An Adaptive Stiffness Control Method, System, Device and Medium for a Flattened Axial Flux Motor

Abstract

The present invention provides an adaptive stiffness control method, system, device and medium for a flat axial flux motor, comprising the following steps: Step 1, real-time sensing of the operating parameters of the motor through a variety of sensors installed on the flat axial flux motor. By introducing advanced sensor technology, data processing algorithms and intelligent control strategies, the present invention realizes precise adaptive control of the stiffness of the flat axial flux motor, enabling the motor to always maintain the optimal stiffness state under different operating conditions, thereby improving the efficiency, power density and dynamic response performance of the motor. At the same time, the adaptive stiffness control can effectively reduce the vibration and noise caused by stiffness changes, improving the stability and reliability of the system; the control method of the present invention can adapt to different load changes and working environments, and has broad application prospects. This method realizes the automatic adjustment of the motor stiffness without manual intervention, improving the intelligent level of control.

Description

Technical Field

[0001] The present invention relates to the technical field of flux motors, and particularly relates to an adaptive stiffness control method, system, device and medium for a flat axial flux motor. Background Art

[0002] With the rapid development of technology, flat axial flux motors have shown broad application prospects in many fields due to their unique structural advantages. Flat axial flux motors have the advantages of compact structure, high power density, and high efficiency. Their compact structure gives them great advantages in application scenarios with limited space, such as electric vehicles, aerospace, etc., being able to provide more installation space for other components, while reducing the overall weight and improving the mobility and endurance of the equipment. The characteristic of high power density enables them to output a large amount of power within a small volume, meeting the requirements for high-power drives in fields such as industrial automation and robotics. The high energy conversion efficiency not only reduces the operating cost but also meets the requirements of energy conservation and environmental protection, playing an important role in new energy power generation, energy-saving household appliances, etc. However, there are still certain problems:

[0003] First, existing motor control methods are difficult to achieve precise control of the motor stiffness in the face of complex and changeable operating environments. The stiffness of the motor is affected by various factors, such as load changes, temperature changes, manufacturing errors of the motor, installation accuracy, operating environment, etc. In terms of load changes, different load types have different requirements for motor stiffness. A sudden increase or decrease in load will cause a change in the internal stress distribution of the motor, affecting the electromagnetic force between the stator and the rotor, and thus affecting the motor stiffness. Temperature changes will affect the material properties of the motor, such as winding resistance, magnetic permeability, etc., thereby changing the electromagnetic and mechanical properties of the motor and affecting the stiffness. Manufacturing errors will lead to uneven air gaps, affecting the magnetic field distribution and electromagnetic force; low installation accuracy will generate vibration and noise, reducing stability and reliability; humidity, dust, etc. in the operating environment will also affect the motor performance and stiffness;

[0004] Second, due to the inability to precisely control the motor stiffness, the full performance of the motor is greatly restricted. The change in the motor stiffness will directly affect the output performance of the motor, such as speed, torque, efficiency, etc. When the stiffness decreases, the output torque decreases, the speed becomes unstable, and the efficiency decreases, resulting in the equipment driven by the motor being unable to work properly, affecting production efficiency and product quality. For example, in a numerically controlled machine tool, insufficient motor stiffness will affect the machining accuracy and surface quality; in an electric vehicle, it will affect the vehicle's acceleration performance, endurance and ride comfort. At the same time, the change in the motor stiffness will also affect the stability of the entire system. When the stiffness decreases, the resonance frequency of the system will change, easily causing resonance and oscillation, increasing noise, and even damaging system components. In fields with high requirements for system stability such as aerospace and medical equipment, it may bring serious safety hazards;

[0005] Therefore, an adaptive stiffness control method, system, device and medium for a flat axial flux motor are proposed. Summary of the Invention

[0006] In view of this, embodiments of the present invention hope to provide an adaptive stiffness control method, system, device and medium for a flat axial flux motor to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0007] To solve the above technical problems, a technical solution adopted in this application is: an adaptive stiffness control method for a flat axial flux motor, including the following steps:

[0008] Step 1: Through a variety of sensors installed on the flat axial flux motor, the operating parameters of the motor are sensed in real time, motor operation data is obtained, and the motor operation data is transmitted to the control system;

[0009] Step 2: Preprocess the obtained motor operation data to remove high-frequency noise and outliers in the motor operation data;

[0010] Step 3: Based on the electromagnetic characteristics, mechanical structure and operating environment of the motor, establish a stiffness model of the flat axial flux motor;

[0011] Step 4: Use the preprocessed motor operation data and the established stiffness model to preliminarily evaluate the current stiffness value of the motor, and dynamically set a reasonable target stiffness value according to the specific application scenario and performance requirements of the motor;

[0012] Step 5: Compare the currently evaluated current stiffness value with the set target stiffness, calculate the deviation between the two to obtain a stiffness deviation value;

[0013] Step 6: According to the stiffness deviation value, adjust the control parameters of the motor through an intelligent control algorithm to perform adaptive adjustment of the motor stiffness. If the stiffness deviation value is positive and large and the deviation change rate is positive and large, increase the control parameters. If the stiffness deviation value is negative and small and the deviation change rate is negative and small, decrease the control parameters;

[0014] Step 7: During the adjustment process, continuously monitor the operating state of the motor and transmit the feedback information back to the control system. The control system further optimizes the adjustment strategy according to the feedback information.

[0015] Preferably as a further option of this technical solution, in Step 1, the sensors include a current sensor, a voltage sensor, a speed sensor, a temperature sensor and a load sensor;

[0016] The motor operation data includes electrical parameter data, mechanical parameter data, and environmental parameter data;

[0017] The electrical parameter data includes current, voltage, power, and resistance;

[0018] The mechanical parameter data includes rotational speed, torque, vibration, and noise;

[0019] The environmental parameter data includes temperature, humidity, and air pressure.

