A motor control method, device, and storage medium

Abstract

This application provides a motor control method, apparatus, and storage medium that can improve the matching degree between the boundary range of the sliding mode observation model and the observation error, thereby improving the response speed of motor control. The method includes: performing an Nth observation of the motor current based on the sliding mode observation model to obtain the Nth observation error, where N is a positive integer greater than 0; based on the Nth observation error, the boundary range of the sliding mode observation model, and the cumulative number of boundary violations in the (N-1)th observation; if N is greater than or equal to a preset value, updating the boundary range of the sliding mode observation model according to the boundary violation rate, where the boundary violation rate is the ratio of the cumulative number of boundary violations in the Nth observation to N; and controlling the motor according to the updated sliding mode observation model.

Description

Technical Field

[0001] This application relates to the field of motor control technology, and in particular to a motor control method, device, and storage medium. Background Technology

[0002] The characteristics of a permanent magnet synchronous motor (PMSM) require that the rotor position be synchronized during control. Currently, rotor position information can be estimated using sensorless technology, and the motor can be controlled through a field-oriented control (FOC) algorithm. For example, an adaptive sliding mode observer (SMO) model can be used to estimate the motor current and rotor position. The core idea of ​​this approach is to adjust the boundary range of the sliding mode observer model using operating parameters such as rotor speed or rotor torque.

[0003] However, the operating parameters such as rotor speed or rotor torque are only indirectly related to the observation error of the sliding mode observation model, and the degree of matching between the adaptively adjusted boundary range and the observation error in the above scheme is poor. Summary of the Invention

[0004] The present application provides a motor control method, device, and storage medium that can improve the matching degree between the boundary range of the sliding mode observation model and the observation error, thereby improving the response speed of motor control.

[0005] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0006] Firstly, a motor control method is provided, which can be applied to a control device. The method includes: performing an Nth observation of the motor current based on a sliding mode observation model to obtain the Nth observation error, where N is a positive integer greater than 0; determining the cumulative number of boundary violations in the Nth observation based on the Nth observation error, the boundary range of the sliding mode observation model, and the cumulative number of boundary violations in the (N-1)th observation; updating the boundary range of the sliding mode observation model based on the boundary violation rate if N is greater than or equal to a preset value; and controlling the motor based on the updated sliding mode observation model. The boundary violation rate is the ratio between the cumulative number of boundary violations in the Nth observation and N. The cumulative number of boundary violations in the Nth observation is the number of observation errors exceeding the boundary range of the sliding mode observation model out of the Nth observation error. The Nth observation error is determined by performing N observations of the motor current based on the sliding mode observation model.

[0007] It can be understood that "out of bounds" refers to the observation error of the control device when observing the current based on the sliding mode observation model exceeding the boundary range of the sliding mode observation. Furthermore, when N is greater than or equal to a preset value, the control device updates the boundary range of the sliding mode observation model. This can be understood as the control device acquiring information on the out-of-bounds occurrence of the observation error of the sliding mode observation model over a period of time based on multiple observations of the motor current, and updating the boundary range of the sliding mode observation model accordingly.

[0008] In this embodiment, the control device can determine the ratio (i.e., the out-of-bounds rate) between the number of times the observation error is outside the boundary range and the total number of observations, based on multiple observations of the motor current. It then updates the boundary range according to this out-of-bounds rate, ensuring the updated boundary range matches the operating conditions over a given period. This improves the matching degree between the boundary range and the observation error, thereby enhancing the motor's control performance. It is understood that compared to adjusting the boundary range based on indirect variables such as rotor speed or torque, the actual out-of-bounds situation of the observation error of the sliding mode observation model over a period of time more directly and accurately reflects the actual operating state of the sliding mode observation model, thus effectively adjusting the boundary range and improving the matching degree between the boundary range and the observation error.

[0009] In one possible implementation, the boundary range of the sliding mode observation model is updated based on the boundary violation rate. This includes: performing closed-loop processing on the boundary violation rate based on the target boundary violation rate to obtain the updated boundary range; and updating the boundary range of the sliding mode observation model based on the updated boundary range. In other words, the control device performs closed-loop processing on the current boundary violation rate based on the target boundary violation rate to output the updated boundary range. This eliminates the need for complex algorithms, saving the control device's computational power and facilitating deployment.

[0010] In one possible implementation, motor control is performed based on the updated sliding mode observation model, including: if the Nth observation error exceeds the updated boundary range, determining the updated electromotive force factor output by the sliding mode observer based on the updated boundary range; and controlling the motor based on the electromotive force factor. In other words, after the control device updates the boundary range of the sliding mode observation model, the control device controls the motor based on the updated boundary range and the Nth observation error, thus enabling timely control of the motor using the updated boundary range and improving the response speed of motor control.

[0011] In one possible implementation, the motor current is observed for the Nth time according to the sliding mode observation model to obtain the Nth observation error. This includes: observing the motor current for the Nth time according to the sliding mode observation model to obtain the Nth current error between the Nth observed current and the Nth measured current; and determining the Nth observation error based on the Nth current error and the gain coefficient of the sliding mode observation model. In other words, the control device determines the Nth observation error based on the Nth current error and the gain coefficient of the sliding mode observation model, making the electromotive force factor output by the sliding mode observation model equivalent to the Nth observation error. Therefore, the observation error is equivalent to the back electromotive force of the motor. This allows the control device to determine whether the boundary has been exceeded based on the boundary range of the sliding mode observation model, directly linking the operating condition with the boundary range to improve the matching between the operating condition and the boundary range.

