A novel sensorless speed measurement method for DC brushed motors

Abstract

The present invention discloses a novel sensorless speed measurement method for a DC brushed motor, which relates to the technical field of motor control. The method includes: collecting the phase voltage waveform during the operation of the motor, performing Fourier transform, calculating the frequency of the motor passing through the carbon brush commutation, and converting to obtain the motor operating speed V1; calculating the back electromotive force of the motor according to the magnitude of the DC component of the phase voltage Fourier transform, and converting to obtain the speed V2; obtaining the reference speed V3 through an externally connected encoder, performing data fusion model calibration to obtain the calibrated Kalman parameters; performing data fusion on V1 and V2 according to the calibrated Kalman parameters to obtain the new motor speed V. The present invention can achieve encoder-free feedback motor speed control, effectively solve the influence of motor temperature rise on the calculation speed of the back electromotive force, and also avoid the problem of inaccurate speed measurement caused by the insignificant disturbance of the carbon brush commutation waveform due to slow speed.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and more particularly, to a novel sensorless speed measurement method for a DC brushed motor. Background Art

[0002] For the control of traditional DC brushed motors, an encoder is generally used as the speed closed-loop feedback to achieve accurate real-time speed and position control. For applications without a speed sensor such as an encoder, it is necessary to establish a motor model to estimate the current speed of the motor. Currently, it is widely used to estimate the motor speed using the back electromotive force of the motor, that is, calculated using the following formula: where E is the back electromotive force of the motor, U is the voltage between two phases of the motor, I is the current flowing through the motor, R is the internal resistance of the motor winding, L is the inductance of the motor winding, represents the differential value of the motor current with respect to time.

[0003] Theoretically, the back electromotive force calculated in this way can represent the speed of the motor. However, during the actual operation of the motor, the temperature rise caused by the motor operation will cause changes in the internal resistance R and the inductance L of the motor, resulting in inaccurate calculation of the back electromotive force of the motor. Therefore, temperature compensation is required. And to perform temperature compensation, a temperature sensor needs to be added, which will increase the cost of the system on the one hand and reduce the stability of the system on the other hand. Summary of the Invention

[0004] In order to overcome the above problems or at least partially solve the above problems, an embodiment of the present invention provides a novel sensorless speed measurement method for a DC brushed motor, which can effectively solve the influence of motor temperature rise on the calculation speed of the back electromotive force, and can also avoid the problem of inaccurate speed measurement caused by the unobvious disturbance of the carbon brush commutation waveform due to the slow speed.

[0005] The embodiments of the present invention are implemented as follows:

[0006] In a first aspect, an embodiment of the present invention provides a novel sensorless speed measurement method for a DC brushed motor, including the following steps:

[0007] Collect the phase voltage waveform during the operation of the motor, perform Fourier transform, calculate the frequency of the motor passing through the carbon brush commutation, and convert it to obtain the motor operating speed V1;

[0008] Calculate the magnitude of the back electromotive force of the motor according to the magnitude of the DC component of the phase voltage Fourier transform, and convert it to obtain the speed V2;

[0009] Obtain the reference speed V3 through an external encoder measurement, collect and calibrate the data fusion model according to V1, V2, and V3 under various preset conditions to obtain the calibrated Kalman parameters;

[0010] According to the calibrated Kalman parameters, data fusion is performed on V1 and V2 to calculate the new motor speed V.

[0011] To solve the problems in the prior art, the present invention uses the voltage fluctuation generated when the motor undergoes carbon brush commutation to calculate the operating speed V1 of the motor. At the same time, in combination with the back electromotive force E of the motor, the calculated motor speed V2 is used for data fusion to obtain a new motor speed V. The motor speed calculated by this method can effectively solve the influence of motor temperature rise on the calculation speed of the back electromotive force, and can also avoid the problem of inaccurate speed measurement caused by the slow speed and the unobvious disturbance of the carbon brush commutation waveform.

