Motor controller operation control method based on harmonic suppression and dynamic load matching

Abstract

The present application relates to the technical field of motor control, and discloses a motor controller operation control method based on harmonic suppression and dynamic load matching, comprising the following steps: S101, collecting operation state data of the motor controller; S102, pre-processing the operation state data, extracting power disturbance features, electromagnetic-mechanical-acoustic coupling features and noise-power response features, and composing a three-dimensional feature parameter vector; S103, modeling the three-dimensional feature parameter vector in a feature space, constructing a silent operation feature model using a clustering algorithm, and defining a silent operation feature interval; S104, comparing and analyzing the three-dimensional feature parameter vector with the silent operation feature interval, and adjusting the controller parameters. The present application realizes accurate discrimination of the silent state of the motor controller and adaptive optimization of the controller parameters, effectively improving the silent performance and operation stability of the motor system.

Description

Technical Field

[0001] The present invention belongs to the technical field of motor control, and in particular relates to a motor controller operation control method based on harmonic suppression and dynamic load matching. Background Art

[0002] Motor controllers are widely used in applications such as drones, power tools, and electric two-wheelers, where operational stability and noise control are critical. As end-user requirements for comfort, quietness, and responsiveness continue to rise, achieving optimal silent operation without sacrificing power performance has become a key technical challenge.

[0003] In existing technologies, harmonic control is typically optimized through fixed modulation strategies, such as sine waves and SVPWM, or through control parameter adjustment based on a single noise threshold. However, these methods have two limitations: 1. Adjustment strategies often rely on empirical settings, lacking a deep understanding and modeling of the coupling relationship between controller state and noise changes, making them difficult to adapt to dynamic changes in multiple operating conditions; 2. Existing silent control often uses rule-based algorithms based on amplitude judgment, lacking a comprehensive analysis of multi-dimensional state characteristics and a data-driven discrimination mechanism, making it prone to misjudgment or adjustment lag. In addition, the uncontrollability and complexity of environmental noise also pose challenges to the accurate extraction and identification of noise characteristics. Summary of the Invention

[0004] The present invention provides a motor controller operation control method based on harmonic suppression and dynamic load matching, which solves the technical problems in related technologies that the adjustment strategy relies on empirical rules, cannot accurately identify silent deviation behavior under multi-dimensional states, and lacks the ability to model the coupling characteristics of the motor controller and noise.

[0005] The present invention provides a motor controller operation control method based on harmonic suppression and dynamic load matching, comprising the following steps:

[0006] S101, collecting operating status data of a motor controller at a preset sampling interval within a first preset time period, the operating status data including output power, current, voltage, commutation frequency, speed, and ambient noise signal;

[0007] S102: Preprocess the running status data and extract the following features:

[0008] Power disturbance signature constructed based on output power and speed;

[0009] Electromagnetic-mechanical-acoustic coupling characteristics constructed based on environmental noise signals;

[0010] Noise-power response characteristics constructed based on output power and ambient noise signals;

[0011] The power disturbance feature, the noise spectrum structure shift feature and the noise-power response feature are combined into a three-dimensional feature parameter vector;

[0012] S103, performing feature space modeling on the three-dimensional feature parameter vector, constructing a silent operation feature model using a clustering algorithm, and defining a silent operation feature interval;

[0013] S104: Compare and analyze the three-dimensional characteristic parameter vector of the current sampling period with the silent operation characteristic interval. When the three-dimensional characteristic parameter vector does not belong to the silent operation characteristic interval and it is determined that the current state is non-silent operation, adjust the controller parameters, which include: PWM duty cycle, commutation angle, modulation mode and current limit.

[0014] Furthermore, the running status data is pre-processed, specifically including:

[0015] Normalize the output power, current, voltage, commutation frequency and speed;

[0016] Perform bandpass filtering and background noise removal on the environmental noise signal.

