Method and device for monitoring and controlling operation of a direct current motor speed regulator

Abstract

The embodiment of the application provides a kind of DC motor speed regulator's operation monitoring control method, device, comprising: obtaining the rotor temperature observation value of the temperature sensor of DC motor;Obtain ambient temperature data;According to the rotor temperature data and ambient temperature data, calculate the temperature sensing coefficient;The error amount parameter of DC motor is adjusted by the temperature sensing coefficient, and the adjustment error amount parameter is obtained;According to the adjustment error amount parameter and system preset input, fuzzy convolution inference is carried out, and the output internal control amount is obtained;The operation of DC motor is controlled by the internal control amount, gradually extract more complex features, reduce the parameter quantity of fuzzy inference model, improve the calculation efficiency, and reduce the risk of overfitting through weight parameter sharing, effectively reduce the uncertainty and nonlinearity of brushless DC motor control system, the controller in the embodiment of the application has better robustness.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method for monitoring and controlling the operation of a DC motor speed controller, a device for monitoring and controlling the operation of a DC motor speed controller, a DC motor speed controller, and a computer-readable storage medium. Background Technology

[0002] A DC motor is a rotating electric machine that converts DC electrical energy into mechanical energy or vice versa. It mainly consists of a stator and a rotor. The stator is the stationary part and is composed of the main magnetic poles, commutating poles, frame, end covers, and brush assembly. The rotor is the rotating part and is composed of a shaft, armature core, armature windings, commutator, and fan.

[0003] DC motors are typically speed-regulated using PID controllers. PID speed control is widely used in industrial automation and precision control. The performance of a PID controller depends on the precise tuning of its three parameters (proportional, integral, and derivative). Tuning these parameters often requires specialized knowledge and experience, and the parameters may need to be readjusted for different motor and load characteristics. PID controllers have limited adaptability to nonlinear systems or systems with large parameter variations. For DC motor systems with complex dynamic characteristics, PID controllers may not provide optimal speed control performance. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a method for monitoring and controlling the operation of a DC motor speed controller, a device for monitoring and controlling the operation of a DC motor speed controller, a DC motor speed controller, and a computer-readable storage medium to overcome or at least partially solve the above problems.

[0005] To address the aforementioned problems, this invention discloses a method for monitoring and controlling the operation of a DC motor speed controller, wherein the DC motor is connected to a PID controller, a temperature sensor, and a position sensor; the method includes:

[0006] Obtain the rotor temperature observation value from the temperature sensor of the DC motor;

[0007] Obtain ambient temperature data;

[0008] The temperature sensitivity coefficient is calculated based on the rotor temperature data and the ambient temperature data.

[0009] The error parameters of the DC motor are adjusted by the temperature sensitivity coefficient to obtain the adjustment error parameters;

[0010] Based on the adjustment error parameters and the system preset input, fuzzy convolution inference is performed to obtain the output internal control quantity;

[0011] The operation of the DC motor is controlled by the internal control variables.

[0012] Preferably, the temperature sensor includes a first temperature sensor, a second temperature sensor, and a third temperature sensor distributed at different positions on the motor stator; acquiring the rotor temperature data from the DC motor temperature sensor includes:

[0013] The first temperature data from the first temperature sensor is obtained;

[0014] The second temperature data from the second temperature sensor is obtained;

[0015] The third temperature data from the third temperature sensor is obtained;

[0016] A temperature set sequence is formed by combining the first temperature data, the second temperature data, and the third temperature data. The temperature set sequence is then subjected to a threshold limit operation and the mean is calculated to generate the rotor temperature observation value.

[0017] Preferably, the step of calculating the temperature sensitivity coefficient based on the rotor temperature observation value and ambient temperature data includes:

[0018] The rotor temperature observation value is corrected using the ambient temperature data through linear regression to obtain the temperature sensitivity coefficient.

[0019] Preferably, the step of adjusting the error parameter of the DC motor through the temperature sensitivity coefficient to obtain the adjustment error parameter includes:

[0020] A relationship model between the temperature sensitivity coefficient and the error parameters is established. The error parameters of the DC motor are adjusted according to the relationship model to obtain the adjusted error parameters.

[0021] Preferably, the step of performing fuzzy convolutional inference based on the adjustment error parameter and the system preset input to obtain the output internal control quantity includes:

[0022] The error parameter is obtained by subtracting the actual output from the preset input. This error parameter and the adjustment error parameter are then mapped to the predicted numerical level to obtain the quantization mapping error parameter and the quantization adjustment error parameter.

[0023] Determine the fuzzy subsets of the quantization mapping error parameters and the quantization adjustment error parameters;

[0024] The membership degree of the control quantity is obtained through a preset inference table based on the membership degree of the quantization mapping error parameter and the quantization adjustment error parameter.

