Minimum energy tracking method for micro direct current motor based on current waveform decomposition

Abstract

The application discloses a minimum energy consumption tracking method of a micro DC motor based on current waveform decomposition and belongs to the technical field of micro motor control. The method comprises the following steps: a given driving voltage is used to drive the motor to run, the magnetic field and current signals during rotation are synchronously collected, the motor rotation period is determined based on the magnetic field signals, three-layer verification is sequentially completed, and the effective period is screened out; in the effective period, the energy consumption and load index are calculated by means of the current data; the load variation trend is determined according to the dynamic reference value and the average load index; if the load rises, the voltage is first increased to the convergence of the load index, and then decreased to carry out the lowest energy consumption search; if the load decreases, the voltage is directly decreased to carry out the lowest energy consumption search, and finally the motor lowest energy consumption operation condition is determined. The application does not depend on motor parameters, does not need offline calibration, has low calculation complexity, is suitable for being deployed on a resource-limited embedded microcontroller platform, and can effectively distinguish the influence of voltage regulation and load change.

Description

Technical Field

[0001] This invention belongs to the field of micro motor control technology, specifically relating to a method for tracking the minimum energy consumption of a micro DC motor based on current waveform decomposition. Background Technology

[0002] Miniature brushed DC motors are widely used in power-constrained microelectromechanical systems (MEMS) such as capsule robots and implantable medical devices. In these applications, the motors typically operate under periodic, time-varying loads, and the load conditions may change with the working environment. Examples include spring-compression periodic loads, barrel cam-to-linear (R2L) pumping mechanisms in implantable drug delivery pumps, peristaltic propulsion mechanisms in capsule robots, and miniature peristaltic pump mechanisms—scenarios where the current waveform alternates between high and low load segments within a single motor rotation cycle. To optimize energy consumption, the system needs to be able to detect load changes online to adjust the drive strategy promptly.

[0003] Existing load detection methods mainly include the current mean threshold method and the observer-based load torque estimation method.

[0004] The average current threshold method calculates the arithmetic mean of current samples over one motor rotation cycle as a load indicator. A load change is determined when the deviation of the average current from the reference value exceeds a preset threshold. This method is simple to calculate and does not rely on motor parameters. However, its steady-state current is affected by both the drive voltage and load torque, making it unable to distinguish between current changes caused by voltage regulation and those caused by load changes. When the system needs to gradually adjust the drive voltage during energy optimization, the change in the average current includes contributions from both the voltage regulation operation itself and potential load changes. This overlap significantly increases the risk of misjudgment.

[0005] The observer-based load torque estimation method uses the mathematical model of the motor and measurable signals to estimate the load torque in real time. This method requires known motor parameters such as armature resistance, back electromotive force constant, torque constant, and viscous damping coefficient.

[0006] However, in micro motor systems, some of these parameters are difficult to identify accurately, while others drift with temperature and operating conditions. In particular, the viscous damping coefficient is difficult to measure accurately in micro systems, the armature resistance changes with winding temperature, and the gearbox efficiency itself is not constant. These uncertainties make the estimation results prone to systematic biases, limiting their engineering feasibility in practical deployments.

[0007] Therefore, there is a need for an online load detection method that is independent of motor parameters and can reliably distinguish between load changes and voltage adjustment effects during drive voltage regulation. Summary of the Invention

[0008] To address the existing technical problems, this invention proposes a method for tracking the minimum energy consumption of a micro DC motor based on current waveform decomposition.

[0009] The specific technical solution is as follows:

[0010] The motor is driven to rotate under a set driving voltage, and the magnetic field signal and current signal during the rotation process are collected in real time.

[0011] The motor rotation cycle is identified based on magnetic field signals, and each cycle undergoes a three-layer validity verification, including peak time range check, statistical value range verification, and prominence check. If it passes, it is recorded as a valid cycle.

[0012] Within each effective cycle, energy consumption and adaptive thresholds are calculated based on current signals, feature quantities are extracted, and load indicators are calculated.

[0013] Based on the dynamic reference load index and the average load index of multiple consecutive effective periods, the direction of load change is determined. If the load increases, a two-stage re-tracking is performed: first, the driving voltage is gradually increased until the average load index converges, and then the driving voltage is gradually decreased from the converged voltage to perform energy consumption reduction search.

