Model prediction fault-tolerant control method and system for dual three-phase permanent magnet synchronous motor

Abstract

The invention relates to a model prediction fault-tolerant control method and system for a dual three-phase permanent magnet synchronous motor, and the method comprises the steps: estimating the lumped disturbance in the operation process of the motor in real time based on an extended state observer, and carrying out the feed-forward compensation of the reference voltage of the motor according to the lumped disturbance information; and based on the compensated voltage reference value, constructing a dynamic voltage vector set and correcting an initial voltage vector set, searching an optimal voltage vector in the dynamic voltage vector set, and driving the motor through three-phase decoupling space vector pulse width modulation. According to the field loss fault-tolerant control strategy designed by the invention, phase current harmonics caused by local field loss faults of the dual three-phase permanent magnet synchronous motor can be effectively suppressed, and the steady-state precision and the dynamic response capability of the system under the local field loss faults are enhanced.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, specifically to a model prediction fault-tolerant control method and system for a dual three-phase permanent magnet synchronous motor based on an extended state observer when a local demagnetization fault occurs. Background Technology

[0002] Dual three-phase permanent magnet synchronous motors (PMSMs) are widely used in high-power applications such as rail transit and industrial drive systems due to their high torque density, high power capacity, and high operating efficiency. Compared to traditional three-phase PMSMs, dual three-phase PMSMs offer more flexible control and stronger fault tolerance. However, similar to traditional three-phase PMSMs, the permanent magnets in dual three-phase PMSMs can experience localized demagnetization under conditions such as high temperatures. Demagnetization leads to a decrease in the air gap flux linkage amplitude, resulting in electromagnetic torque fluctuations, phase current distortion, and control performance degradation. In certain critical application scenarios, motor shutdown can lead to significant economic losses or safety risks, and on-site maintenance conditions are limited, making immediate shutdown for maintenance difficult. Therefore, a fault-tolerant control strategy capable of maintaining stable motor operation after a demagnetization fault is urgently needed.

[0003] Currently, research on fault-tolerant control of demagnetization faults in permanent magnet synchronous motors (PMSMs) mainly focuses on three-phase structures. For dual three-phase PMSMs, related research is relatively limited. Most current research focuses on the detection of demagnetization faults, with some studies utilizing the 5th and 7th harmonic back electromotive forces of dual three-phase PMSMs to detect and compensate for demagnetization faults. This algorithm is relatively simple in principle and can achieve demagnetization fault detection and fault-tolerant control to a certain extent. However, demagnetization faults are usually accompanied by changes in motor resistance and inductance parameters, which affects the algorithm's performance. Furthermore, the algorithm's demagnetization detection range is limited, impacting the effectiveness of demagnetization fault-tolerant control. Summary of the Invention

[0004] To address the current problem of demagnetization-tolerant control in permanent magnet synchronous motors, the present invention aims to provide a model prediction-tolerant control method and system for dual three-phase permanent magnet synchronous motors.

[0005] To solve the above-mentioned technical problems, the technical solution provided by the present invention is as follows: A model-predictive fault-tolerant control method for a dual three-phase permanent magnet synchronous motor, the method comprising: S1, the local loss-of-excitation fault detection module receives the current and voltage values ​​measured by the current and voltage sensors, and estimates the lumped disturbance of the dual three-phase permanent magnet synchronous motor based on the extended state observer of the hyperlocal model; it then transmits the lumped disturbance to... d x - q y Subspace deadbeat predictive current control module and d - qSubspace model predicts current control module; S2, d x - q y The subspace deadbeat predictive current control module performs delay compensation to obtain d. x axis, q y The predicted shaft current value is used to obtain the voltage reference value based on the predicted current value and the deadbeat principle. S3, d - q The subspace model predicts that the current control module performs delay compensation, constructs a dynamic voltage vector set and corrects the initial voltage vector set, and searches for... dq Subspace optimal voltage vector; S4, the VSD conversion module transforms the physical quantities of the six phases of the stator to... , , Subspace; Synchronous Rotation Coordinate Module will subspace Transformation of physical quantities in subspace to synchronous rotating coordinate system dq and d x - q y Coordinate system; S5, the three-phase decoupled SVPWM module uses the three-phase decoupled SVPWM algorithm to convert the optimal voltage vector into the PWM drive signal required by the six-bridge arm inverter, driving the dual three-phase permanent magnet synchronous motor.

[0006] Optionally, the discrete mathematical model of the dual three-phase permanent magnet synchronous motor using the hyperlocal model in step S1 is as follows:

[0007] The mathematical model of the extended state observer is

[0008] in, T s The sampling period; β 1 and β 2 is a constant. β 1 = 2 oh c , β 2= ,in It is the bandwidth of the extended state observer; α It is the gain matrix α = diag( α d , α q ,α dx , α qy ); i sr It is a current vector. , i d 、i q 、 i dx 、i qy They are d, q, and d respectively. x q y Shaft current value; z 1 is i sr The estimated value, z 1=[ , , , ] T , , , , They are d, q, and d respectively. x q y shaft current estimate; z 2 is an estimate of the lumped disturbance. = [ , ， , ] T , , , , They are d, q, and d respectively. x q y Estimates of axis lumped disturbances; u sr It is d, q, d x q y Voltage vector composed of axial voltages u sr =[ u d , u q , u dx , u qy ] T ，u d , u q , udx , u qy They are d, q, and d respectively. x q y Axis voltage vector components, Indicates the first time, These are d, q, and d of the hyperlocal model, respectively. x q y Shaft lumped disturbance; Represents d, q, d x q y The deviation between the estimated and true values ​​of the shaft current vector. Let d, q, and d represent the values ​​in the hyperlocal model, respectively. x q y The coefficient of shaft voltage.

