Synchronous reluctance motor MTPA control method for optimizing online extremum search method

Abstract

The invention discloses a synchronous reluctance motor MTPA control method for optimizing an online extremum search method, and relates to the field of motor control. The method comprises the following steps: applying an extremum search method to a maximum torque / current ratio (MTPA) in a synchronous reluctance motor (SynRM); and under a steady state condition, constructing a lookup table by using an optimized current vector angle obtained by online extremum search. And under subsequent similar working conditions, the data in the lookup table is directly called, so that a repeated search process is avoided, and the control efficiency is improved. When the operation of the motor does not reach the steady state, the existing data in the lookup table are used for interpolation calculation, and the MTPA current vector angle under the current working condition is estimated, so that the MTPA control strategy is expanded to the unsteady state condition. The method aims at challenges caused by parameter nonlinearity and dependence on steady-state conditions in a traditional MTPA control strategy. Through combination of an online extremum search method and real-time lookup table construction, the method improves the dynamic response speed, expands the application to an unsteady state condition, and significantly improves the efficiency and performance of SynRM.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a synchronous reluctance motor MTPA control method that optimizes an online extreme value search method. Background Art

[0002] In engineering practice, in order to achieve high-precision MTPA control, the common practice is to calibrate the motor used and measure the torque output under different dq axis current distribution conditions. However, the traditional calibration method has many problems and limitations, including time-consuming, cumbersome, and insufficient parameter versatility. In order to obtain accurate and detailed parameters, it is often necessary to examine the output of the motor under different bus voltages and in the full speed range. In addition, the influence of motor temperature on motor performance and measurement accuracy must be considered, which requires a lot of repetitive work, which is time-consuming and cumbersome. The introduction of online search algorithms can dynamically adjust and optimize motor control parameters in real time according to the current working state and environmental conditions of the motor, thereby improving the accuracy and stability of motor control and better meeting the control requirements under different working conditions. The introduction of online search algorithms into motor control systems is of great significance, which can further improve the performance and efficiency of motor control. Summary of the invention

[0003] The purpose of the present invention is to provide a synchronous reluctance motor MTPA control method that optimizes the online extreme value search method, which can enable the system to operate efficiently under complex working conditions, save costs, and improve the overall operating efficiency.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A synchronous reluctance motor MTPA control method using an optimized online extreme value search method, characterized by comprising:

[0006] The MTPA control of the synchronous reluctance motor is realized using the online extreme value search method. A steady-state detection (SSD) module is established to determine whether the drive is in transient or steady-state mode based on the rate of change of torque and speed. The SSD module generates an enable signal to enable or disable the extreme value search method.

[0007] In the steady-state process: the search step of the online extreme value search method is optimized by a simplified gradient descent method, that is, a larger step size is used to increase the search speed when it is far away from the MTPA operating point, and a smaller search step size is used when it is close to the MTPA operating point until the current vector angle converges, and the converged current angle in the steady state is recorded in the online search result lookup table.

[0008] In the transition phase: make full use of the optimization values ​​obtained in the past search phase to reduce unnecessary calculations and adjustments to the system operating point, improve the system's response speed and stability, and interpolate data that is not available in the storage area by reading the MTPA data that has been searched near the current value to give an estimated reference current vector angle.

[0009] Step 1: Determine whether the drive is in transient or steady-state mode based on the rate of change of torque and speed and establish a steady-state detection (SSD) module.

[0010] Step 2: Use the simplified gradient descent method to optimize the search step size of the online extreme value search method.

[0011] Step 3: Construct an online search result lookup table to record the current angle that converges under stable conditions.

[0012] Step 4: Add a pre-judgment module. If the optimal value for the corresponding working condition has been searched and stored, it can be called directly, thereby skipping this search phase and avoiding repeated search phases.

[0013] Step 5: When the motor is not in the intermediate transition stage of steady state, for the data not in the storage area, the MTPA data that has been searched near the current value is read for interpolation calculation to give an estimated reference current vector angle.

[0014] Optionally, a steady state detection (SSD) module is established to determine whether the system is in a stable state.

