Method for adjusting rotating speed of brushless direct current motor

Abstract

The invention relates to the technical field of motor control, in particular to a brushless direct current motor rotating speed adjusting method which comprises the following steps: acquiring monitoring parameters of a motor; determining ideal state parameters of the motor based on the monitoring parameters; determining actual state parameters of the motor based on the ideal state parameters and the monitoring parameters; determining control parameters of the motor based on the actual state parameters; and based on the control parameters and a preset state model, carrying out adaptive adjustment on the rotating speed of the motor. According to the method, adaptive adjustment of the rotating speed of the motor can be realized, so that the rotating speed of the motor can be timely and accurately adjusted.

Description

Technical Field

[0001] The invention relates to the technical field of motor control, and in particular to a speed regulation method for a brushless DC motor in the technical field of motor control. Background Art

[0002] In industrial and household appliances, brushless direct current motors (BLDC) are widely used in various scenarios, such as electric vehicles, fans, air conditioning compressors, etc. BLDC motors have the advantages of high efficiency, low noise, and long life, so they are becoming more and more popular. The control and regulation of BLDC motors are the key to their application, among which speed control is an important task because speed control directly affects the performance and power consumption of the motor.

[0003] In related technologies, a common adaptive control method is based on the Model Reference Adaptive Control (MRAC) algorithm, which compares the actual response of the motor with a pre-established model and dynamically adjusts the control parameters according to the comparison results to achieve adaptive regulation of the motor. However, this method is sensitive to changes in motor parameters and external interference, and it is difficult to achieve fast and accurate speed regulation. Summary of the invention

[0004] In order to solve the technical problem that the motor speed cannot be adjusted quickly and accurately, the purpose of the present invention is to provide a brushless DC motor speed adjustment method, and the technical solution adopted is as follows: In a first aspect, an embodiment of the present invention provides a method for adjusting the speed of a brushless DC motor, the method comprising: Get the monitoring parameters of the motor; Based on the monitoring parameters, determining the ideal state parameters of the motor; Determining actual state parameters of the motor based on the ideal state parameters and the monitoring parameters; Determining control parameters of the motor based on the actual state parameters; Based on the control parameters and the preset state model, the rotation speed of the motor is adaptively adjusted.

[0005] In combination with the first aspect above, in some possible implementations, obtaining the monitoring parameters of the motor includes: Obtaining the speed, power data, voltage data and current data of the motor; The rotation speed, the power data, the voltage data and the current data are preprocessed to obtain the monitoring parameters.

[0006] In the above scheme, the accuracy of the monitoring parameters can be improved by preprocessing the collected motor speed, power data, voltage data and current data.

[0007] In combination with the first aspect, in some possible implementations, determining the actual state parameter of the motor based on the ideal state parameter and the monitoring parameter includes: Determining the actual power of the motor based on the torque of the motor and the speed of the monitoring parameter; Determine the neighborhood range of any extreme point based on the slope corresponding to any extreme point in the current data in the monitoring parameter and the slope of the data points in the neighborhood data of any extreme point; Determine the correlation between different data based on the neighborhood range of any extreme value point, the actual power and the ideal power in the ideal state parameter; The actual state parameter is determined based on the correlation, the actual power and the power in a time period corresponding to any extreme point.

[0008] In the above scheme, by monitoring the neighborhood range of each extreme point in the motor data in the parameters, the correlation between different data in the neighborhood range is analyzed, and then the correlation is combined with the actual power and the power in the time period corresponding to the extreme point. The actual state parameters of the motor can be obtained more accurately, which facilitates the calculation of the control parameters of the motor through the actual state parameters.

[0009] In combination with the first aspect above, in some possible implementations, determining the neighborhood range of any extreme point based on the slope corresponding to any extreme point in the current data in the monitoring parameter and the slope of data points in the neighborhood data of any extreme point includes: Determine the slope of the ith extreme point and the slope of the ijth data point; Determine a first difference between the slope of the i-th extreme point and the slope of the ij-th data point; Determine a second difference between the slope of the it-th data point and the slope of the it-1-th data point; wherein i is an integer less than or equal to the number of extreme value points and greater than or equal to 1, t is a positive integer less than or equal to i, and j is an integer greater than or equal to 1 and less than or equal to t; Based on the first difference and the second difference, a neighborhood range of the i-th extreme point is determined.

[0010] In the above scheme, for each extreme point, by analyzing the difference between the slopes of different extreme points and the slopes of data points in the neighborhood data, the neighborhood cutoff point of each extreme point can be accurately analyzed, thereby obtaining the neighborhood range of each extreme point.