[0020] Preferably, as a further aspect of the present technical solution, in step two, the high-frequency noise and outliers in the motor operation data are removed by using the mean filtering algorithm and the Grubbs criterion detection method respectively;

[0021] Set a data sequence , the calculation formula for the data Y after mean filtering is:

[0022] ;

[0023] where m is the size of the filtering window;

[0024] Set a data sequence , the formulas for calculating its average value and standard deviation S are respectively:

[0025] ;

[0026] ;

[0027] For a certain data , the formula for calculating is:

[0028] ;

[0029] where is usually called the Grubbs statistic, which is used to judge whether there are outliers in the data set in the Grubbs criterion; represents the jth data point in the data set, and j is the serial number of this data point in the sequence; is the average value of the data set, that is, the arithmetic mean of all data points; S represents the standard deviation of the data set, which measures the degree of dispersion of each data point in the data set relative to the average value;

[0030] If , then is an outlier, where is the Grubbs critical value, which is determined by the significance level and the number of data .

[0031] As a further optimization of this technical solution, in step three, the stiffness model comprehensively considers the influence of the electromagnetic field parameters, mechanical structure parameters, and temperature parameters of the motor on stiffness. The specific formula is:

[0032] ;

[0033] where K is the motor stiffness, E is the electromagnetic field parameter of the motor, M is the mechanical structure parameter of the motor, and T is the temperature parameter.

[0034] As a further optimization of this technical solution, in step four, the formula for calculating the current stiffness value of the motor is:

[0035] ;

[0036] where F is the force applied to the motor, which is calculated from the load and the electromagnetic parameters of the motor, is the deformation of the motor, which is estimated based on the structure and operating parameters of the motor.

[0037] As a further optimization of this technical solution, in step five, the formula for calculating the stiffness deviation value is:

[0038] ;

[0039] where is the target stiffness value, is the current stiffness value.

[0040] As a further optimization of this technical solution, in step six, the intelligent control algorithm includes fuzzy control algorithm, neural network control algorithm, and genetic algorithm;

[0041] The control parameters include current-related parameters, voltage-related parameters, magnetic field-related parameters, mechanical structure-related parameters, and control strategy-related parameters;

[0042] The current-related parameters include stator current amplitude, rotor current amplitude, and current phase;

[0043] The voltage-related parameters include stator voltage amplitude and rotor voltage amplitude;

[0044] The magnetic field-related parameters include magnetic field strength and magnetic flux;

[0045] The mechanical structure-related parameters include air gap length and mechanical spring coefficient;

[0046] The control strategy-related parameters include control algorithm parameters and control period.

[0047] To solve the above technical problems, another technical solution adopted by this application is: an adaptive stiffness control system for a flat axial flux motor, comprising: a sensor module, a data preprocessing module, a model establishment module, a stiffness evaluation module, a target setting module, a deviation calculation module, a parameter adjustment module, and a feedback and optimization module;

[0048] The sensor module is used to sense the operating parameters of the flat axial flux motor in real time, obtain the motor operating data including electrical parameter data, mechanical parameter data, and environmental parameter data, and transmit the data to the control system; wherein, the electrical parameter data includes current, voltage, power, and resistance; the mechanical parameter data includes rotational speed, torque, vibration, and noise; the environmental parameter data includes temperature, humidity, and air pressure;

[0049] The data preprocessing module is used to remove high-frequency noise in the motor operating data by using the mean filtering algorithm and remove outliers by using the Grubbs criterion detection method;

[0050] The model establishment module is used to establish a stiffness model of the flat axial flux motor based on the electromagnetic characteristics, mechanical structure, and operating environment of the motor;

[0051] The stiffness evaluation module is used to preliminarily evaluate the current stiffness value of the motor by using the preprocessed motor operating data and the established stiffness model;

[0052] The target setting module is used to dynamically set a reasonable target stiffness value according to the specific application scenario and performance requirements of the motor;

[0053] The deviation calculation module is used to compare the currently evaluated current stiffness value with the set target stiffness, calculate the deviation between the two, and obtain the stiffness deviation value;

[0054] The parameter adjustment module is used to adjust the control parameters of the motor through an intelligent control algorithm according to the stiffness deviation value for adaptive adjustment of the motor stiffness; if the stiffness deviation value is positive and large and the deviation change rate is positive and large, increase the control parameters; if the stiffness deviation value is negative and small and the deviation change rate is negative and small, decrease the control parameters;

[0055] The feedback and optimization module is used to continuously monitor the operating state of the motor and transmit the feedback information back to the control system, and the control system further optimizes the adjustment strategy according to the feedback information.

[0056] To solve the above technical problems, another technical solution adopted by this application is: a computer device, comprising: a processor;

[0057] a memory for storing executable instructions;

[0058] Among them, the processor is used to read the executable instructions from the memory and execute the executable instructions to implement the adaptive stiffness control method of a flat axial flux motor as described above.

[0059] To solve the above technical problems, another technical solution adopted by this application is: a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to implement the adaptive stiffness control method of a flat axial flux motor as described above.

[0060] Due to the above technical solutions adopted in the embodiments of the present invention, it has the following advantages:

[0061] 1. By introducing advanced sensor technology, data processing algorithms, and intelligent control strategies, the present invention realizes precise adaptive control of the stiffness of a flat axial flux motor, enabling the motor to always maintain the best stiffness state under different operating conditions, thereby improving the efficiency, power density, and dynamic response performance of the motor. At the same time, adaptive stiffness control can effectively reduce vibrations and noises caused by stiffness changes, improving the stability and reliability of the system;

[0062] 2. The control method of the present invention can adapt to different load changes and working environments, and has broad application prospects. This method realizes automatic adjustment of the motor stiffness without manual intervention, improving the intelligent level of control.