[0012] In one possible implementation, before controlling the motor based on the updated sliding mode observation model, the method provided by the first aspect further includes: updating the gain coefficient of the sliding mode observation model based on the Nth current error, provided that N is greater than or equal to a preset value. It is understood that, as explained in the aforementioned description of the working principle of the sliding mode observation model, the boundary range and gain coefficient of the sliding mode observation model are key parameters affecting its output. Therefore, updating only the boundary range may lead to a mismatch between the boundary range and the gain coefficient, thus affecting the accuracy of estimating the rotor's angular velocity and / or phase angle based on the electromotive force factor output by the sliding mode observation model. In other words, by synchronously updating the boundary range and gain coefficient of the sliding mode observation model, the control device can ensure that the updated boundary range matches the gain coefficient, thereby improving the accuracy of the electromotive force factor output by the sliding mode observation model. This allows for the rapid and accurate estimation of the rotor's angular velocity and / or phase angle based on the electromotive force factor, thereby improving the performance of motor control.

[0013] In one possible implementation, updating the gain coefficient of the sliding mode observation model based on the Nth current error includes: performing closed-loop processing on the gain coefficient of the sliding mode observation model based on the Nth current error to obtain the updated gain coefficient; and updating the gain coefficient of the sliding mode observation model based on the updated gain coefficient. In other words, the control device performs closed-loop processing on the current error based on the target current error to output the updated gain coefficient. This eliminates the need for complex algorithms, saving the control device's computational power and facilitating deployment.

[0014] In one possible implementation, motor control is performed based on the updated sliding mode observation model, including: updating the Nth observation error based on the Nth current error and the updated gain coefficient; determining the updated electromotive force factor output by the sliding mode observer based on the updated boundary range if the updated Nth observation error exceeds the updated boundary range; and controlling the motor based on the electromotive force factor. In other words, after the control device updates the sliding mode observation, it controls the motor based on the updated boundary range, the updated gain coefficient, and the Nth observation error. This allows for timely control of the motor using the updated boundary range and gain coefficient, thereby improving the response speed of motor control.

[0015] Secondly, a control device is provided for implementing the various methods described above. This control device can be the control device described in the first aspect or any implementation thereof, or a device containing the aforementioned control device, or a device included in the aforementioned control device, such as a chip. The control device includes modules, units, or means that implement the methods described above. These modules, units, or means can be implemented in hardware, software, or by hardware executing corresponding software. The hardware or software includes one or more modules or units corresponding to the functions described above.

[0016] In some possible designs, the control device may include a processing module. This processing module can be used to implement the processing functions described in the first aspect above and any of its possible implementations.

[0017] Thirdly, a control device is provided, comprising: at least one processor; the processor being configured to execute a computer program or instructions to cause the control device to perform the methods of any of the above aspects.

[0018] In one possible implementation, the control device further includes the memory. Optionally, the memory is coupled to the processor; the memory may be integrated with the processor, or it may be independent of the processor. Optionally, the processor is used to execute computer programs or instructions stored in the memory.

[0019] In one possible implementation, the memory is independent of the control device.

[0020] In one possible implementation, the control device also includes a communication interface for communicating with modules (e.g., motors) outside the control device.

[0021] The control device may be the control device in the first aspect described above or any implementation thereof, or a device containing the control device described above, or a device contained in the control device described above, such as a chip.

[0022] Fourthly, a computer-readable storage medium is provided that stores a computer program or instructions that, when executed on a control device, enable the control device to perform the method described in the first aspect or any implementation thereof.

[0023] Fifthly, a computer program product containing instructions is provided, which, when run on a control device, enables the control device to execute the method described in the first aspect or any implementation thereof.

[0024] In a sixth aspect, a control device (e.g., a chip or chip system) is provided, the control device including a processor for implementing the functions involved in any of the above aspects or any implementation thereof.

[0025] In some possible designs, the control device includes a memory for storing necessary program instructions and data.

[0026] In some possible designs, when the device is a chip system, it can be composed of chips or contain chips and other discrete components.

[0027] The technical effects of any of the design methods in aspects two through six can be found in the technical effects of the different design methods in aspect one above, and will not be repeated here.

[0028] In a seventh aspect, a control system for an electric motor is provided, the control system comprising: a control device according to the first aspect and any implementation thereof. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram illustrating the working principle of a sliding mode observation model provided in an embodiment of this application;

[0031] Figure 2 This is a schematic flowchart of a motor control method provided in an embodiment of this application;

[0032] Figure 3 This is a schematic diagram of updating the boundary range of a sliding mode observation model based on the target boundary crossing rate in a closed loop, as provided in an embodiment of this application.

[0033] Figure 4This is a schematic diagram of updating the gain coefficient of a sliding mode observation model based on the target current error closed loop, according to an embodiment of this application.

[0034] Figure 5 This is a schematic diagram of a process for synchronously updating the boundary range and gain coefficient of a sliding mode observation model, provided in an embodiment of this application.

[0035] Figure 6 This is a schematic diagram of the structure of a control device provided in an embodiment of this application. Detailed Implementation

[0036] To facilitate understanding of the embodiments of this application, the following points will be explained before introducing the embodiments of this application.

[0037] 1. To make the above-mentioned objectives, features, and advantages of the embodiments of this application more apparent and understandable, the embodiments of this application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described in the specific embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] 2. "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device or apparatus. This application does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or apparatus. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or apparatus. The type of memory can be any form of storage medium, and this application does not limit this.

[0039] 3. In the embodiments of this application, the descriptions such as "when," "under the circumstances," "if," and "if" all refer to the fact that the device or apparatus will make corresponding processing under certain objective circumstances. They are not time limits, nor do they require the device or apparatus to have a judgment action when it is implemented, nor do they mean that there are other limitations.

[0040] 4. In the embodiments of this application, for ease of description, when numbering or indexing is involved, the numbering can start from 1 continuously, or it can start from 0 continuously, or it can start from any integer continuously, without any specific limitation.