[0015] Optionally, a simplified gradient descent method is used to optimize the search step size of the online extreme value search method.

[0016] Optionally, an online search result lookup table is constructed to record the current angle that converges in a stable state. According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects.

[0017] Optionally, a pre-judgment module is added. If the optimal value for the corresponding working condition has been searched and stored, it can be directly called, thereby skipping this search phase and avoiding repeated search phases.

[0018] The present invention discloses a synchronous reluctance motor MTPA control method that optimizes the online extreme value search method. The method comprises: determining whether the motor is in a steady state or quasi-steady state mode according to the change of torque and speed through a steady state detection (Steady State Detect, SSD) module. This module determines the activation or deactivation of the MTPA online search module to ensure that the search process is only performed when the motor is in a steady state; in view of the problem that the traditional online extreme value search method has high steady-state accuracy but slow search speed, a variable search step size is used to optimize its search speed. By timely adjusting the search step size, a larger step size can be used for rapid approximation when the deviation from the MTPA operating point is large, and a smaller step size can be used for precise search when approaching the MTPA operating point to avoid oscillation; it is proposed to use the high-precision MTPA control result obtained by the online extreme value search method in a steady state, establish an optimal value lookup table in real time to save the repeated search stage, and expand the control strategy to non-steady-state conditions. In this way, when the motor encounters the same working condition in the subsequent operation, the value in the lookup table can be directly called, thereby avoiding repeated search processes and improving control efficiency; when the motor operation has not reached a steady state, the existing optimized value is used to approximate the real MTPA reference value in the dynamic process through the interpolation algorithm to reduce the deviation between the working point after reaching the steady state condition and the actual MTPA working point. The optimized online extreme value search method makes the MTPA control of the synchronous reluctance motor more precise, more responsive, and able to adapt to a wider range of operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 It is a schematic diagram of the control flow of the synchronous reluctance motor MTPA using the optimized online extreme value search method of the present invention.

[0021] Figure 2 It is the block diagram of the extreme value search method and the steady-state judgment block diagram of the present invention.

[0022] Among them, (a) is the flow chart of the steady-state discrimination module; (b) is the block diagram of the extreme value search method;

[0023] Figure 3 It is a schematic diagram of the simplified gradient descent method under the constant torque load of the motor in this embodiment and a flow chart of the search result storage module.

[0024] Among them, (a) is a schematic diagram of the simplified gradient descent method under constant torque load; (b) is a flow chart of the search result storage module.

[0025] Figure 4 This is a flow chart of the interpolation module of the synchronous reluctance motor control system of the present invention.

[0026] Figure 5 This is a flow chart of the pre-judgment module of the synchronous reluctance motor control system of the present invention.

[0027] Figure 6 The present invention establishes a write data module test for the lookup table online.

[0028] Among them, (a) is the current simulation waveform of the search process; (b) is the current vector angle simulation waveform of the search process; (c) is the search value convergence judgment logic value; (d) storage area data;

[0029] Figure 7 The present invention establishes online the reading data of the lookup table and the interpolation output module test.

[0030] Among them, (a) is the motor torque simulation waveform; (b) is the stator current simulation waveform; (c) is the motor speed simulation waveform; (d) is the current vector angle output value; DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] The purpose of the present invention is to provide a synchronous reluctance motor MTPA control method that optimizes the online extreme value search method, which can significantly reduce the stator current of the motor, improve energy efficiency, and has good dynamic performance and steady-state accuracy.

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] like Figure 1 As shown, the present invention provides a high-performance control method for a synchronous reluctance motor MTPA, comprising:

[0035] Step 100: Establishing MTPA control of the extreme value search method and establishing a steady-state check module (SSD); specifically including:

[0036] Whether the drive is in transient or steady-state mode is determined based on the rate of change of torque and speed. Based on the determined motor operating state conditions, the SSD module generates an enable signal, expressed as e(k); the motor state is divided according to the value of e(k).