[0014] If the voltage is reduced, the driving voltage will be gradually reduced starting from the current driving voltage to perform a power consumption reduction search;

[0015] In the energy consumption reduction search, if the average energy consumption of multiple consecutive effective cycles under the current drive voltage is lower than the recorded historical minimum average energy consumption, then the historical minimum average energy consumption and its corresponding drive voltage are updated, and the drive voltage is further reduced for the next round of search; otherwise, the drive voltage corresponding to the historical minimum average energy consumption is taken as the minimum energy consumption operating point.

[0016] Furthermore, the magnetic field signal is generated by fixing a permanent magnet at the end of the output shaft of the miniature DC motor.

[0017] Furthermore, the process of identifying the motor rotation cycle based on the magnetic field signal is as follows: if the magnetic field value of the current magnetic field signal is greater than the threshold and greater than the adjacent magnetic field value, then the current magnetic field signal is considered to be a valid peak value, and every two adjacent valid peak values ​​form a rotation cycle.

[0018] Furthermore, the peak time range check is defined as the time interval between two valid peak values ​​being greater than or equal to the lowest time interval threshold and less than or equal to the highest time threshold;

[0019] The statistical value range is verified to be such that the mean, peak, and valley values ​​of the current within the rotation cycle all satisfy the physical constraints.

[0020] The spuriousness check is performed when the difference between the peak and trough values ​​of the current during the rotation cycle is greater than or equal to the minimum spuriousness threshold.

[0021] Furthermore, the characteristic quantities include a high current component, a baseline current component, and a high load duration; the high current component is the arithmetic mean of all currents above the high current adaptive threshold within the effective period; the baseline current component is the arithmetic mean of all currents below the low current adaptive threshold within the effective period; and the high load duration is the length of the continuous time interval within the effective period during which the current exceeds the high current adaptive threshold.

[0022] Furthermore, the load index is calculated by subtracting the high current component from the baseline current component and then multiplying the result by the high load duration.

[0023] Furthermore, the calculation process for the average load index during the two-stage retracking process is as follows:

[0024] Under the current driving voltage, skip the first few effective cycles, and start from the first effective cycle that has not been skipped, calculate the arithmetic mean of the load index for multiple consecutive effective cycles, which is the average load index.

[0025] Furthermore, the update process of the dynamic reference load index is as follows:

[0026] Calculate the load index for each effective cycle at the minimum energy consumption operating point. Skip the first few effective cycles and start from the first effective cycle that has not been skipped. Calculate the arithmetic mean of the load index for multiple consecutive effective cycles as the updated reference load index.

[0027] The beneficial effects of this invention are:

[0028] (1) It does not depend on motor parameters. The load index L of this invention is constructed only based on the statistical characteristics of the current sampling sequence. It does not require known motor parameters such as armature resistance, back electromotive force constant, torque constant or viscous damping coefficient, nor does it require offline parameter calibration. It is not sensitive to parameter drift and temperature changes.

[0029] (2) It can effectively distinguish the effects of voltage regulation and load changes. The load index calculation formula defined in this invention amplifies the response of the index to load changes during the driving voltage regulation process, while the response to pure voltage regulation is relatively small, thereby effectively reducing the risk of misjudgment.

[0030] (3) Low computational complexity. Feature extraction in each cycle only involves comparison, summation and division operations, without the need for matrix operations or iterative optimization, making it suitable for deployment on resource-constrained embedded microcontroller platforms; at the same time, it enables the system to respond to load fluctuations in a timely manner and quickly converge to the minimum energy consumption operating point, ensuring the real-time tracking and energy efficiency optimization capabilities.

[0031] (4) It has the characteristic of indicating the inflection point near the minimum energy consumption operating point. When the driving voltage drops below the minimum energy consumption operating point, the high load duration increases sharply, resulting in obvious inflection characteristics of the load index, which can be directly used as the stage transition criterion in the minimum energy consumption search algorithm.

[0032] (5) The present invention introduces a permanent magnet to generate a magnetic field signal, which can provide a stable periodic reference for motor rotation that is independent of the current, enabling the system to accurately identify each rotation cycle and perform three-layer validity verification, thereby significantly improving the ability to screen the effective cycle under load fluctuations or electrical interference, and improving the accuracy and robustness of energy efficiency optimization without relying on expensive sensors. Attached Figure Description

[0033] Figure 1 This is an overall flowchart of the method of the present invention.

[0034] Figure 2 The high current component on the current waveform during one motor rotation cycle Baseline current component and high load duration A diagram with annotations.

[0035] Figure 3 This is a schematic diagram illustrating the change of driving voltage over time in a specific embodiment of the present invention.