[0041] 5. In the embodiments of this application, "observation" and "estimation (or calculation)" are different. "Observation" refers to the real-time monitoring (or acquisition) of certain states (e.g., motor states) or parameters (e.g., motor back EMF) of the system using a sliding mode observer (SMO), sliding mode controller (SMC), or sliding mode observation model. This process depends on the design of the sliding mode controller or sliding mode observer. "Estimation," based on "observation," uses algorithms or data models to approximate or infer states or parameters that cannot be directly measured (e.g., rotor speed). Furthermore, in the embodiments of this application, the terms "sliding mode controller" and "sliding mode observer" can be used interchangeably; this is explained uniformly here and will not be repeated below.

[0042] 6. In the description of the embodiments of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0043] To facilitate understanding of the technical solutions provided in the embodiments of this application, the relevant technical terms and concepts involved in the embodiments of the application will be introduced first.

[0044] First, sliding mode observation model

[0045] The sliding mode observation model is a model built based on the mathematical model of the motor. By using the output of the sliding mode observation model, the back electromotive force of the motor can be estimated, and then the angular velocity and / or phase angle of the rotor can be obtained, thereby indirectly obtaining the position of the rotor.

[0046] For example, the mathematical model of the sliding mode observation model can be obtained by formula (1).

[0047]

[0048] See formula (1), Let R be the motor current error, which is the difference between the observed current observed by the sliding mode model and the measured current (or actual current). Let L be the stator resistance of the motor. Let L be the stator inductance of the motor. s For measuring voltage (or actual voltage). denoted as , where is the observed back electromotive force. z is the iteration factor output by the sliding mode observation model.

[0049] It should be understood that the iteration factor z output by the sliding mode observation model can be processed (e.g., low-pass filtering) to obtain the back electromotive force of the motor. The back electromotive force of the motor contains the phase angle information of the motor rotor, and the angular velocity and / or phase angle of the rotor can be estimated based on the back electromotive force of the motor.

[0050] In addition, for ease of description, "iteration factor" is replaced with "electromotive force factor" in the embodiments of this application. Unless the difference between the two is emphasized below, the two can be used interchangeably. This is explained uniformly here and will not be repeated below.

[0051] Furthermore, in the embodiments of this application, the terms "sliding mode observation model", "sliding mode observer" and "sliding mode controller" can be used interchangeably, which will be explained uniformly here and will not be repeated below.

[0052] Second, the working principle of the sliding mode observation model.

[0053] The following is combined Figure 1 This explains the working principle of the sliding mode observation model.

[0054] Figure 1 This is a schematic diagram illustrating the working principle of a sliding mode observation model provided in an embodiment of this application. Figure 1 As shown, the synchronizer is used to calculate the number of times the sliding mode observation model observes the motor current within one cycle. It should be understood that the synchronizer can be set to 0 (or reset) before starting observation in the next cycle.

[0055] like Figure 1As shown, after the voltage is input to the motor, the sliding mode observation model observes the motor current, obtaining both the observed current and the measured current (or actual current). It can be understood that the sliding mode observation model can measure the motor current using an inverter bridge, or other circuits or devices, to obtain the measured current. Additionally, the observed current is the current obtained through observation using the sliding mode observation model.

[0056] It is understandable that the sliding mode observation model can determine the current error of the motor based on the difference between the observed current and the measured current, and judge the current error to determine the electromotive force factor z.

[0057] For example, such as Figure 1 As shown, if the current error is within the boundary range, then the electromotive force factor z = K1. For example, K1 = K slid Error / MaxSMCerror. K slid represents the gain coefficient of the sliding mode observation model. Error represents the current error. MaxSMC represents the boundary range of the sliding mode observation model.

[0058] It is understandable that the gain coefficient of the sliding mode observation model can also be called the sliding mode gain, which determines the convergence speed of the estimated electromotive force factor z. If the sliding mode gain is too large, the observed value may be greater than the measured value, which will cause the motor control process to jitter. If the sliding mode gain is too small, it will lead to a slow convergence speed, which will result in a slow response speed of the motor control.

[0059] Furthermore, the boundary range of the sliding mode observation model can also be called the sliding boundary or boundary layer. The sliding boundary is used to ensure that the motor's current error reaches a preset sliding surface within the sliding boundary and operates stably along it. For example, when the current error is located within the sliding boundary, the control input of the sliding mode observation model becomes smooth. It can be understood that if the sliding boundary is too small, the motor control process will have greater jitter. If the sliding boundary is too large, the motor control response speed will be slower.

[0060] It should be understood that the amplitude of the back electromotive force of the motor is different under different operating conditions. If the sliding mode boundary is fixed, it will lead to a deterioration in the performance of motor control.

[0061] In addition, for the sake of simplicity, the term boundary range will be used below to refer to sliding mode boundary or boundary layer, etc. This will be explained uniformly here and will not be repeated below.

[0062] like Figure 1 As shown, if the current error is outside the boundary range, it can be considered an out-of-bounds error, and the out-of-bounds count is incremented by 1. Furthermore, if the current error is less than 0, then z = -K2; if the current error is greater than 0, then z = K2. For example, K2 = -K slid .

[0063] It should be understood that adaptive boundary ranges are used to adapt to various operating conditions. Currently, related schemes based on adaptive boundary ranges typically use indirect variables related to current error, such as rotor speed or torque, to adjust the boundary range. Since rotor speed or torque are indirect variables, this leads to a linear relationship between the adjusted boundary range and the peak value of current error fluctuations; for example, the boundary range might be 1.2 or 1.3 times the peak value of current error fluctuations. In other words, a large difference between the boundary range obtained in this way and the average peak value of current error fluctuations can result in large jitter or slow response speed, leading to poor motor control performance.