[0025] By using the membership degree of the control quantity as the input of the convolutional layer and setting the temperature sensing coefficient as the weight coefficient of the convolutional layer, the specific membership degree of the output control quantity is obtained.

[0026] The internal control quantity is calculated by the membership degree of the specific control quantity.

[0027] Preferably, the internal control quantity includes a first internal control quantity, a second internal control quantity, and a third internal control quantity; the calculation of the internal control quantity through the membership degree of the specific control quantity includes:

[0028] Defuzzification is performed on the membership degree of a specific control variable to determine the first internal control variable, the second internal control variable, and the third internal control variable.

[0029] This invention discloses an operation monitoring and control device for a DC motor speed controller, wherein the DC motor is connected to a PID controller, a temperature sensor, and a position sensor; the device includes:

[0030] The first acquisition module is used to acquire the rotor temperature observation value of the temperature sensor of the DC motor;

[0031] The second acquisition module is used to acquire ambient temperature data;

[0032] The calculation module is used to calculate the temperature sensitivity coefficient based on the rotor temperature data and the ambient temperature data.

[0033] The adjustment module is used to adjust the error parameters of the DC motor through the temperature sensing coefficient to obtain the adjustment error parameters;

[0034] The fuzzy convolution inference module is used to perform fuzzy convolution inference based on the adjustment error parameter and the system preset input to obtain the output internal control quantity.

[0035] The control module is used to control the operation of the DC motor through the internal control quantities.

[0036] Preferably, the temperature sensor includes a first temperature sensor, a second temperature sensor, and a third temperature sensor distributed at different positions on the motor stator; the first acquisition module includes:

[0037] The first acquisition submodule is used to acquire the first temperature data from the first temperature sensor;

[0038] The second acquisition submodule is used to acquire the second temperature data from the second temperature sensor;

[0039] The third acquisition submodule is used to acquire the third temperature data of the third temperature sensor;

[0040] The mean calculation submodule is used to form a temperature set sequence from the first temperature data, the second temperature data, and the third temperature data, perform a threshold limit operation on the temperature set sequence, and calculate the mean to generate the rotor temperature observation value.

[0041] This invention discloses a DC motor speed controller, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described DC motor speed controller operation monitoring and control method.

[0042] This invention discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described DC motor speed controller operation monitoring and control method.

[0043] The embodiments of the present invention have the following advantages:

[0044] In this embodiment of the invention, a DC motor is connected to a PID controller, a temperature sensor, and a position sensor. The operation monitoring and control method of the DC motor speed controller includes: acquiring the rotor temperature observation value of the DC motor's temperature sensor; acquiring ambient temperature data; calculating a temperature sensing coefficient based on the rotor temperature data and ambient temperature data; adjusting the error parameter of the DC motor using the temperature sensing coefficient to obtain an adjustment error parameter; performing fuzzy convolution inference based on the adjustment error parameter and a preset system input to obtain an output internal control quantity; controlling the operation of the DC motor using the internal control quantity, gradually extracting more complex features, reducing the number of parameters in the fuzzy inference model, improving computational efficiency, and reducing the risk of overfitting through weight parameter sharing, effectively reducing the uncertainty and nonlinearity of the brushless DC motor control system. The controller in this embodiment of the invention has better robustness. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of an embodiment of a DC motor speed controller operation monitoring and control method according to an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the architecture of a DC motor speed controller operation monitoring and control system according to an embodiment of the present invention;

[0048] Figure 3This is a schematic diagram of the architecture of a DC motor speed controller operation monitoring and control system according to an embodiment of the present invention;

[0049] Figure 4 This is a structural block diagram of an embodiment of a DC motor speed controller operation monitoring and control device according to an embodiment of the present invention;

[0050] Figure 5 This is an internal structural diagram of a DC motor speed controller according to one embodiment. Detailed Implementation

[0051] To make the technical problems, technical solutions, and beneficial effects solved by the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0052] In one core concept of this invention, temperature is a crucial factor for the sustained operation of a motor, especially for brushless DC motors using electronic commutation, which have numerous electronic components. Considering the influence of temperature is one of the key innovations of the PID control, regulation, monitoring, and control in this invention. Specifically, a temperature set sequence is subjected to threshold limiting and mean calculation to generate rotor temperature observations. These observations are then corrected using linear regression based on the ambient temperature data to obtain a temperature sensitivity coefficient. Furthermore, a convolutional layer is added between the parameter fuzzy inference and defuzzification processes to reduce the number of parameters in the fuzzy inference model, improve model computation efficiency, and reduce the uncertainty and nonlinearity of the brushless DC motor control system.