[0017] Furthermore, a power disturbance feature is constructed based on the output power and the rotational speed. The output power at the current moment, the output power at the previous moment, and the rotational speed at the previous moment are combined to obtain a first intermediate feature. The power disturbance feature is obtained by taking the absolute value of the ratio of the first intermediate feature to the output power at the previous moment.

[0018] An electromagnetic-mechanical-acoustic coupling feature is constructed based on the ambient noise signal. A second intermediate feature is obtained based on the covariance of the sideband spectrum envelope and the current harmonic envelope. A third intermediate feature is obtained by combining the output power at the current moment, the output power at the previous moment, and the speed at the previous moment. The electromagnetic-mechanical-acoustic coupling feature is obtained by performing a weighted combination of the second and third intermediate features and the energy of the frequency band.

[0019] A noise-power response feature is constructed based on the output power and the ambient noise signal, wherein the amplitude of the ambient noise signal is subtracted from its mean and divided by the standard deviation to construct a normalized noise offset as the fourth intermediate feature; the amplitude of the output power is subtracted from its mean and divided by the standard deviation of the power to construct a normalized power offset as the fifth intermediate feature; the difference between the fourth intermediate feature and the fifth intermediate feature is squared and summed within the sampling window to obtain the noise-power response feature.

[0020] Furthermore, the clustering algorithm adopted by the silent operation feature model is a density clustering algorithm, which divides the high-density area of ​​the feature space as the silent operation feature interval according to the distribution density of the three-dimensional feature parameter vector.

[0021] Furthermore, the parameter settings of the density clustering algorithm are dynamically adjusted according to the current output power and commutation frequency of the controller. The neighborhood radius parameter is proportional to the output power, and the minimum sample number parameter is proportional to the commutation frequency.

[0022] Furthermore, the silent operation characteristic interval is the highest density cluster identified by the density clustering algorithm in the three-dimensional characteristic parameter vector samples in the historical silent state, which is used to represent the characteristic distribution of the motor controller in a stable operation state.

[0023] Furthermore, the method of determining whether the system is currently in silent operation includes:

[0024] Determine whether the current three-dimensional feature parameter vector belongs to a cluster in the silent operation feature interval;

[0025] It is determined whether the Euclidean distance between the current three-dimensional feature parameter vector and the cluster center in the silent operation feature interval exceeds a deviation threshold.

[0026] Furthermore, the controller parameter adjustment strategy is determined based on the dominant offset dimension of each feature in the current three-dimensional feature parameter vector. If the noise-power response feature is the dominant offset, the PWM duty cycle is adjusted first; if the electromagnetic-mechanical-acoustic coupling feature is the dominant offset, the commutation angle is adjusted first.

[0027] The dominant offset dimension is determined based on the normalized offset value of each dimension of the three-dimensional feature parameter vector relative to the standard silent state, and the dominant offset dimension is the feature component with the largest offset value.

[0028] Furthermore, the adjustment amplitude of the controller parameters is set according to the offset of the current three-dimensional characteristic parameter vector relative to the silent operation characteristic interval. The adjustment amplitude is defined by a piecewise function. When the offset is within a first preset interval, a first adjustment rate is used for adjustment. When the offset exceeds a second preset threshold, a second adjustment rate greater than the first adjustment rate is used for adjustment, and a fixed compensation step is superimposed.

[0029] The beneficial effects of the present invention are as follows: by constructing power disturbance characteristics, electromagnetic-mechanical-acoustic coupling characteristics, and noise-power response characteristics to form a three-dimensional characteristic parameter vector, and combining it with a density clustering algorithm to establish a silent operation characteristic model, the present invention improves the ability to accurately identify silent states. Compared with traditional methods based on empirical rules or single threshold judgments, the present invention has higher operational stability and robustness, can achieve on-demand dynamic optimization of controller parameters, significantly reduce noise levels during operation, and improve the silent performance and user experience of terminal devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the motor controller operation control method based on harmonic suppression and dynamic load matching of the present invention. DETAILED DESCRIPTION

[0031] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0032] like Figure 1 As shown, the motor controller operation control method based on harmonic suppression and dynamic load matching includes the following steps:

[0033] S101, collecting operating status data of a motor controller at a preset sampling interval within a first preset time period, the operating status data including output power, current, voltage, commutation frequency, speed, and ambient noise signal;

[0034] S102: Preprocess the running status data and extract the following features:

[0035] Power disturbance signature constructed based on output power and speed;

[0036] Electromagnetic-mechanical-acoustic coupling characteristics constructed based on environmental noise signals;

[0037] Noise-power response characteristics constructed based on output power and ambient noise signals;

[0038] The power disturbance feature, the noise spectrum structure shift feature and the noise-power response feature are combined into a three-dimensional feature parameter vector;

[0039] S103, performing feature space modeling on the three-dimensional feature parameter vector, constructing a silent operation feature model using a clustering algorithm, and defining a silent operation feature interval;

[0040] S104: Compare and analyze the three-dimensional characteristic parameter vector of the current sampling period with the silent operation characteristic interval. When the three-dimensional characteristic parameter vector does not belong to the silent operation characteristic interval and it is determined that the current state is non-silent operation, adjust the controller parameters, which include: PWM duty cycle, commutation angle, modulation mode and current limit.

[0041] In one embodiment of the present invention, the motor controller's operating status data is collected at a preset sampling interval within a first preset time period to construct continuous time series data for subsequent feature extraction and state modeling. The output power is calculated by sampling the product of voltage and current (measured in W). Current is collected using a Hall effect sensor (measured in A). Voltage is collected by the controller's power management module (measured in V). The commutation frequency is obtained by detecting the controller's PWM drive update frequency (measured in Hz). The speed is collected by an encoder (measured in rpm). Ambient noise signals are collected by an external microphone and converted to digital form.

[0042] In one embodiment of the present invention, preprocessing the operating status data specifically includes:

[0043] Normalize the output power, current, voltage, commutation frequency and speed;

[0044] Perform bandpass filtering and background noise removal on the environmental noise signal.

[0045] Bandpass filtering is used to retain the main audio components generated during motor operation while suppressing wind noise, power supply noise, and high-frequency electronic interference signals. The optimal filter frequency range can be set from 200Hz to 8000Hz, and can be dynamically adjusted based on the motor structure and noise characteristics of the specific application.

[0046] Background noise removal adopts a method based on silent segment mean spectrum estimation, that is, background noise samples are collected when the motor is no-load or stopped to form a background spectrum reference. During actual operation, spectral subtraction processing is performed on the real-time noise signal to suppress uncontrollable noise components and improve the discrimination and stability of noise characteristics.

[0047] In one embodiment of the present invention, the calculation formula of the power disturbance characteristic is: ,in, It represents the power disturbance characteristic, indicating the degree of deviation of the dynamic coupling relationship between the motor output power and speed, and reflects the stability of the mechanical transmission system. When harmonics are injected or the load suddenly changes, the characteristic value increases, indicating that the power-speed transmission path is distorted. k represents the index of the sampling time, represents the output power at the kth sampling moment, represents the output power at the k-1th sampling moment, represents the speed at the k-1th sampling moment, and represents the first weight coefficient and the second weight coefficient, Represents a very small positive number to prevent the denominator from being zero;

[0048] The calculation formula of electromagnetic-mechanical-acoustic coupling characteristics is:

[0049] ,in, It represents the electromagnetic-mechanical-acoustic coupling characteristics, which is used to quantify the driving contribution of the controller current harmonics to the electromagnetic noise. It reflects the conversion efficiency of electric energy-mechanical vibration-acoustic energy through the covariance correlation between the current harmonic envelope and the noise spectrum. f represents the frequency. Represents the switching frequency and its sidebands. The sidebands represent a spectrum area around the switching frequency. represents the spectral energy at frequency f, represents the sideband spectrum envelope, represents the current harmonic envelope, represents the covariance between the sideband spectrum envelope and the current harmonic envelope, and Represent the standard deviation of the sideband spectrum envelope and the current harmonic envelope, Indicates the preset sampling interval, represents the speed at the kth sampling moment, represents the first preset constant, 、 and represent the third weight coefficient, the fourth weight coefficient and the fifth weight coefficient respectively;