[0009] Optionally, in step S2, according to

[0010] Delay compensation is performed to obtain d x axis, q y Predicted shaft current and The predicted current value is obtained based on the discrete mathematical model of the dual three-phase permanent magnet synchronous motor using the hyperlocal model; the voltage reference value is calculated based on the deadbeat principle. and .

[0011] , in and It is d x axis, q y Shaft current reference value.

[0012] Optionally, the delay compensation in step S3 includes: Predict the currents along the d and q axes at the next moment based on the deadbeat principle. and Discrete mathematical model is ; It is the sampling time; , It is the first k The d-axis and q-axis currents at time t; , They are the first k The d-axis and q-axis voltages at time t.

[0013] Optionally, constructing the dynamic voltage vector set in step S3 includes: motor Control and set the d-axis current reference value. The upper and lower limits are respectively and ,according to

[0014] The upper and lower limits of the d-axis voltage can be obtained. Set an adjustment factor and its upper and lower limits , And satisfy 0 < <1; Q-axis voltage upper and lower limits Then according to

[0015] Dynamic adjustment to obtain d - q Shaft reference voltage upper and lower limits; where , ; divide the interval N d Divide the interval into equal parts. N q Divide into equal parts to obtain the initial voltage vector set. .

[0016] Optionally, the correction of the initial voltage vector set in step S3 includes: Based on the initial voltage vector set Center voltage vector ,pass

[0017] Two voltage vectors are obtained , ,in , , , ,according to , Length of the module and To analyze whether it is necessary to Make corrections; if and All do not exceed the inverter bus voltage If so, no correction is needed; express yes The voltage vector is obtained by inverse synchronous rotation transformation. express yes Obtained by inverse synchronous rotation transformation. The center voltage vector is at - Subspace voltage vector, The center voltage vector is at - Subspace voltage vector, Represents the initial voltage vector set; like > or > First, scale to , and d x -q y The subspace voltage vector remains unchanged, while the dq subspace voltage vector changes. The calculation is as follows: ; As a corrected voltage vector set The first element; Rotate Δ in a counterclockwise direction i get ; calculate ;judge Does it not exceed If it exceeds the limit, then stop; otherwise, continue. Add correction voltage vector set In the middle; rotation have to And repeat; the termination condition is that the number of elements in the modified voltage vector set reaches the upper limit. ,or ( k The modulus of (= 2,3,…) exceeds ; yes The scaled voltage vector, and After scaling, and Things will also change. Become , Become ; It is after scaling The changing voltage vector It is after scaling The changing voltage vector; It is a corrected voltage vector set; After the first rotation The voltage vector obtained by the change After the first rotation The voltage vector obtained from the change; yes After rotation, by The voltage vector obtained from the change; like > or > ,Will Reduced to a module length equal to ;d x -q y The subspace voltage vector remains unchanged, while the dq subspace voltage vector changes with... Change, gain and , As the first element in the corrected voltage vector set; for Perform a synchronous rotation transformation to obtain ; Detection by Calculated Does the module length not exceed If the value exceeds the limit, terminate the correction of the voltage vector set; otherwise, set the voltage vector set to... Add the corrected voltage vector set and continue rotating. Repeat until the termination condition is met.

[0018] Optionally, the search for the optimal voltage vector in the dq subspace in step S3 includes: Traversal The voltage vector in the equation, substitute it into , get Under the same voltage vector, the following predictions were made using the same method: ; , Let represent the predicted value of the dq subspace current vector at time k+2. It is the predicted value of the d-axis current at time k+2. It is the predicted value of the q-axis current at time k+2; This represents the predicted value of the dq subspace current vector at time k+3. In this context, n represents the nth time after the kth time. It is the current vector reference value. , This is the d-axis current reference value. This is the q-axis current reference value; According to the value function

[0019] The value function value corresponding to each voltage vector is obtained. Q , R It is a weight matrix. It is the difference between the voltage vector and the previous optimal voltage vector; the voltage vector with the smallest value function value is selected as the optimal voltage vector, and the optimal voltage vector is output to the three-phase decoupled SVPWM module.

[0020] Optionally, step S5 includes: using a three-phase decoupling SVPWM algorithm to convert the six-bridge inverter into two parallel three-phase inverters.

[0021] Optionally, step S5 includes: based on the input optimal voltage vector , and , The three-phase decoupled SVPWM algorithm is used to separate the voltage vectors. and The signals are converted and combined into the drive signals for the six-arm inverter.

