Permanent magnet synchronous motor double-vector model predictive control method

Abstract

The invention discloses a permanent magnet synchronous motor double-vector model predictive control method which is completely different from traditional double-vector predictive control, after a first voltage vector is selected, a generated predictive error is used for selecting a second voltage vector, and therefore the accuracy of model prediction is improved. Compared with traditional permanent magnet synchronous motor double-vector model predictive control, the method has smaller steady-state torque ripple and current harmonic.

Description

Technical Field

[0001] The 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 are widely used in aerospace, medical and health, electric vehicles and other fields due to their simple structure, high efficiency and high power density. In high-precision fields such as aerospace and automotive electric, the excellent dynamic and static performance of permanent magnet motors has always been the goal of researchers in the industry. Classic control methods for permanent magnet motors include field oriented control and direct torque control.

[0003] The core idea of ​​field-oriented control is to transform the three-phase system into a two-phase system through coordinate transformation, thus achieving the decoupling control of the two variables of PMSM torque and flux linkage, and making the AC motor equivalent to a DC motor for control. Field-oriented control uses a proportional (PI) controller, which requires parameter tuning, and its dynamic performance is easily affected by the motor parameters. The PI controller of field-oriented control is often a cascade structure, with the innermost loop being the current loop and the outer loop being the speed loop. The bandwidth of the speed loop needs to be smaller than the bandwidth of the current loop, and the dynamic response and steady-state characteristics of the motor cannot be taken into account at the same time.

[0004] Direct torque control is to directly control the torque through the switching state of the inverter, calculate the electromagnetic torque and flux amplitude in the stator coordinate system respectively, and do not need to decouple the stator current. Its control objects are the electromagnetic torque tracking error and the stator flux tracking error, and they are allowed to fluctuate within a certain range. Direct torque control uses two hysteresis comparators to directly control the electromagnetic torque in the stator coordinate system of the motor, and then control the switching state of the inverter. Its biggest advantage is that the torque response is relatively fast and does not require a PI controller. It is used in air compressors, mixers, conveyor belts and other occasions that require large starting torque and fast torque response. In recent years, with the rise of electric vehicles, direct torque control algorithms have been valued in this field. The direct torque control algorithm itself also has many shortcomings: the switching frequency is not constant; the steady-state current and torque pulsation are large; the control performance is poor at low speed, accompanied by relatively large noise.

[0005] The main idea of ​​the permanent magnet motor model predictive control strategy is: in each control cycle, based on the system model and constraints, the optimization problem is solved online to obtain the optimal control effect. In recent years, with the rapid development of microprocessors, model predictive control technology has been widely used in servo motor drive systems. The outstanding advantage of model predictive control is fast torque response, which enables the servo motor drive system to have good dynamic performance. Model predictive control is divided into finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC) according to different control principles. CCS-MPC uses optimal control theory to directly obtain the optimal voltage vector that meets the constraints of the PMSM system, and combines the modulation strategy to realize the control of the motor. FCS-MPC makes full use of the discrete characteristics of power electronic devices, takes all switching states as candidate vectors, and obtains the optimal switching state according to the value function. Model predictive control has fast dynamic response capability and excellent steady-state characteristics, which can meet the application requirements of permanent magnet synchronous motors in the field of high-performance control.

[0006] The dual-vector model predictive control uses a duty cycle control method to synthesize the optimal voltage vector. The voltage vector applied by this method is no longer a voltage vector with seven fixed directions and amplitudes, giving the motor more selection states, which greatly improves the steady-state performance of the motor. However, the current dual-vector model predictive control of permanent magnet motors requires traversing all voltage vectors to select the optimal voltage vector. The procedure is complex and requires a large amount of calculation. Few related patents have studied this issue. Summary of the invention

[0007] In order to overcome the shortcomings of the prior art, the present invention provides a dual-vector model predictive control method for a permanent magnet synchronous motor. This method is completely different from the traditional dual-vector predictive control. After the first voltage vector is selected, the prediction error generated is used to select the second voltage vector, thereby improving the accuracy of the model prediction. The present invention has smaller steady-state torque fluctuations and current harmonics than the traditional dual-vector model predictive control of a permanent magnet synchronous motor.

[0008] The technical solution adopted by the present invention to solve the technical problem is 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: Perform one-step delay prediction based on step 1:

[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 generate 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 through the following cost 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 switch 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 according to the size of the two value functions, that is:

[0035]

[0036] Where d1 and d2 represent the duty ratios 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 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 executes 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, the computer program product comprising a computer storage medium storing a computer program, the computer program comprising instructions executable by at least one processor, and the above-mentioned model predictive control method is implemented when the instructions are executed by the at least one processor.

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

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

[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 conventional 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 It is a switch signal combination diagram corresponding to the double 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 a block diagram for realizing the dual-vector model predictive control of the permanent magnet synchronous motor in the present invention. DETAILED DESCRIPTION

[0049] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[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 value 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, Prediction 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 ratios 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: Perform one-step delay prediction based on step 1: 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 generate 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 through the following cost 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 switch 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 according to the size of the two value functions, that is: Where d1 and d2 represent the duty ratios 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. A computer program, characterized in that The computer program enables a computer to execute the method as claimed in claim 1.

3. 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 executes the method as claimed in claim 1.

4. 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 as claimed in claim 1 is implemented.

5. A chip, characterized in that: include: A processor, used 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.

6. 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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