[0063] The above summary is only for the purpose of the specification and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0065] Figure 1 It is a schematic flowchart of the adaptive stiffness control method for the flat axial flux motor of the present invention;

[0066] Figure 2 It is a schematic diagram of the functional modules of the adaptive stiffness control system for the flat axial flux motor of the present invention;

[0067] Figure 3A schematic structural diagram of a computer device provided by an embodiment of the present invention.

[0068] Reference numerals: 10, computer device; 1002, processor; 1004, memory; 1006, transmission device. Detailed implementation manners

[0069] The following describes the embodiments of the present disclosure in detail with reference to the accompanying drawings.

[0070] It should be clear that the following uses specific specific examples to illustrate the implementation manners of the present disclosure. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present disclosure.

[0071] It should be noted that the following describes various aspects of the embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement a device and / or practice a method. In addition, this device and / or this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0072] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present disclosure schematically. Only the components related to the present disclosure are shown in the diagrams, rather than being drawn according to the number, shape and size of the components in actual implementation. The type, quantity and proportion of each component in its actual implementation can be an arbitrary change, and the component layout type may also be more complex.

[0073] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0074] Figure 1It is a schematic flowchart of an adaptive stiffness control method for a flat axial flux motor according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application is not limited to Figure 1 the process sequence shown. As Figure 1 shown: An adaptive stiffness control method for a flat axial flux motor includes the following steps:

[0075] Step 1: Through a variety of sensors installed on the flat axial flux motor, the operating parameters of the motor are sensed in real time, the motor operating data is obtained, and the motor operating data is transmitted to the control system;

[0076] Specifically: First, select a variety of sensors suitable for the flat axial flux motor. These sensors should be able to accurately sense the key operating parameters of the motor, and install these sensors at appropriate positions on the motor to ensure that the required operating parameters can be accurately measured; Then, the sensors convert the sensed operating parameters into electrical signals, and these electrical signals are converted into digital data after being processed; Finally, the collected motor operating data is transmitted to the control system by wired or wireless means. The control system can be a dedicated computer or an embedded system for receiving, processing, and analyzing this data.

[0077] Step 2: Preprocess the obtained motor operating data to remove high-frequency noise and outliers in the motor operating data;

[0078] Among them, high-frequency noise is usually caused by factors such as electrical interference and mechanical vibration during the operation of the motor, and the mean filtering algorithm is used to remove it; Outliers are usually caused by factors such as sensor failures and data acquisition errors, and the Grubbs criterion detection method is used to remove them; The data after preprocessing should be stored in the storage medium of the control system for subsequent analysis and processing. At the same time, this data can also be transmitted to other systems or devices for further analysis and monitoring.

[0079] Step 3: Based on the electromagnetic characteristics, mechanical structure, and operating environment of the motor, establish a stiffness model of the flat axial flux motor;

[0080] Among them, electromagnetic characteristics: The magnetic flux path of the flat axial flux motor is different from that of a common radial motor. Its air gap is planar, and the direction of the air gap magnetic field is parallel to the axis direction of the motor; The electromagnetic characteristics of components such as permanent magnets, windings, and stator cores in the motor have an important impact on the stiffness of the motor;

[0081] Mechanical structure: The mechanical structure characteristics of components such as the rotor, stator, bearings, and shaft of the motor determine the stiffness and stability of the motor; Parameters such as the diameter, thickness, and material of the rotor, as well as the slot shape and number of slots of the stator, will affect the stiffness of the motor;

[0082] Operating environment: During operation, the motor is affected by environmental factors such as temperature, humidity, and vibration; these environmental factors cause changes in the material properties of the motor, thereby affecting its stiffness;

[0083] Among them, the specific steps to establish the stiffness model of the flat axial flux motor are as follows:

[0084] First, according to the electromagnetic characteristics and mechanical structure of the motor, determine the key parameters affecting stiffness, including the magnetization intensity of the permanent magnet, the current density of the winding, the slot shape and number of slots of the stator, the diameter and thickness of the rotor, etc.;

[0085] Then, based on the basic principles of electromagnetics and mechanics, establish a mathematical stiffness model of the motor, which should be able to reflect the stiffness characteristics of the motor under different working conditions;

[0086] Next, consider the influence of environmental factors such as temperature and humidity on the motor stiffness in the model, obtain the stiffness data of the motor under different environmental conditions through experiments or simulation methods, and correct the model;

[0087] Finally, through experiments or simulation methods, verify the accuracy and reliability of the established stiffness model. If there are significant differences between the model and the experimental results, the model needs to be further corrected and optimized.

[0088] Step 4: Use the preprocessed motor operation data and the established stiffness model to preliminarily evaluate the current stiffness value of the motor, and dynamically set a reasonable target stiffness value according to the specific application scenario and performance requirements of the motor;

[0089] Among them, the specific steps to preliminarily evaluate the current stiffness value of the motor are as follows:

[0090] First, input the preprocessed motor operation data (such as current, voltage, speed, temperature, etc.) into the established stiffness model;

[0091] Then, use the stiffness model to calculate the current stiffness value of the motor according to the input operation data. The stiffness value is a scalar (representing the overall stiffness) or a vector (representing the stiffness in each direction);

[0092] Finally, compare the preliminarily evaluated stiffness value with the experimentally measured value to verify the accuracy of the model. If there are differences, the model can be fine-tuned or recalibrated.

[0093] Among them, the specific steps to dynamically set a reasonable target stiffness value are as follows:

[0094] First, analyze the specific application scenario of the motor, such as high-speed operation, heavy load, high-precision positioning, etc., and determine the requirements for the motor stiffness under different application scenarios;

[0095] Then, considering the performance requirements of the motor, such as vibration suppression, noise control, efficiency optimization, etc., these requirements will directly affect the setting of the target stiffness value;

[0096] Next, according to the real-time operation data and working condition changes of the motor, dynamically adjust the target stiffness value. For example, under heavy load conditions, it is necessary to increase the stiffness of the motor to improve stability; while at high speeds, it is necessary to reduce the stiffness to reduce vibration and noise;

[0097] Finally, based on the dynamically set target stiffness value, design corresponding control strategies, which include adjusting control parameters such as current, voltage, speed, etc., to achieve precise control of the motor stiffness.