[0022] A model-predictive fault-tolerant control system for a dual three-phase permanent magnet synchronous motor, the control system comprising: The local loss-of-excitation fault detection module receives current and voltage values ​​measured by current and voltage sensors, and estimates the lumped disturbance of the dual three-phase permanent magnet synchronous motor based on an extended state observer using a hyperlocal model; it then transmits the lumped disturbance to... d x - q y Subspace deadbeat predictive current control module and d - q Subspace model predicts current control module; d x - q y The subspace deadbeat predictive current control module performs delay compensation to obtain d. x axis, q y The predicted shaft current value is used to obtain the voltage reference value based on the predicted current value and the deadbeat principle. d - q The subspace model predicts the current control module, performs delay compensation, constructs a dynamic voltage vector set and corrects the initial voltage vector set, and searches... dq Subspace optimal voltage vector; The VSD conversion module transforms the physical quantities of the six phases of the stator to... , , subspace; Synchronous rotation coordinate module, subspace Transformation of physical quantities in subspace to synchronous rotating coordinate system dq and d x -q y Coordinate system; The three-phase decoupled SVPWM module uses the three-phase decoupled SVPWM algorithm to convert the optimal voltage vector into the PWM drive signal required by the six-bridge inverter, driving the dual three-phase permanent magnet synchronous motor.

[0023] A model prediction fault-tolerant control system for a dual three-phase permanent magnet synchronous motor includes: a processor coupled to a memory for storing programs or instructions, wherein when the program or instructions are executed by the processor, the system performs the aforementioned method.

[0024] A chip system includes at least one processor and an interface for receiving data and / or signals, wherein the at least one processor is configured to perform the methods described above.

[0025] A computer-readable storage medium having instructions stored thereon that, when executed on a computer, cause the computer to perform the methods described above.

[0026] A computer program product having a computer program stored thereon, characterized in that the computer program, when executed, implements the above-described method.

[0027] This invention estimates the lumped disturbance of a dual three-phase permanent magnet synchronous motor based on an extended state observer and an improved model predictive current controller. The improved model predictive current controller based on a hyperlocal model, combined with the calculation of the optimal voltage vector using the lumped disturbance, can effectively counteract the effects of local demagnetization, thereby suppressing phase current harmonics, reducing torque ripple, and ensuring that the motor can maintain good dynamic and steady-state operating performance under local demagnetization fault conditions. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This diagram represents the demagnetization control system of a dual three-phase permanent magnet synchronous motor.

[0030] Figure 2 Indicates when a demagnetization fault occurs dq Schematic diagram of subspace rotor flux linkage.

[0031] Figure 3 A schematic diagram illustrating the selection of the initial voltage vector set.

[0032] Figure 4 This diagram illustrates the steps involved in correcting voltage vector scaling.

[0033] Figure 5 This diagram illustrates the steps involved in correcting the voltage vector rotation.

[0034] Figure 6 This is a flowchart illustrating the corrected voltage vector algorithm. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0037] In various embodiments of the present invention, it should be understood that the sequence number of each process described below does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0038] In addition, the terms "system" and "network" are often used interchangeably in this embodiment of the invention.

[0039] In the embodiments provided in this application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0040] Furthermore, in this embodiment of the invention, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0041] In this embodiment of the invention, the term "multiple" refers to two or more, and other quantifiers are similar.

[0042] The model prediction fault-tolerant control method for dual three-phase permanent magnet synchronous motors of the present invention includes: S1, the local loss-of-excitation fault detection module receives the current and voltage values ​​measured by the current and voltage sensors, and estimates the lumped disturbance of the dual three-phase permanent magnet synchronous motor based on the extended state observer of the hyperlocal model; it then transmits the lumped disturbance to... d x - q y Subspace deadbeat predictive current control module and d - q Subspace model predicts current control module; S2, d x - q y The subspace deadbeat predictive current control module performs delay compensation to obtain d. x axis, q y The predicted shaft current value is used to obtain the voltage reference value based on the predicted current value and the deadbeat principle. S3, d - q The subspace model predicts that the current control module performs delay compensation, constructs a dynamic voltage vector set and corrects the initial voltage vector set, and searches for... dq Subspace optimal voltage vector; S4, the VSD conversion module transforms the physical quantities of the six phases of the stator to... , , Subspace; Synchronous Rotation Coordinate Module will subspace Transformation of physical quantities in subspace to synchronous rotating coordinate system dq and d x - q y Coordinate system; S5, the three-phase decoupled SVPWM module uses the three-phase decoupled SVPWM algorithm to convert the optimal voltage vector into the PWM drive signal required by the six-bridge arm inverter, driving the dual three-phase permanent magnet synchronous motor.