[0037] Step 200: When the motor is in a steady state, the gradient descent method is used to optimize the step size selection in the MTPA search process, and an online search result lookup table is constructed and the converged current vector angle is recorded. Specifically, it includes:

[0038] When using the traditional extreme value search method, the search is usually performed with a given step size, and its search speed is an inherent disadvantage. The execution of the MTPA online search depends on the determination and maintenance of the steady-state conditions, so it is necessary to improve the utilization efficiency of the steady-state stage as much as possible. The present invention uses gradient descent to optimize the search step size, thereby accelerating the convergence speed of the online maximum torque / maximum power tracking (MTPA) search results. And record the converged current vector angle in the online search result lookup table, so that it can be directly called in subsequent approximate working conditions, eliminating the process of repeated searches and improving steady-state performance.

[0039] Step 300: When the motor is not in a steady state, the data not stored in the storage area is interpolated by reading the searched MTPA data near the current value to provide an estimated reference current vector angle. Specifically, it includes:

[0040] In the intermediate transition stage of the motor's non-steady state, due to the lack of previously stored optimal values, directly applying the reference value of the previous stage may not achieve accurate MTPA control, affecting the motor performance. To solve this problem, an interpolation calculation method can be used to estimate the reference current vector angle of the current stage using the existing MTPA data near the current value in the storage area. This method can provide an estimate that is closer to the actual MTPA operating point, which helps reduce the search cycle required after steady state, improve the dynamic response of the motor, and promote the motor to return to steady state faster, thereby optimizing the MTPA control effect.

[0041] Step 400: Before the motor executes the MTPA search phase, a pre-judgment module is added to realize real-time judgment of the working condition of the current motor, and the data corresponding to similar working conditions are matched in the established online MTPA lookup table. If the optimal value of the corresponding working condition has been searched and stored, it can be directly called, which specifically includes:

[0042] This module collects key parameters such as the motor's stator current and speed in real time, and sets steady-state condition thresholds, such as the rate of change of current and speed. The rate of change of these parameters is calculated and compared with the threshold to determine whether the motor is in a steady state. If the motor is in a steady state, the MTPA search algorithm is started to find the optimal current vector angle; if it is not in a steady state, the search is not started to avoid misjudgment. The system will continuously monitor and update the steady-state judgment results in real time, and record the optimal parameters in the steady state in the lookup table so that they can be directly applied in subsequent similar working conditions to improve control efficiency.

[0043] Based on the above technical solution, the following embodiments are provided.

[0044] In the following control link example, the motor parameters are shown in Table 1:

[0045] Table 1

[0046]

[0047]

[0048] First, determine the operating conditions of the synchronous reluctance motor:

[0049] like Figure 2 As shown in (a): The Steady State Detect (SSD) module is a key module for MTPA control. Its purpose is to determine whether the drive is in transient or steady-state mode based on the rate of change of torque and speed. Torque is generally not easy to measure online, and the estimation also has accuracy issues, so current approximation is used instead. According to the determined motor operating state conditions, the SSD module generates an enable signal, expressed as e(k), which determines the activation or deactivation of the extreme value search method MTPA module. The extreme value search method module is responsible for the search of the MTPA control current vector angle. It is necessary to ensure that the module should enable or disable MTPA tracking according to the transient or steady state of the motor. The following is the judgment method of the SSD module:

[0050] (1) Transition process (e(k) = 0): If the torque change rate (approximated by current) or the change rate of torque and speed exceeds their respective thresholds, the motor is considered to be in the transition process. In this case, the SSD module sets e(k) to 0 and disables the MTPA online search module.

[0051] (2) Steady-state operation (e(k) = 1): If the rate of change of torque and speed are both below their respective thresholds, indicating that neither torque nor speed is changing rapidly, the motor is considered to be in steady-state operation. In this case, the SSD module sets e(k) to 1, activates the extreme value search module and enables MTPA tracking.

[0052] (3) Quasi-steady-state operation (e(k) = 2): If the rate of change of the torque is within its threshold range, but the rate of change of the speed exceeds its threshold, indicating a condition of constant torque but changing speed (quasi-steady state), the SSD module sets e(k) to 2. Similar to steady-state operation, this also activates the online search module and enables MTPA search. According to the above steady-state discrimination principle, when the module output value e(k) is non-zero, the MTPA online search procedure can be started.