[0036] Figure 4 This is a comparison diagram of the changes in motor current waveforms under different driving voltages in a specific embodiment of the present invention.

[0037] Figure 5 This is a graph showing the change of load index L with driving voltage under different load conditions in a specific embodiment of the present invention.

[0038] Figure 6 This is a schematic diagram of the electronic device terminal structure of the present invention. Detailed Implementation

[0039] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. Technical features in various embodiments of the present invention can be combined accordingly without mutual conflict.

[0040] The overall process of the online detection method for load changes of a micro DC motor based on current waveform decomposition proposed in this invention is as follows: Figure 1As shown, the specific content is as follows:

[0041] Step 1: Electromagnetic signal acquisition and motor rotation cycle detection.

[0042] A permanent magnet (such as a miniature neodymium iron boron permanent magnet) is fixed at the end of the output shaft of a miniature brushed DC motor to convert the rotation of the output shaft into a magnetic field change signal. Simultaneously, a magnetometer (in a specific embodiment of this invention, a MEMSIC MMC5983MA triaxial magnetometer module) is fixedly installed near the motor, and the motor must be within the signal receiving range of the magnetometer. A galvanometer is directly connected to the motor to measure the current generated when the motor rotates (in a specific embodiment of this invention, a Texas Instruments INA219 current monitoring chip is used for current sampling).

[0043] A driving voltage is applied to the motor, causing it to rotate. Based on the direction of motor rotation, the magnetic field component parallel to the motor's trajectory in the X, Y, and Z directions of the magnetometer is selected as the single axis with the strongest signal amplitude (for example, when the motor rotates around the y-axis, the y-axis is selected as the single axis with the strongest signal amplitude). This is used as a periodic detection signal source. When the output shaft rotates, it drives the permanent magnet to rotate. Each rotation of the permanent magnet generates a complete magnetic field change cycle at the magnetometer.

[0044] The output of the single axis of the magnetometer, which serves as the periodic detection signal source, is continuously and discretely sampled. Local maxima are found in the discrete sequence. That is, when the magnetic field value at a certain sampling point is higher than that at its adjacent sampling points, and the peak amplitude reaches a preset threshold, that point is determined to correspond to a valid peak value. The time interval between two adjacent valid peak values ​​is one motor rotation cycle. .

[0045] The peak detection method is used because the magnetic field signal has a clear periodicity, and the peak position is less sensitive to DC offset, making it less prone to boundary misjudgment due to baseline drift. The magnetometer communicates with the microcontroller via an I2C bus. Since multiple I2C devices exist in the system simultaneously (including the current sensor INA219 and the magnetometer MMC5983MA), a mutex mechanism is used to ensure the atomicity of bus access.

[0046] Step 2: Three-layer cycle validity verification.

[0047] In actual operation, the magnetometer signal may be affected by electromagnetic interference, mechanical vibration, or sampling noise. To ensure the reliability of periodic detection, each detected period undergoes three-layer verification sequentially:

[0048] The first layer is the peak time range check.

[0049] Detected time interval between adjacent peaks Must meet ,in and These are the lower and upper limits of the time interval between adjacent peak values, respectively, set by the user based on the expected speed range corresponding to the current drive voltage, with a margin of ±20%. If If the cycle exceeds this range, it is marked as invalid and discarded.

[0050] The second layer is the verification of the statistical value range.

[0051] Within an initially accepted period, the mean, peak, and valley values ​​of the current samples are calculated to verify whether they are within a physically reasonable range. For example, the mean and valley values ​​of the current should not be negative, and the peak value should not exceed the stall current of the motor.

[0052] The third layer is the prominence check.

[0053] The peak-to-valley difference of the current within a cycle must meet the minimum prominence requirement, i.e. ,in This is the peak current. This is the current valley value. To meet the minimum protrusion requirement, the value should be 30% to 80% of the rated no-load current. If the current waveform is too flat, it indicates that the cycle does not contain effective load information and should not be used for subsequent calculations.

[0054] Through the above three-layer verification mechanism, the system can effectively filter out invalid periodic data and ensure the data quality of subsequent feature extraction.

[0055] Step 3: Load indicators and energy consumption calculation.

[0056] (3.1) Adaptive threshold calculation

[0057] Within each valid verification cycle, the peak value is first obtained from the current sampling sequence. Valley value Then, two adaptive thresholds are calculated:

[0058]

[0059]

[0060] in, As a high-current adaptive threshold, the position of 90% of the peak-to-valley difference is selected to identify high-current sampling points close to the peak value; As a low-current adaptive threshold, the position at 10% of the peak-to-valley difference is used to select low-current sampling points close to the baseline level.