[0043] The discrete mathematical model of the dual three-phase permanent magnet synchronous motor using the hyperlocal model in step S1 is as follows:

[0044] The mathematical model of the extended state observer is

[0045] in, T s The sampling period; β 1 and β 2 is a constant. β 1 = 2 oh c , β 2= ,in It is the bandwidth of the extended state observer; α It is the gain matrix α = diag( α d , α q , α dx , α qy ); i sr It is a current vector. , i d 、i q 、 i dx 、i qy They are d, q, and d respectively. x q y Shaft current value; z 1 is i sr The estimated value, z 1=[ , , , ] T , , , , They are d, q, and d respectively. x q y shaft current estimate; z 2 is an estimate of the lumped disturbance. = [ , ， , ] T , , , , They are d, q, and d respectively. x q y Estimates of axis lumped disturbances; u sr It is d, q, d x q y Voltage vector composed of axial voltages u sr =[ ud , u q , u dx , u qy ] T ，u d , u q , u dx , u qy They are d, q, and d respectively. x q y Axis voltage vector components, Indicates the first time, These are d, q, and d of the hyperlocal model, respectively. x q y Shaft lumped disturbance; Represents d, q, d x q y The deviation between the estimated and true values ​​of the shaft current vector. Let d, q, and d represent the values ​​in the hyperlocal model, respectively. x q y The coefficient of shaft voltage.

[0046] In step S2, according to

[0047] Delay compensation is performed to obtain the predicted values ​​of the dx-axis and qy-axis currents. and The predicted current value is obtained based on the discrete mathematical model of the dual three-phase permanent magnet synchronous motor using the hyperlocal model; the voltage reference value is calculated based on the deadbeat principle. and

[0048] , in and These are the reference values ​​for the dx-axis and qy-axis currents.

[0049] The delay compensation in step S3 includes: Predict the currents along the d and q axes at the next moment based on the deadbeat principle. and Discrete mathematical model is ; It is the sampling time; , It is the first k The d-axis and q-axis currents at time t; , They are the first k The d-axis and q-axis voltages at time t.

[0050] Step S3 involves constructing a dynamic voltage vector set, including: motor Control and set the d-axis current reference value. The upper and lower limits are respectively and ,according to

[0051] The upper and lower limits of the d-axis voltage can be obtained. Set an adjustment factor and its upper and lower limits , And satisfy 0 < <1; Q-axis voltage upper and lower limits Then according to

[0052] Dynamic adjustment to obtain d - q Shaft reference voltage upper and lower limits; where , ; divide the interval N d Divide the interval into equal parts. N q Divide into equal parts to obtain the initial voltage vector set. .

[0053] Step S3 involves correcting the initial voltage vector set, including: Based on the initial voltage vector set Center voltage vector ,pass

[0054] Two voltage vectors are obtained , ,in , , , ,according to , Length of the module and To analyze whether it is necessary to Make corrections; if and All do not exceed the inverter bus voltage If so, no correction is needed; express yes The voltage vector is obtained by inverse synchronous rotation transformation. express yes Obtained by inverse synchronous rotation transformation. The center voltage vector is at - Subspace voltage vector, The center voltage vector is at - Subspace voltage vector, Represents the initial voltage vector set; like > or > First, scale to , and d x -q y The subspace voltage vector remains unchanged, while the dq subspace voltage vector changes. The calculation is as follows: ; As a corrected voltage vector set The first element; Rotate Δ in a counterclockwise direction i get ; calculate ;judge Does it not exceed If it exceeds the limit, then stop; otherwise, continue. Add correction voltage vector set In the middle; rotation have to And repeat; the termination condition is that the number of elements in the modified voltage vector set reaches the upper limit. ,or ( k The modulus of (= 2,3,…) exceeds ; yes The scaled voltage vector, and After scaling, and Things will also change. Become , Become ; It is after scaling The changing voltage vector It is after scaling The changing voltage vector; It is a corrected voltage vector set; After the first rotation The voltage vector obtained by the change After the first rotation The voltage vector obtained from the change; yes After k rotations, by The voltage vector obtained from the change.

[0055] like > or > ,Will Reduced to a module length equal to ;d x -q y The subspace voltage vector remains unchanged, while the dq subspace voltage vector changes with... Change, gain and , As the first element in the corrected voltage vector set; for Perform a synchronous rotation transformation to obtain ; Detection by Calculated Does the module length not exceed If the value exceeds the limit, terminate the correction of the voltage vector set; otherwise, set the voltage vector set to... Add the corrected voltage vector set and continue rotating. Repeat until the termination condition is met.

[0056] Step S3 involves searching for the optimal voltage vector in the dq subspace, which includes: Traversal The voltage vector in the equation, substitute it into , get Under the same voltage vector, the following predictions were made using the same method: ; , Let represent the predicted value of the dq subspace current vector at time k+2. It is the predicted value of the d-axis current at time k+2. It is the predicted value of the q-axis current at time k+2; This represents the predicted value of the dq subspace current vector at time k+3. In this context, n represents the nth time after the kth time. It is the current vector reference value. , This is the d-axis current reference value. This is the q-axis current reference value; According to the value function

[0057] The value function value corresponding to each voltage vector is obtained. Q , R It is a weight matrix. It is the difference between the voltage vector and the previous optimal voltage vector; the voltage vector with the smallest value function value is selected as the optimal voltage vector, and the optimal voltage vector is output to the three-phase decoupled SVPWM module.

