A double vector model predictive control method for permanent magnet synchronous motor

Abstract

The application discloses a double-vector model predictive control method for a permanent magnet synchronous motor, which is completely different from traditional double-vector predictive control, and after selection of a first voltage vector, a second voltage vector is selected by using a generated prediction error, so that the accuracy of model prediction is improved. The application has smaller steady-state torque fluctuation and current harmonic than traditional double-vector model predictive control for a permanent magnet synchronous motor.

Description

Technical Field

[0001] The present invention belongs to the technical field of motors, and in particular relates to a dual-vector model predictive control method for a permanent magnet synchronous motor. Background Art

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in aerospace, healthcare, electric vehicles, and other fields due to their simple structure, high efficiency, and high power density. In these high-tech fields, the superior dynamic and static performance of PMSMs has long been a goal for researchers. Classic control methods for PMSMs include field-oriented control (FOC) and direct torque control (DTC).

[0003] The core concept of field-oriented control (FOC) is to transform a three-phase system into a two-phase system through coordinate transformation, achieving decoupled control of the PMSM torque and flux, allowing AC motors to be controlled as if they were DC motors. FOC utilizes a proportional (PI) controller, which requires parameter tuning and is susceptible to motor parameters affecting its dynamic performance. PI controllers for FOC often have a cascade structure, with the innermost loop being the current loop and the outer loop being the speed loop. The speed loop's bandwidth must be smaller than the current loop's, and this compromises the motor's dynamic response and steady-state characteristics.

[0004] Direct torque control (DTC) directly controls torque through the switching state of the inverter. Electromagnetic torque and flux amplitude are calculated separately in the stator coordinate system, eliminating the need for stator current decoupling. The control targets are electromagnetic torque tracking error and stator flux tracking error, allowing them to fluctuate within a certain range. Using two hysteresis comparators, DTC directly controls the electromagnetic torque in the motor's stator coordinate system, thereby controlling the switching state of the inverter. Its greatest advantage is its fast torque response, eliminating the need for a PI controller. It is used in applications such as air compressors, blenders, and conveyors that require high starting torque and fast torque response. In recent years, with the rise of electric vehicles, DTC algorithms have gained significant attention in this field. However, DTC algorithms themselves have several drawbacks: unstable switching frequency; large steady-state current and torque ripple; poor control performance at low speeds, and considerable noise.

[0005] The main idea behind the model predictive control strategy for permanent magnet motors (PMSMs) is to solve the optimization problem online within each control cycle based on the system model and constraints to achieve the optimal control effect. In recent years, with the rapid development of microprocessors, MPC technology has been widely used in servo motor drive systems. Its outstanding advantage is its fast torque response, which enables servo motor drive systems to possess excellent dynamic performance. Based on different control principles, MPC is categorized into finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). CCS-MPC utilizes optimal control theory to directly determine the optimal voltage vector that satisfies the PMSM system constraints and combines it with a modulation strategy to achieve motor control. FCS-MPC leverages the discrete nature of power electronic devices, treating all switching states as candidate vectors and determining the optimal switching state based on a cost function. MPC boasts fast dynamic response and excellent steady-state characteristics, meeting the application requirements of high-performance control of permanent magnet synchronous motors.

[0006] Dual-vector model predictive control uses duty cycle control to synthesize the optimal voltage vector. This method no longer applies seven fixed-direction and fixed-amplitude voltage vectors, giving the motor more options and significantly improving its steady-state performance. However, current dual-vector model predictive control for permanent magnet motors requires iterating through all voltage vectors to select the optimal one. This process is complex and computationally intensive, leading to limited patent research on this issue. Summary of the Invention

[0007] To overcome the shortcomings of existing technologies, the present invention provides a dual-vector model predictive control method for permanent magnet synchronous motors. This method, unlike traditional dual-vector predictive control, uses the prediction error generated after selecting the first voltage vector to select the second voltage vector, thereby improving model prediction accuracy. This method achieves lower steady-state torque ripple and current harmonics than traditional dual-vector model predictive control for permanent magnet synchronous motors.

