Self-adaptive rigidity control method, system and equipment of flattened axial flux motor and medium thereof

Abstract

The invention provides a self-adaptive rigidity control method, system and equipment for a flattened axial flux motor and a medium thereof, and the method comprises the following steps: 1, sensing the operation parameters of the motor in real time through a plurality of sensors installed on the flattened axial flux motor; according to the invention, by introducing an advanced sensor technology, a data processing algorithm and an intelligent control strategy, precise adaptive control of the stiffness of the flattened axial flux motor is realized, and the motor can always keep an optimal stiffness state under different operation conditions, so that the efficiency, the power density and the dynamic response performance of the motor are improved, and meanwhile, the reliability of the motor is improved. Vibration and noise caused by rigidity change can be effectively reduced through self-adaptive rigidity control, and the stability and reliability of the system are improved; the control method can adapt to different load changes and working environments, has a wide application prospect, realizes automatic adjustment of the motor rigidity, does not need manual intervention, and improves the intelligent level of control.

Description

Technical Field

[0001] The present invention relates to the field of flux motor technology, and in particular to an adaptive stiffness control method, system, device and medium thereof for a flattened axial flux motor. Background Art

[0002] With the rapid development of science and technology, the flattened axial flux motor has shown broad application prospects in many fields with its unique structural advantages. The flattened axial flux motor has the advantages of compact structure, high power density and high efficiency. Its compact structure gives it great advantages in application scenarios with limited space, such as electric vehicles, aerospace and other fields. It can provide more installation space for other components, while reducing the overall weight and improving the maneuverability and endurance of the equipment. The characteristics of high power density enable it to output greater power in a smaller volume, meeting the needs of high-power drives in industrial automation, robotics and other fields. Efficient energy conversion efficiency not only reduces operating costs, but also meets the requirements of energy conservation and environmental protection, and plays an important role in new energy power generation, energy-saving home appliances and other fields. However, there are still certain problems: 1. Existing motor control methods are difficult to achieve precise control of motor stiffness when faced with complex and changeable operating environments. The stiffness of the motor will be affected by many factors, such as load changes, temperature changes, motor manufacturing errors, 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 changes in the internal stress distribution of the motor, affecting the electromagnetic force between the stator and 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 characteristics 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 produce vibration and noise, reducing stability and reliability; humidity and dust in the operating environment will also affect the performance and stiffness of the motor; Second, the inability to precisely control the stiffness of the motor greatly limits the full performance of the motor. Changes in 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, causing the equipment driven by the motor to fail to work properly, affecting production efficiency and product quality. For example, in CNC machine tools, insufficient motor stiffness will affect processing accuracy and surface quality; in electric vehicles, it will affect the vehicle's acceleration performance, cruising range, and ride comfort. At the same time, changes in motor stiffness will also affect the stability of the entire system. When the stiffness decreases, the resonant frequency of the system will change, which can easily cause resonance and oscillation, increase noise, and even damage system components. In fields such as aerospace and medical equipment that have high requirements for system stability, this may pose serious safety hazards; To this end, an adaptive stiffness control method, system, device and medium of a flattened axial flux motor are proposed. Summary of the invention

[0003] In view of this, the embodiments of the present invention hope to provide an adaptive stiffness control method, system, device and medium of a flattened axial flux motor to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0004] In order to solve the above technical problems, a technical solution adopted in the present application is: an adaptive stiffness control method for a flat axial flux motor, comprising the following steps: Step 1: Using a variety of sensors installed on the flat axial flux motor, the motor's operating parameters are sensed in real time, the motor's operating data is acquired, and the motor's operating data is transmitted to the control system; Step 2: preprocess the acquired motor operation data to remove high-frequency noise and abnormal values ​​in the motor operation data; Step 3: Establish a stiffness model of the flat axial flux motor based on the electromagnetic characteristics, mechanical structure and operating environment of the motor; 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 based on the specific application scenario and performance requirements of the motor; Step 5: Compare the current stiffness value obtained by the preliminary evaluation with the set target stiffness, calculate the deviation between the two, and obtain the stiffness deviation value; Step 6: According to the stiffness deviation value, the control parameters of the motor are adjusted by the intelligent control algorithm to perform adaptive adjustment of the motor stiffness. If the stiffness deviation value is positive and the deviation change rate is positive, the control parameter is increased; if the stiffness deviation value is negative and the deviation change rate is negative, the control parameter is decreased; Step 7: During the adjustment process, the operating status of the motor is continuously monitored and the feedback information is transmitted back to the control system. The control system further optimizes the adjustment strategy based on the feedback information.