[0058] Step S5 includes: using the three-phase decoupling SVPWM algorithm to convert the six-bridge inverter into two three-phase inverters connected in parallel.

[0059] Step S5 includes: based on the input optimal voltage vector , and , The three-phase decoupled SVPWM algorithm is used to separate the voltage vectors. and The signals are converted and combined into the drive signals for the six-arm inverter.

[0060] A model predictive fault-tolerant control system for a dual three-phase permanent magnet synchronous motor includes a local loss-of-excitation fault detection module. This module receives current and voltage values ​​measured by current and voltage sensors and uses an ESO based on a hyperlocal model to estimate the lumped disturbance of the dual three-phase permanent magnet synchronous motor. The lumped disturbance is then transmitted to... d - q Model predictive current control module and d x - q y The subspace deadbeat predictive current control module reduces the impact of disturbances on the motor.

[0061] The hyperlocal model of a dual three-phase permanent magnet synchronous motor is shown in the following equation. , in F d , F q , F dx , F qy It is the component of the lumped disturbance on the coordinate axis. , , , Each of these is a coefficient greater than 0, and must satisfy... , , = .

[0062] The discrete mathematical model of the dual three-phase permanent magnet synchronous motor using the hyperlocal model is shown in the following equation. , The mathematical model of ESO is shown in the following equation. , in, T s The sampling period; β 1 and β 2 is a constant. β 1 = 2 oh c , β 2= ,in It is the bandwidth of the extended state observer; α It is the gain matrix α = diag( α d , α q , α dx , α qy ); i sr It is a current vector. , i d 、i q 、 i dx 、i qy They are d, q, and d respectively. x q y Shaft current value; z 1 is i sr The estimated value, z 1=[ , , , ] T , , , , They are d, q, and d respectively. x q y shaft current estimate; z 2 is an estimate of the lumped disturbance. = [ , , ] T , , , , They are d, q, and d respectively. x q y Estimates of axis lumped disturbances; u sr It is d, q, d x q y Voltage vector composed of axial voltages u sr =[ u d , u q , u dx , u qy ] T ，u d , u q , u dx , u qy They are d, q, and d respectively. x q y Axis voltage vector components, Indicates the first time, These are d, q, and d of the hyperlocal model, respectively. x q y Shaft lumped disturbance; Represents d, q, d x q y The deviation between the estimated and true values ​​of the shaft current vector. Let d, q, and d represent the values ​​in the hyperlocal model, respectively. x q y The coefficient of shaft voltage.

[0063] The above system also includes The subspace deadbeat predictive current control module, connected to the aforementioned local demagnetization fault detection module, employs a hyperlocal mathematical model with the dx-axis current reference value. qy axis current reference value dx-axis current value qy axis current value , , Using this as input, calculate the reference values ​​for the voltage along the dx and qy axes. and Based on the hyperlocal model and deadbeat principle of a dual three-phase permanent magnet synchronous motor, the delay compensation formula is as follows: , The mathematical model of the control module and the equivalent feedforward compensation are shown in the following equation. .

[0064] Under normal circumstances, and The value is relatively small, so the controller output of this subspace is not limited.

[0065] The above system also includes d - q The subspace model predictive current control module, connected to the aforementioned local loss-of-magnetism fault detection module, operates on the principle of dynamically generating a voltage vector set based on the deadbeat principle, and then iterating through the set to find the optimal voltage vector. Considering that the voltage reference value obtained from the traditional deadbeat principle may exceed the linear modulation region of the SVPWM algorithm, causing a decrease in the dynamic performance of the control system and making it susceptible to parameter perturbations, an improvement is made... The subspace model current controller mainly includes delay compensation, voltage vector set selection, and optimal voltage vector calculation.

[0066] The delay compensation of the current control module in the dq subspace model predicts the current at the next moment based on the deadbeat principle. and These are the predicted d-axis and q-axis current values ​​for the next time step. The discrete mathematical model is shown in the following equation. , It is the sampling time; , It is the first k The d-axis and q-axis currents at time t; , They are the first k The d-axis and q-axis voltages at time t.

[0067] for d - q Selection of voltage vector set for the model predictor current controller in subspace, and calculation. d - q d-axis reference voltage upper and lower limits. First, for the d-axis current reference value, assume its upper and lower limits are respectively... , The upper and lower limits of the d-axis voltage can be obtained according to the following formula.

[0068] .

[0069] Set an adjustment factor and its upper and lower limits , And satisfy 0 < <1, q-axis voltage upper and lower limits Then adjust dynamically according to the following formula: .

[0070] in , .

[0071] The initial voltage vector set is calculated based on the previous upper and lower limits of the d-axis and q-axis voltage vectors. First calculate Center voltage vector ,Bundle and and d x - q y Output of Subspace Model-Free Predictive Current Controller , As center vector = [ , , , ] T ,in among them = Then divide the interval N d Divide the interval into equal parts. N q Divide the data into equal parts and obtain the initial voltage vector set as shown in Figure 3.