[0098] Step Five: Compare the currently obtained stiffness value from the preliminary evaluation with the set target stiffness, calculate the deviation between the two to obtain the stiffness deviation value;

[0099] Specifically: First, directly compare the currently obtained stiffness value with the target stiffness value. If the currently obtained stiffness value is a vector (including stiffness components in multiple directions), then it is necessary to compare the stiffness components in each direction separately;

[0100] Then, according to the comparison result, calculate the deviation between the currently obtained stiffness value and the target stiffness value. The deviation can be a scalar (representing the overall deviation) or a vector (representing the deviations in each direction). The deviation value can be obtained through a simple subtraction operation, that is, the target stiffness value minus the currently obtained stiffness value;

[0101] Finally, analyze the calculated deviation value to determine the magnitude and direction of the deviation. The magnitude of the deviation reflects the degree of difference between the currently obtained stiffness value and the target stiffness value, while the direction of the deviation indicates the direction that needs to be adjusted.

[0102] Step Six: According to the stiffness deviation value, adjust the control parameters of the motor through an intelligent control algorithm to perform adaptive adjustment of the motor stiffness. If the stiffness deviation value is positive and large and the deviation change rate is positive and large, then increase the control parameters. If the stiffness deviation value is negative and small and the deviation change rate is negative and small, then decrease the control parameters;

[0103] Among them, the adaptive adjustment process of the motor stiffness is as follows:

[0104] First, obtain the real-time operation data of the motor through sensors or a data acquisition system, and calculate the stiffness deviation value and its change rate;

[0105] Then, according to the real-time monitored stiffness deviation value and its change rate, execute the intelligent control algorithm. The intelligent control algorithm calculates the control parameter values that need to be adjusted according to the preset rules or models;

[0106] Next, apply the calculated control parameter values to the control system of the motor, and achieve the adaptive adjustment of the motor stiffness by adjusting the output of the control system;

[0107] Finally, according to the adjusted motor stiffness value, calculate the stiffness deviation value and its change rate again. If the deviation is still large, continue to adjust the control parameters until the stiffness of the motor reaches or approaches the target stiffness value.

[0108] Step 7: During the adjustment process, continuously monitor the operating state of the motor and transmit the feedback information back to the control system. The control system further optimizes the adjustment strategy according to the feedback information;

[0109] Specifically: Adjust and optimize the existing control strategy according to the feedback motor operating state information. For example, if it is detected that the motor vibration is too large, the control parameters need to be adjusted to reduce the vibration; if the motor temperature is too high, the load needs to be reduced or heat dissipation measures need to be increased; if intelligent control algorithms (such as fuzzy control, neural networks, etc.) are used, the algorithm can be iterated and optimized according to the feedback information. Through continuous learning and adjustment, the algorithm can more accurately predict and control the operating state of the motor; the control system has an adaptive ability and can automatically adjust the control parameters and strategies according to the real-time operating state of the motor. The control system can identify the changes of the motor in real time and take corresponding measures to maintain the stable operation and performance optimization of the motor.

[0110] In one embodiment, specifically, in Step 1, the sensors include a current sensor, a voltage sensor, a speed sensor, a temperature sensor, and a load sensor;

[0111] Among them, the current sensor is used to monitor the current change during the operation of the motor, and the current data is an important indicator for evaluating the motor load, efficiency, and potential overheating risk;

[0112] The voltage sensor is used to monitor the stability of the motor input voltage, and the voltage data helps to judge the power quality and whether the motor is within the normal operating voltage range;

[0113] The speed sensor is used to monitor the speed of the motor in real time, and the speed data is an important basis for evaluating the motor performance, load capacity, and potential faults (such as bearing wear);

[0114] The temperature sensor is used to monitor the temperature of the motor and its surrounding environment, and the temperature data is crucial for preventing motor overheating, ensuring the effectiveness of the cooling system, and extending the motor life;

[0115] The load sensor is used to measure the load size borne by the motor, and the load data helps to evaluate the load-bearing capacity of the motor and adjust the control strategy to adapt to different load conditions.

[0116] Motor operation data includes electrical parameter data, mechanical parameter data and environmental parameter data;

[0117] Electrical parameter data includes current, voltage, power and resistance;

[0118] Current: This includes the current values of different parts such as stator current and rotor current, which can reflect the load condition and operating status of the motor. For example, excessive current means that the motor is overloaded;

[0119] Voltage: The motor's input voltage, phase voltage, etc. Voltage stability is crucial to the normal operation of the motor. Abnormal voltage fluctuations affect the motor's performance and life;

[0120] Power: active power, reactive power, apparent power, etc. Power data can help evaluate the energy efficiency and operating costs of the motor;

[0121] Resistance: Motor winding resistance, etc. Changes in resistance indicate motor winding failure or aging.

[0122] Mechanical parameter data includes speed, torque, vibration and noise;

[0123] Speed: The rotation speed of a motor is one of the important indicators for measuring its output performance. Different application scenarios have different requirements for speed.

[0124] Torque: The torque output by the motor. Torque is related to the load. For motors driving different loads, torque data can reflect their working capacity.

[0125] Vibration: The amplitude and frequency of vibrations generated when the motor is running. Excessive vibrations may indicate improper motor installation, bearing wear, or other mechanical failures.

[0126] Noise: The intensity and frequency characteristics of the sound emitted during motor operation. Abnormal noise indicates damage or failure of internal motor parts.