[0064] Based on this, the embodiments of this application provide the following technical solutions, which can improve the matching degree between the boundary range of the sliding mode observation model and the observation error, thereby improving the response speed of motor control.

[0065] In one possible implementation, the control device performs the Nth observation of the motor current based on the sliding mode observation model, obtaining the Nth observation error. The control device then determines the cumulative number of out-of-bounds errors in the Nth observation based on the Nth observation error, the boundary range of the sliding mode observation model, and the cumulative number of out-of-bounds errors from the (N-1)th observation. If N is greater than or equal to a preset value, the control device updates the boundary range of the sliding mode observation model based on the out-of-bounds rate. The control device then controls the motor according to the updated sliding mode observation model. Here, the cumulative number of out-of-bounds errors in the Nth observation is the number of observation errors exceeding the boundary range of the sliding mode observation model from the Nth observation error. The Nth observation error is determined by performing N observations of the motor current based on the sliding mode observation model. Furthermore, the out-of-bounds rate is the ratio between the cumulative number of out-of-bounds errors in the Nth observation and N. Additionally, N is a positive integer greater than 0.

[0066] It is understandable that "going out of bounds" means that the observation error of the control device when observing the current according to the sliding mode observation model exceeds the boundary range of the sliding mode observation.

[0067] In addition, when N is greater than or equal to a preset value, the control device updates the boundary range of the sliding mode observation model. This can be understood as follows: based on multiple observations of the motor current, the control device obtains the out-of-bounds situation of the observation error of the sliding mode observation model over a period of time, and updates the boundary range of the sliding mode observation model according to the out-of-bounds situation.

[0068] In other words, the control device can determine the ratio (i.e., the out-of-bounds rate) between the number of times the observation error is outside the boundary range and the total number of observations based on multiple observations of the motor current. Then, it updates the boundary range according to the out-of-bounds rate so that the updated boundary range matches the operating conditions in the current period, thereby improving the matching degree between the boundary range and the observation error and improving the control performance of the motor.

[0069] It is understandable that, compared to adjusting the boundary range based on indirect variables such as rotor speed or torque, the observation error of the sliding mode observation model over a period of time (e.g., Figure 1 The actual out-of-bounds situation of the current error described in the paper can more directly and accurately reflect the actual operating state of the sliding mode observation model, thereby effectively adjusting the boundary range and improving the matching degree between the boundary range and the observation error.

[0070] It should be understood that the aforementioned control device may be: one or more application-specific integrated circuits (ASICs), one or more central processing units (CPUs), one or more microcontroller units (MCUs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms. The embodiments of this application do not specifically limit this.

[0071] In addition, the aforementioned control device can also be referred to as a controller, control module, control circuit, control chip, or control unit, etc. This will be explained uniformly here and will not be repeated below.

[0072] It is understood that the above-mentioned control device can be applied to appliances or equipment such as range hoods, integrated stoves, or air conditioner compressors, and this application embodiment does not specifically limit it.

[0073] The above scheme will be described in detail below with reference to the accompanying drawings.

[0074] It should be understood that the names of the motor-related parameters or the names of the information carrying the motor obtained by the various devices or modules in the following embodiments of this application are just examples. In actual implementation, they may be other names. This application does not specifically limit them.

[0075] Furthermore, while this application uses a control device as the execution subject for illustration, it does not limit the scope of this application. The execution subject of each method embodiment in this application can be a device or module, a device included in that device or module, or a device containing that device or module. For example, the execution subject can be a control device or control module, an electrical appliance or device containing a control device or control module, or a control chip included in the control device. It is understood that each method embodiment in this application can be implemented by a logic node, logic module, or software capable of implementing some or all of the functions of the control device.

[0076] Figure 2 This is a schematic flowchart of a motor control method provided in an embodiment of this application. Figure 2 As shown, the method includes the following steps S201 to S204.

[0077] S201. The control device performs the Nth observation of the motor current based on the sliding mode observation model, and obtains the Nth observation error. Where N is a positive integer greater than 0.

[0078] S202. The control device determines the cumulative number of boundary violations in the Nth observation based on the Nth observation error, the boundary range of the sliding mode observation model, and the cumulative number of boundary violations in the (N-1)th observation. The cumulative number of boundary violations in the Nth observation is the number of observation errors in the Nth observation errors that exceed the boundary range of the sliding mode observation model. The Nth observation error is determined by performing N observations on the motor current based on the sliding mode observation model.

[0079] S203. When N is greater than or equal to a preset value, the control device updates the boundary range of the sliding mode observation model according to the boundary violation rate. The boundary violation rate is the ratio of the cumulative number of boundary violations in the Nth observation to N.

[0080] S204. The control device controls the motor according to the updated sliding mode observation model.

[0081] Steps S201 to S204 are described below.

[0082] For step S201

[0083] It can be understood that the Nth time in step S201 is a count starting from 1 within a cycle. Furthermore, the cycle can refer to the period during which the control device observes the motor current, or it can refer to the period during which the control device controls the motor; this embodiment does not specifically limit this.

[0084] It should be understood that in the embodiments of this application, N is a positive integer greater than 0, such as 1, 2, 3, or a larger value.

[0085] In addition, N in the embodiments of this application can also be understood (or represented) as an index or number, and is not limited thereto.

[0086] It is understandable that, when N can be represented as an index or number, in some cases, the counting or indexing may start from 0 within a cycle, and thus the Nth time is actually the N-1th time. This will be explained in a unified manner here and will not be repeated below.

[0087] It should be understood that the number of observations (e.g., N times) by the control device to observe the motor current according to the sliding mode observation model in step S201 can be determined by a synchronizer. For example, the synchronizer could be... Figure 1 The synchronizer in the control device observes the motor current once according to the sliding mode observation model, and the synchronizer updates the observation count by incrementing it by 1.

