Operation monitoring control method and device of direct current motor speed regulator

Abstract

The embodiment of the invention provides an operation monitoring control method and device for a direct-current motor speed regulator. The method comprises the following steps: acquiring a rotor temperature observation value of a temperature sensor of a direct-current motor; acquiring environment temperature data; calculating a temperature sensing coefficient according to the rotor temperature data and the environment temperature data; adjusting an error parameter of the direct current motor through the temperature sensing coefficient to obtain an adjusted error parameter; performing fuzzy convolution reasoning according to the adjustment error parameter and a system preset input quantity to obtain an output internal control quantity; the operation of the direct current motor is controlled through the internal control quantity, more complex features are extracted step by step, the number of parameters of a fuzzy reasoning model is reduced, the calculation efficiency is improved, the risk of overfitting is reduced through weight parameter sharing, the uncertainty and nonlinearity of a brushless direct current motor control system are effectively reduced, and the control precision of the brushless direct current motor is improved. The controller in the embodiment of the invention has better robustness.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an operation monitoring and control method of a DC motor speed regulator, an operation monitoring and control device of a DC motor speed regulator, a DC motor speed regulator and a computer-readable storage medium. Background Art

[0002] A DC motor is a rotating motor that converts DC power into mechanical energy or mechanical energy into DC power. It mainly includes a stator and a rotor. The stator is the stationary part, which consists of the main magnetic poles, commutation poles, a frame, an end cover, and a brush device. The rotor is the rotating part, which consists of a shaft, an armature core, an armature winding, a commutator, and a fan.

[0003] DC motors are generally regulated by PID controllers. PID speed regulation has been widely used in industrial automation and precision control. The performance of PID controllers depends on the precise setting of its three parameters (proportional, integral, and differential). The setting process of these parameters often requires professional knowledge and experience, and the parameters may need to be readjusted for different motor and load characteristics. PID controllers have limited adaptability when facing nonlinear systems or systems with large parameter changes. For DC motor systems with complex dynamic characteristics, PID controllers may not provide the best speed regulation performance. Summary of the invention

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

[0005] In order to solve the above problems, an embodiment of the present invention discloses an operation monitoring and control method of a DC motor speed regulator, wherein the DC motor is connected to a PID controller, a temperature sensor and a position sensor; the method comprises:

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

[0007] Get the ambient temperature data;

[0008] Calculate the temperature sensitivity coefficient according to the rotor temperature data and the ambient temperature data;

[0009] The error parameter of the DC motor is adjusted by the temperature sensitivity coefficient to obtain an adjusted error parameter;

[0010] Perform fuzzy convolution reasoning according to the adjustment error amount parameter and the system preset input amount to obtain the output internal control amount;

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

[0012] Preferably, the temperature sensor comprises a first temperature sensor, a second temperature sensor and a third temperature sensor distributed at different positions of the motor stator; the rotor temperature data of the temperature sensor of the DC motor is obtained, including:

[0013] Acquire first temperature data of the first temperature sensor;

[0014] Acquiring second temperature data of the second temperature sensor;

[0015] Acquiring third temperature data of the third temperature sensor;

[0016] A temperature set sequence is formed by the first temperature data, the second temperature data and the third temperature data, and a threshold limiting operation is performed on the temperature set sequence and a mean value is calculated to generate a rotor temperature observation value.

[0017] Preferably, the temperature sensitivity coefficient is calculated according to the rotor temperature observation value and the ambient temperature data, including:

[0018] The rotor temperature observation value is corrected by the ambient temperature data in a linear regression manner to obtain a temperature sensitivity coefficient.

[0019] Preferably, the step of adjusting the error parameter of the DC motor by the temperature sensitivity coefficient to obtain the adjusted error parameter comprises:

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

[0021] Preferably, the fuzzy convolution reasoning is performed according to the adjustment error amount parameter and the system preset input amount to obtain the output internal control amount, including:

[0022] Subtracting the actual output of the system from the preset input of the system to obtain an error parameter, mapping the error parameter and the adjustment error parameter to the predicted digital level to obtain a quantized mapped error parameter and a quantized adjustment error parameter;

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

[0024] Obtaining the control amount membership through a preset inference table according to the membership of the quantization mapping error parameter and the quantization adjustment error parameter;

[0025] Using the control amount membership as the input of the convolution layer and setting the temperature sensitivity coefficient as the weight coefficient of the convolution layer to obtain the output specific control amount membership;