[0127] Environmental parameter data include temperature, humidity and air pressure;

[0128] Temperature: Motor body temperature, ambient temperature, etc. Excessive motor temperature can lead to insulation damage, shortened lifespan, and other problems. Therefore, temperature data is very important for thermal management and protection of the motor;

[0129] Humidity: The humidity of the surrounding environment. High humidity environment affects the insulation performance and reliability of the motor;

[0130] Air pressure: In some special application scenarios, air pressure also affects the operation of the motor.

[0131] In one embodiment, specifically, in step 2, high-frequency noise and abnormal values in the motor operation data are removed by using a mean filtering algorithm and a Grubbs criterion detection method respectively;

[0132] Mean filtering is a simple and effective signal processing technique that reduces noise by smoothing the signal. , the calculation formula of the data Y after mean filtering is:

[0133] ;

[0134] Where m is the filter window size;

[0135] Grubbs' criterion is a statistical test method based on normal distribution, which is used to identify outliers in a data set. , calculate its average value The formulas for and standard deviation S are:

[0136] ;

[0137] ;

[0138] For a certain data ,calculate The formula is:

[0139] ;

[0140] in, Often called the Grubbs statistic, it is used in the Grubbs criterion to determine whether there are outliers in a data set; Represents the jth data point in the data set, where j is the sequence number of the data point in the sequence; is the mean of the data set, that is, the arithmetic mean of all data points; S represents the standard deviation of the data set, which measures the degree of dispersion of each data point in the data set relative to the mean;

[0141] like ,but is an outlier, where is the Grubbs critical value, and the significance level and the number of data Sure;

[0142] By removing high-frequency noise and outliers from motor operation data, the accuracy and reliability of the data are improved, providing stronger support for subsequent adaptive stiffness control.

[0143] In one embodiment, specifically, in step three, the stiffness model comprehensively considers the effects of the electromagnetic field parameters, mechanical structure parameters, and temperature parameters of the motor on stiffness. The specific formula is as follows:

[0144] ;

[0145] where K is the motor stiffness, E is the electromagnetic field parameter of the motor, M is the mechanical structure parameter of the motor, and T is the temperature parameter.

[0146] Among them, the electromagnetic field parameters include current, voltage, magnetic flux, etc. These parameters directly affect the electromagnetic force and torque of the motor, and thus affect the stiffness of the motor. In the model, it is necessary to consider the influence of the changes of these parameters on stiffness and incorporate them into the stiffness model through appropriate functional relationships;

[0147] The mechanical structure parameters include the size of the motor, material properties, bearing stiffness, etc. These parameters determine the mechanical performance and structural stability of the motor. In the model, it is necessary to consider the influence of these parameters on the motor stiffness, especially how they interact with the electromagnetic field parameters to jointly determine the stiffness characteristics of the motor;

[0148] The temperature parameter reflects the temperature state of the motor during operation. The change of temperature will affect the material properties and mechanical performance of the motor, and thus affect the stiffness of the motor. In the model, it is necessary to consider the influence of temperature on the motor stiffness and incorporate it into the stiffness model through appropriate thermodynamic relationships.

[0149] In one embodiment, specifically, in step four, the calculation formula for the current stiffness value of the motor is as follows:

[0150] ;

[0151] where F is the force applied to the motor, which is calculated from the load and the electromagnetic parameters of the motor, is the deformation of the motor, which is estimated based on the structure and operating parameters of the motor;

[0152] Among them, the force applied to the motor can be calculated from the load and the electromagnetic parameters of the motor. Specifically, the force F applied to the motor can be expressed as the product of the electromagnetic torque and the radius of the motor rotor (in a rotating motor), or as the product of the electromagnetic force and the axial length of the motor (in a linear motor); the electromagnetic torque or electromagnetic force can be calculated from the electromagnetic parameters such as the current, voltage, and magnetic flux of the motor, and these parameters can be measured in real time by sensors;

[0153] The deformation of the motor can be estimated based on the motor's structure and operating parameters. In some cases, the deformation can be directly measured by a displacement sensor. In the absence of direct measurement means, the deformation can be estimated through the motor's mechanical structure parameters (such as elastic modulus, geometric dimensions, etc.) and operating parameters (such as load, speed, etc.). The estimation method involves complex mechanical analysis and calculations and needs to be customized according to the specific structure and operating conditions of the motor.

[0154] In one embodiment, specifically, in step five, the calculation formula for the stiffness deviation value is:

[0155] ;

[0156] Where, is the target stiffness value, is the current stiffness value;

[0157] Among them, the target stiffness value is the expected stiffness value determined according to the motor's design requirements, operating conditions or control strategy. It can be a fixed value or a value dynamically adjusted according to operating conditions;

[0158] The current stiffness value is the stiffness value of the motor under the current operating state obtained through real-time measurement and calculation, which reflects the actual stiffness characteristics of the motor under the current conditions.

[0159] In one embodiment, specifically, in step six, the intelligent control algorithm includes a fuzzy control algorithm, a neural network control algorithm, and a genetic algorithm;

[0160] Among them, the fuzzy control algorithm is a control method based on fuzzy set theory and fuzzy logic reasoning. It is suitable for dealing with complex, uncertain or difficult-to-precisely-model systems; in motor stiffness control, the fuzzy control algorithm can dynamically adjust control parameters according to the motor's operating state and stiffness deviation value through fuzzy rules and reasoning to achieve adaptive control of stiffness;

[0161] The neural network control algorithm is a control method based on artificial neural networks. It has powerful non-linear mapping ability and self-learning ability and can adapt to complex and changeable control environments; in motor stiffness control, the neural network control algorithm can establish a mapping relationship between motor stiffness and control parameters through training and learning, and then, according to the real-time measured stiffness deviation value, predict and adjust control parameters through the neural network to achieve precise control of stiffness;

[0162] The genetic algorithm is an optimization algorithm based on the principles of biological evolution. It iteratively optimizes control parameters by simulating natural selection and genetic mechanisms to find the optimal control strategy. In motor stiffness control, the genetic algorithm can be used to optimize the combination and value range of control parameters to improve the stiffness and stability of the motor. Through the iterative optimization of the genetic algorithm, a set of optimal control parameters can be found to minimize the stiffness deviation value of the motor.