[0005] As a further preferred embodiment of the present technical solution, in step 1, the sensor includes a current sensor, a voltage sensor, a rotation speed sensor, a temperature sensor and a load sensor; The motor operation 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 include rotation speed, torque, vibration and noise; The environmental parameter data include temperature, humidity and air pressure.

[0006] As a further preferred embodiment of the present technical solution, in step 2, the removal of high-frequency noise and abnormal values ​​in the motor operation data utilizes a mean filtering algorithm and a Grubbs criterion detection method respectively; Set a set of data series , the calculation formula of the data Y after mean filtering is: ; Where m is the filter window size; Set a set of data series , calculate its average value The formulas for and standard deviation S are: ; ; For a certain data ,calculate The formula is: ; 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 ordinal number of the 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; like ,but is an outlier, among which is the Grubbs critical value, and the significance level and the number of data Sure.

[0007] As a further preferred embodiment of the present 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 the stiffness, and the specific formula is: ; Among them, 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.

[0008] As a further preferred embodiment of the present technical solution, in step 4, the current stiffness value calculation formula of the motor is: ; Where F is the force on the motor, which is calculated by 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.

[0009] As a further preferred embodiment of the present technical solution, in step 5, the calculation formula of the stiffness deviation value is: ; in, is the target stiffness value, is the current stiffness value.

[0010] As a further preferred embodiment of the present technical solution, in step six, 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 stator current amplitude, rotor current amplitude and current phase; The voltage-related parameters include stator voltage amplitude and rotor voltage amplitude; The magnetic field related parameters include magnetic field intensity and magnetic flux; The mechanical structure related parameters include air gap length and mechanical spring coefficient; The control strategy related parameters include control algorithm parameters and control cycle.

[0011] To solve the above technical problems, another technical solution adopted in the present application is: an adaptive stiffness control system for a flattened axial flux motor, comprising: a sensor module, a data preprocessing module, a model building 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 used to sense the operating parameters of the flattened axial flux motor in real time, obtain 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 speed, torque, vibration and noise; the environmental parameter data includes temperature, humidity and air pressure; The data preprocessing module is used to remove high-frequency noise in the motor operation data by using a mean filtering algorithm and to remove abnormal values ​​by using a Grubbs criterion detection method; The model building module is used to build a stiffness model of the flattened axial flux motor based on the electromagnetic characteristics, mechanical structure and operating environment of the motor; The stiffness evaluation module is used to preliminarily evaluate the current stiffness value of the motor by using the preprocessed motor operation data and the established stiffness model; 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; The deviation calculation module is used to compare the current stiffness value obtained by preliminary evaluation with the set target stiffness, calculate the deviation between the two, and obtain the stiffness deviation value; 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, 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, the control parameter is increased; if the stiffness deviation value is negative and small and the deviation change rate is negative and small, the control parameter is reduced; The feedback and optimization module is used to continuously monitor the operating status of the motor and transmit the feedback information back to the control system, and the control system further optimizes the adjustment strategy based on the feedback information.

[0012] To solve the above technical problem, another technical solution adopted by the present application is: a computer device, comprising: a processor; A memory for storing executable instructions; Wherein, 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 the flattened axial flux motor as described above.

[0013] To solve the above technical problems, another technical solution adopted in the present application is: a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the adaptive stiffness control method of a flattened axial flux motor as described above.

[0014] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution: 1. The present invention realizes precise adaptive control of the stiffness of the flattened axial flux motor by introducing advanced sensor technology, data processing algorithm and intelligent control strategy, which enables 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, adaptive stiffness control can effectively reduce the vibration and noise caused by stiffness changes, and improve the stability and reliability of the system; 2. The control method of the present invention can adapt to different load changes and working environments and has broad application prospects. The method realizes automatic adjustment of motor stiffness without manual intervention, thereby improving the intelligent level of control.