[0053] Reference Figure 1 This diagram illustrates an embodiment of a DC motor speed controller operation monitoring and control method according to an embodiment of the present invention. The DC motor is connected to a PID controller, a temperature sensor, and a position sensor; specifically, it may include the following steps:

[0054] Step S101: Obtain the rotor temperature observation value from the temperature sensor of the DC motor;

[0055] In this embodiment of the invention, the type of DC motor may include a brushless DC motor, such as... Figure 2As shown, the DC motor can be connected to a PID controller, a temperature sensor, and a position sensor. The PID controller can be connected to a fuzzy controller. Specifically, the fuzzy controller calculates the error parameter e based on the difference between the actual system output Y(t) and the preset system input X(t). It receives the error parameter to obtain the proportional parameter adjustment ΔKp, integral parameter adjustment ΔKi, and derivative parameter adjustment ΔKd. It inputs these three parameters to the PID controller to obtain the control quantity U(t) at time t. The operation of the DC motor is controlled by the control quantity U(t). The adjusted error parameter e is related to the temperature data from the temperature sensor.

[0056] In this embodiment of the invention, the specific structure of the brushless DC motor may include a motor rotor made of permanent magnet material, a motor stator with coil windings, a power supply, power electronic devices and integrated circuits, and the power electronic devices and integrated circuits may include the aforementioned PID controller, temperature sensor, position sensor and electronic commutator, etc. This embodiment of the invention does not impose too many restrictions on the specific structure of the brushless DC motor.

[0057] Specifically, in this embodiment of the invention, the temperature sensor includes a first temperature sensor, a second temperature sensor, and a third temperature sensor distributed at different positions on the stator of the motor; acquiring the rotor temperature data of the DC motor includes: acquiring first temperature data from the first temperature sensor; acquiring second temperature data from the second temperature sensor; acquiring third temperature data from the third temperature sensor; forming a temperature set sequence using the first temperature data, the second temperature data, and the third temperature data; performing a threshold limiting operation on the temperature set sequence and calculating the average value to generate the rotor temperature observation value.

[0058] Regarding the installation location of the temperature sensor, it can be evenly distributed at three endpoints of the same transverse circumferential cross-section of the motor stator, forming a triangular distribution. This is used to measure the rotor temperature. For example, the first temperature data may include (x1, x2, x3, x4, x5, x6, x7), the second temperature data may include (x8, x9, x10, x11, x12, x13, x14), and the third temperature data may include (x15, x16, x17, x18, x19, x20, x21). The temperature set sequence formed by these three data points is (x1, x2, x3, x4, x5, x6, x7, x8). x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21); The temperature set sequence is subjected to a threshold limiting operation. This threshold limiting operation refers to deleting temperature data that is higher than the highest threshold or lower than the lowest threshold, eliminating interference from abnormal temperature data, and improving the accuracy of data adjustment. If the temperature set sequence after the threshold limiting operation is (x1, x2, x4, x5, x7, x8, x9, x11, x12, x13, x16, x19, x20, x21), then the average value of the above 14 temperature data is calculated as the rotor temperature observation value.

[0059] The rotor temperature observation value described above is merely one example of an embodiment of the present invention. Rotor temperature observation values ​​can also be obtained in other ways, and the embodiments of the present invention do not impose excessive limitations on them. It should be noted that the temperature sensor can include various non-contact temperature sensors, and the embodiments of the present invention do not impose excessive limitations on them.

[0060] Step S102: Obtain ambient temperature data;

[0061] In a further embodiment of the present invention, the ambient temperature data can be the temperature of the external environment in which the DC motor is located, which can be obtained through an external temperature sensor.

[0062] Step S103: Calculate the temperature sensitivity coefficient based on the rotor temperature data and ambient temperature data;

[0063] In a further embodiment of the present invention, the step of calculating the temperature sensitivity coefficient based on the rotor temperature observation value and the ambient temperature data includes: correcting the rotor temperature observation value using the ambient temperature data through linear regression to obtain the temperature sensitivity coefficient.

[0064] In this embodiment of the invention, the ambient temperature data is set as the independent variable x, and the rotor temperature observation value is set as the dependent variable y, y = λx + b. λ is determined as the temperature sensitivity coefficient, which represents the correlation coefficient between the rotor temperature observation value and the ambient temperature data. Of course, the above linear regression method is only an example of this embodiment of the invention, and the temperature sensitivity coefficient can also be calculated by other linear regression methods.

[0065] Step S104: Adjust the error parameter of the DC motor using the temperature sensitivity coefficient to obtain the adjustment error parameter;

[0066] In practical application of this invention, the step of adjusting the error parameter e of the DC motor through the temperature sensing coefficient to obtain the adjustment error parameter includes:

[0067] A relationship model between the temperature sensitivity coefficient and the error parameters is established. The error parameters of the DC motor are adjusted according to the relationship model to obtain the adjusted error parameters.