[0011] In combination with the first aspect, in some possible implementations, determining the neighborhood range of the i-th extreme point based on the first difference and the second difference includes: Based on the first difference and the second difference, determining the possibility that the it-th data point is a data change cutoff point, and the possibility that the i+t-th data point is a data change cutoff point; Based on the possibility that the ith data point is a data change cutoff point and the possibility that the i+tth data point is a data change cutoff point, the neighborhood range of the ith extreme value point is determined.

[0012] In the above scheme, for any extreme point, by analyzing the possibility that different data points in the neighborhood of the extreme point are data change cutoff points, the neighborhood range of the extreme point can be quickly and accurately obtained.

[0013] In combination with the first aspect, in some possible implementations, determining the correlation between different data based on the neighborhood range of any extreme point, the actual power, and the ideal power in the ideal state parameter includes: Determining an energy loss function of the motor based on the actual power and the ideal power; Determine the data value of any data point within the neighborhood of any extreme value point; In the target data segment of the energy loss function, determining a target value that matches the location of any one of the data points; wherein the location of the target data segment matches the any one of the extreme value points; Based on the difference between the data value of any data point and the target value, and the number of data points within the neighborhood of any extreme point, the correlation between the neighborhood of any extreme point and the target data segment is determined.

[0014] In the above scheme, by analyzing the difference between the target data point and any extreme point in the energy loss function, the correlation between any extreme point and the target data segment can be obtained, so that the actual state parameters of the motor can be obtained through the correlation.

[0015] In combination with the first aspect, in some possible implementations, determining the actual state parameter based on the correlation, the actual power, and the power in the time period corresponding to any extreme point includes: Determine the power difference between the maximum power and the minimum power in the time period corresponding to any extreme value point; Fusing the power difference and the actual power to obtain a fused power; The fusion power is adjusted based on the correlation to obtain the actual state parameter.

[0016] In the above scheme, the power difference and the actual power are fused and adjusted through correlation to obtain the actual state parameters that can accurately reflect the motion state of the motor.

[0017] In combination with the first aspect, in some possible implementations, determining the control parameter of the motor based on the actual state parameter includes: The actual state parameter is adjusted using a preset value to obtain an adjusted actual state parameter; Based on the adjusted actual state parameter and the actual power of the motor, a control parameter of the motor is determined.

[0018] In the above scheme, the actual state parameters and the actual power of the motor are comprehensively considered in the process of calculating the control parameters, so that the obtained control parameters are more accurate.

[0019] In combination with the first aspect above, in some possible implementations, the adaptively adjusting the speed of the motor based on the control parameter and the preset state model includes: Inputting the control parameters into the preset state model to obtain an output result; The output result is used as an input parameter of a preset adaptive control model to adaptively adjust the rotation speed of the motor.

[0020] In the above solution, the output result of the preset state model is used as the input parameter of the preset adaptive control model, so as to realize the adaptive adjustment of the motor speed.

[0021] In combination with the first aspect above, in some possible implementations, the method further includes: Determining a steady-state gain of a system to which the motor belongs based on a rotation speed or an output torque of the motor; Determining a damping ratio of the system based on overshoot and oscillation behavior of the system to which the motor belongs; determining a time constant for matching the system based on the speed at which the motor reaches a steady-state value; Based on the time constant, the steady-state value and the damping ratio, the steady-state gain is adjusted to construct the preset state model.

[0022] In the above scheme, the steady-state gain of the system to which the motor belongs is calculated through the motor speed and output torque, and then the preset state model of the system is constructed in combination with the damping ratio of the system and the matching time constant, so that the motor can be adaptively controlled according to the control parameters through the preset state model.

[0023] In a second aspect, a brushless DC motor speed regulating device is provided, the device comprising: An acquisition module is used to acquire monitoring parameters of the motor; A first determination module, configured to determine an ideal state parameter of the motor based on the monitoring parameter; A second determination module, configured to determine an actual state parameter of the motor based on the ideal state parameter and the monitoring parameter; A third determination module, configured to determine a control parameter of the motor based on the actual state parameter; The regulating module is used to adaptively regulate the rotation speed of the motor based on the control parameters and a preset state model.

[0024] According to a third aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method according to the first aspect or any possible implementation of the first aspect.

[0025] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation manner of the first aspect.

[0026] The present invention has the following beneficial effects: after obtaining the monitoring parameters of the motor, the ideal state parameters of the motor are calculated by the monitoring parameters. Afterwards, the ideal state parameters and the monitoring parameters are combined to determine the actual state parameters, so that the ideal state parameters can be fully considered in the actual state parameters, thereby making the actual state parameters more accurate. The control parameters of the motor are determined by the actual state parameters, so that the speed of the motor can be adaptively adjusted by the control parameters and the preset state model. In this way, by inputting the control parameters into the preset state model, the speed of the motor can be adaptively adjusted, so that the speed of the motor can be adjusted in a timely and accurate manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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 work.