[0012] Based on the first aspect, in some embodiments of the present invention, the method of collecting the phase voltage waveform during the operation of the motor, performing Fourier transform, calculating the frequency of the motor passing through the carbon brush commutation, and converting to obtain the motor operating speed V1 includes the following steps:

[0013] Collect and determine the two-phase voltages Va and Vb of the motor according to the voltage waveform generated during the operation of the motor;

[0014] Calculate the motor phase voltage Vab = Va - Vb;

[0015] Perform Fourier transform on Vab to obtain the spectrum data of the motor phase voltage;

[0016] Extract and determine the operating speed V1 of the motor commutation according to the frequency of the AC component with the maximum amplitude in the spectrum data.

[0017] Based on the first aspect, in some embodiments of the present invention, the method of collecting and calibrating the data fusion model according to V1, V2, and V3 under multiple preset conditions to obtain the calibrated Kalman parameters includes the following steps:

[0018] Collect V1, V2, and V3 at different preset speeds and temperatures to obtain the corresponding data sequences;

[0019] Optimize the parameters of the Kalman data fusion algorithm based on the data sequences until V3 = f(V1, V2) is satisfied, complete the calibration, and record the Kalman parameters at this time.

[0020] In a second aspect, an embodiment of the present application provides an electronic device, which includes a memory for storing one or more programs; a processor. When the one or more programs are executed by the processor, the method according to any one of the first aspects described above is implemented.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the above first aspects is implemented.

[0022] The embodiments of the present invention at least have the following advantages or beneficial effects:

[0023] The embodiments of the present invention provide a novel sensorless speed measurement method for a DC brushed motor. The method calculates the operating speed V1 of the motor by using the voltage fluctuation generated when the motor passes through the carbon brush commutation. At the same time, the motor speed V2 calculated from the back electromotive force E of the motor is combined, and after data fusion, a new motor speed V is obtained. The motor speed calculated by this method can effectively solve the influence of motor temperature rise on the calculation speed of the back electromotive force, and can also avoid the problem of inaccurate speed measurement caused by the inconspicuous disturbance of the carbon brush commutation waveform due to the slow speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 It is a flowchart of a novel sensorless speed measurement method for a DC brushed motor according to an embodiment of the present invention;

[0026] Figure 2 It is a waveform diagram of Vab in an embodiment of the present invention;

[0027] Figure 3 It is a flowchart for calculating V1 in a novel sensorless speed measurement method for a DC brushed motor according to an embodiment of the present invention;

[0028] Figure 4 It is a structural block diagram of an electronic device provided by an embodiment of the present invention.

[0029] Description of reference numerals: 101. Memory; 102. Processor; 103. Communication interface. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and illustrated in the drawings here can be arranged and designed in various different configurations.

[0031] Accordingly, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0032] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not require further definition and explanation in subsequent drawings.

[0033] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0034] In the description of the embodiments of the present invention, "a plurality of" represents at least two.

[0035] Embodiment:

[0036] As Figures 1 - 3 shown, in a first aspect, an embodiment of the present invention provides a novel sensorless speed measurement method for a DC brushed motor, including the following steps:

[0037] S1. Collect the phase voltage waveform during motor operation, perform Fourier transform, calculate the frequency of the motor passing through the carbon brush commutation, and convert to obtain the motor operating speed V1.

[0038] Further, as Figure 3 shown, it includes:

[0039] S11. Collect and determine the two-phase voltages Va and Vb of the motor according to the voltage waveform generated during motor operation.

[0040] S12. Calculate the motor phase voltage Vab = Va - Vb.

[0041] S13. Perform Fourier transform on Vab to obtain the spectrum data of the motor phase voltage.

[0042] S14. Extract and determine the operating speed V1 of the motor commutation based on the frequency of the AC component with the maximum amplitude in the spectrum data. Because during the operation of the motor, when commuting through the carbon brushes, phase voltage fluctuations will occur. This fluctuation signal appears as a periodic AC signal superimposed on the motor phase voltage signal in the frequency domain. By performing Fourier transform to extract this frequency signal, the motor commutation frequency can be obtained.