[0015] The above summary is for illustrative purposes only 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 accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a schematic flow chart of the adaptive stiffness control method of the flattened axial flux motor of the present invention; Figure 2 It is a schematic diagram of the functional modules of the adaptive stiffness control system of the flattened axial flux motor of the present invention; Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention.

[0018] Figure numerals: 10, computer device; 1002, processor; 1004, memory; 1006, transmission device. DETAILED DESCRIPTION

[0019] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0020] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a 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 embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0021] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0022] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0023] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0024] Figure 1 FIG. 1 is a flow chart 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 is limited. Figure 1 As shown: An adaptive stiffness control method for a flat axial flux motor comprises the following steps: Step 1: Using a variety of sensors installed on the flat axial flux motor, the motor's operating parameters are sensed in real time, the motor's operating data is acquired, and the motor's operating data is transmitted to the control system; Specifically: First, select a variety of sensors suitable for flattened axial flux motors. These sensors should be able to accurately sense the key operating parameters of the motor. Install these sensors at appropriate locations 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, which are converted into digital data after processing. Finally, the collected motor operating data is transmitted to the control system via wired or wireless means. The control system can be a dedicated computer or embedded system for receiving, processing and analyzing these data.

[0025] Step 2: preprocess the acquired motor operation data to remove high-frequency noise and abnormal values ​​in the motor operation data; Among them, high-frequency noise is usually caused by electrical interference, mechanical vibration and other factors during the operation of the motor, and is removed by the mean filtering algorithm; outliers are usually caused by sensor failure, data acquisition errors and other factors, and are removed by the Grubbs criterion detection method; the preprocessed data should be stored in the storage medium of the control system for subsequent analysis and processing. At the same time, these data can also be transmitted to other systems or equipment for further analysis and monitoring.

[0026] Step 3: Establish a stiffness model of the flat axial flux motor based on the electromagnetic characteristics, mechanical structure and operating environment of the motor; Among them, electromagnetic characteristics: the magnetic flux path of the flattened axial flux motor is different from that of the ordinary radial motor. Its air gap is planar, and the direction of the air gap magnetic field is parallel to the direction of the motor axis. The electromagnetic characteristics of the permanent magnets, windings, stator core and other components in the motor have an important influence on the stiffness of the motor. Mechanical structure: The mechanical structural characteristics of the motor's rotor, stator, bearings, shaft and other components determine the motor's stiffness and stability; the rotor's diameter, thickness, material, and the stator's slot shape, slot number and other parameters will affect the motor's stiffness; Operating environment: The motor will be affected by environmental factors such as temperature, humidity, and vibration during operation; these environmental factors cause the material properties of the motor to change, thus affecting its stiffness; Among them, the specific steps of establishing the stiffness model of the flattened axial flux motor are: First, according to the electromagnetic characteristics and mechanical structure of the motor, the key parameters affecting the stiffness are determined. These parameters include the magnetization intensity of the permanent magnet, the current density of the winding, the slot shape and number of the stator, the diameter and thickness of the rotor, etc. Then, based on the basic principles of electromagnetism and mechanics, a mathematical model of the motor's stiffness is established. This model should be able to reflect the motor's stiffness characteristics under different working conditions. Next, the influence of environmental factors such as temperature and humidity on the stiffness of the motor is considered in the model. Through experiments or simulation methods, the stiffness data of the motor under different environmental conditions is obtained, and the model is modified. Finally, the accuracy and reliability of the established stiffness model are verified through experiments or simulation methods. If there is a large difference between the model and the experimental results, the model needs to be further modified and optimized.

[0027] 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 based on the specific application scenario and performance requirements of the motor; Among them, the specific steps for preliminarily evaluating the current stiffness value of the motor are: First, the preprocessed motor operation data (such as current, voltage, speed, temperature, etc.) is input into the established stiffness model; Then, the stiffness model is used to calculate the current stiffness value of the motor according to the input operation data. The stiffness value is a scalar (indicating the overall stiffness) or a vector (indicating the stiffness in each direction). Finally, the initially estimated stiffness values ​​are compared with experimental measurements to verify the accuracy of the model and, if discrepancies exist, the model can be fine-tuned or recalibrated.