[0028] Figure 1 It is a schematic diagram of the implementation flow of a brushless DC motor speed regulation method provided by an embodiment of the present invention; Figure 2 It is another implementation flow diagram of a method for adjusting the speed of a brushless DC motor provided by an embodiment of the present invention; Figure 3 It is another implementation flow diagram of a method for adjusting the speed of a brushless DC motor provided by an embodiment of the present invention; Figure 4 It is another implementation flow diagram of a method for adjusting the speed of a brushless DC motor provided by an embodiment of the present invention; Figure 5 It is another implementation flow diagram of a method for adjusting the speed of a brushless DC motor provided by an embodiment of the present invention; Figure 6 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a brushless DC motor speed regulation method proposed by the present invention, its specific implementation, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0030] Among them, in the description of the embodiments of the present invention, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a way to describe the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" refers to two or more than two.

[0031] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0032] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0033] The following is a detailed description of a method for adjusting the speed of a brushless DC motor provided by the present invention in conjunction with the accompanying drawings. Figure 1 , which shows a flow chart of a method for adjusting the speed of a brushless DC motor provided by an embodiment of the present invention, the method comprising: 101, obtaining monitoring parameters of the motor.

[0034] Here, the motor may be a brushless DC motor. The monitoring parameters of the motor include at least: the rotation speed, power data, voltage data and current data of the motor.

[0035] In some possible implementations, the speed, power data, voltage data and current data of the motor are collected, and then the speed, power data, voltage data and current data are preprocessed to obtain the monitoring parameters. For example, the speed, power data, voltage data and current data are subjected to noise reduction processing to make the obtained monitoring parameters more accurate; in this way, by preprocessing the collected speed, power data, voltage data and current data of the motor, the accuracy of the monitoring parameters can be improved.

[0036] In some possible implementations, the brushless DC motor consists of a motor body and a driver, and is a typical mechatronics product. Since the brushless DC motor operates in an automatic control mode, it does not need an additional starting winding on the rotor like a synchronous motor that starts under heavy load under variable frequency speed regulation, nor does it produce oscillation and loss of step when the load changes suddenly. The driver is composed of power electronic devices and integrated circuits, etc., and its functions are: receiving the start, stop, and brake signals of the motor to control the start, stop, and brake of the motor; receiving position sensor signals and forward and reverse signals to control the on and off of each power tube in the inverter bridge to generate continuous torque; receiving speed instructions and speed feedback signals to control and adjust the speed; providing protection and display, etc. In order to accurately control the motor, it is necessary to analyze the operating status of the motor and make real-time adjustments according to the operating status.

[0037] First, it is necessary to collect the monitoring parameters of the brushless DC motor. In this embodiment, the speed sensor is used to collect the speed data of the motor, and the torque sensor is used to obtain the real-time torque value of the motor, and the actual power data, voltage data, and current data of the motor are obtained. The above-mentioned collected data are all time series data, and the monitoring parameters can be expressed as .

[0038] 102. Determine ideal state parameters of the motor based on the monitoring parameters.

[0039] Here, the ideal power is calculated by monitoring the voltage data and the current data in the parameters, and the ideal power is used as the ideal state parameter of the motor. In some possible implementations, the ideal power is obtained by multiplying the current data and the voltage data in the monitoring parameters, thereby obtaining the ideal state parameter of the motor.

[0040] 103. Determine actual state parameters of the motor based on the ideal state parameters and the monitoring parameters.

[0041] Here, the actual state parameters of the motor are used to represent the real-time state of the motor. In some possible implementations, the actual state parameters of the motor are calculated by combining the ideal state parameters with the monitoring parameters. The above step 103 can be performed by Figure 2 The steps shown achieve: 201. Determine the actual power of the motor based on the torque of the motor and the rotational speed in the monitoring parameter.

[0042] Here, when adjusting the motor speed, the motor speed is adjusted according to the difference between the ideal value and the actual value of each monitoring parameter under the ideal model. Therefore, it is necessary to first obtain the actual power state and ideal power of the motor according to the power, speed and torque of the motor, as shown in formulas (1) and (2): (1); (2); in, Indicates the ideal real-time power of the motor, express The voltage at the moment, express The current at the moment; Indicates the actual power, Indicates the motor speed, Indicates torque.

[0043] 202 , determining a neighborhood range of any extreme point based on a slope corresponding to any extreme point in the current data in the monitoring parameter and a slope of a data point in the neighborhood data of any extreme point.