[0043] In some embodiments of the present invention, sample the two-phase voltages Va and Vb of the motor and calculate the motor phase voltage Vab = Va - Vb. Through experimental tests, the waveform of Vab collected is as Figure 2 shown. It can be clearly seen from the waveform diagram that the motor phase voltage waveform shows periodic changes, and the main frequency of the change is the commutation frequency of the motor, that is, the motor speed. Perform Fourier transform on Vab to obtain the spectrum data of the motor phase voltage. In this application, the fast Fourier transform FFT, which is more suitable for embedded system calculation, is used. The specific implementation method is to calculate Vab at a sampling frequency of 1KHZ, and at the same time store 1024 consecutive data. Perform FFT operation on these 1024 consecutive Vab data to obtain the spectrum data of the Vab data waveform. From the data of the FFT transform in the figure, this data waveform has a DC component, followed by a component with a frequency of 464HZ. This frequency is the commutation frequency of the motor and can characterize the motor speed. At the same time, the magnitude of the DC component can also characterize the magnitude of the motor speed to a certain extent. Combining these two data can calculate the reliable operating speed of the motor. Then extract the frequency component. After FFT operation, a series of discrete data with different frequencies and different amplitudes can be obtained. Among them, the DC component can characterize the magnitude of the back electromotive force E, and the point with the largest amplitude in the AC component is the commutation frequency. In the above figure, it is 464HZ. According to the frequency, the commutation speed V1 of the motor can be calculated.

[0044] S2. Calculate the magnitude of the motor back electromotive force based on the magnitude of the DC component of the Fourier transform of the phase voltage, and convert it to obtain the speed V2;

[0045] In some embodiments of the present invention, based on the magnitude of the DC component amplitude after FFT operation, the motor back electromotive force can be obtained, and then the motor back electromotive force speed V2 can be calculated. Because the back electromotive force of the motor is proportional to the operating speed of the motor, and the magnitude of the motor back electromotive force is the magnitude of the DC component of the motor phase voltage, the motor back electromotive force speed V2 can be determined by performing Fourier transform on the phase voltage and according to the magnitude of its DC component.

[0046] S3. Obtain the reference speed V3 through actual measurement by an external encoder, collect and calibrate the data fusion model based on V1, V2, and V3 under various preset conditions to obtain the calibrated Kalman parameters;

[0047] In some embodiments of the present invention, before performing data fusion on the motor speed V1 and the back electromotive force E, an accurate speed is required as a reference, and this reference speed needs to be accurate. This system obtains the actual operating speed of the motor based on a speed encoder installed on the motor, and sets this actual operating speed as the reference speed V3.

[0048] S4. According to the calibrated Kalman parameters, perform data fusion on V1 and V2 to calculate the new motor speed V.

[0049] Furthermore, it includes: collecting V1, V2, and V3 at preset different speeds and different temperatures to obtain corresponding data sequences; optimizing the parameters of the Kalman data fusion algorithm based on the data sequences until V3 = f(V1, V2) is satisfied, completing the calibration, and recording the Kalman parameters at this time.

[0050] In some embodiments of the present invention, based on the above-obtained three speeds, V1 is the motor speed calculated according to the disturbance of the motor phase voltage waveform; V2 is the speed calculated according to the motor back electromotive force model; V3 is the speed actually feedback by the encoder (reference speed), and a set of models are established to achieve the following effect: V3 = f(V1, V2). To obtain the correct calculation model V3 = f(V1, V2), a large amount of data needs to be collected, and data analysis needs to be carried out under the following several variables:

[0051] 1. Speed change. Increase the speed by 10% until it reaches 100% speed, and collect data for analysis.

[0052] 2. Temperature change. Increase the temperature by 20°C on the motor surface until it reaches 120°C, and collect data for analysis.