[0068] The expression for the relational model is as follows:

[0069]

[0070] Where θ represents the adjustment error parameter; λ represents the temperature sensitivity coefficient; e t Let t represent the error parameter at time t, where t = 0, 1, 2, 3, ..., n, and n is an integer; Q represents the first empirical coefficient; the error parameter refers to the difference between the actual output Y(t) of the system and the preset input X(t). The error parameter e of the DC motor is adjusted using the temperature sensitivity coefficient through a relational model to obtain the adjusted error parameter. This adjusted error parameter is an error function that is linearly related to the temperature sensitivity coefficient. The temperature factor is added to the PID control of the DC motor to improve the durability and longevity of the equipment.

[0071] Step S105: Perform fuzzy convolution inference based on the adjustment error parameters and the system preset input to obtain the output internal control quantity;

[0072] In this embodiment of the invention, after obtaining the adjustment error parameter, the adjustment error parameter and the system preset input can be used for fuzzy convolution inference in a fuzzy convolution controller to obtain the output internal control quantity; specifically, the step of performing fuzzy convolution inference based on the adjustment error parameter and the system preset input to obtain the output internal control quantity includes:

[0073] The error parameter is obtained by subtracting the actual output from the preset input. This error parameter and the adjustment error parameter are then mapped to the predicted numerical level to obtain the quantization mapping error parameter and the quantization adjustment error parameter.

[0074] Determine the fuzzy subsets of the quantization mapping error parameters and the quantization adjustment error parameters;

[0075] The membership degree of the control quantity is obtained through a preset inference table based on the membership degree of the quantization mapping error parameter and the quantization adjustment error parameter.

[0076] By using the membership degree of the control quantity as the input of the convolutional layer and setting the temperature sensing coefficient as the weight coefficient of the convolutional layer, the specific membership degree of the output control quantity is obtained.

[0077] The internal control quantity is calculated by the membership degree of the specific control quantity.

[0078] like Figure 3 As shown, the fuzzy convolution controller may include a fuzzification module, a fuzzy inference module, a convolutional layer module, and a defuzzification module. Of course, it may also include other modules, but the embodiments of the present invention do not impose too many restrictions on this.

[0079] Specifically applied to the embodiments of the present invention, the error quantity parameter and the adjustment error quantity parameter are first quantized, that is, the error quantity parameter and the adjustment error quantity parameter are mapped to the predicted numerical level, and the error quantity parameter and the adjustment error parameter are continuously updated to obtain the quantized mapping error quantity parameter and the quantized adjustment error quantity parameter.

[0080] Furthermore, after obtaining the quantization mapping error parameter and the quantization adjustment error parameter, multiple fuzzy subsets can be determined. These fuzzy subsets may include: NB, NM, NS, ZO, PS, PM, PB, where NB represents negative large, NM represents negative medium, NS represents negative small, ZO represents zero, PS represents positive small, PM represents positive medium, and PB represents positive large. A preset inference table is established, and the membership degree of the control quantity is obtained according to the preset inference. This can be achieved through the TRAPMF membership function. This embodiment of the invention does not impose too many restrictions on this. The membership degree of the control quantity is then used as the input of the convolutional layer, and the temperature sensing coefficient is set as the weight coefficient of the convolutional layer to obtain the specific membership degree of the output control quantity. The internal control quantity is calculated through the specific membership degree of the control quantity. The internal control quantity includes a first internal control quantity, a second internal control quantity, and a third internal control quantity. The first internal control quantity is the proportional parameter adjustment quantity △Kp, the second internal control quantity is the integral parameter adjustment quantity △Ki, and the third internal control quantity is the differential parameter adjustment quantity △Kd.

[0081] In a preferred embodiment of the present invention, the internal control quantity includes a first internal control quantity, a second internal control quantity, and a third internal control quantity; the step of calculating the internal control quantity through the membership degree of the specific control quantity includes: performing defuzzification processing on the membership degree of the specific control quantity, such as defuzzification processing by the centroid method, to determine the first internal control quantity, the second internal control quantity, and the third internal control quantity.

[0082] It should be noted that the above steps such as error quantization, error fuzzification, parameter fuzzy inference, and defuzzification can be data processing steps set by those skilled in the art according to the actual situation. The embodiments of the present invention do not impose too many restrictions on this. The innovation of this step is that a convolutional layer is added between the membership degree and the control quantity. By utilizing the characteristics of convolution operation, more complex features are gradually extracted, the number of parameters of the fuzzy inference model is reduced, the computational efficiency is improved, and the risk of overfitting is reduced through weight parameter sharing.