[0061] This adaptive design allows the threshold to automatically adjust with changes in operating conditions, eliminating the need for manual preset of a fixed value. A value of 90% can filter out isolated current spikes to some extent, while a value of 10% can prevent intermediate values ​​in the transition region from being mixed into the calculation of the baseline current.

[0062] While performing adaptive threshold calculation, this invention also calculates the single-cycle energy consumption for that period. Perform the following discrete integral calculation.

[0063] Since the drive voltage is set by the DC-DC converter in each search step and remains constant throughout the cycle, the sampling interval between two consecutive AD conversions of the ammeter is... Then the energy consumption of the nth effective cycle for;

[0064]

[0065] in, It is the sum of all current sample values ​​within this cycle. This is the arithmetic mean of the current within that period. This represents the total number of current sampling points within that cycle. The length of the motor's rotation cycle (given in step one). The instantaneous current value corresponding to the kth sampling point , This is the driving voltage.

[0066] This discrete integral calculation is related to the high current component AC, the baseline current component DC, and the high load duration. The feature extraction shares the same current sampling sequence, eliminating the need for additional sampling or storage.

[0067] Furthermore, the energy consumption in this invention The subsequent load metrics are two independent variables, each with its own function: The objective function used for subsequent energy consumption reduction search, and the load index used as the detection criterion for subsequent load changes.

[0068] (3.2) , and Feature extraction.

[0069] Based on the adaptive threshold calculated in (3.1), three features are extracted from the current sampling sequence of this period:

[0070] High current component All exceeding this period The arithmetic mean of the current samples represents the average current level in the high-load section.

[0071] Baseline current component All below during this period The arithmetic mean of the current samples represents the baseline current level in the low-load segment.

[0072] High load duration The current exceeds during this cycle. The length of the continuous time interval, which in this invention is defined as the length corresponding to the first and last intervals exceeding a certain threshold in the discrete implementation. The time span between sampling points.

[0073] like Figure 2 The diagram shows the current distribution over time during a typical motor rotation cycle. It can be observed that the current waveform exhibits a distinct high and low load segment structure within a typical motor rotation cycle. During the spring compression phase, the motor experiences a larger mechanical load, causing the current to rise; during the non-compression phase, the load is smaller, and the current drops back to the baseline level. Therefore, the three characteristic quantities proposed in this invention have significant advantages. Capture the current amplitude during high load periods. Capture the baseline level of low-load segments. Capture the duration of high-load segments.

[0074] It should be noted that here and The naming does not refer to the classic meaning of alternating current and direct current, but rather to the high current component and baseline current component extracted from a motor rotation cycle, respectively. Its definition focuses on distinguishing between the current level in the high load segment and the current level in the low load segment.

[0075] (3.3) Construction of load index and multi-period averaging.

[0076] Based on the three features extracted in (3.2), a load index is constructed:

[0077]

[0078] in, As a load indicator, from a dimensional analysis perspective, The unit is (millicoulomb), in physical terms, represents the amount of extra charge transferred relative to the baseline level during the high-load segment of each motor rotation cycle.

[0079] The load index calculated from this It can encode two independent dimensions of information simultaneously:

[0080] Amplitude information This reflects the current increment caused by the load, specifically the rise in current during high-load periods relative to the baseline current. This component depends primarily on the magnitude of the additional load torque and is approximately independent of the drive voltage.

[0081] Time information This reflects the duration of the high-load segment and is affected by both the drive voltage and the load torque. When the drive voltage decreases, the motor speed decreases, and the time required to traverse the same high-load angular displacement segment increases. The corresponding increase.

[0082] The product of the two makes L sensitive to the following two situations: (1) when the load increases, Increase and (2) When the driving voltage deviates from the minimum energy consumption operating point and moves towards a lower voltage direction, although the response of L is amplified; Basically unchanged, but A sharp increase, leading to The rise indicates a clear turning point.

[0083] Compared to using only the average current as a load metric, the load metric constructed in this invention... It can effectively distinguish between current changes caused by voltage regulation and those caused by load variations. The average current is affected by both the drive voltage and the load, and these two factors are intertwined and difficult to separate; while... In Insensitive to voltage changes Although affected by voltage, its change pattern can be distinguished from the change caused by load changes in the overall response of L.