[0072] According to the center vector Determine whether the initial voltage vector set needs to be corrected. (Definition) and and according to the following formula , Two voltage vectors are obtained , ,in ， .like and Not exceeding If so, no correction is needed; express yes The voltage vector is obtained by inverse synchronous rotation transformation. express yes Obtained by inverse synchronous rotation transformation. The center voltage vector is at - Subspace voltage vector, The center voltage vector is at - Subspace voltage vector, This represents the initial voltage vector set.

[0073] like > or > First, scale to ,and u dxqy constant, u dq Change, according to the following formula

[0074] It can be calculated . As a corrected voltage vector set The first element.

[0075] Rotate Δ in a counterclockwise direction i ,have to Then, following the same formula...

[0076] It can be obtained Then make a judgment. Does it not exceed If it exceeds the limit, the process will stop. Otherwise, it will stop. Add correction voltage vector set In the middle. Then rotate again. have to Then repeat step S4.2, terminating when the number of elements in the modified voltage vector set reaches the upper limit. ,or The length of the module exceeds .

[0077] like > or > ,Will Reduced to a module length equal to , u dxqy constant, u dq along with Change, gain and , As the first element in the corrected voltage vector set. Similarly, for Perform a synchronous rotation transformation to obtain And detect by Calculated Does the module length not exceed If it exceeds the limit, then terminate the correction of the voltage vector set; otherwise, then... Add the corrected voltage vector set and continue rotating. Repeat the above steps until the termination condition is met.

[0078] The optimal vector calculation part of the model predictive current controller, from Select the optimal voltage vector from the given information. Iterate through the data. The voltage vector in the equation is substituted into the following formula. , get .

[0079] Under the same voltage vector, the following predictions were made using the same method: According to the formula of the value function , The value function value corresponding to each voltage vector is calculated, and the voltage vector with the smallest value function value is selected as the optimal voltage vector. Then, the optimal voltage vector is output to the three-phase decoupled SVPWM module.

[0080] The system also includes a coordinate transformation module, including a VSD transformation module, which transforms the physical quantities of the six phases of the stator to... , , Subspace; Synchronous Rotation Coordinate Module, subspace x - y Transformation of physical quantities in subspace to synchronous rotating coordinate system and d x - q y Coordinate system.

[0081] The system also includes a three-phase decoupled SVPWM module, connected to the above... Deadbeat predictive current control module in subspace and Subspace model predictive current control module. The six-arm inverter is equivalent to two parallel three-phase inverters using a three-phase decoupled SVPWM algorithm. Let the input voltage vectors of the two three-phase inverters be respectively... u svm1 ,u svm2 These two voltage vectors are related to the optimal input voltage vector. , and dx - qy Voltage vector reference value , The expression is as follows: , `conj()` is used to find the conjugate of a complex number. The voltage vectors of two parallel three-phase inverters are obtained from the above formula. and In this configuration, ABC acts as an inverter SVM1, and UVW acts as an inverter SVM2. Then, a three-phase decoupling SVPWM algorithm can be used to separate the inverters. and The signals are converted and combined into the drive signals for the six-arm inverter.

[0082] Figure 1 illustrates an embodiment of a model predictive fault-tolerant control system for a dual three-phase permanent magnet synchronous motor according to the present invention. First, voltage and current sensors acquire the current voltage and current signals. and The voltage and current vectors are then transformed into a synchronous rotating coordinate system via a coordinate transformation module. , The position sensor acquires the current position signal of the motor. i m It is converted into an angular velocity signal by a differentiator. oh m The fault detection module is based on , , i m Calculate the lumped disturbance , Then the angular velocity reference value and oh m Subtract the two numbers and pass the result to the PI controller to obtain the result. q Shaft current reference value After that, regarding d x - q y The deadbeat predictive current controller in the subspace uses a current reference value. , , And calculate based on the principle of no beat time. d x and q y Shaft voltage reference value ;for d - q Subspace model predicts current controller, through , , , , i m , Calculate the optimal voltage vector in the dq subspace. After passing through the reverse synchronous rotation module, the signal is transmitted to the three-phase decoupled SVPWM module to generate a level signal, which enables the motor to run, thus forming a closed-loop control system.

[0083] The methods in the above embodiments can be implemented by a processor calling a program stored in memory (which can be the device's memory or external memory). That is, the device may include a processor that executes the methods in the above embodiments by calling a program in memory. The processor here can be an integrated circuit with signal processing capabilities, such as a CPU. The device can be implemented by one or more integrated circuits configured to implement the above methods. For example: one or more ASICs, or one or more microprocessors (DSPs), or one or more FPGAs, or a combination of at least two of these integrated circuit forms. Alternatively, a combination of the above implementation methods can be used.

[0084] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0086] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0088] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0089] In another embodiment of this application, a computer-readable storage medium is also provided, which stores computer-executable instructions. When at least one processor of the device executes the computer-executable instructions, the device performs the method described in the above-described partial embodiments.

[0090] In another embodiment of this application, a computer program product is also provided, the computer program product including computer-executable instructions stored in a computer-readable storage medium; at least one processor of the device can read the computer-executable instructions from the computer-readable storage medium, and the at least one processor executes the computer-executable instructions to cause the device to perform the methods described in some of the above embodiments.