[0083] Specifically, the input to the convolutional layer is the membership degree of the control quantity, and the temperature sensitivity coefficient is used as the weight coefficient of the convolutional layer. The process is repeated until the loss function of the convolutional layer converges, and the output is the membership degree of the specific control quantity after the convolution operation. The convolutional layer may include multiple convolutional kernels. The convolutional kernels slide on the membership degree of the control quantity and perform local operations to extract features. The size, shape and weight coefficients (i.e., temperature sensitivity coefficients) of the convolutional kernels can be determined through the training process. On the other hand, parameters such as the stride, padding and receptive field of the convolutional layer can also be set. This embodiment of the invention does not impose too many restrictions on these parameters.

[0084] Step S106: Control the operation of the DC motor through the internal control quantity.

[0085] After obtaining the first internal control quantity, which is the proportional parameter adjustment quantity △Kp, the second internal control quantity, which is the integral parameter adjustment quantity △Ki, and the third internal control quantity, which is the derivative parameter adjustment quantity △Kd, the initial proportional parameter adjustment quantity Kpo, the initial integral parameter adjustment quantity Kio, and the initial derivative parameter adjustment quantity Kdo output by the PID controller are added to the proportional parameter adjustment quantity △Kp, the integral parameter adjustment quantity △Ki, and the derivative parameter adjustment quantity △Kd, respectively, to obtain the proportional parameter adjustment quantity Kp, the integral parameter adjustment quantity Ki, and the derivative parameter adjustment quantity Kd. The speed of the DC motor is controlled according to the proportional parameter adjustment quantity Kp, the integral parameter adjustment quantity Ki, and the derivative parameter adjustment quantity Kd. This embodiment of the invention does not impose too many limitations on this.

[0086] In this embodiment of the invention, a DC motor is connected to a PID controller, a temperature sensor, and a position sensor. The operation monitoring and control method of the DC motor speed controller includes: acquiring the rotor temperature observation value of the DC motor's temperature sensor; acquiring ambient temperature data; calculating a temperature sensing coefficient based on the rotor temperature data and ambient temperature data; adjusting the error parameter of the DC motor using the temperature sensing coefficient to obtain an adjustment error parameter; performing fuzzy convolution inference based on the adjustment error parameter and a preset system input to obtain an output internal control quantity; controlling the operation of the DC motor using the internal control quantity, gradually extracting more complex features, reducing the number of parameters in the fuzzy inference model, improving computational efficiency, and reducing the risk of overfitting through weight parameter sharing, effectively reducing the uncertainty and nonlinearity of the brushless DC motor control system. The controller in this embodiment of the invention has better robustness.

[0087] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that this embodiment is not limited to the described order of actions, because according to this embodiment, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this embodiment.

[0088] Reference Figure 4 This diagram illustrates a structural block diagram of an embodiment of a DC motor speed controller operation monitoring and control device. The DC motor is connected to a PID controller, a temperature sensor, and a position sensor. Specifically, it may include the following modules:

[0089] The first acquisition module 301 is used to acquire the rotor temperature observation value of the temperature sensor of the DC motor;

[0090] The second acquisition module 302 is used to acquire ambient temperature data;

[0091] Calculation module 303 is used to calculate the temperature sensitivity coefficient based on the rotor temperature data and ambient temperature data;

[0092] The adjustment module 304 is used to adjust the error parameter of the DC motor through the temperature sensing coefficient to obtain the adjustment error parameter;

[0093] The fuzzy convolution inference module 305 is used to perform fuzzy convolution inference based on the adjustment error parameter and the system preset input to obtain the output internal control quantity.

[0094] The control module 306 is used to control the operation of the DC motor through the internal control quantities.

[0095] Preferably, the temperature sensor includes a first temperature sensor, a second temperature sensor, and a third temperature sensor distributed at different positions on the motor stator; the first acquisition module includes:

[0096] The first acquisition submodule is used to acquire the first temperature data from the first temperature sensor;

[0097] The second acquisition submodule is used to acquire the second temperature data from the second temperature sensor;

[0098] The third acquisition submodule is used to acquire the third temperature data of the third temperature sensor;

[0099] The mean calculation submodule is used to form a temperature set sequence from the first temperature data, the second temperature data, and the third temperature data, perform a threshold limit operation on the temperature set sequence, and calculate the mean to generate the rotor temperature observation value.

[0100] Preferably, the computing module includes:

[0101] The linear regression submodule is used to correct the rotor temperature observation value using the ambient temperature data through linear regression to obtain the temperature sensitivity coefficient.