[0084] Compared to observer-based load torque estimation, The calculation does not depend on any motor parameters, and does not require known armature resistance, back electromotive force constant, torque constant or viscous damping coefficient, so it is not affected by parameter calibration error and temperature drift.

[0085] To reduce the impact of measurement noise on energy consumption estimation and load indicators, calculations were performed for M consecutive steady-state cycles. The arithmetic mean of L is used as the energy consumption estimate under the current driving voltage. and load index estimation :

[0086]

[0087]

[0088] in, These are the single-cycle energy consumption and single-cycle load index for the j-th steady-state cycle, respectively.

[0089] The choice of M involves a trade-off between noise suppression and response speed. A larger M results in better estimation stability because measurement noise cancels out between cycles, but also increases the measurement time required for each voltage level. The longer the value, the longer the total response time. In this invention, M=3 is preferred.

[0090] Step 4: Re-track the minimum energy consumption operating point based on load changes and energy consumption.

[0091] (4.1) Initialization of reference values ​​and location of the first minimum energy consumption operating point.

[0092] After system startup, an energy consumption descent search is first performed under initial load to locate the minimum energy consumption operating point. Initial value:

[0093] From the upper limit of voltage (In this specific embodiment, Starting with 5.0V, use the preset voltage step size. (In this specific embodiment, Starting with 0.2V, gradually decrease the voltage and calculate at each voltage level. And compared with the lowest recorded energy consumption Comparison, among which the first voltage level Its corresponding historical lowest energy consumption is the energy consumption calculated by itself.

[0094] like Figure 3 As shown, in a specific embodiment of the present invention, the driving voltage decreases in a stepwise manner according to a preset voltage step size. Each voltage level is maintained for a certain period of time to collect current and magnetic field data for multiple consecutive effective rotation cycles, and to calculate the average single-cycle energy consumption and average load index under that voltage level. Through this stepwise voltage reduction method, the search for the minimum energy consumption operating point can be completed along the voltage decreasing direction without avoiding complex bidirectional searches.

[0095] The comparison results and specific measures are as follows:

[0096] like ,Will Updated to ,Will After updating to the current voltage level, continue searching for the next lower voltage level; if If the search is terminated, the driving voltage will be returned to [value]. The corresponding voltage level is used as the initial minimum energy consumption operating point. .

[0097] The feasibility of the above unidirectional descending search is based on single-cycle energy consumption. Regarding drive voltage The single-valley characteristic: within the feasible region, It first decreases monotonically as the voltage decreases, and then passes the minimum point. It then monotonically increases; therefore, the first observed increase in energy consumption can be considered as having passed the threshold. No need for two-way probing.

[0098] The system in After stable operation, calculate continuous One effective cycle (in this specific embodiment) The arithmetic mean of the load index L (=3) is used as the initial reference value. And store it in memory for later comparison.

[0099] (4.2) Steady-state monitoring and load change determination.

[0100] The system in It runs continuously and updates every M effective cycles. and compared with the pre-stored reference value Compare:

[0101] when When the load increases, the original... The voltage being too low for a new, higher load poses two risks: First, the current voltage may already be close to the stall voltage under the new load, and directly reducing the voltage could cause the motor to stall; second, starting the search from a low voltage makes it impossible to determine the new load. The direction. Therefore, a two-stage re-tracking process is executed, namely (4.3) and (4.4);

[0102] when When the load is determined to decrease, the original The energy consumption is too high for the new, lower load, there is no risk of motor stalling, and the new minimum energy consumption operating point must be located at the current level. The lower voltage side is then directly entered into the direct buck search process of (4.4); otherwise, the current state is maintained. Unchanged, continue steady-state monitoring.

[0103] in, To preset a positive threshold, The two thresholds are set in stages according to the current speed range of the motor.

[0104] (4.3) Boost Search

[0105] When the load increases, the original The voltage being too low for a new, higher load poses two risks: First, the current voltage may already be close to the stall voltage under the new load, and directly reducing the voltage could cause the motor to stall; second, starting the search from a low voltage makes it impossible to determine the new load. The direction.

[0106] Starting from the current V*, with a preset voltage step size Gradually increase the voltage. Calculate after each voltage increment. and the previous drive voltage Compare, if the following conditions are met:

[0107]

[0108] in, The preset convergence threshold L is used. The current driving voltage, This is the driving voltage from the previous cycle.

[0109] Then determine Convergence has occurred, the first phase has ended, and the voltage is currently at convergence. This marks the beginning of the second phase.