[0091] The above description and accompanying drawings provide specific example embodiments and implementations. However, the described subject matter can be embodied in a variety of different forms, and therefore, the covered or claimed subject matter is intended to be construed as not being limited to any of the example embodiments set forth herein. A fairly broad scope is intended for the claimed or covered subject matter. Among other things, the subject matter can be embodied as a method, apparatus, component, system, or non-transitory computer-readable medium for storing computer code. Thus, embodiments can take the form of, for example, hardware, software, firmware, storage medium, or any combination thereof. For example, the method embodiments described above can be implemented by a component, apparatus, or system including a memory and a processor by executing computer code stored in said memory.

[0092] References to features, advantages, or similar language anywhere in this specification do not imply that all features and advantages achievable with this technical solution are included or should be included in any single implementation thereof. Rather, language relating to features and advantages is to be understood as meaning that a particular feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of this technical solution. Therefore, discussions of features and advantages, and similar language anywhere in this specification may, but do not necessarily, refer to the same embodiments.

[0093] Furthermore, the features, advantages, and characteristics described in this technical solution can be combined in any suitable manner in one or more embodiments. In view of the description herein, those skilled in the art will recognize that this technical solution can be practiced without one or more specific features or advantages of a particular embodiment. In other instances, additional features and advantages that may not be present in all embodiments of this technical solution may be found in certain embodiments.

Claims

1. A model predictive fault-tolerant control method for a dual three-phase permanent magnet synchronous motor, characterized in that, The method includes: S1, the local loss-of-excitation fault detection module receives the current and voltage values ​​measured by the current and voltage sensors, and estimates the lumped disturbance of the dual three-phase permanent magnet synchronous motor based on the extended state observer of the hyperlocal model; it then transmits the lumped disturbance to... d x - q y Subspace deadbeat predictive current control module and d - q Subspace model predicts current control module; S2, d x - q y The subspace deadbeat predictive current control module performs delay compensation to obtain d. x axis, q y The predicted shaft current value is used to obtain the voltage reference value based on the predicted current value and the deadbeat principle. S3, d - q The subspace model predicts that the current control module performs delay compensation, constructs a dynamic voltage vector set and corrects the initial voltage vector set, and searches for... dq Subspace optimal voltage vector; S4, the VSD conversion module transforms the physical quantities of the six phases of the stator to... , , Subspace; Synchronous Rotation Coordinate Module will subspace Transformation of physical quantities in subspace to synchronous rotating coordinate system dq and d x - q y Coordinate system; S5, the three-phase decoupled SVPWM module uses the three-phase decoupled SVPWM algorithm to convert the optimal voltage vector into the PWM drive signal required by the six-bridge arm inverter, driving the dual three-phase permanent magnet synchronous motor.

2. The method according to claim 1, characterized in that, The discrete mathematical model of the dual three-phase permanent magnet synchronous motor using the hyperlocal model in step S1 is as follows: ； The mathematical model of the extended state observer is ； in, T s The sampling period; β 1 and β 2 is a constant. β 1 = 2 ω c , β 2 = ,in It is the bandwidth of the extended state observer; α It is the gain matrix α = diag( α d , α q , α dx , α qy ); i sr It is a current vector. , i d 、i q 、 i dx 、i qy They are d, q, and d respectively. x q y Shaft current value; z 1 is i sr The estimated value, z 1=[ , , , ] T , , , , They are d, q, and d respectively. x q y shaft current estimate; z 2 is an estimate of the lumped disturbance. = [ , ， , ] T , , , , They are d, q, and d respectively. x q y Estimates of axis lumped disturbances; u sr It is d, q, d x q y Voltage vector composed of axial voltages u sr =[ u d , u q , u dx , u qy ] T ，u d , u q , u dx , u qy They are d, q, and d respectively. x q y Axis voltage vector components, Indicates the first time, These are d, q, and d of the hyperlocal model, respectively. x q y Shaft lumped disturbance; Represents d, q, d x q y The deviation between the estimated and true values ​​of the shaft current vector. Let d, q, and d represent the values ​​in the hyperlocal model, respectively. x q y The coefficient of shaft voltage.

3. The method according to claim 2, characterized in that, In step S2, according to ； Delay compensation is performed to obtain the predicted values ​​of the dx-axis and qy-axis currents. and The predicted current value is obtained based on the discrete mathematical model of the dual three-phase permanent magnet synchronous motor using the hyperlocal model. The voltage reference value was calculated based on the deadbeat principle. and , ， in and These are the reference values ​​for the dx-axis and qy-axis currents.

4. The method according to claim 3, characterized in that, The delay compensation in step S3 includes: Predict the currents along the d and q axes at the next moment based on the deadbeat principle. and Discrete mathematical model is ； It is the sampling time; , It is the first k The d-axis and q-axis currents at time t; , They are the first k The d-axis and q-axis voltages at time t.