[0044] Here, the slope of the data point in the neighborhood data of any extreme point can be the slope of the data point in the neighborhood data of the extreme point. For any extreme point, by analyzing whether the data point in the neighborhood data of the extreme point is a neighborhood cutoff point, the neighborhood range of the extreme point is obtained. In some possible implementations, the above step 202 can be performed by Figure 3 The steps shown achieve: 301, determine the slope of the i-th extreme point and the slope of the ij-th data point.

[0045] Here, the ijth data is the neighborhood data whose data value is less than the i-th extreme point.

[0046] 302, determining a first difference between the slope of the i-th extreme value point and the slope of the ij-th data point.

[0047] Here, the slope of the ith extreme point is subtracted from the slope of the ijth data point, and the absolute value of the obtained difference is taken to obtain the first difference.

[0048] 303 , determine a second difference between the slope of the it th data point and the slope of the it-1 th data point.

[0049] Wherein, i is an integer less than or equal to the number of extreme value points and greater than or equal to 1, t is a positive integer less than or equal to i, and j is an integer greater than or equal to 1 and less than or equal to t. The slope of the it-th data point is subtracted from the slope of the it-1th data point, and the absolute value of the obtained difference is taken to obtain the second difference.

[0050] 304. Determine a neighborhood range of the i-th extreme point based on the first difference and the second difference.

[0051] Here, for different extreme value points, multiple first differences and multiple second differences of corresponding pairs can be obtained. Multiple first differences are added and averaged. The average is subtracted from the second difference to obtain the first difference. The probability that the data point is the data change cutoff point is calculated. If the probability is high, it means that the data point is the data change cutoff point. Based on multiple data change cutoff points, the neighborhood range of the i-th extreme point is obtained. In this way, for each extreme point, by analyzing the difference between the slopes of different extreme points and the slopes of data points in the neighborhood data, the neighborhood cutoff point of each extreme point can be accurately analyzed, thereby obtaining the neighborhood range of each extreme point.

[0052] In some possible implementations, the above step 304 may be implemented by the following process: First, based on the first difference and the second difference, determine the possibility that the it-th data point is a data change cutoff point, and the possibility that the i+t-th data point is a data change cutoff point.

[0053] Then, based on the possibility that the ith data point is a data change cutoff point and the possibility that the i+tth data point is a data change cutoff point, the neighborhood range of the ith extreme value point is determined.

[0054] In some possible implementations, if the running state of the motor changes dramatically within a short period of time, it may cause the motor to fail. Therefore, when the current change is detected to be instantaneous, it means that the motor state has changed dramatically. Therefore, the maximum value point in the current data is first obtained, and then the mutation data interval is obtained. The calculation process is shown in formula (3): (3); Wherein, t is a positive integer less than or equal to i. Indicates the current data The data points are the possible extent of the data change cutoff point. Indicates The slope of the extreme point, Indicates The slope of the data point, Indicates The slope of the data point, Indicates The slope of the data point.

[0055] Similarly, obtain Indicates The data points are the possible degree of data change cutoff point, and the first threshold is preset ,when , Greater than , then this point is The neighborhood cutoff point of the extreme point is then Composition In this way, for any extreme point, by analyzing the possibility that different data points in the neighborhood of the extreme point are the data change cutoff points, the neighborhood range of the extreme point can be obtained quickly and accurately.

[0056] 203 : Determine correlations between different data based on a neighborhood range of any extreme value point, the actual power, and an ideal power in the ideal state parameter.

[0057] Here, by analyzing the difference between the actual power and the ideal power, and combining the difference with the neighborhood range of any extreme point, the correlation between different data can be obtained. In some possible implementations, the above step 203 can be performed by Figure 4 The steps shown achieve: 401. Determine an energy loss function of the motor based on the actual power and the ideal power.

[0058] Here, since the motor will generate energy loss due to friction, motor heating, etc. during the actual rotation process, resulting in a difference between the actual output power of the motor and the ideal output power, it is necessary to adjust the state parameters of the motor according to the changes in the real-time power data of the motor power.

[0059] According to the above ideal power and actual power, the energy loss function of the motor is obtained, as shown in formula (4): (4); in, represents an energy loss function, which can represent the real-time power loss of the brushless motor.

[0060] The motor does not change linearly in the actual operating environment. For example, electric vehicles may accelerate, decelerate, or brake suddenly during driving. Under various working conditions, the actual operating power of the motor will vary greatly from the ideal power. Therefore, in order to protect the motor from overvoltage and prevent the motor from burning due to excessive instantaneous power, it is necessary to adjust it linearly.

[0061] According to the energy loss function obtained above, a real-time energy loss curve is obtained, wherein the horizontal axis of the energy loss curve is time and the vertical axis is power loss; and then the motor operation state is obtained according to the change of the energy loss curve and the current range.

[0062] 402. Determine a data value of any data point within a neighborhood range of any extreme value point.