[0053] Based on the above data collection and analysis, the corresponding data sequences are obtained as shown in the following table:

[0054]

[0055] By using the collected corresponding data sequences, we adopt the Kalman filtering algorithm as the basis for data fusion to make the data calculated from V1 and V2 as close as possible to V3. Among them, V1 and V2 each have their own advantages and disadvantages in the actual measurement process. When the speed is low, due to the small amplitude of the back electromotive force, the corresponding fluctuation value is also small, so the accuracy at this time is V2 > V1. When the speed is high, the corresponding voltage fluctuation will be more obvious, and the accuracy at this time is V1 > V2. By using Kalman for data fusion, a more stable and reliable motor speed V3 can be obtained. Through the above formula calculation, we can calculate the running speed of the motor without a speed encoder. After testing, this method is not affected by the motor temperature, current magnitude, etc., and has strong universality. Once the model is established, it is generally applicable to motors of the same specification.

[0056] To solve the problems in the prior art, the present invention uses the voltage fluctuation generated when the motor undergoes carbon brush commutation to calculate the running speed V1 of the motor, and combines it with the motor speed V2 calculated from the back electromotive force E of the motor. After data fusion, a new motor speed V is obtained. The motor speed calculated by this method can effectively solve the influence of motor temperature rise on the calculation speed of the back electromotive force, and can also avoid the problem of inaccurate speed measurement caused by the insignificant disturbance of the carbon brush commutation waveform at low speeds.

[0057] As Figure 4 shown, in a second aspect, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; a processor 102. When the one or more programs are executed by the processor 102, the method according to any one of the above first aspects is implemented.

[0058] It further includes a communication interface 103, and the memory 101, the processor 102, and the communication interface 103 are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules, and the processor 102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 101. The communication interface 103 can be used for signaling or data communication with other node devices.

[0059] Among them, the memory 101 can be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electric Erasable Programmable Read-Only Memory (EEPROM), etc.

[0060] The processor 102 can be an integrated circuit chip with signal processing capabilities. The processor 102 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0061] In the embodiments provided in this application, it should be understood that the disclosed methods, systems, and methods can also be implemented in other ways. The method and system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the methods, systems, and computer program products according to multiple embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0062] In addition, in each embodiment of the present application, the functional modules can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0063] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor 102, the method according to any one of the above first aspects is implemented. If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0064] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0065] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present application is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A novel sensorless speed measurement method for a brushless DC motor, characterized in that: The following steps are involved: Collect the phase voltage waveform when the motor is running, perform Fourier transform, calculate the frequency of the motor's carbon brush commutation, and convert it to the motor's running speed V1; According to the magnitude of the DC component of the phase voltage Fourier transform, the magnitude of the motor back electromotive force is calculated and converted to obtain the speed V2; The reference speed V3 is measured by an external encoder, and the data fusion model is calibrated based on V1, V2 and V3 under various preset conditions to obtain the calibrated Kalman parameters. According to the calibrated Kalman parameters, data fusion is performed on V1 and V2 to calculate the new motor speed V; The method of collecting and calibrating the data fusion model according to V1, V2 and V3 under multiple preset conditions to obtain calibrated Kalman parameters includes the following steps: Collect V1, V2, and V3 at different preset speeds and temperatures to obtain corresponding data sequences; Optimize the Kalman data fusion algorithm parameters based on the data sequence until the , complete the calibration, and record the Kalman parameters at this time.

2. A novel sensorless speed measurement method for a brushless DC motor according to claim 1, characterized in that: The method of collecting the phase voltage waveform when the motor is running, performing Fourier transform, calculating the frequency of the motor through carbon brush commutation, and converting the motor running speed V1 includes the following steps: Collect and determine the two-phase voltages Va and Vb of the motor according to the voltage waveform generated when the motor is running; Calculate the motor phase voltage Vab=Va-Vb; Perform Fourier transform on Vab to obtain the frequency spectrum data of the motor phase voltage; The motor commutation running speed V1 is determined based on the frequency of the AC component with the maximum amplitude in the spectrum data.

3. An electronic device, characterized in that: include: A memory for storing one or more programs; processor; When the one or more programs are executed by the processor, the method according to any one of claims 1 to 2 is implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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