5. The method according to claim 4, characterized in that, Step S3 involves constructing a dynamic voltage vector set, including: motor Control and set the d-axis current reference value. The upper and lower limits are respectively and ,according to ； The upper and lower limits of the d-axis voltage can be obtained. Set an adjustment factor and its upper and lower limits , And satisfying 0 < <1; Q-axis voltage upper and lower limits Then according to ； Dynamic adjustment to obtain d - q Shaft reference voltage upper and lower limits; where , ; divide the interval N d Divide the interval into equal parts. N q Divide into equal parts to obtain the initial voltage vector set. .

6. The method according to claim 5, characterized in that, Step S3 involves correcting the initial voltage vector set, including: Based on the initial voltage vector set Center voltage vector ,pass ； Two voltage vectors are obtained , ,in , , , ,according to , Length of the module and To analyze whether it is necessary to Make corrections; if and All do not exceed the inverter bus voltage If so, no correction is needed; express yes The voltage vector is obtained by inverse synchronous rotation transformation. express yes Obtained by inverse synchronous rotation transformation, The center voltage vector is at - Subspace voltage vector, The center voltage vector is at - Subspace voltage vector, Represents the initial voltage vector set; like > or > First, scale to , and d x -q y The subspace voltage vector remains unchanged, while the dq subspace voltage vector changes. The calculation is as follows: ; As a corrected voltage vector set The first element; Rotate Δ in a counterclockwise direction θ get ; calculate ;judge Does it not exceed If it exceeds the limit, then stop; otherwise, continue. Add correction voltage vector set In the middle; rotation have to And repeat; the termination condition is that the number of elements in the modified voltage vector set reaches the upper limit. ,or ( k The modulus of (= 2,3,…) exceeds ; yes The scaled voltage vector, and After scaling, and Things will also change. Become , Become ; It is after scaling The changing voltage vector It is after scaling The changing voltage vector; It is a corrected voltage vector set; After the first rotation The voltage vector obtained by the change After the first rotation The voltage vector obtained from the change; yes After k rotations, by The voltage vector obtained from the change; like > or > ,Will Reduced to a module length equal to ;d x -q y The subspace voltage vector remains unchanged, while the dq subspace voltage vector changes with... Change, gain and , As the first element in the corrected voltage vector set; for Perform a synchronous rotation transformation to obtain ; Detection by Calculated Does the module length not exceed If the value exceeds the limit, terminate the correction of the voltage vector set; otherwise, set the voltage vector set to... Add the corrected voltage vector set and continue rotating. Repeat until the termination condition is met.

7. The method according to claim 6, characterized in that, Search in step S3 dq The optimal voltage vector in the subspace includes: Traversal The voltage vector in the equation, substitute it into ， get Under the same voltage vector, the following predictions were made using the same method: ; , Let represent the predicted value of the dq subspace current vector at time k+2. It is the predicted value of the d-axis current at time k+2. It is the predicted value of the q-axis current at time k+2; This represents the predicted value of the dq subspace current vector at time k+3. In this context, n represents the nth time after the kth time. It is the current vector reference value. , This is the d-axis current reference value. This is the q-axis current reference value; According to the value function ； The value function value corresponding to each voltage vector is obtained. Q , R It is a weight matrix. It is the difference between the voltage vector and the previous optimal voltage vector; the voltage vector with the smallest value function value is selected as the optimal voltage vector, and the optimal voltage vector is output to the three-phase decoupled SVPWM module.

8. The method according to claim 1, characterized in that, Step S5 includes: using the three-phase decoupling SVPWM algorithm to convert the six-bridge inverter into two three-phase inverters connected in parallel.

9. The method according to claim 8, characterized in that, Step S5 includes: based on the input optimal voltage vector , and , The three-phase decoupled SVPWM algorithm is used to separate the voltage vectors. and The signals are converted and combined into the drive signals for the six-arm inverter.

10. A model predictive fault-tolerant control system for a dual three-phase permanent magnet synchronous motor, characterized in that, The control system includes: The local loss-of-excitation fault detection module receives current and voltage values ​​measured by current and voltage sensors, and estimates the lumped disturbance of the dual three-phase permanent magnet synchronous motor based on an extended state observer using a hyperlocal model; it then transmits the lumped disturbance to... d x - q y Subspace deadbeat predictive current control module and d - q Subspace model predicts current control module; d x - q y The subspace deadbeat predictive current control module performs delay compensation to obtain d. x axis, q y The predicted shaft current value is used to obtain the voltage reference value based on the predicted current value and the deadbeat principle. d - q The subspace model predicts the current control module, performs delay compensation, constructs a dynamic voltage vector set and corrects the initial voltage vector set, and searches... dq Subspace optimal voltage vector; The VSD conversion module transforms the physical quantities of the six phases of the stator to... , , subspace; Synchronous rotation coordinate module, subspace Transformation of physical quantities in subspace to synchronous rotating coordinate system dq and d x - q y Coordinate system; The three-phase decoupled SVPWM module uses the three-phase decoupled SVPWM algorithm to convert the optimal voltage vector into the PWM drive signal required by the six-bridge inverter, driving the dual three-phase permanent magnet synchronous motor.

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