[0063] Here, taking any extreme point as the i-th extreme point as an example, the data value of each data point is obtained within the neighborhood of the i-th extreme point.

[0064] 403 : Determine a target value matching the position of any data point in the target data segment of the energy loss function.

[0065] The location of the target data segment matches any of the extreme points. For example, any of the extreme points is the i-th extreme point, and the i-th data segment is determined to be the target data segment in the energy loss function.

[0066] 404. Determine the correlation between any extreme point and the target data segment based on the difference between the data value of any data point and the target value, and the number of data points within the neighborhood of any extreme point.

[0067] Here, the current data and energy loss data are normalized separately and placed at the same order of magnitude. The data correlation is obtained based on the neighborhood range of the data mutation point in the determined current data and the change of the data in the energy loss curve. The calculation process is shown in formula (5): (5); in, Indicates current data Middle Neighborhood range of extreme points and energy loss function The target data segment at the corresponding position in The correlation between any extreme point and the target data segment. Indicates current data Middle In the neighborhood of the extreme point The value of the data point, Indicates energy loss data The target data segment at the corresponding position in Middle The target value for each data point, Indicates The number of data points in the neighborhood of an extreme point, In this way, by analyzing the difference between the target data point and any extreme point in the energy loss function, the correlation between any extreme point and the target data segment can be obtained, so that the actual state parameters of the motor can be obtained through the correlation.

[0068] 204. Determine the actual state parameter based on the correlation, the actual power, and the power in a time period corresponding to any extreme point.

[0069] Here, by combining the correlation of different data, the actual power, and the power in the time period corresponding to any extreme point, the actual operating state of the motor can be accurately analyzed, that is, a more accurate actual state parameter can be obtained. In this way, by monitoring the neighborhood range of each extreme point in the motor data in the parameters, the correlation between different data in the neighborhood range is analyzed, and then the correlation is combined with the actual power and the power in the time period corresponding to the extreme point, the actual state parameter of the motor can be obtained more accurately, which is convenient for calculating the control parameters of the motor through the actual state parameter.

[0070] In some possible implementations, the above step 204 can be performed by Figure 5 The steps shown achieve: 501 , determine the power difference between the maximum power and the minimum power in a time period corresponding to any extreme point.

[0071] 502. Fuse the power difference with the actual power to obtain a fused power.

[0072] Here, the fused power is obtained by multiplying the power difference and the actual power.

[0073] 503. Adjust the fusion power based on the correlation to obtain the actual state parameter.

[0074] Here, according to the correlation between the neighborhood range of any extreme point and the target data segment, the actual state parameters of the motor are obtained, as shown in formula (6): (6); in, Indicates The actual state parameters of the period corresponding to the extreme point, Indicates The actual motor power in the period corresponding to the extreme point, Indicates The extreme point corresponds to the maximum value of the motor power in the time period. Indicates The extreme point corresponds to the minimum power value in the time period. Represents a linear normalized function. It should be noted that the state parameters of the motor at other non-extreme points are represented by the original power data. In this way, the power difference and the actual power are adjusted by correlation to obtain the actual state parameters that can accurately reflect the motor motion state.

[0075] 104. Determine a control parameter of the motor based on the actual state parameter.

[0076] Here, the control parameters of the motor are calculated by combining the actual power with the actual state parameters, so as to realize adaptive control of the motor through the control parameters.

[0077] In some possible implementations, first, the actual state parameter is adjusted by a preset value to obtain an adjusted actual state parameter; wherein the preset value may be a custom data or an empirical value. In an embodiment of the present invention, the preset value may be 1. The actual state parameter is subtracted from the preset value to obtain the adjusted actual state parameter. Thereafter, the control parameter of the motor is determined based on the adjusted actual state parameter and the actual power of the motor. For example, the control parameter of the motor is obtained by multiplying the adjusted actual state parameter and the actual power of the motor. In this way, the actual state parameter and the actual power of the motor are comprehensively considered in the process of calculating the control parameter, so that the obtained control parameter is more accurate.

[0078] In some possible implementations, the control parameters are obtained according to the actual state parameters of the motor obtained above and the error between the output of the actual motor model and the output of the reference model. The specific implementation process is as follows: Because the input power and output power of the motor cannot be equal, the motor needs to overcome friction to do work and also generate heat. Therefore, when making adjustments, it is necessary to make adjustments based on the smoothness of the motor operation. The smoother the motor runs, the less adjustment is required, and the greater the fluctuation of the motor operation, the more real-time adjustment is required. Therefore, according to the above calculation, the control parameters of the motor at different time points are obtained, as shown in formula (7): (7); in, Indicates The control parameters of the time period corresponding to the extreme point, Indicates The actual motor power in the period corresponding to the extreme point, Indicates The actual state parameters of the period corresponding to the extreme points.