[0088] Furthermore, the synchronizer can be deployed inside the control device, or independently outside the control device, or it can be implemented through software or hardware deployed inside the control device; there are no limitations on this.

[0089] It is understood that a synchronizer can be used to synchronize the current of a motor when the control device uses a sliding mode observation model. For example, a synchronizer can be used within a synchronization cycle (such as the observation cycle or control cycle of the motor current by the aforementioned control device).

[0090] In addition, the synchronizer can also be called a synchronization counter or other names, and this application embodiment does not specifically limit it.

[0091] The observation errors involved in step S201 are described below.

[0092] It should be understood that in the embodiments of this application, the observation error may be a current error, or the observation error may be determined based on the current error and the gain coefficient of the sliding mode observation model.

[0093] It is understandable that the current error is... Figure 1 The current error involved refers to the deviation between the observed current and the measured current obtained by the control device based on the sliding mode observation model when observing the current of the motor.

[0094] For example, the Nth observation error can be: the control device performs the Nth observation of the motor current according to the sliding mode observation model, and obtains the Nth current error between the Nth observed current and the Nth measured current.

[0095] It should be understood that the observation error is determined based on the current error and the gain coefficient of the sliding mode observation model. This is equivalent to the electromotive force factor z output by the sliding mode observation model (i.e., z in formula (1)). The observation error is equivalent to the back electromotive force of the motor. Thus, the boundary range of the sliding mode observation model is used to determine whether the boundary is exceeded. The working condition can be directly associated with the boundary range to improve the matching between the working condition and the boundary range.

[0096] In one possible implementation, the control device performs the Nth observation of the motor current based on the sliding mode observation model to obtain the Nth observation error (i.e., step S201), including:

[0097] S201-1. The control device performs the Nth observation of the motor current according to the sliding mode observation model, and obtains the Nth current error between the Nth observed current and the Nth measured current.

[0098] It is understandable that the implementation details of step S201-1 can be found in [link to documentation]. Figure 1 The relevant explanations will not be repeated here.

[0099] S201-2. Determine the Nth observation error based on the Nth current error and the gain coefficient of the sliding mode observation model.

[0100] It can be understood that the Nth observation error can be equal to the product of the Nth current error and the gain coefficient of the sliding mode observation model, or the Nth observation error can be a linear combination of the Nth current error and the gain coefficient of the sliding mode observation model. This application does not specifically limit this.

[0101] In other words, the control device determines the Nth observation error based on the Nth current error and the gain coefficient of the sliding mode observation model. It can make the electromotive force factor output by the sliding mode observation model equivalent to the Nth observation error. Thus, the observation error is equivalent to the back electromotive force of the motor. Based on the observation error and the boundary range of the sliding mode observation model, it can determine whether the boundary has been exceeded. It can directly associate the operating condition with the boundary range to improve the matching between the operating condition and the boundary range.

[0102] For step S202

[0103] It is understood that the boundary range of the sliding mode observation model can be [-Zmax,+Zmax], or [-Zmax,+∞], or [-∞,+Zmax], and the embodiments of this application do not specifically limit it.

[0104] Furthermore, the value of Zmax mentioned above depends on the actual implementation, and this application embodiment does not impose specific limitations on it.

[0105] It should be understood that in step S202, the control device can determine whether the Nth observation error is out of bounds, i.e. whether the Nth observation error is within the boundary range, based on the Nth observation error and the boundary range of the sliding mode observation model.

[0106] It is understood that in step S202, the control device can update the cumulative number of out-of-bounds errors in the (N-1)th observation based on the out-of-bounds error of the Nth observation, thereby obtaining the cumulative number of out-of-bounds errors in the Nth observation. Optionally, the control device updates the cumulative number of out-of-bounds errors in the (N-1)th observation by adding 1 to the cumulative number of out-of-bounds errors in the (N-1)th observation.

[0107] Furthermore, if the error of the Nth observation does not exceed the limit, the control device may not update the cumulative number of exceedances for the (N-1)th observation.

[0108] It can also be understood that in step S202, each time the control device observes the current of the motor according to the sliding mode observation model, it will determine whether the observation error obtained from each observation exceeds the limit, and then the number of exceeding the limit can be accumulated in sequence, so as to accurately determine the number of exceeding the limit.

[0109] In addition, the control device can also save the observation error obtained from each observation, and if N is greater than or equal to a preset value, determine the number of observation errors that exceed the boundary range of the sliding mode observation model in N observation errors. This can avoid determining whether the observation error exceeds the boundary in each observation.

[0110] It is understandable that determining the cumulative number of boundary violations in the Nth observation can be achieved using a boundary violation recorder. Furthermore, the boundary violation recorder can be deployed within the control device, deployed independently outside the control device, or implemented through software or hardware deployed within the control device; there are no limitations on this.

[0111] In addition, the boundary crossing recorder can also use other names, and there are no restrictions on this.

[0112] For step S203

[0113] It is understood that the preset value can be 20 times, 30 times, 40 times, or a larger value, depending on the actual implementation. This application does not specifically limit this.

[0114] In addition, the embodiments of this application update the boundary range of the sliding mode observation model based on the working state of the sliding mode observation model over a period of time. Therefore, the control device needs to obtain the working state of the sliding mode observation model over a period of time, that is, the number of times the multiple observation errors output by the sliding mode observation model exceed the boundary range.

[0115] It's understandable that by setting preset values, the control device can update the boundary range of the sliding mode observation model based on the boundary violation rate, avoiding unnecessary updates. For example, suppose there are 5 observations, and the number of boundary violations is 2, resulting in a boundary violation rate of 40%, which is relatively high. However, this number of boundary violations might be due to sudden noise. If the update is based on a 40% boundary violation rate in this case, the average peak value of the boundary range and the actual fluctuation of the observation error might be larger, affecting the performance of the motor control.