[0008] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0009] Step 1: The mathematical model of the permanent magnet synchronous motor is:

[0010]

[0011] Among them, i d and i q is the dq axis current, Rs is the motor phase resistance, L d and L q Represents the dq axis inductance of the motor, ω e is the electrical angular velocity of the motor, ψ f is the permanent magnet flux, u d and u q Represents the dq axis voltage of the motor;

[0012] After Euler discretization, the mathematical model of the motor is:

[0013]

[0014] Among them, i d (k+1) and i q (k+1) represents the predicted current value of the dq axis at time (k+1), i d (k) and i q (k) represents the measured current value of the dq axis at time k, u d (k) and u q (k) represents the measured voltage value of the dq axis at time k,

[0015] Step 2: Based on step 1, make one-step delay prediction:

[0016]

[0017] Among them, i d (k+2) and i q (k+2) represents the predicted current value of the dq axis at time (k+2), i d (k+1) and i q (k+1) represents the predicted current value of the dq axis at time (k+1), u d (k+1) and u q (k+1) represents the predicted voltage value of the dq axis at time (k+1);

[0018] Step 3: Select the first optimal voltage vector using the following cost function 1(1):

[0019]

[0020] Among them, g j Represents the value function value corresponding to different voltage vectors, i d * and i q * Represents the reference current value of the dq axis;

[0021] Step 4: At the initial stage of the program, the first optimal voltage vector is selected and applied to the motor, which will produce a prediction error:

[0022]

[0023] in, and represents the current prediction error of the dq axis,

[0024] The prediction errors are divided into four categories according to the error value:

[0025]

[0026] In order to offset the prediction error, when selecting the second voltage vector, consider the input current in the opposite direction of the current prediction error. Then the prediction equation of the second voltage vector is:

[0027]

[0028] in, and represents the predicted dq axis current at time (k+1), and Represents the dq-axis voltage value at time k;

[0029] Step 5: Select the second optimal voltage vector using the following value function (2):

[0030]

[0031] in, represents the value of value function 2, and is a reference value, here

[0032] According to the principle that the second vector changes the switching signal only once based on the first vector, the total number of alternative voltage vectors is reduced from 7 to 3, and the value function 2 is changed to:

[0033]

[0034] Step 6: Calculate the corresponding duty cycle size based on the size of the two value functions, that is:

[0035]

[0036] Where d1 and d2 represent the duty cycles corresponding to the first and second optimal voltage vectors, and Represents the cost function values ​​corresponding to the first and second optimal voltage vectors.

[0037] A computer program enables a computer to execute the above-mentioned model predictive control method.

[0038] An electronic device comprises: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the above-mentioned model predictive control method.

[0039] A computer-readable storage medium stores a computer program, which implements the above-mentioned model predictive control method when executed by a processor.

[0040] A chip includes: a processor for calling and running a computer program from a memory, so that a device equipped with the chip executes the above-mentioned model predictive control method.

[0041] A computer program product comprises a computer storage medium storing a computer program, wherein the computer program comprises instructions executable by at least one processor, and when the instructions are executed by the at least one processor, the above-mentioned model predictive control method is implemented.

[0042] The beneficial effects of the present invention are as follows:

[0043] (1) Superior dynamic performance: Compared with the traditional dual-vector model predictive control of permanent magnet synchronous motors, the method proposed in this invention has similar fast dynamic response capabilities.

[0044] (2) Steady-state performance optimization: Through current and torque testing and comparison under different load conditions, the present invention has smaller steady-state torque fluctuations and current harmonics than the traditional permanent magnet synchronous motor dual-vector model predictive control. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a prediction error classification diagram of the present invention.

[0046] Figure 2 This is a switching signal combination diagram corresponding to the dual vector of the present invention.

[0047] Figure 3 This is the dual-vector selection method proposed by the present invention.

[0048] Figure 4 It is an implementation block diagram of the dual-vector model predictive control of the permanent magnet synchronous motor in the present invention. DETAILED DESCRIPTION

[0049] The present invention will be further described below with reference to the accompanying drawings and examples.

[0050] The prediction error classification diagram is as follows Figure 1 As shown, the switch signal combination diagram corresponding to the dual vector is as follows Figure 2 As shown, the dual vector selection method proposed by the present invention is as follows Figure 3 As shown, Figure 4 The figure shows the implementation block diagram of the dual-vector model predictive control of the permanent magnet synchronous motor. The implementation steps are as follows:

[0051] Step 1: The mathematical model of the permanent magnet synchronous motor is:

[0052]

[0053] Among them, i d and i q is the dq axis current, R s is the motor phase resistance, L d and L q Represents the dq axis inductance of the motor, ω e is the electrical angular velocity of the motor, ψ f is the permanent magnet flux, u d and u q Represents the dq axis voltage of the motor.

[0054] After Euler discretization, the mathematical model of the motor is Among them, i d (k+1) and i q (k+1) represents the predicted current value of the dq axis at time (k+1), i d (k) and i q (k) represents the measured current value of the dq axis at time k, u d (k) and u q (k) represents the measured voltage value of the dq axis at time k,

[0055] Step 2: Due to the program calculation and the digital delay of the device, a delay prediction step is required based on step 1:

[0056]

[0057] Among them, i d (k+2) and i q (k+2) represents the predicted current value of the dq axis at time (k+2), i d (k+1) and i q (k+1) represents the predicted current value of the dq axis at time (k+1), u d (k+1) and u q (k+1) represents the predicted voltage value of the dq axis at time (k+1).