[0079] 105. Based on the control parameters and the preset state model, adaptively adjust the rotation speed of the motor.

[0080] In some possible implementations, the preset state model can be constructed by the following process: First, based on the speed or output torque of the motor, the steady-state gain of the system to which the motor belongs is determined; secondly, based on the overshoot and oscillation behavior of the system to which the motor belongs, the damping ratio of the system is determined; thirdly, based on the speed at which the motor reaches the steady-state value, the time constant matched by the system is determined; finally, based on the time constant, the steady-state value and the damping ratio, the steady-state gain is adjusted to construct the preset state model. In this way, the steady-state gain of the system to which the motor belongs is calculated through the speed and output torque of the motor, and then the preset state model of the system is constructed in combination with the damping ratio and the matching time constant of the system, so that the motor can be adaptively controlled according to the control parameters through the preset state model.

[0081] In some possible implementations, when controlling a brushless DC motor, adjustments are made through the dynamic changes of its various monitoring parameters. The motor is mainly regulated by adjusting its speed. Different speeds have different torques of the motor, and the speed and torque are inversely proportional, that is, the faster the speed, the smaller the torque; the lower the speed, the greater the torque. When the motor load is large, in order to protect the motor from being burned out due to excessive power, a larger torque is required to start, and the corresponding motor speed is smaller at this time. Therefore, dynamic adjustment is required based on the relationship between the motor speed, power, and torque.

[0082] Before adjusting the speed of the motor, a preset state model (i.e., an ideal state model) is first determined. The model is used to represent the state of the brushless DC motor under the ideal state. The system response function of the preset state model is shown in formula (8): (8); in, represents the system response function, represents the time constant, represents the damping ratio, Indicates steady-state gain. Specifically: Steady-state gain Determines the steady-state gain of the system, which is proportional to the final speed or output torque of the motor. s represents the steady-state value of the motor at the current moment.

[0083] Time constant : Related to the speed at which the motor reaches its steady-state value. The smaller the time constant, the faster the system responds.

[0084] Damping ratio : It is related to the overshoot and oscillation behavior of the system. When the damping ratio is 1, the system is critically damped and has no overshoot; when it is less than 1, the system will have a certain overshoot; when it is greater than 1, the system is overdamped and has a slow response speed.

[0085] It can be seen from formula (8) that in order to ensure the stable operation of the motor, the steady-state gain K and the time constant It plays a decisive role, and the steady-state gain K determines the stability of the system. Therefore, the steady-state gain K needs to be adjusted according to the various operating parameters of the motor so that the motor can run stably.

[0086] In the above step 105, the output result is obtained by inputting the control parameter into the preset state model; and the output result is used as the input parameter of the preset adaptive control model to realize adaptive adjustment of the speed of the motor. The preset adaptive control model may be a proportional integral derivative (PID) model. The calculated control parameter is input into the preset state model for adaptive control. The control logic is: the output G(s) of the preset state model is used as the control parameter of the PID model to adjust the speed of the motor.

[0087] In an embodiment of the present invention, after collecting the monitoring parameters of the motor, the ideal state parameters of the motor are calculated by the monitoring parameters. Afterwards, the ideal state parameters and the monitoring parameters are combined to determine the actual state parameters, so that the ideal state parameters can be fully considered in the actual state parameters, thereby making the actual state parameters more accurate. The control parameters of the motor are determined by the actual state parameters, so that the speed of the motor can be adaptively adjusted by the control parameters and the preset state model. In this way, by inputting the control parameters into the preset state model, the adaptive adjustment of the speed of the motor can be achieved, so that the speed of the motor can be adjusted in a timely and accurate manner.

[0088] An embodiment of the present invention provides a brushless DC motor speed regulating device, the device comprising: An acquisition module is used to acquire monitoring parameters of the motor; A first determination module, configured to determine an ideal state parameter of the motor based on the monitoring parameter; A second determination module, configured to determine an actual state parameter of the motor based on the ideal state parameter and the monitoring parameter; A third determination module, configured to determine a control parameter of the motor based on the actual state parameter; The regulating module is used to adaptively regulate the rotation speed of the motor based on the control parameters and a preset state model.

[0089] In some possible implementations, the acquisition module is also used to acquire the rotational speed, power data, voltage data and current data of the motor; and pre-process the rotational speed, power data, voltage data and current data to obtain the monitoring parameters.

[0090] In some possible implementations, the second determination module is also used to determine the actual power of the motor based on the torque of the motor and the speed in the monitoring parameters; determine the neighborhood range of any extreme point based on the slope corresponding to any extreme point in the current data in the monitoring parameters and the slope of the data point in the neighborhood data of any extreme point; determine the correlation between different data based on the neighborhood range of any extreme point, the actual power and the ideal power in the ideal state parameter; determine the actual state parameter based on the correlation, the actual power and the power in the time period corresponding to any extreme point.