[0110] The physical meaning of convergence is that the motor's operating state under the current voltage has become stable, and the speed is high enough to allow the time to pass through the high load section. The fact that the voltage-energy consumption curve no longer shortens significantly with increasing voltage indicates that the motor has moved out of the stall risk zone and has been positioned to the right of the voltage-energy consumption curve (i.e., ...). ), and it is safe to begin searching towards lower voltage.

[0111] (4.4) Precise search for decreasing energy consumption

[0112] Starting from the current driving voltage, according to the preset voltage step size Perform a unidirectional descending search, calculating at each voltage level. With the lowest historical average energy consumption Compare:

[0113] like Then Updated to and update Given the current voltage level, continue searching for the next lower voltage level; if If the search is terminated, the driving voltage will be returned to [value]. The corresponding voltage level will serve as the new minimum energy consumption operating point. .

[0114] The voltage reduction search process can also include two stages: a coarse search stage and a fine search stage. In the coarse search stage, a larger voltage step size is used. Adjust the drive voltage and collect M valid cycles for each level. The local minimum range of the driving voltage is located; then, in the fine search phase, a smaller voltage step size is used. Further refinement within this range to determine the minimum energy consumption operating point. ,in 0.2V is preferred.

[0115] (4.5) Load detection protection during the search process.

[0116] In the unidirectional descending search process, each step of the pressure reduction will cause... The voltage rises (because the voltage drops) (Increase). If not protected, this change may be misinterpreted by the steady-state monitoring module as a new round of "load increase" event, thereby triggering the first stage of voltage boost again, causing the algorithm to oscillate infinitely between voltage boost and voltage buck.

[0117] To avoid this problem, this invention sets a protection flag when entering the direct buck search, temporarily disabling the direct buck search based on... Load change detection; waiting for the search to complete and the system to stabilize. Then, remove the protection mark and recalculate the continuity. The average load index for each effective period will be the reference value. Replace with the newly calculated average value of L, and then resume normal steady-state monitoring.

[0118] (4.6) Algorithm continuous operation and transient skipping strategy.

[0119] After obtaining the minimum energy consumption operating point, the system returns to steady-state monitoring state. It continues to run as a new operating point until the next load change event triggers a new round of retracking, thereby achieving continuous online tracking of the minimum energy consumption operating point under periodic time-varying load conditions.

[0120] Meanwhile, after the drive voltage switch occurs, the motor current and speed need to go through several cycles of transient process to reach a new steady state. If a load segment event occurs during the transient period, the current increment caused by the load will be superimposed on the transient current, causing the energy consumption estimate of this cycle to deviate from the steady state value. If these transient cycles are directly included in the statistics of energy consumption and load indicators, a systematic deviation will be introduced.

[0121] Therefore, this invention employs a transient skip strategy: after each drive voltage switching event is triggered, starting from the next valid cycle, the previous cycle is discarded first. Each transient period does not participate in the accumulation (in this specific embodiment). =2); from the first Starting from one effective cycle, accumulation will begin again. One steady-state cycle of data is used to calculate the average load index under the current drive voltage.

[0122] To verify the effectiveness of the method proposed in this invention, further experiments were designed for verification.

[0123] The miniature brushed DC motor used in this experiment is a 6mm diameter motor, paired with a planetary gearbox with a reduction ratio of 136.02:1, achieving a transmission efficiency of approximately 65% ​​and corresponding to a motor output speed of approximately 300 rpm. The motor drives a spring-compression type periodic load, using two springs of different stiffness to represent light and heavy load conditions, respectively.

[0124] The microcontroller utilizes the Nordic Semiconductor nRF5340DK development kit and runs the Zephyr real-time operating system. The drive voltage is provided by a Maxim MAX77643 DC-DC converter, with an output voltage range of 0.8 to 5.15V and a step accuracy of 0.025V. Current sampling employs a Texas Instruments INA219 current monitoring chip, configured in 12-bit ADC resolution mode, with a conversion time of approximately 532μs per conversion and an effective sampling rate of approximately 50Hz. The INA219 calculates the current value by measuring the voltage drop across the shunt resistor connected in series in the power supply path and transmits the data to the microcontroller via the I2C bus. The magnetometer is a MEMSIC MMC5983MA triaxial magnetometer, which provides 18-bit resolution magnetic field measurement with a sampling rate configured at 300Hz.

[0125] The values ​​for each algorithm parameter are: voltage step size Voltage limit Transient skip cycle number Average number of cycles M = 3; load change judgment threshold .