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

[0027] Preferably, the internal control amount includes a first internal control amount, a second internal control amount and a third internal control amount; the internal control amount is obtained by calculating the membership degree of the specific control amount, including:

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

[0029] The embodiment of the present invention discloses an operation monitoring and control device for a DC motor speed regulator, wherein the DC motor is connected to a PID controller, a temperature sensor and a position sensor; the device comprises:

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

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

[0032] A calculation module, used for calculating a temperature sensitivity coefficient according to the rotor temperature data and the ambient temperature data;

[0033] An adjustment module, used for adjusting an error parameter of the DC motor by using the temperature sensitivity coefficient to obtain an adjusted error parameter;

[0034] A fuzzy convolution inference module, used for performing fuzzy convolution inference according to the adjustment error amount parameter and the system preset input amount to obtain an output internal control amount;

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

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

[0037] A first acquisition submodule, used to acquire first temperature data from the first temperature sensor;

[0038] A second acquisition submodule, used to acquire second temperature data from the second temperature sensor;

[0039] A third acquisition submodule, used to acquire third temperature data of the third temperature sensor;

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

[0041] The embodiment of the present invention discloses a DC motor speed regulator, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned operation monitoring and control method of the DC motor speed regulator when executing the computer program.

[0042] The embodiment of the present invention discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the operation monitoring and control method of the DC motor speed regulator are implemented.

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

[0044] In an embodiment of the present 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 regulator includes: obtaining a rotor temperature observation value of a temperature sensor of the DC motor; obtaining ambient temperature data; calculating a temperature sensitivity coefficient according to the rotor temperature data and the ambient temperature data; adjusting an error parameter of the DC motor by the temperature sensitivity coefficient to obtain an adjusted error parameter; performing fuzzy convolution reasoning according to the adjusted error parameter and a preset input of the system to obtain an output internal control quantity; controlling the operation of the DC motor by the internal control quantity, gradually extracting more complex features, reducing the number of parameters of the fuzzy reasoning model, improving calculation efficiency, and reducing the risk of overfitting by sharing weight parameters, effectively reducing the uncertainty and nonlinearity of the brushless DC motor control system, and the controller in the embodiment of the present invention has better robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

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

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

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

[0050] Figure 5 The figure is an internal structure diagram of a DC motor speed regulator according to an embodiment. DETAILED DESCRIPTION

[0051] In order to make the technical problems, technical solutions and beneficial effects solved by the embodiments of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0052] In a core concept of an embodiment of the present invention, temperature is an important factor in the durability of motor operation, especially for a brushless DC motor using electronic commutation, because it has a large number of electronic components. Considering the influence of temperature is one of the important innovations of the PID control, regulation, monitoring and control of the embodiment of the present invention. Specifically, the temperature set sequence is subjected to threshold limiting operation and mean calculation to generate rotor temperature observation values, which are corrected by the ambient temperature data using linear regression to obtain the temperature sensitivity coefficient. On the other hand, a convolution layer is added between parameter fuzzy reasoning and defuzzification to reduce the number of parameters of the fuzzy reasoning model, improve the model calculation efficiency, and reduce the uncertainty and nonlinearity of the brushless DC motor control system.

[0053] Reference Figure 1 , shows a schematic diagram of an embodiment of a method for monitoring and controlling the operation of a DC motor speed regulator according to an embodiment of the present invention, wherein the DC motor is connected to a PID controller, a temperature sensor and a position sensor; the method may specifically include the following steps:

[0054] Step S101, obtaining a rotor temperature observation value of a temperature sensor of a DC motor;

[0055] In the embodiment of the present invention, the type of the 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 is used to calculate the error parameter e according to the difference between the actual output quantity Y(t) of the system and the preset input quantity X(t) of the system, receive the error parameter to obtain the output proportional parameter adjustment quantity △Kp, integral parameter adjustment quantity △Ki, and differential parameter adjustment quantity △Kd; the proportional parameter adjustment quantity △Kp, integral parameter adjustment quantity △Ki, and differential parameter adjustment quantity △Kd are input to the PID controller to obtain the output control quantity U(t) at time t, and the operation of the DC motor is controlled by the control quantity U(t), wherein the adjusted error parameter e is related to the temperature data of the temperature sensor.