[0091] In some possible implementations, the second determination module is also used to determine the slope of the i-th extreme point and the slope of the ij-th data point; determine the first difference between the slope of the i-th extreme point and the slope of the ij-th data point; determine the second difference between the slope of the it-th data point and the slope of the it-1-th data point; wherein i is an integer less than or equal to the number of extreme points and greater than or equal to 1, t is a positive integer less than or equal to i, and j is an integer greater than or equal to 1 and less than or equal to t; based on the first difference and the second difference, determine the neighborhood range of the i-th extreme point.

[0092] In some possible implementations, the second determination module is further used to determine the possibility of the it-th data point being a data change cutoff point and the possibility of the i+t-th data point being a data change cutoff point based on the first difference and the second difference; and determine the neighborhood range of the i-th extreme point based on the possibility of the it-th data point being a data change cutoff point and the possibility of the i+t-th data point being a data change cutoff point.

[0093] In some possible implementations, the second determination module is further used to determine the energy loss function of the motor based on the actual power and the ideal power; determine the data value of any data point within the neighborhood range of any extreme point; determine a target value that matches the location of any data point in the target data segment of the energy loss function; wherein the location of the target data segment matches any extreme point; based on the difference between the data value of any data point and the target value, and the number of data points within the neighborhood range of any extreme point, determine the correlation between the neighborhood range of any extreme point and the target data segment.

[0094] In some possible implementations, the second determination module is also used to determine the power difference between the maximum power and the minimum power in the time period corresponding to any extreme point; fuse the power difference and the actual power to obtain the fused power; and adjust the fused power based on the correlation to obtain the actual state parameter.

[0095] In some possible implementations, the third determination module is further used to adjust the actual state parameter using a preset value to obtain an adjusted actual state parameter; and determine the control parameter of the motor based on the adjusted actual state parameter and the actual power of the motor.

[0096] In some possible implementations, the adjustment module is further used to input the control parameter into the preset state model to obtain an output result; and use the output result as an input parameter of a preset adaptive control model to adaptively adjust the rotation speed of the motor.

[0097] In some possible implementations, the adjustment module is also used to determine the steady-state gain of the system to which the motor belongs based on the speed or output torque of the motor; determine the damping ratio of the system based on the overshoot and oscillation behavior of the system to which the motor belongs; determine the time constant matched by the system based on the speed at which the motor reaches a steady-state value; and adjust the steady-state gain based on the time constant, the steady-state value and the damping ratio to construct the preset state model.

[0098] Optionally, the transmission medium can be a wired link (such as but not limited to coaxial cable, optical fiber and digital subscriber line (DSL)) or a wireless link (such as but not limited to wireless Fidelity (WIFI), Bluetooth and mobile device network).

[0099] It should be noted that: the device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the embodiment of a brushless DC motor speed regulation method provided in the above embodiment belongs to the same concept, and its specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0100] Figure 6 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 6 As shown, the electronic device 600 includes: a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602, wherein when the processor 602 executes the computer program 603, the device can execute any of the above-mentioned brushless DC motor speed regulation methods. In addition, the embodiment of the present invention also protects a device, which may include a memory and a processor, wherein the memory stores an executable program code, and the processor is used to call and execute the executable program code to execute a brushless DC motor speed regulation method provided by the embodiment of the present invention.

[0101] This embodiment can divide the functional modules of the device according to the above method example. For example, it can correspond to each functional module, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation. In the case of dividing each module corresponding to each function, the device may also include a signal uploading module, a determination module, an adjustment module, etc. It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here.

[0102] It should be understood that the device provided in this embodiment is used to perform the above-mentioned method for adjusting the speed of a brushless DC motor, so the same effect as the above-mentioned implementation method can be achieved. In the case of an integrated unit, the device may include a processing module and a storage module. Among them, when the device is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device to execute mutual program codes, etc. Among them, the processing module can be a processor or a controller, which can implement or execute various exemplary logic boxes, modules and circuits described in combination with the disclosure of the present invention. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module can be a memory.

[0103] In addition, the device provided in the embodiment of the present invention may be a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute a brushless DC motor speed regulation method provided in the above embodiment. This embodiment also provides a computer-readable storage medium, in which a computer program code is stored, and when the computer program code is run on a computer, the computer executes the above-mentioned related method steps to implement a brushless DC motor speed regulation method provided in the above embodiment.

