0118] In addition, this embodiment also provides a computer-readable storage medium, in which a computer program / instruction is stored, and the computer program / instruction is programmed or configured to execute the SOGI filtered motor single vector model-free predictive control method through a processor.

[0119] In addition, this embodiment also provides a computer program product, including a computer program / instruction, which is programmed or configured to execute the SOGI filtered motor single vector model-free predictive control method through a processor.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the functions in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0121] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A motor single vector model-free predictive control method with SOGI filtering, characterized in that: The steps include: S101, performing Clark transformation on the three-phase current sampling signal of the motor stator, converting it into a stationary coordinate system, and then using a SOGI filter to filter out high-frequency noise signals; S102, using the current signal after filtering out the noise signal for the updating mechanism of the current differential table in the differential method model-free predictive current control to obtain the voltage table and current differential table of eight basic voltage vectors; S103, using the voltage table and current differential table of the eight basic voltage vectors to respectively predict the stator currents of the α-axis and the β-axis at time k+1, and selecting the optimal voltage vector V according to the preset cost equation opt generating a single vector control signal to act on a voltage source inverter; In step S102, the current signal after filtering out the noise signal is used for the function expression of the updating mechanism of the current differential table in the differential method model-free predictive current control: In the above formula, Δi αx (k) and Δi βx (k) are the current difference values ​​of the α-axis and β-axis at the updated time k, Δi αx (k-1) and Δi βx (k-1) are the current difference values ​​of the α-axis and β-axis at the k-1 moment before the update, Δi αo (k) and Δi βo (k) are the α-axis and β-axis current differential values ​​at time k corresponding to the basic voltage vector selected in the previous control cycle, Δi αo (k-1) and Δi βo (k-1) are the current differential values ​​of the α-axis and β-axis at the k-1 moment corresponding to the basic voltage vector selected in the previous control cycle, U αo (k-1) and U βo (k-1) are the α-axis and β-axis components of the basic voltage vector acting at time k-1, U αo (k-2) and U βo (k-2) are the α-axis and β-axis components of the basic voltage vector acting at time k-2, U αx (k-1) and U βx (k-1) are the α-axis and β-axis components of the x-axis basic voltage vector that is not acting at time k-2, respectively. αx (k-2) and U βx (k-2) are the α-axis and β-axis components of the x-numbered basic voltage vector that is not in effect at time k-2, and the value range of x is 0 to 7.

2. The motor single vector model-free predictive control method with SOGI filtering according to claim 1 is characterized in that: The function expression of the SOGI filter in step S101 is: or: In the above formula, iα(k) and iα(k-2) are the α-axis currents input to the SOGI filter at time k and k-2 respectively, iα′(k), iβ′(k), iα′(k-1), iβ′(k-1) and iα′(k-2), iβ′(k-2) are the α-axis and β-axis currents output by the SOGI filter at time k, k-1 and k-2 respectively, b0, b2, qb0, qb1, qb2, a1 and a2 are coefficients, z is the complex frequency parameter in the z-transform, and: In the above formula, k1 is the filter gain coefficient, ω is the angular frequency of the tracking signal, T s is the control period, and s is the complex frequency parameter in Laplace transform.

3. The motor single vector model-free predictive control method with SOGI filtering according to claim 2 is characterized in that: The transfer function of the SOGI filter in step S101 is: In the above formula, H d (z) and H q (z) are the transfer functions of the two orthogonal output signals of the SOGI filter on the d-axis and q-axis respectively.

4. The motor single vector model-free predictive control method with SOGI filtering according to claim 1 is characterized in that: The function expression for predicting the stator current of the α-axis and the β-axis at time k+1 in step S103 is: iα(k+1)=iα(k)+Δi αx (k), iβ(k+1)=iβ(k)+Δi βx (k), In the above formula, iα(k+1) and iβ(k+1) are the predicted stator currents of the α-axis and β-axis at the k+1 moment, iα(k) and iβ(k) are the predicted stator currents of the α-axis and β-axis at the k moment, respectively. Δi αx (k) and Δi βx (k) are the updated current difference values ​​of the α-axis and β-axis respectively.

5. The motor single vector model-free predictive control method with SOGI filtering according to claim 1 is characterized in that: The function expression of the cost equation in step S103 is: g=(iref-iα(k+1)) 2 +(iref-iβ(k+1)) 2 , In the above formula, g is the cost equation, iref is the reference current, iα(k+1) and iβ(k+1) are the predicted stator currents of the α-axis and β-axis at the time k+1, respectively.

6. The motor single vector model-free predictive control method with SOGI filtering according to claim 5 is characterized in that: In step S103, the optimal voltage vector V is selected according to a preset cost equation. opt It means selecting the basic voltage vector with the smallest cost equation as the optimal voltage vector V from the voltage table and current difference table of the eight basic voltage vectors. opt A single vector control signal is generated to act on the voltage source inverter.

7. A motor single vector model-free predictive control system with SOGI filtering, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the motor single vector model-free predictive control method with SOGI filtering as claimed in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program / instruction stored therein, characterized in that: The computer program / instruction is programmed or configured to execute the motor single vector model-free predictive control method with SOGI filtering as claimed in any one of claims 1 to 6 through a processor.

9. A computer program product comprising a computer program / instructions, characterized in that The computer program / instruction is programmed or configured to execute the motor single vector model-free predictive control method with SOGI filtering as claimed in any one of claims 1 to 6 through a processor.

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