[0050] The sideband spectrum envelope refers to the energy distribution envelope curve of the noise signal spectrum extracted from the frequency band extending up and down the set frequency deviation range with the controller switching frequency as the center, expressed as a set of frequency point energy sequences;

[0051] Current harmonic envelope refers to extracting the amplitude distribution curve of harmonics after Fourier transform of current to form the envelope sequence of current harmonic energy;

[0052] The noise-power response characteristic is calculated as: ,in, Represents the noise-power response characteristic, which is used to measure the synchronization of output power fluctuations and ambient noise fluctuations. In silent mode, power and noise fluctuations are synchronized; when out of step, the characteristic value increases, indicating abnormal controller modulation. K represents the number of sampling moments, represents the amplitude of the ambient noise signal at the kth sampling moment, represents the average value of the amplitude of the ambient noise signal within the first preset time period, Represents the standard deviation of the amplitude of the environmental noise signal, represents the average value of the output power within the first preset time period, represents the standard deviation of the output power;

[0053] The power disturbance feature, noise spectrum structure offset feature and noise-power response feature are combined into a three-dimensional feature parameter vector for subsequent feature space modeling input. The three-dimensional feature parameter vector is expressed as: , Represents a three-dimensional feature parameter vector.

[0054] In one embodiment of the present invention, feature space modeling is performed on the three-dimensional feature parameter vector to construct a silent operation feature model.

[0055] The three-dimensional characteristic parameter vector consists of a power disturbance feature, an electromagnetic-mechanical-acoustic coupling feature, and a noise-power response feature, and is used to characterize the operating state of the motor controller in each sampling period.

[0056] The silent operation feature model is constructed using a density-based clustering algorithm, preferably the DBSCAN algorithm. This algorithm does not require pre-defined cluster centers and can automatically identify high-density areas based on the distribution density of three-dimensional feature parameter vectors. It is applicable to non-convex spatial structures and helps improve the modeling accuracy and robustness of the silent operation model under complex feature distributions.

[0057] During the training phase, a large amount of operating status data from the motor controller in silent and stable operation is collected to extract the corresponding three-dimensional feature parameter vectors. This vector is then input into a density clustering algorithm for cluster analysis. The clustering algorithm classifies each sample point based on its neighborhood density in the feature space. Multiple clusters are identified using a preset density threshold. The cluster with the highest cluster density and the least state perturbation is the silent operation cluster and is defined as the "silent operation characteristic interval."

[0058] The two core parameters of the density clustering algorithm are the neighborhood radius parameter and the minimum number of samples parameter, which represent the radius of the neighborhood range of a sample point and the minimum number of neighboring points required to form a dense area, respectively. To improve model adaptability, the present invention preferably dynamically adjusts these two parameters based on the current operating state of the controller: the neighborhood radius parameter is proportional to the controller's output power, and the minimum number of samples parameter is positively correlated with the commutation frequency, allowing the clustering algorithm to adapt to the state characteristic distribution under different operating intensities.

[0059] During the operation phase, the silent operation characteristic model is used to determine whether the current controller is in a silent state. Specifically, the three-dimensional feature parameter vector extracted during the current sampling period is input into the modeled feature space and compared with the silent operation characteristic interval. If the current vector falls within this characteristic interval, the controller is determined to be in a silent operation state. If it deviates from this interval, the state is determined to be abnormal or non-silent, which in turn triggers the adaptive adjustment process of the controller parameters.