[0163] The control parameters include current-related parameters, voltage-related parameters, magnetic field-related parameters, mechanical structure-related parameters, and control strategy-related parameters.

[0164] The current-related parameters include the stator current amplitude, rotor current amplitude, and current phase.

[0165] Stator current amplitude: Adjusting the magnitude of the stator current can change the magnetic field strength and torque output of the motor, thereby affecting the stiffness of the motor. For example, increasing the stator current amplitude will increase the electromagnetic force of the motor, thereby improving the stiffness of the motor.

[0166] Rotor current amplitude: Changes in the rotor current will also affect the electromagnetic characteristics of the motor. By adjusting the rotor current amplitude, the stiffness characteristics of the motor can be changed to a certain extent.

[0167] Current phase: Reasonably adjusting the phase relationship between the stator current and the rotor current can optimize the magnetic field distribution and torque output of the motor, thereby affecting the stiffness performance of the motor.

[0168] The voltage-related parameters include the stator voltage amplitude and the rotor voltage amplitude (if applicable).

[0169] Stator voltage amplitude: Changing the stator voltage amplitude can affect the magnetic field strength and speed of the motor. Within a certain range, appropriately increasing the stator voltage amplitude can enhance the output ability of the motor and also play a role in adjusting the stiffness of the motor.

[0170] Rotor voltage amplitude (if applicable): For some motor types, adjusting the rotor voltage can also affect the operating characteristics and stiffness of the motor.

[0171] The magnetic field-related parameters include the magnetic field strength and magnetic flux.

[0172] Magnetic field strength: By adjusting the excitation current of the motor or other means to change the magnetic field strength, the electromagnetic force and stiffness of the motor can be directly affected. For example, increasing the magnetic field strength can increase the attractive or repulsive force of the motor, thereby improving the stiffness of the motor.

[0173] Magnetic flux: Controlling the magnitude of the magnetic flux can change the electromagnetic characteristics and mechanical properties of the motor. Reasonably adjusting the magnetic flux can achieve adaptive adjustment of the motor stiffness.

[0174] The parameters related to the mechanical structure include the air-gap length and the mechanical spring coefficient;

[0175] Air-gap length: In some motors with special designs, the electromagnetic coupling and mechanical characteristics of the motor can be changed by adjusting the air-gap length between the stator and rotor of the motor, thereby affecting the stiffness of the motor;

[0176] Mechanical spring coefficient: If components such as mechanical springs are included in the motor system, the overall stiffness of the motor can be changed by adjusting the spring coefficient.

[0177] The parameters related to the control strategy include control algorithm parameters and control period;

[0178] Control algorithm parameters: For example, in the fuzzy control algorithm, by adjusting the weights of the fuzzy rules, the parameters of the membership function, etc., the response characteristics of the control strategy for motor stiffness adjustment can be changed;

[0179] Control period: Adjusting the execution period of the control algorithm can affect the real-time performance and accuracy of the control, and thus have an impact on the adaptive adjustment of the motor stiffness.

[0180] In summary, through the adaptive stiffness control method for a flat axial-flux motor provided by this embodiment, the flat axial-flux motor can dynamically adjust the control parameters according to the real-time operation data and the target stiffness requirement, realizing the adaptive adjustment of stiffness. This can not only improve the operation stability and load capacity of the flat axial-flux motor, but also reduce energy consumption and extend the service life. At the same time, this method has good adaptability and robustness, and can cope with the changes in different application scenarios and complex environments.

[0181] Figure 2 It is a schematic diagram of the functional modules of an adaptive stiffness control system for a flat axial-flux motor according to an embodiment of the present application. As Figure 2 shown, an adaptive stiffness control system for a flat axial-flux motor includes: a sensor module, a data preprocessing module, a model establishment module, a stiffness evaluation module, a target setting module, a deviation calculation module, a parameter adjustment module, and a feedback and optimization module;

[0182] The sensor module is used to sense the operation parameters of the flat axial-flux motor in real time, obtain the motor operation data including electrical parameter data, mechanical parameter data, and environmental parameter data, and transmit the data to the control system; among them, the electrical parameter data includes current, voltage, power, and resistance; the mechanical parameter data includes rotational speed, torque, vibration, and noise; the environmental parameter data includes temperature, humidity, and air pressure; providing accurate and comprehensive motor operation data for the control system is the basis for subsequent data processing and control strategy formulation;

[0183] A data preprocessing module, which is used to remove high-frequency noise in the motor operation data by using the mean filtering algorithm and remove outliers by using the Grubbs criterion detection method; improve the accuracy and reliability of the data, and provide clean and effective data for subsequent data analysis and control strategy formulation;

[0184] A model establishment module, which is used to establish a stiffness model of a flat axial-flux motor based on the electromagnetic characteristics, mechanical structure and operating environment of the motor; provide a theoretical basis for stiffness evaluation, so that the control system can preliminarily evaluate the stiffness of the motor based on the model;

[0185] A stiffness evaluation module, which is used to preliminarily evaluate the current stiffness value of the motor by using the preprocessed motor operation data and the established stiffness model; provide real-time stiffness information for subsequent stiffness control, which is the key for the control system to achieve adaptive control;

[0186] A target setting module, which is used to dynamically set a reasonable target stiffness value according to the specific application scenario and performance requirements of the motor; provide a clear control target for the control system, so that the control system can adjust the stiffness of the motor specifically;