[0102] 4. The operation monitoring and control device for a DC motor speed controller according to claim 3, characterized in that the adjustment module comprises:

[0103] The error parameter adjustment submodule is used to establish a relationship model between the temperature sensitivity coefficient and the error parameter. Based on the relationship model, the error parameter of the DC motor is adjusted to obtain the adjusted error parameter.

[0104] Preferably, the fuzzy convolutional inference module includes:

[0105] The quantization mapping submodule is used to subtract the system's actual output from the system's preset input to obtain the error parameter. This error parameter and the adjustment error parameter are then mapped to the predicted numerical level to obtain the quantization mapping error parameter and the quantization adjustment error parameter.

[0106] The determination submodule is used to determine the fuzzy subset of the quantization mapping error parameters and the quantization adjustment error parameters;

[0107] The inference submodule is used to obtain the membership degree of the control quantity through a preset inference table based on the membership degree of the quantization mapping error parameter and the quantization adjustment error parameter.

[0108] The submodule is configured to use the membership degree of the control quantity as the input of the convolutional layer and set the temperature sensing coefficient as the weight coefficient of the convolutional layer to obtain the membership degree of the specific control quantity output.

[0109] The calculation submodule is used to calculate the internal control quantity through the membership degree of the specific control quantity.

[0110] Preferably, the internal control quantity includes a first internal control quantity, a second internal control quantity, and a third internal control quantity; the calculation submodule includes:

[0111] The defuzzing processing unit is used to perform defuzzing processing on the membership degree of a specific control quantity to determine the first internal control quantity, the second internal control quantity, and the third internal control quantity.

[0112] Each module in the aforementioned DC motor speed controller's operation monitoring and control system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the DC motor speed controller in hardware form or independent of it, or they can be stored in the memory of the DC motor speed controller in software form, so that the processor can call and execute the corresponding operations of each module.

[0113] The operation monitoring and control system of the DC motor speed controller provided above can be used to execute the operation monitoring and control method of the DC motor speed controller provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0114] In one embodiment, a DC motor speed controller is provided, the internal structure of which can be shown in the following diagram. Figure 5 As shown, the DC motor speed controller includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for monitoring and controlling the operation of a DC motor speed controller. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the DC motor speed controller housing, or an external keyboard, touchpad, or mouse.

[0115] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the DC motor speed controller to which the present application is applied. A specific DC motor speed controller may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0116] In one embodiment, a DC motor speed controller is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above embodiment.

[0117] Obtain the rotor temperature observation value from the temperature sensor of the DC motor;

[0118] Obtain ambient temperature data;

[0119] The temperature sensitivity coefficient is calculated based on the rotor temperature data and the ambient temperature data.

[0120] The error parameters of the DC motor are adjusted by the temperature sensitivity coefficient to obtain the adjustment error parameters;

[0121] Based on the adjustment error parameters and the system preset input, fuzzy convolution inference is performed to obtain the output internal control quantity;

[0122] The operation of the DC motor is controlled by the internal control variables.

[0123] Preferably, the temperature sensor includes a first temperature sensor, a second temperature sensor, and a third temperature sensor distributed at different positions on the motor stator; acquiring the rotor temperature data from the DC motor temperature sensor includes:

[0124] The first temperature data from the first temperature sensor is obtained;

[0125] The second temperature data from the second temperature sensor is obtained;

[0126] The third temperature data from the third temperature sensor is obtained;

[0127] A temperature set sequence is formed by combining the first temperature data, the second temperature data, and the third temperature data. The temperature set sequence is then subjected to a threshold limit operation and the mean is calculated to generate the rotor temperature observation value.

[0128] Preferably, the step of calculating the temperature sensitivity coefficient based on the rotor temperature observation value and ambient temperature data includes:

[0129] The rotor temperature observation value is corrected using the ambient temperature data through linear regression to obtain the temperature sensitivity coefficient.

[0130] Preferably, the step of adjusting the error parameter of the DC motor through the temperature sensitivity coefficient to obtain the adjustment error parameter includes:

[0131] A relationship model between the temperature sensitivity coefficient and the error parameters is established. The error parameters of the DC motor are adjusted according to the relationship model to obtain the adjusted error parameters.

[0132] Preferably, the step of performing fuzzy convolutional inference based on the adjustment error parameter and the system preset input to obtain the output internal control quantity includes:

[0133] The error parameter is obtained by subtracting the actual output from the preset input. This error parameter and the adjustment error parameter are then mapped to the predicted numerical level to obtain the quantization mapping error parameter and the quantization adjustment error parameter.

[0134] Determine the fuzzy subsets of the quantization mapping error parameters and the quantization adjustment error parameters;

[0135] The membership degree of the control quantity is obtained through a preset inference table based on the membership degree of the quantization mapping error parameter and the quantization adjustment error parameter.