[0116] In other words, by triggering the control device to update the boundary range based on the out-of-bounds rate when N is greater than the preset value, the control device can be updated based on multiple measurements, thereby improving the degree to which the out-of-bounds rate reflects the actual working conditions and reducing the impact of sudden noise on the out-of-bounds rate.

[0117] The boundary range of the control device for updating the sliding mode observation model is described below.

[0118] In one possible implementation, the control device updates the boundary range of the sliding mode observation model according to the boundary violation rate (i.e., step S203), including:

[0119] S203-1 The control device performs closed-loop processing on the boundary rate based on the target boundary rate to obtain the updated boundary range.

[0120] It is understood that the target out-of-bounds rate can be 0, 0.01, 0.02, or 0.03, depending on the actual implementation. This application does not specifically limit this.

[0121] In addition, the out-of-bounds rate in step S203-1 can also be understood as the current out-of-bounds rate, or the out-of-bounds rate at the Nth observation.

[0122] It should be understood that the relevant implementation of step S203-1 can be found in [reference needed]. Figure 3 .like Figure 3 As shown, the control device can perform closed-loop processing on the boundary rate according to the target boundary rate, so that the boundary rate after closed-loop iteration is close to the target boundary rate, and then output the updated boundary range.

[0123] For example, Figure 3 The corresponding mathematical model can be expressed as: in, This represents the updated boundary range. pid (*) indicates a proportional-integral-derivative (PID) control function. TarZovr is the target out-of-bounds rate. RefZovr is the out-of-bounds rate.

[0124] It should be understood that the closed-loop processing involved in the embodiments of this application can employ various closed-loop strategies, and there is no limitation on them. For example, a proportional loop (or proportional control), an integral loop (or integral control), a proportional-integral (PI) loop (or PI control), or a proportional-integral-derivative (PID) control can be used, etc. These are uniformly described here and will not be elaborated further below.

[0125] S203-2. The control device updates the boundary range of the sliding mode observation model based on the updated boundary range.

[0126] In other words, the control device performs closed-loop processing on the current boundary rate based on the target boundary rate to output the updated boundary range. This eliminates the need for complex algorithms, thereby saving the control device's computing power and making it easier to deploy.

[0127] For step S204

[0128] In one possible implementation, the control device controls the motor based on the updated sliding mode observation model (i.e., step S204), including:

[0129] S204-1. When the error of the Nth observation exceeds the updated boundary range, the control device determines the updated electromotive force factor of the sliding mode observer output based on the updated boundary range.

[0130] It is understandable that after the control device updates the boundary range during the Nth observation, it can use the updated boundary range to determine whether the error of the Nth observation exceeds the limit. If the error of the Nth observation does not exceed the limit, then the control device uses the proportional coefficient of linear control (e.g., ...) based on the observation error. Figure 1 K in slid ·Error / MaxSMCerror), obtain the electromotive force factor z, the control device can estimate the rotor angular velocity and / or phase angle based on the electromotive force factor z, and then control the motor.

[0131] It can also be understood that if the error of the Nth observation exceeds the limit, the control device executes step S204-1, that is, determines the electromotive force factor z based on the updated boundary range. For example, in the case of the error of the Nth observation exceeding the limit, the electromotive force factor z can be the updated boundary range.

[0132] For example, when the Nth observation error exceeds the boundary and the Nth observation error is greater than 0, the electric factor z can be the updated boundary range. The positive value (i.e.) When the Nth observation error exceeds the boundary, and the Nth observation error is less than 0, the electric factor z can be the updated boundary range. The negative value (i.e. ).

[0133] S204-2. The control device controls the motor based on the electromotive force factor.

[0134] In other words, after the control device updates the boundary range of the sliding mode observation model, the control device controls the motor based on the updated boundary range and the Nth observation error. This allows the updated boundary range to be used to control the motor in a timely manner, thereby improving the response speed of motor control.

[0135] It should be understood that, as explained in the preamble of the specific implementation method regarding the working principle of the sliding mode observation model, another key parameter of the sliding mode observation model is its gain system. When updating the boundary range of the sliding mode observation model, the gain coefficient of the sliding mode observation model is updated synchronously, which can improve the response speed of motor control. This will be explained below.

[0136] Optionally, Figure 2 The method shown further includes, before the control device controls the motor according to the updated sliding mode observation model (i.e., step S204):

[0137] S205. When N is greater than or equal to a preset value, the control device updates the gain coefficient of the sliding mode observation model according to the Nth current error.

[0138] As can be understood, as explained in the working principle of the sliding mode observation model, the boundary range and gain coefficient of the sliding mode observation model are key parameters affecting the output results of the sliding mode observation model. Therefore, if only the boundary range of the sliding mode observation model is updated, it may lead to a mismatch between the boundary range and the gain coefficient, thereby affecting the accuracy of estimating the rotor's angular velocity and / or phase angle based on the electromotive force factor output by the sliding mode observation model.

[0139] In other words, by synchronously updating the boundary range and gain coefficient of the sliding mode observation model, the control device can make the updated boundary range match the gain coefficient, thereby improving the accuracy of the electromotive force factor output by the sliding mode observation model. Based on this electromotive force factor, the rotor's angular velocity and / or phase angle can be estimated quickly and accurately, thus improving the performance of motor control.

[0140] It is understandable that, based on step S205, the updated sliding mode observation model in step S204 is a sliding mode observation model that synchronously updates the boundary range and gain coefficient.