[0028] Among them, the specific steps for dynamically setting a reasonable target stiffness value are: First, analyze the specific application scenarios of the motor, such as high-speed operation, heavy load, high-precision positioning, etc., to determine the requirements for motor stiffness in different application scenarios; Then, consider the motor performance requirements, such as vibration suppression, noise control, efficiency optimization, etc. These requirements will directly affect the setting of the target stiffness value; Next, the target stiffness value is dynamically adjusted based on the real-time operating data and working condition changes of the motor. For example, under heavy load conditions, the stiffness of the motor needs to be increased to improve stability; while under high-speed operation, the stiffness needs to be reduced to reduce vibration and noise. Finally, based on the dynamically set target stiffness value, corresponding control strategies are designed. These strategies include adjusting control parameters such as current, voltage, and speed to achieve precise control of the motor stiffness.

[0029] Step 5: Compare the current stiffness value obtained by the preliminary evaluation with the set target stiffness, calculate the deviation between the two, and obtain the stiffness deviation value; Specifically: First, the current stiffness value is directly compared with the target stiffness value. If the current stiffness value is a vector (containing stiffness components in multiple directions), the stiffness component in each direction needs to be compared separately; Then, according to the comparison result, the deviation between the current stiffness value and the target stiffness value is calculated. The deviation can be a scalar (indicating the overall deviation) or a vector (indicating the deviation in each direction). The deviation value can be obtained by a simple subtraction operation, that is, the target stiffness value minus the current stiffness value; Finally, the calculated deviation value is analyzed to determine the size and direction of the deviation. The size of the deviation reflects the degree of difference between the current stiffness value and the target stiffness value, while the direction of the deviation indicates the direction that needs to be adjusted.

[0030] Step 6: According to the stiffness deviation value, the control parameters of the motor are adjusted by the intelligent control algorithm to perform adaptive adjustment of the motor stiffness. If the stiffness deviation value is positive and the deviation change rate is positive, the control parameter is increased; if the stiffness deviation value is negative and the deviation change rate is negative, the control parameter is decreased; Among them, the adaptive adjustment process of motor stiffness is: First, the real-time operation data of the motor is obtained through sensors or data acquisition systems, and the stiffness deviation value and its change rate are calculated; Then, according to the stiffness deviation value and its change rate monitored in real time, the intelligent control algorithm is executed, and the intelligent control algorithm calculates the control parameter value that needs to be adjusted according to the preset rules or models; Next, the calculated control parameter value is applied to the control system of the motor, and the output of the control system is adjusted to achieve adaptive adjustment of the motor stiffness; Finally, according to the adjusted motor stiffness value, the stiffness deviation value and its change rate are calculated again. If the deviation is still large, the control parameters are adjusted continuously until the stiffness of the motor reaches or approaches the target stiffness value.

[0031] Step 7: During the adjustment process, the running status of the motor is continuously monitored, and the feedback information is transmitted back to the control system, and the control system further optimizes the adjustment strategy according to the feedback information; Specifically: According to the feedback information of the motor operating status, the existing control strategy is adjusted and optimized. For example, if the motor vibration is detected to be 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 an intelligent control algorithm (such as fuzzy control, neural network, etc.) is 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 status of the motor; the control system has adaptive capabilities and can automatically adjust the control parameters and strategies according to the real-time operating status of the motor. The control system can identify changes in the motor in real time and take corresponding measures to maintain stable operation and performance optimization of the motor.

[0032] In one embodiment, specifically, in step 1, the sensor includes a current sensor, a voltage sensor, a rotation speed sensor, a temperature sensor and a load sensor; Among them, the current sensor is used to monitor the current changes when the motor is running. The current data is an important indicator for evaluating the motor load, efficiency and potential overheating risk; The voltage sensor is used to monitor the stability of the motor input voltage. The voltage data helps to determine the quality of the power supply and whether the motor is within the normal operating voltage range; The speed sensor is used to monitor the speed of the motor in real time. The speed data is an important basis for evaluating the motor performance, load capacity and potential faults (such as bearing wear); Temperature sensors are used to monitor the temperature of the motor and its surroundings. Temperature data is critical to preventing motor overheating, ensuring the effectiveness of the cooling system, and extending the life of the motor. The load sensor is used to measure the load borne by the motor. The load data helps to evaluate the motor's carrying capacity and adjust the control strategy to adapt to different load conditions.