[0126] To verify the characteristic that the load index L exhibits an "inflection point" change near the minimum energy consumption operating point V*, this experiment collected data on L as a function of the driving voltage under both light and heavy load conditions. A changing curve.

[0127] During the aforementioned voltage drop search process, the changes in drive voltage and the motor current waveforms under different drive voltages are as follows: Figure 3 and Figure 4 As shown, as the driving voltage gradually decreases, the motor speed decreases, the time length corresponding to a single rotation cycle increases, the duration of the current under high load gradually lengthens, and the periodic fluctuations in the current waveform become more pronounced. This phenomenon indicates that changes in the driving voltage affect the time distribution characteristics of the current waveform; therefore, this invention focuses on the duration of high load. With high current component Baseline current component Together used to build load metrics .

[0128] Experimental results are as follows Figure 5 As shown, it can be observed that: Higher than In the region where L decreases slowly with increasing voltage, the curve is relatively flat; in near At that time, L began to show a clear upward trend; Below In the region where L increases sharply, a clear transition characteristic is formed.

[0129] This transition characteristic provides a criterion for the boost search in this invention: when L changes from a sharp increase to a gradual increase, it can be determined that the motor has left the stall risk area and is located on the right side of the voltage-energy consumption curve. It is safe to proceed to the second stage of descending search.

[0130] In addition, comparing the two load conditions The curves show that L is generally higher under heavy load conditions than under light load conditions, and Migrating to the higher voltage side conforms to the present invention regarding load and Physical analysis of the relationship.

[0131] To quantitatively evaluate the energy-saving effect of the method of the present invention after load changes, the tracking energy-saving rate is defined as follows:

[0132]

[0133] Its physical meaning is: if the system does not re-search after a load change, it will continue to operate. , and re-search and work The percentage of energy saved is compared to the actual energy consumption. Tests were conducted under both light load and heavy load switching directions, and the results are shown in Table 1:

[0134] Table 1

[0135]

[0136] Experimental results show that the method of the present invention can correctly detect load changes and trigger the corresponding re-tracking process within M = 3 effective cycles under both switching directions, with a tracking energy saving rate of over 18%.

[0137] For the system embodiments, since they basically correspond to the method embodiments, relevant details can be found in the descriptions of the method embodiments; the implementation methods of the modules will not be repeated here. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0138] The system embodiments of the present invention can be applied to any device with data processing capabilities, such as a computer or other similar device. The system embodiments can be implemented in software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of any data processing device loading the corresponding computer program instructions from non-volatile memory into memory for execution.

[0139] It should also be noted that the minimum energy consumption tracking method for micro DC motors based on current waveform decomposition in the above embodiments can essentially be executed by a computer program. Therefore, similarly, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer electronic device corresponding to the method provided in the above embodiments, which includes a memory and a processor;

[0140] The memory is used to store computer programs;

[0141] The processor is configured to implement the minimum energy consumption tracking method for micro DC motors based on current waveform decomposition in the above embodiments when executing the computer program.

[0142] From a hardware perspective, such as Figure 6 The diagram shown is a hardware structure diagram provided in this embodiment. In addition to the processor, memory, network interface and non-volatile memory shown in the diagram, any device with data processing capabilities in the embodiment may also include other hardware depending on the actual function of the device with data processing capabilities, which will not be described in detail here.

[0143] When the logical instructions in the aforementioned memory can be 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 the present invention, 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, which is stored in a storage medium.

[0144] Therefore, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer-readable storage medium corresponding to the method provided in the above embodiments. The storage medium stores a computer program, which, when executed by a processor, can realize the minimum energy consumption tracking method for micro DC motors based on current waveform decomposition in the above embodiments.

[0145] It is understood that the computer-readable storage medium can be an internal storage unit of any data processing device described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device of any data processing device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units and external storage devices of any data processing device. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0146] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, all technical solutions obtained through equivalent substitution or transformation fall within the protection scope of the present invention.