[0053] Step 2: Use the gradient descent method to optimize the step size selection in the MTPA search process when the motor is in steady state, build an online search result lookup table and record the converged current vector angle.

[0054] like Figure 3 As shown in (a): Taking the real MTPA operating point as the dividing line, when the motor operating point is on the left side of the dividing line, the gradient of the stator current change is less than zero; when the operating point is on the right side of the dividing line, the gradient of the stator current change is greater than zero; when the motor operates near the MTPA operating point, the slope of the curve becomes more and more gentle, and when it reaches the MTPA operating point, the gradient of the stator current change is greater than zero.

[0055] The idea of ​​the gradient descent method is to search along the gradient direction of the objective function, and comprehensively determine the position of the motor operating point on the constant torque curve based on the real-time estimated gradient sign and the size of the absolute value, and then determine the appropriate search step size at this time, that is, when the motor is far away from the MTPA operating point, a larger step size is used to increase the search speed, and when it is close to the MTPA operating point, a smaller search step size is used to avoid oscillation and obtain good accuracy, until the motor operating point is transferred to the vicinity of the MTPA operating point and the search value converges. According to the principle of the aforementioned search algorithm, it is impossible to obtain the function of the current to the current vector angle under a specific working condition (torque), so it is impossible to calculate numerically based on the function expression. The process of online search is to change the current vector angle discretely with a smaller step size. Due to the limitation of the dynamic response time of the motor, there is no need to calculate the gradient at high speed. Therefore, the gradient of the current to the current vector angle can be approximately simplified and calculated based on the difference between the current before and after the current vector angle changes. In the above analysis, the calculation process of the simplified stator current drop gradient is simple, which can be used to select the search step size. This simplified method also does not contain motor parameters and is easy to implement.

[0056] According to the size of the gradient, the relative gradient size is multiplied by the minimum search step size, and the corresponding search step size is assigned to different working conditions.

[0057] In order to better monitor the running state of the motor and determine whether the extreme value search method has reached convergence, the torque of the motor is generally not easy to measure. It is necessary to monitor the measurable parameters of the motor in real time, such as current and speed. Refer to the aforementioned steady-state condition judgment module, but set stricter criteria to prevent the storage of erroneous data. Based on this, the convergence of the search process data can be effectively monitored. If the search result converges, it means that the motor has achieved MTPA control under this condition. At this time, the optimal value corresponding to this condition can be recorded in real time in the memory.

[0058] The third step is that when the motor is not in the intermediate transition stage when the motor is not in the steady state, the data that is not stored in the storage area is interpolated by reading the MTPA data that has been searched near the current value to give an estimated reference current vector angle.

[0059] like Figure 4 As shown: For convenience, the current is divided into intervals of 1A as an example. The interpolation process starts from reading all the existing MTPA optimized and corrected reference values ​​in an interval near the current value of the current stage, and then selecting the two closest reference values ​​that meet the conditions for linear interpolation. Considering that the MTPA current vector angles of different motors are generally close when they are not saturated, if there is no reference value that meets the conditions in the light saturation interval with a smaller current value, the theoretical value is used for interpolation; in the deep saturation interval with a larger current, the inductance saturation of different motors may be different, so the maximum reference value obtained by optimization is directly used, and the default is that the deep saturation area has been reached.

[0060] When the motor is not in the intermediate transition stage when it is not in steady state (or the working condition where steady state condition is difficult to reach), parameter optimization cannot be performed, and the storage area does not have the previously stored optimization value. If the reference value of the previous stage is still used, the most accurate MTPA control may not be achieved, so the output torque potential cannot be fully utilized, which will be unfavorable for the motor to reach steady state condition again, and the establishment of steady state condition is crucial for MTPA control based on extreme value search. Given that the current vector angle at MTPA generally shows a monotonically increasing trend as the current increases. Therefore, in the intermediate transition stage, for the data that is not in the storage area, the MTPA data that has been searched near the current value is read for interpolation calculation to give an estimated reference current vector angle. The advantage of this method is that it is closer to the real MTPA control current vector angle of the current stage than the traditional direct use of the reference value of the previous search stage, which helps to reduce the interpolation between the current vector angle after entering the steady state and the real MTPA control vector angle, and helps to reduce the search cycle after entering the steady state in the future. In addition, it can also improve the dynamic response characteristics of the motor in the transition stage, which is also conducive to the motor entering the steady state faster.