[0187] A deviation calculation module, which is used to compare the currently evaluated current stiffness value with the set target stiffness, calculate the deviation between the two, and obtain the stiffness deviation value; provide a control basis for the parameter adjustment module, so that the control system can adjust the control parameters of the motor according to the deviation value;

[0188] A parameter adjustment module, which is used to adjust the control parameters of the motor through an intelligent control algorithm according to the stiffness deviation value, and perform adaptive adjustment of the motor stiffness; if the stiffness deviation value is positive and large and the deviation change rate is positive and large, increase the control parameters; if the stiffness deviation value is negative and small and the deviation change rate is negative and small, decrease the control parameters; realize the adaptive control of the motor stiffness, so that the stiffness of the motor can approach the target stiffness value in real time and accurately;

[0189] A feedback and optimization module, which is used to continuously monitor the operating state of the motor and transmit the feedback information back to the control system, and the control system further optimizes the adjustment strategy according to the feedback information; improve the stability and adaptability of the control system, so that the control system can continuously optimize the control strategy according to the actual situation and improve the performance and stability of the motor.

[0190] In summary, the adaptive stiffness control system for a flat axial flux motor provided in this embodiment realizes real-time and accurate adaptive control of the stiffness of the flat axial flux motor by integrating a sensor module, a data preprocessing module, a model establishment module, a stiffness evaluation module, a target setting module, a deviation calculation module, a parameter adjustment module, and a feedback and optimization module. These modules work together to form a highly intelligent and adaptive control system, providing a strong guarantee for the stable operation and performance improvement of the motor.

[0191] For other details of the implementation technical solutions of each module in the adaptive stiffness control system of the flat axial flux motor in the above embodiment, reference can be made to the description in the adaptive stiffness control method for a flat axial flux motor in the above embodiment, which will not be elaborated here.

[0192] It should be noted that each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0193] The embodiment of the present application provides a computer device, which includes a processor and a memory. At least one instruction or at least one program segment is stored in the memory, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement an adaptive stiffness control method for a flat axial flux motor as provided in the above method embodiment.

[0194] Figure 3 The hardware structure diagram of a device for implementing the adaptive stiffness control method for a flat axial flux motor provided in the embodiment of the present application is shown. The device can participate in constituting or include the device or system provided in the embodiment of the present application. As Figure 3 shown, the computer device 10 may include one or more processors 1002 (the processor may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 1004 for storing data, and a transmission device 1006 for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 3 the structure shown is only schematic and does not limit the structure of the above electronic device. For example, the computer device 10 may further include more or fewer components than those Figure 3 shown, or have the same asFigure 3 The different configurations shown.

[0195] It should be noted that one or more of the above-mentioned processors and / or other data processing circuits can generally be referred to as "data processing circuits" herein. The data processing circuit can be embodied in software, hardware, firmware, or any combination thereof, in whole or in part. In addition, the data processing circuit can be a single independent processing module, or incorporated in whole or in part into any one of the other components in the computer device 10 (or mobile device). As involved in the embodiments of the present application, the data processing circuit is a kind of processor control (such as the selection of a variable resistance terminal path connected to an interface).

[0196] The memory 1004 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to an adaptive stiffness control method for a flat axial flux motor in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, that is, to implement the above-mentioned method. The memory 1004 can include high-speed random access memory, and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 1004 can further include a memory remotely located relative to the processor, and these remote memories can be connected to the computer device 10 through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0197] The transmission device 1006 is used to receive or send data via a network. Specific examples of the above-mentioned network can include the wireless network provided by the communication provider of the computer device 10. In one instance, the transmission device 1006 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device 1006 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0198] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables the user to interact with the user interface of the computer device 10 (or mobile device).

[0199] For the detailed description of this embodiment, reference can be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0200] The embodiment of the present application further provides a computer-readable storage medium, which can be disposed in a server to store at least one instruction or at least one program related to an adaptive stiffness control method for a flat axial-flux motor in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the adaptive stiffness control method for a flat axial-flux motor provided in the above method embodiment.

[0201] Optionally, in this embodiment, the above storage medium may be located in at least one of multiple network servers in a computer network. Optionally, in this embodiment, the above storage medium may include, but is not limited to: various media such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), external hard drives, magnetic disks, or optical discs that can store program codes.

[0202] For the detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0203] The embodiment of the present invention further provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the adaptive stiffness control method for a flat axial-flux motor provided in the above various optional embodiments.

[0204] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are only examples and not limitations. It cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present disclosure. In addition, the above specific details are only for the purpose of illustration and easy understanding, rather than limitations. The above details do not limit the present disclosure to necessarily adopt the above specific details for implementation.

[0205] In this disclosure, 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. The block diagrams of devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open-ended terms, meaning "including but not limited to", and can be used interchangeably with each other. The words "or" and "and" used herein refer to the word "and / or" and can be used interchangeably with it, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to" and can be used interchangeably with it.

[0206] In addition, as used herein, the "or" used in the listing of items starting with "at least one" indicates a disjunctive listing. So, for example, the listing of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Further, the term "exemplary" does not mean that the examples described are preferred or better than other examples.

[0207] It should also be noted that in the systems and methods of this disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of this disclosure.

[0208] Various changes, substitutions, and alterations to the technologies described herein can be made without departing from the teachings defined by the appended claims. Moreover, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and acts described above. Current or later-developed processes, machines, manufactures, compositions of events, means, methods, or acts that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or acts within their scope.

[0209] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0210] The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the present disclosure to the form disclosed herein. Although several example aspects and embodiments have been discussed above, those of ordinary skill in the art will recognize some variations, modifications, alterations, additions, and subcombinations thereof.