[0060] In one embodiment of the present invention, in order to accurately judge whether the current state of the motor controller is in silent operation, the system adopts a dual-strategy judgment mechanism based on the constructed silent operation feature model: first, the discrimination label of the clusterer is preferably used to classify the three-dimensional feature parameter vector of the current sampling period. If the vector is identified as a member of the cluster in the silent operation feature interval, it is directly judged to be in a silent state; second, when the clusterer does not output a valid label for the current feature point, or there is a fuzzy boundary situation, the Euclidean distance between the current three-dimensional feature parameter vector and the cluster center in the silent operation feature interval is calculated. If the distance exceeds the preset deviation threshold, it is judged to be a deviation from the silent state. This combined judgment method takes into account both model recognition accuracy and generalization robustness, and is suitable for atypical states such as feature drift that may occur during operation.

[0061] In one embodiment of the present invention, the adjustment strategy of the controller parameters is not fixedly corresponding, but is dynamically decided based on the dominant offset degree of different feature dimensions in the three-dimensional feature parameter vector. When it is determined that the current operating state deviates from the silent operation feature interval, the dominant offset dimension in the power disturbance feature, electromagnetic-mechanical-acoustic coupling feature and noise-power response feature is first analyzed. If the noise-power response feature offset is the most significant, the strategy of adjusting the PWM duty cycle is prioritized to quickly suppress the noise energy output; if the electromagnetic-mechanical-acoustic coupling feature changes mainly, the commutation angle is prioritized to optimize the modulation harmonic behavior. This method realizes the closed-loop logic of "identifying the main cause and precise adjustment", improves the adjustment efficiency and control accuracy, and has strong practical engineering adaptability.

[0062] In actual operation, the three-dimensional characteristic parameter vector of each sampling period is used to analyze its offset value relative to the standard silent state. The offset value is calculated as follows: , Indicates the offset value of the nth dimension, represents the features of the nth dimension, The feature of the nth dimension representing the standard silent state, It represents the standard deviation of the feature of the nth dimension in the standard silent state. In this embodiment, the dimension with the largest offset value is taken as the offset dominant dimension.

[0063] In one embodiment of the present invention, the adjustment amplitude of the controller parameters is set according to the offset between the current three-dimensional characteristic parameter vector and the silent operation characteristic interval, and a piecewise nonlinear adjustment function is constructed to achieve a balance between smooth and fast adjustment. Specifically, two offset threshold intervals are set: the first preset interval corresponds to a small deviation, and the controller uses a first adjustment rate to gradually adjust the parameters at a lower amplitude to keep the system stable; when the offset exceeds the second preset threshold, it means that the system is in a state of severe deviation. At this time, a second adjustment rate greater than the first adjustment rate is used to quickly recover, and a fixed compensation step is superimposed to enhance the response strength. The intermediate transition interval can adopt linear interpolation or fuzzy control strategy for smooth transition. This segmented strategy improves the fault response efficiency while ensuring the stability of the system, and is suitable for silent control requirements in complex operating environments with multiple working conditions.

[0064] The above describes the embodiments of the present invention, but the present invention is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A motor controller operation control method based on harmonic suppression and dynamic load matching, characterized in that: The following steps are involved: S101, collecting operating status data of a motor controller at a preset sampling interval within a first preset time period, the operating status data including output power, current, voltage, commutation frequency, speed, and ambient noise signal; S102: Preprocess the running status data and extract the following features: Power disturbance signature constructed based on output power and speed; Electromagnetic-mechanical-acoustic coupling characteristics constructed based on environmental noise signals; Noise-power response characteristics constructed based on output power and ambient noise signals; The power disturbance feature, the noise spectrum structure shift feature and the noise-power response feature are combined into a three-dimensional feature parameter vector; S103, performing feature space modeling on the three-dimensional feature parameter vector, constructing a silent operation feature model using a clustering algorithm, and defining a silent operation feature interval; S104: Compare and analyze the three-dimensional characteristic parameter vector of the current sampling period with the silent operation characteristic interval. When the three-dimensional characteristic parameter vector does not belong to the silent operation characteristic interval and it is determined that the current state is non-silent operation, adjust the controller parameters, which include: PWM duty cycle, commutation angle, modulation mode and current limit.