[0033] The motor operation data includes electrical parameter data, mechanical parameter data and environmental parameter data; Electrical parameter data include current, voltage, power and resistance; Current: including the current values ​​of different parts such as stator current and rotor current, which can reflect the load and operating status of the motor. For example, excessive current means that the motor is overloaded; Voltage: The input voltage of the motor, the voltage of each phase, etc. The stability of the voltage is crucial to the normal operation of the motor. Abnormal voltage fluctuations affect the performance and life of the motor. Power: active power, reactive power, apparent power, etc. Power data can help evaluate the energy efficiency and operating cost of the motor; Resistance: Motor winding resistance, etc. Changes in resistance indicate faults or aging of the motor windings.

[0034] Mechanical parameter data include speed, torque, vibration and noise; Speed: The rotation speed of the motor is one of the important indicators to measure the motor output performance. Different application scenarios have different requirements for the speed. Torque: The torque output by the motor. The torque is related to the load. For motors driving different loads, the torque data can reflect their working capacity. 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. Noise: The intensity and frequency characteristics of the sound emitted during the operation of the motor. Abnormal noise indicates damage or failure of the internal parts of the motor.

[0035] Environmental parameter data include temperature, humidity and air pressure; Temperature: Motor body temperature, ambient temperature, etc. Excessive motor temperature can lead to insulation damage, shortened life, and other problems. Therefore, temperature data is very important for thermal management and protection of the motor. Humidity: The humidity of the surrounding environment. High humidity environment affects the insulation performance and reliability of the motor; Air pressure: In some special application scenarios, air pressure also affects the operation of the motor.

[0036] 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; 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: ; Where m is the filter window size; The Grubbs criterion is a statistical test method based on the normal distribution to identify outliers in a data set. , calculate its average value The formulas for and standard deviation S are: ; ; For a certain data ,calculate The formula is: ; 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 ordinal number of the 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; like ,but is an outlier, among which is the Grubbs critical value, and the significance level and the number of data Sure; By removing high-frequency noise and outliers in the motor operation data, the accuracy and reliability of the data are improved, providing stronger support for subsequent adaptive stiffness control.

[0037] In one embodiment, specifically, 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 the stiffness, and the specific formula is: ; Among them, 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.

[0038] 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 then affect the stiffness of the motor. In the model, it is necessary to consider the impact of changes in these parameters on the stiffness and incorporate them into the stiffness model through appropriate functional relationships; Mechanical structural parameters include motor size, material properties, bearing stiffness, etc. These parameters determine the mechanical properties and structural stability of the motor. In the model, it is necessary to consider the impact 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; The temperature parameters reflect the temperature state of the motor during operation. Changes in temperature will affect the material properties and mechanical properties of the motor, and thus affect the stiffness of the motor. In the model, it is necessary to consider the effect of temperature on the motor stiffness and incorporate it into the stiffness model through appropriate thermodynamic relationships.

[0039] In one embodiment, specifically, in step 4, the current stiffness value of the motor is calculated as follows: ; Where F is the force on the motor, which is calculated by the load and the electromagnetic parameters of the motor. is the deformation of the motor, estimated based on the structure and operating parameters of the motor; The force on the motor can be calculated by the load and the electromagnetic parameters of the motor. Specifically, the force F on the motor can be expressed as the product of the electromagnetic torque and the motor rotor radius (in a rotating motor), or as the product of the electromagnetic force and the motor axial length (in a linear motor). The electromagnetic torque or electromagnetic force can be calculated by the electromagnetic parameters of the motor, such as the current, voltage, and magnetic flux, which can be measured in real time by sensors. The deformation of the motor can be estimated based on the structure and operating parameters of the motor. In some cases, the deformation can be directly measured by a displacement sensor. In the absence of direct measurement methods, the deformation can be estimated by the mechanical structure parameters (such as elastic modulus, geometric dimensions, etc.) and operating parameters (such as load, speed, etc.) of the motor. 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.