[0061] The fourth step is to add a pre-judgment module before the motor executes the MTPA search phase to realize real-time judgment of the current working condition of the motor, and match the data corresponding to similar working conditions in the established online MTPA lookup table. If the optimal value of the corresponding working condition has been searched and stored, it can be directly called.

[0062] like Figure 5As shown in the figure: In order to strictly ensure that the recorded value is the correctly optimized value, a certain degree of rapidity must be sacrificed to ensure accuracy. For example, the same optimal current angle output in multiple consecutive search cycles can be regarded as a sign of convergence. Once the convergence condition is met, the optimal current angle corresponding to the current value can be stored for subsequent table lookup operations. In practical applications, the optimized value can be indexed and modified by the sequence number of the variable, thereby realizing the reading and modification of the data. Such a design can effectively manage and utilize the recorded data and improve the real-time performance and stability of the system.

[0063] The module needs to collect key operating parameters of the motor in real time, such as stator current, speed, etc. Steady-state condition setting: Set specific conditions for judging steady state, which usually include thresholds for current change rate, speed change rate, etc. If the change rate of these parameters is lower than the set threshold, the motor can be considered to be in steady state. Change rate calculation: Calculate the change rate of the motor stator current and speed. This can be achieved by comparing the currently collected parameter values ​​with the parameter values ​​at the previous time point. Determine steady state: Compare the calculated change rate with the preset threshold. If the change rate is lower than the threshold, the motor is judged to be in steady state; if the change rate exceeds the threshold, the motor is considered to be in non-steady state. Control decision: According to the steady-state judgment result, decide whether to start the MTPA search algorithm. If the motor is in steady state, start the search algorithm to find the optimal current vector angle; if the motor is in non-steady state, do not start the search algorithm to avoid misjudgment in dynamic changes. Real-time update: During the operation of the motor, the pre-judgment module will continuously monitor and evaluate the operating status of the motor and update the steady-state judgment result in real time. Data recording: When the motor reaches steady state and the MTPA search algorithm finds the optimal current vector angle, the parameters under this condition (such as the current vector angle) are recorded in the lookup table for subsequent use. Lookup table application: In the subsequent operation of the motor, if a similar operating condition is encountered as in the lookup table, the corresponding MTPA parameters can be directly read from the lookup table without searching again, thereby improving control efficiency.

[0064] like Figure 6As shown: (a) is the current simulation waveform of the search process; (b) is the current vector angle simulation waveform of the search process; (c) is the search value convergence judgment logic value; (d) storage area data; It should be noted that in order to test the performance of the online lookup table after optimization of the extreme value search method MTPA control strategy, a torque mutation response test is performed. In order to preliminarily verify the proposed control, for the convenience of analysis, the established lookup table segment interval is 1A. The working condition is consistent with the typical working condition mentioned above. The given speed is accelerated from 0r / min to 500r / min after 1s. The motor performs the search phase of the online extreme value search method MTPA control under two different load conditions of 2N·m and 6N·m. From the simulation analysis, it can be seen that the data writing module can normally write the optimal parameters that have reached convergence, and store the current value and the corresponding current vector angle into the storage area.