[0141] In addition, in the embodiments of this application, the control device updates the boundary range of the sliding mode observation model and the triggering condition for updating the gain coefficient of the sliding mode observation model. These two update processes can be two independent processes, which can be performed simultaneously or separately. The embodiments of this application do not specifically limit this.

[0142] In one possible implementation, the control device updates the gain coefficient of the sliding mode observation model based on the Nth current error (i.e., step S205), including:

[0143] S205-1. The control device performs closed-loop processing on the gain coefficient of the sliding mode observation model based on the Nth current error to obtain the updated gain coefficient.

[0144] It should be understood that step S205-1 is similar to the aforementioned step S203-1, and will be discussed below in conjunction with... Figure 4 Please provide an explanation. For example... Figure 4 As shown, the control device can perform closed-loop processing on the current error according to the target current error, so that the current error after closed-loop iteration is close to the target current error, and then output the updated gain coefficient.

[0145] For example, Figure 4 The corresponding mathematical model can be expressed as: in, f represents the updated gain coefficient. pid (*) indicates a PID control function. TarZero represents the target current error. Zero represents the current error.

[0146] It can be understood that the current error in step S204-1 above can also be understood as the current error or the current error at the Nth observation.

[0147] S205-2. The control device updates the gain coefficient of the sliding mode observation model based on the updated gain coefficient.

[0148] In other words, the control device performs closed-loop processing on the current error based on the target current error to output an updated gain coefficient. This eliminates the need for complex algorithms, thereby saving the computing power of the control device and making it easier to deploy.

[0149] In one possible implementation, the control device controls the motor based on the updated sliding mode observation model, including:

[0150] S204-3. The control device updates the Nth observation error based on the Nth current error and the updated gain coefficient.

[0151] It is understandable that the Nth observation error is determined by the Nth current error and the gain coefficient, therefore the Nth error is updated based on the updated gain coefficient.

[0152] S204-4. If the error of the Nth observation after the update exceeds the updated boundary range, the control device shall determine the electromotive force factor output by the updated sliding mode observer based on the updated boundary range.

[0153] It is understandable that the difference between step S204-4 and step S204-1 is that the Nth observation error in step S204-4 is the updated Nth observation error determined based on the updated gain coefficient.

[0154] S204-5. The control device controls the motor based on the electromotive force factor.

[0155] It is understood that the implementation of step S24-5 can be found in step S204-2, and will not be repeated here.

[0156] In other words, after the control device updates the sliding mode observation, the control device controls the motor based on the updated boundary range, the updated gain coefficient, and the Nth observation error. This allows the updated boundary range and gain coefficient to be used to control the motor in a timely manner, thereby improving the response speed of motor control.

[0157] To facilitate understanding of the control device's synchronous update of the sliding mode observation model's boundary range and gain coefficients, the following will be combined with... Figure 5 Please provide an explanation.

[0158] Figure 5 This is a schematic diagram illustrating a process for synchronously updating the boundary range and gain coefficient of a sliding mode observation model, as provided in an embodiment of this application. Figure 5 In the flowchart shown, the number of observations is Tsmc, the number of out-of-bounds errors is Osmc, the current error is Zero, the observation error is Zsmc, the boundary range is [-Zmax, +Zmax], the out-of-bounds rate is RefZovr, the target out-of-bounds rate is TarZovr, the target current error is TarZero, the sliding mode gain of the sliding mode observation model is Ksmc, and the updated boundary range is... The updated gain coefficient is Zero is the difference between the observed current and the measured current.

[0159] like Figure 5 As shown, after the control device observes the motor current for the Nth time, it determines the current error Zero and the observation error Zsmc = Ksmc * Zero.

[0160] like Figure 5As shown, the control device determines whether Zsmc is greater than +Zmax. If so, it increments the number of out-of-bounds events Osmc by 1, making Zsmc = +Zmax, and then determines whether Tsmc is greater than 100 (i.e., the preset value in step S203). If not, it determines whether Zsmc is greater than or less than -Zmax. If Zsmc is less than -Zmax, it increments the number of out-of-bounds events Osmc by 1, making Zsmc = -Zmax, and then determines whether Tsmc is greater than 100. If not, it directly determines whether Tsmc is greater than 100.

[0161] Understandably, if Tsmc is less than 100, then the control device does not need to be updated, and the process ends.

[0162] Additionally, if Tsmc is greater than 100, the control device determines the following parameters:

[0163] Out-of-bounds rate RefZovr = Osmc / Tsmc, updated boundary range is (See step S203-1), and the updated gain coefficient is as follows: (See step S205-1).

[0164] In addition, the control device can also set the number of out-of-bounds errors Osmc and the number of observations Tsmc to 0.

[0165] In this embodiment, the control device can determine the ratio (i.e., the out-of-bounds rate) between the number of times the observation error is outside the boundary range and the total number of observations based on multiple observations of the motor current. Then, it updates the boundary range according to the out-of-bounds rate so that the updated boundary range matches the operating conditions in the current period, thereby improving the matching degree between the boundary range and the observation error and improving the control performance of the motor.

[0166] The method embodiments provided in this application have been described above. Accordingly, this application also provides a control device for implementing the various methods described above. This control device may be the control device described in the above method embodiments, or a device or apparatus including the above control device, or a component that can be used in the control device.

[0167] Figure 6 This is a schematic diagram of a control device structure provided in an embodiment of this application. Figure 6 As shown, the control device 600 may include modules or units for implementing the methods described in the embodiments above. In one possible design, the control device 600 includes a processing unit 602. Optionally, the control device 600 may further include a storage unit 601 for storing device program code and / or data.

[0168] The control device 600 can be at least one module within the control device in the above embodiments. For example, at least one module within the control device can be a circuit or a chip in the control device.