[0040] In one embodiment, specifically, in step 5, the calculation formula of the stiffness deviation value is: ; in, is the target stiffness value, is the current stiffness value; The target stiffness value is the expected stiffness value determined according to the design requirements, operating conditions or control strategy of the motor. It can be a fixed value or a value dynamically adjusted according to the operating conditions. 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.

[0041] 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; Among them, the fuzzy control algorithm is a control method based on fuzzy set theory and fuzzy logic reasoning. It is suitable for processing complex, uncertain or difficult to accurately model systems. In the motor stiffness control, the fuzzy control algorithm can dynamically adjust the control parameters according to the motor's operating state and stiffness deviation value through fuzzy rules and reasoning to achieve adaptive stiffness control. The neural network control algorithm is a control method based on artificial neural networks. It has powerful nonlinear mapping and self-learning capabilities and can adapt to complex and changing 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. Then, according to the stiffness deviation value measured in real time, the control parameters are predicted and adjusted through the neural network to achieve precise control of stiffness. Genetic algorithm is an optimization algorithm based on the principle 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, 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 genetic algorithm, an optimal set of control parameters can be found to minimize the stiffness deviation of the motor.

[0042] The control parameters include current related parameters, voltage related parameters, magnetic field related parameters, mechanical structure related parameters and control strategy related parameters; Current-related parameters include stator current amplitude, rotor current amplitude and current phase; 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 increasing the stiffness of the motor. Rotor current amplitude: The change of 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; Current phase: Reasonable adjustment of the phase relationship between stator current and rotor current can optimize the magnetic field distribution and torque output of the motor, thereby affecting the stiffness performance of the motor.

[0043] Voltage-related parameters include stator voltage amplitude and rotor voltage amplitude; 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 capacity of the motor and also have a certain effect on the adjustment of the motor stiffness; Rotor voltage amplitude (if applicable): For some motor types, adjustment of the rotor voltage can also affect the running characteristics and stiffness of the motor.

[0044] Magnetic field related parameters include magnetic field strength and magnetic flux; Magnetic field strength: By adjusting the excitation current of the motor or changing the magnetic field strength in other ways, the electromagnetic force and stiffness of the motor can be directly affected. For example, increasing the magnetic field strength can increase the attractive force or repulsive force of the motor, thereby improving the stiffness of the motor. Magnetic flux: Controlling the size of the magnetic flux can change the electromagnetic characteristics and mechanical properties of the motor. Reasonable adjustment of the magnetic flux can achieve adaptive adjustment of the motor stiffness.

[0045] Mechanical structure related parameters include air gap length and mechanical spring constant; Air gap length: In some specially designed motors, 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; Mechanical Spring Constant: If the motor system includes components such as mechanical springs, the spring constant can be adjusted to change the overall stiffness of the motor.

[0046] Control strategy related parameters include control algorithm parameters and control cycle; Control algorithm parameters: For example, in the fuzzy control algorithm, adjusting the weight of the fuzzy rules, the parameters of the membership function, etc. can change the response characteristics of the control strategy to the motor stiffness adjustment; Control cycle: Adjusting the execution cycle of the control algorithm can affect the real-time and accuracy of the control, and thus affect the adaptive adjustment of the motor stiffness.

[0047] In summary, through the adaptive stiffness control method of a flattened axial flux motor provided by this embodiment, the flattened axial flux motor can dynamically adjust the control parameters according to the real-time operating data and the target stiffness requirements to achieve adaptive adjustment of the stiffness, which can not only improve the operating stability and load capacity of the flattened axial flux motor, but also reduce energy consumption and extend the service life. At the same time, the method has good adaptability and robustness, and can cope with changes in different application scenarios and complex environments.