[0058] Step 3: Select the first optimal voltage vector through cost function 1:

[0059]

[0060] Among them, g j Represents the value function value corresponding to different voltage vectors, i d * and i q * Represents the reference current value of the dq axis.

[0061] Step 4: At the initial stage of the program, the first optimal voltage vector is selected and applied to the motor, which will produce a prediction error:

[0062]

[0063] in, and represents the current prediction error of the dq axis, Forecast errors can be divided into four categories according to the error value: like Figure 1 shown.

[0064] In order to offset the prediction error, when selecting the second voltage vector, consider the input current in the opposite direction of the current prediction error. Then the prediction equation of the second voltage vector is:

[0065]

[0066] in, and represents the predicted dq axis current at time (k+1), and Represents the dq-axis voltage value at time k.

[0067] Step 5: Select the second optimal voltage vector through cost function 2:

[0068]

[0069] in, represents the value of value function 2, and is a reference value, here When selecting the second optimal voltage vector, in order to minimize the number of switching times of the power device, according to the principle that the second vector only changes the switching signal once based on the first vector, the total number of alternative voltage vectors is reduced from 7 to 3, which can greatly reduce the amount of calculation, such as Figure 2 As shown. Then the value function 2 can be changed to

[0070] Step 6: The two optimal voltage vectors have been selected, and their respective duty cycles need to be calculated separately. The corresponding duty cycle size is calculated based on the size of the two value functions, that is, Where d1 and d2 represent the duty cycles corresponding to the first and second optimal voltage vectors, and Represents the cost function values ​​corresponding to the first and second optimal voltage vectors.

Claims

1. A dual-vector model predictive control method for a permanent magnet synchronous motor, characterized in that: The steps include: Step 1: The mathematical model of the permanent magnet synchronous motor is: Among them, i d and i q is the dq axis current, R s is the motor phase resistance, L d and L q Represents the dq axis inductance of the motor, ω e is the electrical angular velocity of the motor, ψ f is the permanent magnet flux, u d and u q Represents the dq axis voltage of the motor; After Euler discretization, the mathematical model of the motor is: Among them, i d (k+1) and i q (k+1) represents the predicted current value of the dq axis at time (k+1), i d (k) and i q (k) represents the measured current value of the dq axis at time k, u d (k) and u q (k) represents the measured voltage value of the dq axis at time k, Step 2: Based on step 1, make one-step delay prediction: Among them, i d (k+2) and i q (k+2) represents the predicted current value of the dq axis at time (k+2), i d (k+1) and i q (k+1) represents the predicted current value of the dq axis at time (k+1), u d (k+1) and u q (k+1) represents the predicted voltage value of the dq axis at time (k+1); Step 3: Select the first optimal voltage vector using the following cost function 1(1): Among them, g j Represents the value function value corresponding to different voltage vectors, i d * and i q * Represents the reference current value of the dq axis; Step 4: At the initial stage of the program, the first optimal voltage vector is selected and applied to the motor, which will produce a prediction error: in, and represents the current prediction error of the dq axis, The prediction errors are divided into four categories according to the error value: In order to offset the prediction error, when selecting the second voltage vector, consider the input current in the opposite direction of the current prediction error. Then the prediction equation of the second voltage vector is: in, and represents the predicted dq axis current at time (k+1), and Represents the dq axis voltage value at time k; Step 5: Select the second optimal voltage vector using the following value function (2): in, represents the value of value function 2, and is a reference value, here According to the principle that the second vector changes the switching signal only once based on the first vector, the total number of alternative voltage vectors is reduced from 7 to 3, and the value function 2 is changed to: Step 6: Calculate the corresponding duty cycle size based on the size of the two value functions, that is: Where d1 and d2 represent the duty cycles corresponding to the first and second optimal voltage vectors, and Represents the cost function values ​​corresponding to the first and second optimal voltage vectors.

2. An electronic device, characterized in that: include: processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the method as claimed in claim 1.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to claim 1 is implemented.

4. A chip, characterized in that: include: A processor, configured to call and run a computer program from a memory, so that a device equipped with the chip executes the method as claimed in claim 1.

5. A computer program product, characterized in that The computer program product comprises a computer storage medium storing a computer program, wherein the computer program comprises instructions executable by at least one processor, and when the instructions are executed by the at least one processor, the method according to claim 1 is implemented.

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