[0169] For example, in one embodiment, processing unit 602 is used to: perform the Nth observation of the motor current according to the sliding mode observation model, and obtain the Nth observation error, where N is a positive integer greater than 0. Processing unit 602 is also used to: determine the cumulative number of out-of-bounds occurrences in the Nth observation based on the Nth observation error, the boundary range of the sliding mode observation model, and the cumulative number of out-of-bounds occurrences in the (N-1)th observation. Processing unit 602 is also used to: update the boundary range of the sliding mode observation model according to the out-of-bounds rate when N is greater than or equal to a preset value. Processing unit 602 is also used to: control the motor according to the updated sliding mode observation model. Wherein, the out-of-bounds rate is the ratio between the cumulative number of out-of-bounds occurrences in the Nth observation and N. The cumulative number of out-of-bounds occurrences in the Nth observation is the number of observation errors exceeding the boundary range of the sliding mode observation model in the Nth observation error, which is determined by performing N observations of the motor current according to the sliding mode observation model.

[0170] In one possible design, when the control device 600 is a circuit or chip in the control device, the function of the processing unit 602 can be implemented by one or more processors.

[0171] It is understood that the division of units in the aforementioned control device is merely a logical functional division; one function may correspond to one functional unit, or two or more functions may be integrated into one functional unit. In actual implementation, all or some units may be integrated into one physical entity, or they may be distributed across different physical entities. Furthermore, the aforementioned functional units may be implemented in hardware, software, or a combination of both. Whether a function is executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for specific applications, but such implementations should not be considered beyond the scope of this application.

[0172] In one example, the functional unit in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as: one or more application-specific integrated circuits (ASICs), or one or more central processing units (CPUs), one or more microcontroller units (MCUs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0173] In one example, storage unit 601 may include random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory and / or registers, etc.

[0174] Furthermore, the control device 600 can execute the above-described motor control method, and therefore the technical effects it can achieve can be referred to the above-described method embodiments, which will not be repeated here.

[0175] In one possible implementation, this application also provides a computer-readable storage medium storing a computer program or instructions that, when executed by a computer, implement the functions of the above-described method embodiments.

[0176] In one possible implementation, this application also provides a computer program product that, when executed by a computer, implements the functions of the above-described method embodiments.

[0177] In one possible implementation, this application embodiment also provides a control system for a motor, which includes the control device described in the above method embodiments.

[0178] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device including one or more servers, data centers, etc., that can be integrated with the medium. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium, or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0179] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0180] Those skilled in the art will 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.

[0181] 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 through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0182] In addition, 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.

[0183] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they 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 a portion 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.

[0184] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, the disclosure, and the appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0185] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of the claims and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A motor control method, characterized in that, The method includes: Based on the sliding mode observation model, the current of the motor is observed for the Nth time to obtain the Nth observation error, where N is a positive integer greater than 0; The cumulative number of out-of-bounds observations in the Nth observation is determined based on the Nth observation error, the boundary range of the sliding mode observation model, and the cumulative number of out-of-bounds observations in the (N-1)th observation. The cumulative number of out-of-bounds observations in the Nth observation is the number of observation errors in the Nth observation errors that exceed the boundary range of the sliding mode observation model. The Nth observation error is determined by performing N observations on the motor current based on the sliding mode observation model. If N is greater than or equal to a preset value, the boundary range of the sliding mode observation model is updated according to the boundary crossing rate, wherein the boundary crossing rate is the ratio between the cumulative number of boundary crossings in the Nth observation and N. The motor is controlled based on the updated sliding mode observation model.

2. The method according to claim 1, characterized in that, The step of updating the boundary range of the sliding mode observation model based on the out-of-bounds rate includes: The boundary rate is processed in a closed loop based on the target boundary rate to obtain the updated boundary range. The boundary range of the sliding mode observation model is updated based on the updated boundary range.

3. The method according to claim 1 or 2, characterized in that, The step of controlling the motor based on the updated sliding mode observation model includes: If the Nth observation error exceeds the updated boundary range, the updated electromotive force factor of the sliding mode observer output is determined based on the updated boundary range. The motor is controlled according to the electromotive force factor.

4. The method according to any one of claims 1 to 3, characterized in that, The method of performing the Nth observation of the motor current based on the sliding mode observation model to obtain the Nth observation error includes: Based on the sliding mode observation model, the current of the motor is observed for the Nth time, and the Nth current error between the Nth observed current and the Nth measured current is obtained. The Nth observation error is determined based on the Nth current error and the gain coefficient of the sliding mode observation model.

5. The method according to claim 4, characterized in that, Before controlling the motor based on the updated sliding mode observation model, the method further includes: If N is greater than or equal to the preset value, the gain coefficient of the sliding mode observation model is updated according to the Nth current error.

6. The method according to claim 5, characterized in that, The step of updating the gain coefficient of the sliding mode observation model based on the Nth current error includes: The gain coefficients of the sliding mode observation model are updated by performing closed-loop processing based on the Nth current error. The gain coefficient of the sliding mode observation model is updated based on the updated gain coefficient.

7. The method according to claim 5, characterized in that, The step of controlling the motor based on the updated sliding mode observation model includes: The Nth observation error is updated based on the Nth current error and the updated gain coefficient. If the updated Nth observation error exceeds the updated boundary range, the updated electromotive force factor of the sliding mode observer output is determined based on the updated boundary range. The motor is controlled according to the electromotive force factor.

8. A motor control device, characterized in that, The motor control device includes a module or unit for performing the method according to any one of claims 1-7.

9. A motor control device, characterized in that, The motor control device includes a processor, which is configured to cause the motor control device to perform the method according to any one of claims 1-7 by means of logic circuits and / or executing instructions.

10. A computer-readable storage medium, characterized in that, The storage medium includes instructions that, when executed by a processor, cause the method according to any one of claims 1-7 to be implemented.

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