[0048] Figure 2 is a functional module schematic diagram of an adaptive stiffness control system of a flat axial flux motor according to an embodiment of the present application, such as Figure 2 As shown, an adaptive stiffness control system for a flat axial flux motor includes: a sensor module, a data preprocessing module, a model building 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 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; 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; providing the control system with accurate and comprehensive motor operating data is the basis for subsequent data processing and control strategy formulation; The data preprocessing module is used to remove high-frequency noise in the motor operation data using the mean filtering algorithm and remove outliers 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; The model building module is used to build a stiffness model of the flat axial flux motor based on the electromagnetic characteristics, mechanical structure and operating environment of the motor; it provides a theoretical basis for stiffness evaluation, so that the control system can make a preliminary evaluation of the stiffness of the motor based on the model; The stiffness evaluation module is used to use the preprocessed motor operation data and the established stiffness model to preliminarily evaluate the current stiffness value of the motor. It provides real-time stiffness information for subsequent stiffness control and is the key to the control system to achieve adaptive control. 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; it provides a clear control target for the control system so that the control system can adjust the stiffness of the motor in a targeted manner; The deviation calculation module is used to compare the current stiffness value obtained by preliminary evaluation 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; The parameter adjustment module is used to adjust the control parameters of the motor through the 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 the deviation change rate is positive, the control parameter is increased; if the stiffness deviation value is negative and the deviation change rate is negative, the control parameter is reduced; the adaptive control of the motor stiffness is realized, so that the stiffness of the motor can approach the target stiffness value in real time and accurately; The feedback and optimization module is used to continuously monitor the operating status of the motor and transmit the feedback information back to the control system. The control system further optimizes and adjusts the strategy based on the feedback information; improves the stability and adaptability of the control system, so that the control system can continuously optimize the control strategy according to actual conditions and improve the performance and stability of the motor.

[0049] In summary, the adaptive stiffness control system of a flattened axial flux motor provided in this embodiment realizes real-time and accurate adaptive control of the stiffness of the flattened axial flux motor by integrating a sensor module, a data preprocessing module, a model building module, a stiffness evaluation module, a target setting module, a deviation calculation module, a parameter adjustment module and a feedback and optimization module. The modules work together to form a highly intelligent and adaptive control system, which provides a strong guarantee for the stable operation and performance improvement of the motor.

[0050] For other details about the technical solutions for implementing each module in the adaptive stiffness control system of the flattened axial flux motor in the above embodiment, please refer to the description of an adaptive stiffness control method for a flattened axial flux motor in the above embodiment, which will not be repeated here.

[0051] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between 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 be referred to the partial description of the method embodiment.

[0052] An embodiment of the present application provides a computer device, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement an adaptive stiffness control method for a flattened axial flux motor as provided in the above method embodiment.

[0053] Figure 3 The hardware structure diagram of a device for implementing an adaptive stiffness control method of a flattened axial flux motor provided in an embodiment of the present application is shown. The device may participate in constituting or include the apparatus or system provided in an embodiment of the present application. Figure 3 As 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 also 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. It can be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components as shown, or with Figure 3 Different configurations are shown.

[0054] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer device 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0055] The memory 1004 can be used to store software programs and modules of application software, such as a program instruction / data storage device corresponding to an adaptive stiffness control method for a flattened axial flux motor in an embodiment 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, implementing the above-mentioned method. The memory 1004 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 1004 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer device 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0056] The transmission device 1006 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer device 10. In one example, the transmission device 1006 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 1006 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0057] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer device 10 (or mobile device).

[0058] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0059] An embodiment of the present application also provides a computer-readable storage medium, which can be set in a server to store at least one instruction or at least one program related to an adaptive stiffness control method for a flattened axial flux motor in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement an adaptive stiffness control method for a flattened axial flux motor provided in the above method embodiment.

[0060] Optionally, in this embodiment, the storage medium may be located in at least one of the multiple network servers of the computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0061] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0062] The embodiment of the present invention further provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a 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 an adaptive stiffness control method for a flattened axial flux motor provided in the above various optional embodiments.