Claims

1. An adaptive stiffness control method for a flat axial flux motor, characterized in that, It includes the following steps: Through a variety of sensors installed on the flat axial flux motor, the operating parameters of the motor are sensed in real time, the motor operating data is obtained, and the motor operating data is transmitted to the control system; Wherein the sensors include a current sensor, a voltage sensor, a speed sensor, a temperature sensor and a load sensor; The motor operating data includes electrical parameter data, mechanical parameter data and environmental parameter data; The electrical parameter data includes current, voltage, power and resistance; The mechanical parameter data includes speed, torque, vibration and noise; The environmental parameter data includes temperature, humidity and air pressure; Preprocess the obtained motor operating data to remove high-frequency noise and outliers in the motor operating data; Based on the electromagnetic characteristics, mechanical structure and operating environment of the motor, establish a stiffness model of the flat axial flux motor; Use the preprocessed motor operating data and the established stiffness model to preliminarily evaluate the current stiffness value of the motor, and dynamically set the target stiffness value according to the specific application scenario and performance requirements of the motor; Compare the currently evaluated current stiffness value with the set target stiffness, calculate the deviation between the two to obtain the stiffness deviation value; According to the stiffness deviation value, adjust the control parameters of the motor through an intelligent control algorithm to perform adaptive adjustment of the motor stiffness. If the stiffness deviation value is positive and large and the deviation change rate is positive and large, increase the control parameters. If the stiffness deviation value is negative and small and the deviation change rate is negative and small, decrease the control parameters; wherein the intelligent control algorithm includes a fuzzy control algorithm, a neural network control algorithm and a genetic algorithm; the control parameters include current-related parameters, voltage-related parameters, magnetic field-related parameters, mechanical structure-related parameters and control strategy-related parameters; The current-related parameters include the stator current amplitude, the rotor current amplitude and the current phase; The voltage-related parameters include the stator voltage amplitude and the rotor voltage amplitude; The magnetic field-related parameters include the magnetic field strength and the magnetic flux; The mechanical structure-related parameters include the air gap length and the mechanical spring coefficient; The control strategy-related parameters include control algorithm parameters and control period; During the adjustment process, continuously monitor the operating state of the motor and transmit the feedback information back to the control system, and the control system optimizes the adjustment strategy according to the feedback information.

2. The adaptive stiffness control method for a flat axial flux motor according to claim 1, wherein: Removing the high-frequency noise and outliers in the motor operating data respectively includes using a mean filtering algorithm and a Grubbs criterion detection method.

3. The adaptive stiffness control method of a flat axial flux motor according to claim 1, characterized in that: Based on the electromagnetic characteristics, mechanical structure and operating environment of the motor, establish a stiffness model of the flat axial flux motor, wherein the stiffness model includes the influence of the electromagnetic field parameters, mechanical structure parameters and temperature parameters of the motor on the stiffness, and the specific formula is: ； Wherein, K is the motor stiffness, E is the electromagnetic field parameter of the motor, M is the mechanical structure parameter of the motor, and T is the temperature parameter.

4. The adaptive stiffness control method of a flat axial flux motor according to claim 1, characterized in that: Using the preprocessed motor operating data and the established stiffness model to preliminarily evaluate the current stiffness value of the motor, and dynamically set the target stiffness value according to the specific application scenario and performance requirements of the motor, wherein the calculation formula for the current stiffness value of the motor is: ； Among them, F is the force acting on the motor, which is calculated through the electromagnetic parameters of the load and the motor. is the deformation of the motor, which is estimated according to the structure and operating parameters of the motor.

5. The adaptive stiffness control method of a flat axial flux motor according to claim 1, characterized in that: The current stiffness value obtained from the preliminary evaluation is compared with the set target stiffness, and the deviation between the two is calculated to obtain a stiffness deviation value. The calculation formula for the stiffness deviation value is as follows: ； Among them, is the target stiffness value, is the current stiffness value.

6. An adaptive stiffness control system for a flat axial flux motor, characterized in that, Including: A sensor module, a data preprocessing module, a model establishment module, a stiffness evaluation module, a target setting module, a deviation calculation module, a parameter adjustment module, and a feedback and optimization module; The sensor module is configured to perceive the operating parameters of the flat axial-flux motor in real time, obtain the motor operating data including electrical parameter data, mechanical parameter data, and environmental parameter data, and transmit the data to the control system. Among them, the electrical parameter data includes current, voltage, power, and resistance; the mechanical parameter data includes rotational speed, torque, vibration, and noise; the environmental parameter data includes temperature, humidity, and air pressure; The data preprocessing module is configured to use the mean filtering algorithm to remove the high-frequency noise in the motor operating data and use the Grubbs criterion detection method to remove outliers; The model establishment module is configured to establish a stiffness model of the flat axial-flux motor based on the electromagnetic characteristics, mechanical structure, and operating environment of the motor; The stiffness evaluation module is configured to use the preprocessed motor operating data and the established stiffness model to preliminarily evaluate the current stiffness value of the motor; The target setting module is configured to dynamically set the target stiffness value according to the specific application scenario and performance requirements of the motor; The deviation calculation module is configured to compare the current stiffness value obtained from the preliminary evaluation with the set target stiffness, calculate the deviation between the two, and obtain a stiffness deviation value; The parameter adjustment module is configured to adjust the control parameters of the motor through an intelligent control algorithm according to the stiffness deviation value to perform adaptive adjustment of the motor stiffness. If the stiffness deviation value is positive and large and the deviation change rate is positive and large, increase the control parameters; if the stiffness deviation value is negative and small and the deviation change rate is negative and small, decrease the control parameters; The feedback and optimization module is configured to continuously monitor the operating state of the motor and transmit the feedback information back to the control system, and the control system further optimizes the adjustment strategy according to the feedback information.

7. A computer device, characterized in that, Including: A processor; A memory for storing executable instructions; Among them, the processor is used to read the executable instructions from the memory and execute the executable instructions to implement an adaptive stiffness control method for a flat axial-flux motor as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the processor implements an adaptive stiffness control method for a flat axial-flux motor as described in any one of claims 1 to 5.

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