2. The motor controller operation control method based on harmonic suppression and dynamic load matching according to claim 1 is characterized in that: Preprocess the running status data, including: Normalize the output power, current, voltage, commutation frequency and speed; Perform bandpass filtering and background noise removal on the environmental noise signal.

3. The motor controller operation control method based on harmonic suppression and dynamic load matching according to claim 1, characterized in that: A power disturbance signature is constructed based on the output power and the rotational speed. The output power at the current moment, the output power at the previous moment, and the rotational speed at the previous moment are combined to obtain a first intermediate signature. The power disturbance signature is obtained by taking the absolute value of the ratio of the first intermediate signature to the output power at the previous moment. An electromagnetic-mechanical-acoustic coupling feature is constructed based on the ambient noise signal. A second intermediate feature is obtained based on the covariance of the sideband spectrum envelope and the current harmonic envelope. A third intermediate feature is obtained by combining the output power at the current moment, the output power at the previous moment, and the speed at the previous moment. The electromagnetic-mechanical-acoustic coupling feature is obtained by performing a weighted combination of the second and third intermediate features and the energy of the frequency band. A noise-power response feature is constructed based on the output power and the ambient noise signal, wherein the amplitude of the ambient noise signal is subtracted from its mean and divided by the standard deviation to construct a normalized noise offset as the fourth intermediate feature; the amplitude of the output power is subtracted from its mean and divided by the standard deviation of the power to construct a normalized power offset as the fifth intermediate feature; the difference between the fourth intermediate feature and the fifth intermediate feature is squared and summed within the sampling window to obtain the noise-power response feature.

4. The motor controller operation control method based on harmonic suppression and dynamic load matching according to claim 1, characterized in that: The clustering algorithm used in the silent operation feature model is a density clustering algorithm, which divides the high-density area of ​​the feature space as the silent operation feature interval according to the distribution density of the three-dimensional feature parameter vector.

5. The motor controller operation control method based on harmonic suppression and dynamic load matching according to claim 4, characterized in that: The parameter settings of the density clustering algorithm are dynamically adjusted according to the current output power and commutation frequency of the controller. The neighborhood radius parameter is proportional to the output power, and the minimum number of samples parameter is proportional to the commutation frequency.

6. The motor controller operation control method based on harmonic suppression and dynamic load matching according to claim 1, characterized in that: The silent operation characteristic interval is the highest density cluster identified by the density clustering algorithm in the three-dimensional characteristic parameter vector samples in the historical silent state, and is used to represent the characteristic distribution of the motor controller in a stable operation state.

7. The motor controller operation control method based on harmonic suppression and dynamic load matching according to claim 1, characterized in that: Methods for determining whether the current silent operation state is in the following ways: Determine whether the current three-dimensional feature parameter vector belongs to a cluster in the silent operation feature interval; It is determined whether the Euclidean distance between the current three-dimensional feature parameter vector and the cluster center in the silent operation feature interval exceeds a deviation threshold.

8. The motor controller operation control method based on harmonic suppression and dynamic load matching according to claim 1, characterized in that: The controller parameter adjustment strategy is determined based on the dominant offset dimension of each feature in the current three-dimensional feature parameter vector. If the noise-power response feature is the dominant offset, the PWM duty cycle is adjusted first. If the electromagnetic-mechanical-acoustic coupling feature is the dominant offset, the commutation angle is adjusted first. The dominant offset dimension is determined based on the normalized offset value of each dimension of the three-dimensional feature parameter vector relative to the standard silent state, and the dominant offset dimension is the feature component with the largest offset value.

9. The motor controller operation control method based on harmonic suppression and dynamic load matching according to claim 1, characterized in that: The adjustment amplitude of the controller parameters is set according to the offset of the current three-dimensional characteristic parameter vector relative to the silent operation characteristic interval. The adjustment amplitude is defined by a piecewise function. When the offset is within a first preset interval, a first adjustment rate is adopted. When the offset exceeds a second preset threshold, a second adjustment rate greater than the first adjustment rate is adopted, and a fixed compensation step is superimposed.

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