[0136] By using the membership degree of the control quantity as the input of the convolutional layer and setting the temperature sensing coefficient as the weight coefficient of the convolutional layer, the specific membership degree of the output control quantity is obtained.

[0137] The internal control quantity is calculated by the membership degree of the specific control quantity.

[0138] Preferably, the internal control quantity includes a first internal control quantity, a second internal control quantity, and a third internal control quantity; the calculation of the internal control quantity through the membership degree of the specific control quantity includes:

[0139] Defuzzification is performed on the membership degree of a specific control variable to determine the first internal control variable, the second internal control variable, and the third internal control variable.

[0140] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above embodiments:

[0141] Obtain the rotor temperature observation value from the temperature sensor of the DC motor;

[0142] Obtain ambient temperature data;

[0143] The temperature sensitivity coefficient is calculated based on the rotor temperature data and the ambient temperature data.

[0144] The error parameters of the DC motor are adjusted by the temperature sensitivity coefficient to obtain the adjustment error parameters;

[0145] Based on the adjustment error parameters and the system preset input, fuzzy convolution inference is performed to obtain the output internal control quantity;

[0146] The operation of the DC motor is controlled by the internal control variables.

[0147] Preferably, the temperature sensor includes a first temperature sensor, a second temperature sensor, and a third temperature sensor distributed at different positions on the motor stator; acquiring the rotor temperature data from the DC motor temperature sensor includes:

[0148] The first temperature data from the first temperature sensor is obtained;

[0149] The second temperature data from the second temperature sensor is obtained;

[0150] The third temperature data from the third temperature sensor is obtained;

[0151] A temperature set sequence is formed by combining the first temperature data, the second temperature data, and the third temperature data. The temperature set sequence is then subjected to a threshold limit operation and the mean is calculated to generate the rotor temperature observation value.

[0152] Preferably, the step of calculating the temperature sensitivity coefficient based on the rotor temperature observation value and ambient temperature data includes:

[0153] The rotor temperature observation value is corrected using the ambient temperature data through linear regression to obtain the temperature sensitivity coefficient.

[0154] Preferably, the step of adjusting the error parameter of the DC motor through the temperature sensitivity coefficient to obtain the adjustment error parameter includes:

[0155] A relationship model between the temperature sensitivity coefficient and the error parameters is established. The error parameters of the DC motor are adjusted according to the relationship model to obtain the adjusted error parameters.

[0156] Preferably, the step of performing fuzzy convolutional inference based on the adjustment error parameter and the system preset input to obtain the output internal control quantity includes:

[0157] The error parameter is obtained by subtracting the actual output from the preset input. This error parameter and the adjustment error parameter are then mapped to the predicted numerical level to obtain the quantization mapping error parameter and the quantization adjustment error parameter.

[0158] Determine the fuzzy subsets of the quantization mapping error parameters and the quantization adjustment error parameters;

[0159] The membership degree of the control quantity is obtained through a preset inference table based on the membership degree of the quantization mapping error parameter and the quantization adjustment error parameter.

[0160] By using the membership degree of the control quantity as the input of the convolutional layer and setting the temperature sensing coefficient as the weight coefficient of the convolutional layer, the specific membership degree of the output control quantity is obtained.

[0161] The internal control quantity is calculated by the membership degree of the specific control quantity.

[0162] Preferably, the internal control quantity includes a first internal control quantity, a second internal control quantity, and a third internal control quantity; the calculation of the internal control quantity through the membership degree of the specific control quantity includes:

[0163] Defuzzification is performed on the membership degree of a specific control variable to determine the first internal control variable, the second internal control variable, and the third internal control variable.

[0164] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0165] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0167] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0169] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0170] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0171] The above provides a detailed description of the operation monitoring and control method, the operation monitoring and control device, the DC motor speed controller, and the computer-readable storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for monitoring and controlling the operation of a DC motor speed controller, characterized in that, The DC motor is connected to a PID controller, a temperature sensor, and a position sensor; the method includes: Obtain the rotor temperature observation value from the temperature sensor of the DC motor; Obtain ambient temperature data; The temperature sensitivity coefficient is calculated based on the rotor temperature observations and ambient temperature data. The error parameters of the DC motor are adjusted by the temperature sensitivity coefficient to obtain the adjustment error parameters; Based on the adjustment error parameters and the system preset input, fuzzy convolution inference is performed to obtain the output internal control quantity; The operation of the DC motor is controlled by the internal control quantity; The step of performing fuzzy convolution inference based on the adjusted error parameters and the system preset input to obtain the output internal control quantity includes: The error parameter is obtained by subtracting the actual output from the preset input. This error parameter and the adjustment error parameter are then mapped to the predicted numerical level to obtain the quantization mapping error parameter and the quantization adjustment error parameter. Determine the fuzzy subsets of the quantization mapping error parameters and the quantization adjustment error parameters; The membership degree of the control quantity is obtained through a preset inference table based on the membership degree of the quantization mapping error parameter and the quantization adjustment error parameter. By using the membership degree of the control quantity as the input of the convolutional layer and setting the temperature sensing coefficient as the weight coefficient of the convolutional layer, the specific membership degree of the output control quantity is obtained. The internal control quantity is calculated by the membership degree of the specific control quantity.