[0063] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0064] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0065] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list 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). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0066] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0067] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0068] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0069] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. An adaptive stiffness control method for a flat axial flux motor, characterized in that: The following steps are involved: Through the various sensors installed on the flat axial flux motor, the motor's operating parameters are sensed in real time, the motor's operating data is obtained, and the motor's operating data is transmitted to the control system; Preprocess the acquired motor operation data to remove high-frequency noise and abnormal values ​​in the motor operation data; Based on the electromagnetic characteristics, mechanical structure and operating environment of the motor, a stiffness model of the flat axial flux motor is established; Using the preprocessed motor operation data and the established stiffness model, the current stiffness value of the motor is preliminarily evaluated, and the target stiffness value is dynamically set according to the specific application scenario and performance requirements of the motor; The current stiffness value obtained by preliminary evaluation is compared with the set target stiffness, and the deviation between the two is calculated to obtain the stiffness deviation value; According to the stiffness deviation value, the control parameters of the motor are adjusted through the intelligent control algorithm to perform adaptive adjustment of the motor stiffness. If the stiffness deviation value is positive and the deviation change rate is positive, the control parameter is increased; if the stiffness deviation value is negative and the deviation change rate is negative, the control parameter is decreased; During the adjustment process, the operating status of the motor is continuously monitored and feedback information is transmitted back to the control system. The control system optimizes the adjustment strategy based on the feedback information.

2. The adaptive stiffness control method of a flat axial flux motor according to claim 1, characterized in that: The multiple sensors installed on the flattened axial flux motor sense the motor's operating parameters in real time, obtain the motor's operating data, and transmit the motor's operating data 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 operation 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 include rotation speed, torque, vibration and noise; The environmental parameter data include temperature, humidity and air pressure.

3. The adaptive stiffness control method of a flat axial flux motor according to claim 1, characterized in that: The removal of high-frequency noise and abnormal values ​​in the motor operation data includes using a mean filtering algorithm and a Grubbs criterion detection method respectively.

4. 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, a stiffness model of the flattened axial flux motor is established, 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: ； Among them, 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.

5. The adaptive stiffness control method of a flat axial flux motor according to claim 1, characterized in that: The preprocessed motor operation data and the established stiffness model are used 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 current stiffness value calculation formula of the motor is: ； Where F is the force on the motor, which is calculated by 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.

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

7. The adaptive stiffness control method of a flat axial flux motor according to claim 1, characterized in that: According to the stiffness deviation value, the control parameters of the motor are adjusted by an intelligent control algorithm to perform adaptive adjustment of the motor stiffness. If the stiffness deviation value is positive and the deviation change rate is positive, the control parameter is increased; if the stiffness deviation value is negative and the deviation change rate is negative, the control parameter is decreased, 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 stator current amplitude, rotor current amplitude and current phase; The voltage-related parameters include stator voltage amplitude and rotor voltage amplitude; The magnetic field related parameters include magnetic field intensity and magnetic flux; The mechanical structure related parameters include air gap length and mechanical spring coefficient; The control strategy related parameters include control algorithm parameters and control cycle.

8. An adaptive stiffness control system for a flat axial flux motor, characterized in that: include: Sensor module, data preprocessing module, model building module, stiffness evaluation module, target setting module, deviation calculation module, parameter adjustment module and feedback and optimization module; The sensor module is configured to sense the operating parameters of the flattened axial flux motor in real time, obtain 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 speed, torque, vibration and noise; and the environmental parameter data includes temperature, humidity and air pressure; The data preprocessing module is configured to remove high-frequency noise in the motor operation data by using a mean filtering algorithm and remove abnormal values ​​by using a Grubbs criterion detection method; The model building module is configured to build a stiffness model of the flattened 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 operation 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 a target stiffness value according to specific application scenarios and performance requirements of the motor; The deviation calculation module is configured to compare the current stiffness value obtained by preliminary evaluation with the set target stiffness, calculate the deviation between the two, and obtain the stiffness deviation value; The parameter adjustment module is configured to adjust the control parameters of the motor according to the stiffness deviation value 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, the control parameter is increased; if the stiffness deviation value is negative and small and the deviation change rate is negative and small, the control parameter is reduced; The feedback and optimization module is configured to continuously monitor the operating status of the motor and transmit the feedback information back to the control system, and the control system further optimizes the adjustment strategy based on the feedback information.

9. A computer device, characterized in that: include: processor; A memory for storing executable instructions; 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 flattened axial flux motor as described in any one of claims 1 to 7.

10. 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 a processor, the processor implements an adaptive stiffness control method for a flattened axial flux motor according to any one of claims 1 to 7.

Images