Claims

1. A method for minimum energy consumption tracking of a micro DC motor based on current waveform decomposition, characterized in that, include: The motor is driven to rotate under a set driving voltage, and the magnetic field signal and current signal during the rotation process are collected in real time. The motor rotation cycle is identified based on magnetic field signals, and each cycle undergoes a three-layer validity verification, including peak time range check, statistical value range verification, and prominence check. If it passes, it is recorded as a valid cycle. The peak time range check is defined as the time interval between two valid peaks being greater than or equal to the lowest time interval threshold and less than or equal to the highest time threshold. The statistical value range is verified to be such that the mean, peak, and valley values ​​of the current within the rotation cycle all satisfy the physical constraints. The prominence check is performed when the difference between the peak and trough values ​​of the current within the rotation cycle is greater than or equal to the minimum prominence threshold. Within each effective cycle, energy consumption and adaptive thresholds are calculated based on current signals, feature quantities are extracted, and load indicators are calculated. Based on the dynamic reference load index and the average load index of multiple consecutive effective periods, the direction of load change is determined. If the load increases, a two-stage re-tracking is performed: first, the driving voltage is gradually increased until the average load index converges, and then the driving voltage is gradually decreased from the converged voltage to perform energy consumption reduction search. If the voltage is reduced, the driving voltage will be gradually reduced starting from the current driving voltage to perform a power consumption reduction search; In the energy consumption reduction search, if the average energy consumption of multiple consecutive effective cycles under the current drive voltage is lower than the recorded historical minimum average energy consumption, then the historical minimum average energy consumption and its corresponding drive voltage are updated, and the drive voltage is further reduced for the next round of search; otherwise, the drive voltage corresponding to the historical minimum average energy consumption is taken as the minimum energy consumption operating point.

2. The method for minimum energy consumption tracking of a micro DC motor based on current waveform decomposition according to claim 1, characterized in that, The magnetic field signal is generated by fixing a permanent magnet at the end of the output shaft of a miniature DC motor.

3. The method for minimum energy consumption tracking of a micro DC motor based on current waveform decomposition according to claim 1, characterized in that, The process of identifying the motor rotation cycle based on magnetic field signals is as follows: if the magnetic field value of the current magnetic field signal is greater than the threshold and greater than the adjacent magnetic field value, then the current magnetic field signal is considered to be a valid peak value, and every two adjacent valid peak values ​​form a rotation cycle.

4. The method for minimum energy consumption tracking of a micro DC motor based on current waveform decomposition according to claim 1, characterized in that, The characteristic quantities include high current component, baseline current component, and high load duration; The high current component is the arithmetic mean of all currents above the high current adaptive threshold within the effective period; the baseline current component is the arithmetic mean of all currents below the low current adaptive threshold within the effective period; and the high load duration is the length of the continuous time interval within the effective period during which the current exceeds the high current adaptive threshold.

5. The method for minimum energy consumption tracking of a micro DC motor based on current waveform decomposition according to claim 4, characterized in that, The load index is calculated by subtracting the high current component from the baseline current component and then multiplying the result by the high load duration.

6. The method for minimum energy consumption tracking of a micro DC motor based on current waveform decomposition according to claim 1, characterized in that, The calculation process for the average load index during the two-stage retracking process is as follows: Under the current driving voltage, skip the first few effective cycles, and start from the first effective cycle that has not been skipped, calculate the arithmetic mean of the load index for multiple consecutive effective cycles, which is the average load index.

7. The method for minimum energy consumption tracking of a micro DC motor based on current waveform decomposition according to claim 1, characterized in that, The update process for the dynamic reference load metric is as follows: Calculate the load index for each effective cycle at the minimum energy consumption operating point. Skip the first few effective cycles and start from the first effective cycle that has not been skipped. Calculate the arithmetic mean of the load index for multiple consecutive effective cycles as the updated reference load index.

8. A minimum energy consumption tracking system for a micro DC motor based on current waveform decomposition, used to implement the method of claim 1, characterized in that, include: The signal acquisition module is used to drive the motor to rotate under a set driving voltage and to acquire magnetic field signals and current signals in real time during the rotation process. The cycle verification module is used to identify the rotation cycle of the motor based on the magnetic field signal, and to perform a three-layer validity verification for each cycle, including peak time range check, statistical value range verification and prominence check. If it passes, it is recorded as a valid cycle. The energy consumption and load index calculation module is used to calculate energy consumption and adaptive threshold based on current signal, extract feature quantities and calculate load index in each effective cycle. The load change judgment module is used to determine the direction of load change based on the dynamic reference load index and the average load index of multiple consecutive effective periods. If the load changes, a two-stage re-tracking is performed: first, the driving voltage is gradually increased until the average load index converges, and then the driving voltage is gradually decreased from the converged voltage to perform energy consumption reduction search; if the load changes, the driving voltage is gradually decreased from the current driving voltage to perform energy consumption reduction search.

9. A computer electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to implement, when executing the computer program, the minimum energy consumption tracking method for micro DC motors based on current waveform decomposition as described in any one of claims 1 to 7.

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