2. The operation monitoring and control method for a DC motor speed controller according to claim 1, characterized in that, The temperature sensor includes a first temperature sensor, a second temperature sensor, and a third temperature sensor distributed at different locations on the motor stator; the acquired rotor temperature observation values ​​from the DC motor temperature sensor include: The first temperature data from the first temperature sensor is obtained; The second temperature data from the second temperature sensor is obtained; The third temperature data from the third temperature sensor is obtained; A temperature set sequence is formed by combining the first temperature data, the second temperature data, and the third temperature data. The temperature set sequence is then subjected to a threshold limit operation and the mean is calculated to generate the rotor temperature observation value.

3. The operation monitoring and control method for a DC motor speed controller according to claim 2, characterized in that, The temperature sensitivity coefficient is calculated based on the rotor temperature observations and ambient temperature data, including: The rotor temperature observation value is corrected using the ambient temperature data through linear regression to obtain the temperature sensitivity coefficient.

4. The operation monitoring and control method for a DC motor speed controller according to claim 3, characterized in that, The step of adjusting the error parameter of the DC motor through the temperature sensitivity coefficient to obtain the adjustment error parameter includes: A relationship model between the temperature sensitivity coefficient and the error parameters is established. The error parameters of the DC motor are adjusted according to the relationship model to obtain the adjusted error parameters.

5. The operation monitoring and control method for a DC motor speed controller according to claim 1, characterized in that, The internal control quantities include a first internal control quantity, a second internal control quantity, and a third internal control quantity; the calculation of the internal control quantity through the membership degree of the specific control quantity includes: Defuzzification is performed on the membership degree of a specific control variable to determine the first internal control variable, the second internal control variable, and the third internal control variable.

6. A monitoring and control device for the operation of a DC motor speed controller, characterized in that, The DC motor is connected to a PID controller, a temperature sensor, and a position sensor; the device includes: The first acquisition module is used to acquire the rotor temperature observation value of the temperature sensor of the DC motor; The second acquisition module is used to acquire ambient temperature data; The calculation module is used to calculate the temperature sensitivity coefficient based on the rotor temperature observation value and the ambient temperature data; The adjustment module is used to adjust the error parameters of the DC motor through the temperature sensing coefficient to obtain the adjustment error parameters; The fuzzy convolution inference module is used to perform fuzzy convolution inference based on the adjustment error parameter and the system preset input to obtain the output internal control quantity. A control module is used to control the operation of the DC motor through the internal control quantities; The step of performing fuzzy convolution inference based on the adjusted error parameters and the system preset input to obtain the output internal control quantity includes: The error parameter is obtained by subtracting the actual output from the preset input. This error parameter and the adjustment error parameter are then mapped to the predicted numerical level to obtain the quantization mapping error parameter and the quantization adjustment error parameter. Determine the fuzzy subsets of the quantization mapping error parameters and the quantization adjustment error parameters; The membership degree of the control quantity is obtained through a preset inference table based on the membership degree of the quantization mapping error parameter and the quantization adjustment error parameter. By using the membership degree of the control quantity as the input of the convolutional layer and setting the temperature sensing coefficient as the weight coefficient of the convolutional layer, the specific membership degree of the output control quantity is obtained. The internal control quantity is calculated by the membership degree of the specific control quantity.

7. The operation monitoring and control device for a DC motor speed controller according to claim 6, characterized in that, The temperature sensor includes a first temperature sensor, a second temperature sensor, and a third temperature sensor distributed at different locations on the motor stator; the first acquisition module includes: The first acquisition submodule is used to acquire the first temperature data from the first temperature sensor; The second acquisition submodule is used to acquire the second temperature data from the second temperature sensor; The third acquisition submodule is used to acquire the third temperature data of the third temperature sensor; The mean calculation submodule is used to form a temperature set sequence from the first temperature data, the second temperature data, and the third temperature data, perform a threshold limit operation on the temperature set sequence, and calculate the mean to generate the rotor temperature observation value.

8. A DC motor speed controller, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the operation monitoring and control method for the DC motor speed controller as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the operation monitoring and control method for the DC motor speed controller as described in any one of claims 1 to 5.

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