[0065] like Figure 7 As shown: (a) is the motor torque simulation waveform; (b) is the stator current simulation waveform; (c) is the motor speed simulation waveform; (d) is the current vector angle output value; It should be noted that after completing the online MTPA current vector angle search under two specific load torques, the load torques in the following stages are changed to 2N·m and 4N·m respectively, and a load mutation test is performed to test the storage effect of the online lookup table and the performance of the reading and interpolation modules respectively; From the above simulation analysis: the stator current changes with the torque change and quickly reaches a stable state. The motor speed waveform will change slightly after the load mutation; the output value of the current vector angle, when the current reaches the 2N·m load torque of the existing search value, after the sampling delay and the current drop time, the current vector angle quickly reaches the previously optimized search stage and stabilizes. Under the 4N·m load torque condition that has not been optimized, the interpolation module takes effect and quickly gives a reference current vector angle of 55°, which is very similar to the result obtained by the aforementioned search program. Considering the implicit errors caused by the threshold setting of the search procedure and the influence of other factors, the interpolation output is very close to the true value and remains unchanged after reaching stability.

[0066] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0067] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A synchronous reluctance motor MTPA control method based on an optimized online extreme value search method, characterized in that: include: The MTPA control of the synchronous reluctance motor is realized using the online extreme value search method. A steady state detection (SSD) module is established to determine whether the drive is in transient or steady state mode according to the rate of change of torque and speed. The SSD module generates an enable signal to enable or disable the extreme value search method. In the steady-state process: the search step of the online extreme value search method is optimized by a simplified gradient descent method, that is, a larger step size is used to increase the search speed when it is far away from the MTPA operating point, and a smaller search step size is used when it is close to the MTPA operating point until the current vector angle converges, and the converged current angle in the steady state is recorded in the online search result lookup table. In the transition phase: make full use of the optimization values ​​obtained in the past search phase to reduce unnecessary calculations and adjustments to the system operating point, improve the system's response speed and stability, and interpolate data that is not available in the storage area by reading the MTPA data that has been searched near the current value to give an estimated reference current vector angle. Step 1: Determine whether the drive is in transient or steady-state mode based on the rate of change of torque and speed and establish a steady-state detection (SSD) module. Step 2: Use the simplified gradient descent method to optimize the search step size of the online extreme value search method. Step 3: Construct an online search result lookup table to record the current angle that converges under stable conditions. Step 4: Add a pre-judgment module. If the optimal value for the corresponding working condition has been searched and stored, it can be called directly, thereby skipping this search phase and avoiding repeated search phases. Step 5: When the motor is not in the intermediate transition stage of steady state, for the data not in the storage area, the MTPA data that has been searched near the current value is read for interpolation calculation to give an estimated reference current vector angle.

2. The synchronous reluctance motor MTPA control method of optimizing the online extreme value search method according to claim 1 is characterized in that: The Steady State Detect (SSD) module is established based on the rate of change of torque and speed to determine whether the drive is in transient or steady state mode, as follows: Based on the determined motor operating state conditions, the SSD module generates an enable signal, denoted as e(k), which determines the activation or deactivation of the MTPA module. The MTPA module is responsible for searching the MTPA control current vector angle. It is necessary to ensure that the module should enable or disable MTPA tracking according to the transient or steady state of the motor.

3. The synchronous reluctance motor MTPA control method of optimizing the online extreme value search method according to claim 1 is characterized in that: The simplified gradient descent method is used to optimize the search step size of the online extreme value search method, as follows: Based on the positive and negative sign of the real-time estimated gradient and the size of the absolute value, the position of the motor operating point on the constant torque curve is comprehensively determined at this time, and then the search step size used at this time is determined.

4. The synchronous reluctance motor MTPA control method of optimizing the online extreme value search method according to claim 1 is characterized in that: By adding a pre-judgment module, if the optimal value for the corresponding working condition has been searched and stored, it can be directly called, thereby skipping this search phase and avoiding repeated search phases.

5. The synchronous reluctance motor MTPA control method of optimizing the online extreme value search method according to claim 1 is characterized in that: When the motor is not in the intermediate transition stage when the steady state is not in place, the data not stored in the storage area is interpolated by reading the searched MTPA data near the current value to give an estimated reference current vector angle, including: The interpolation process starts with reading all existing MTPA optimized and corrected reference values ​​in an interval near the current value of the current stage, and then selecting the two closest reference values ​​that meet the conditions for linear interpolation.

6. A control system, characterized in that: Execute the control method according to claims 1-5.

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