[0104] This embodiment also provides a computer program product. When the computer program product is run on a computer, the computer executes the above-mentioned related steps to implement a brushless DC motor speed regulation method provided in the above embodiment. Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be repeated here. Through the description of the above implementation, the technicians in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by the present invention, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiment described above is only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, which may be electrical, mechanical or other forms.

[0105] It should be noted that the sequence of the above-mentioned embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous. The various embodiments in this specification are described in a progressive manner, and the same and similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for adjusting the speed of a brushless DC motor, characterized in that: The method comprises: Get the monitoring parameters of the motor; Based on the monitoring parameters, determining the ideal state parameters of the motor; Determining actual state parameters of the motor based on the ideal state parameters and the monitoring parameters; Determining control parameters of the motor based on the actual state parameters; Based on the control parameters and the preset state model, the rotation speed of the motor is adaptively adjusted.

2. The method according to claim 1, characterized in that The obtaining of the monitoring parameters of the motor includes: Obtaining the speed, power data, voltage data and current data of the motor; The rotation speed, the power data, the voltage data and the current data are preprocessed to obtain the monitoring parameters.

3. The method according to claim 1, characterized in that The determining the actual state parameter of the motor based on the ideal state parameter and the monitoring parameter comprises: Determining the actual power of the motor based on the torque of the motor and the speed of the monitoring parameter; Determine the neighborhood range of any extreme point based on the slope corresponding to any extreme point in the current data in the monitoring parameter and the slope of the data points in the neighborhood data of any extreme point; Determine the correlation between different data based on the neighborhood range of any extreme value point, the actual power and the ideal power in the ideal state parameter; The actual state parameter is determined based on the correlation, the actual power and the power in a time period corresponding to any extreme point.

4. The method according to claim 3, characterized in that The determining the neighborhood range of any extreme point based on the slope corresponding to any extreme point in the current data in the monitoring parameter and the slope of the data points in the neighborhood data of any extreme point includes: Determine the slope of the ith extreme point and the slope of the ijth data point; Determine a first difference between the slope of the i-th extreme point and the slope of the ij-th data point; Determine a second difference between the slope of the it-th data point and the slope of the it-1-th data point; wherein i is an integer less than or equal to the number of extreme value points and greater than or equal to 1, t is a positive integer less than or equal to i, and j is an integer greater than or equal to 1 and less than or equal to t; Based on the first difference and the second difference, a neighborhood range of the i-th extreme point is determined.

5. The method according to claim 4, characterized in that The determining, based on the first difference and the second difference, a neighborhood range of the i-th extreme point comprises: Based on the first difference and the second difference, determining the possibility that the it-th data point is a data change cutoff point, and the possibility that the i+t-th data point is a data change cutoff point; Based on the possibility that the ith data point is a data change cutoff point and the possibility that the i+tth data point is a data change cutoff point, the neighborhood range of the ith extreme value point is determined.

6. The method according to claim 3, characterized in that: The determining of the correlation between different data based on the neighborhood range of any extreme point, the actual power and the ideal power in the ideal state parameter includes: Determining an energy loss function of the motor based on the actual power and the ideal power; Determine the data value of any data point within the neighborhood of any extreme value point; In the target data segment of the energy loss function, determining a target value that matches the location of any one of the data points; wherein the location of the target data segment matches the any one of the extreme value points; Based on the difference between the data value of any data point and the target value, and the number of data points within the neighborhood of any extreme point, the correlation between the neighborhood of any extreme point and the target data segment is determined.

7. The method according to claim 3, characterized in that The determining the actual state parameter based on the correlation, the actual power and the power in the time period corresponding to any extreme point includes: Determine the power difference between the maximum power and the minimum power in the time period corresponding to any extreme value point; Fusing the power difference and the actual power to obtain a fused power; The fusion power is adjusted based on the correlation to obtain the actual state parameter.

8. The method according to claim 1, characterized in that The step of determining the control parameters of the motor based on the actual state parameters includes: The actual state parameter is adjusted using a preset value to obtain an adjusted actual state parameter; Based on the adjusted actual state parameter and the actual power of the motor, a control parameter of the motor is determined.

9. The method according to claim 1, characterized in that: The adaptively adjusting the rotation speed of the motor based on the control parameter and the preset state model includes: Inputting the control parameters into the preset state model to obtain an output result; The output result is used as an input parameter of a preset adaptive control model to adaptively adjust the rotation speed of the motor.

10. The method according to claim 9, characterized in that The method further comprises: Determining a steady-state gain of a system to which the motor belongs based on a rotation speed or an output torque of the motor; Determining a damping ratio of the system based on overshoot and oscillation behavior of the system to which the motor belongs; determining a time constant for matching the system based on the speed at which the motor reaches a steady-state value; Based on the time constant, the steady-state value and the damping ratio, the steady-state gain is adjusted to construct the preset state model.

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