[0056] In the embodiment of the present invention, the specific structure of the brushless DC motor may include a motor rotor made of permanent magnetic material, a motor stator with a coil winding, a power supply, a power electronic device and an integrated circuit, and the power electronic device and the integrated circuit may include the above-mentioned PID controller, temperature sensor, position sensor and electronic commutator, etc. The embodiment of the present invention does not impose too many restrictions on the specific structure of the brushless DC motor;

[0057] Specifically applied to the embodiment of the present invention, the temperature sensor includes a first temperature sensor, a second temperature sensor and a third temperature sensor distributed at different positions of the motor stator; the rotor temperature data of the temperature sensor of the DC motor is obtained, including: obtaining the first temperature data of the first temperature sensor; obtaining the second temperature data of the second temperature sensor; obtaining the third temperature data of the third temperature sensor; forming a temperature set sequence by the first temperature data, the second temperature data and the third temperature data, performing a threshold limiting operation and a mean calculation on the temperature set sequence, and generating a rotor temperature observation value.

[0058] Regarding the installation position of the temperature sensor, it can be evenly distributed at three end points of the same transverse circumferential section of the motor stator, and the three end points are distributed in a triangle to measure the temperature of the rotor. For example, the first temperature data may include (x1, x2, x3, x4, x5, x6, x7), the second temperature data may include (x8, x9, x10, x111, x12, x13, x14), and the third temperature data may include (x15, x16, x17, x18, x19, x20, x21). The temperature set sequence composed of the three is (x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21); perform a threshold limiting operation on the temperature set sequence, wherein the threshold limiting operation refers to deleting temperature data that is higher than a maximum threshold or lower than a minimum 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 calculate the average value of the above 14 temperature data as the rotor temperature observation value.

[0059] The above-mentioned rotor temperature observation value is only an example of an embodiment of the present invention. The rotor temperature observation value can also be obtained by other means, and the embodiment of the present invention does not impose too many restrictions on this. It should be noted that the temperature sensor can include a variety of non-contact temperature sensors, and the embodiment of the present invention does not impose too many restrictions on this.

[0060] Step S102, obtaining ambient temperature data;

[0061] Further applied to the embodiment of the present invention, the ambient temperature data may be the temperature of the external environment in which the DC motor is located, and the temperature of the external environment may be acquired through an external temperature sensor.

[0062] Step S103, calculating a temperature sensitivity coefficient according to the rotor temperature data and the ambient temperature data;

[0063] Further in the embodiment of the present invention, the temperature sensitivity coefficient is calculated based on the rotor temperature observation value and the ambient temperature data, including: using linear regression to correct the rotor temperature observation value through the ambient temperature data to obtain the temperature sensitivity coefficient.

[0064] In an embodiment of the present 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, and λ is determined as the temperature sensitivity coefficient. The temperature sensitivity coefficient represents the correlation coefficient of the rotor temperature observation value infinitely approaching the ambient temperature data. Of course, the above-mentioned linear regression method is only an example of an embodiment of the present invention, and the temperature sensitivity coefficient can also be calculated by other linear regression methods.

[0065] Step S104, adjusting the error parameter of the DC motor by the temperature sensitivity coefficient to obtain an adjusted error parameter;

[0066] In actual application to the present invention, the error parameter e of the DC motor is adjusted by the temperature sensitivity coefficient to obtain the adjusted error parameter, including:

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

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

[0069]

[0070] Among them, θ represents the adjustment error parameter; λ represents the temperature sensitivity coefficient; e t represents the error parameter at time t, t=0, 1, 2, 3·······n, 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) of the system, and the error parameter e of the DC motor is adjusted through the relational model using the temperature sensitivity coefficient to obtain the adjusted error parameter, which refers to an error function that is linearly related to the temperature sensitivity coefficient. The temperature factor is added to the PID control adjustment of the DC motor to improve the durability and durability of the equipment.

[0071] Step S105, performing fuzzy convolution reasoning according to the adjustment error amount parameter and the system preset input amount to obtain an output internal control amount;

[0072] In the embodiment of the present invention, after the adjustment error parameter is obtained, the adjustment error parameter and the system preset input quantity can be subjected to fuzzy convolution reasoning in a fuzzy convolution controller to obtain an output internal control quantity; specifically, the fuzzy convolution reasoning is performed according to the adjustment error parameter and the system preset input quantity to obtain the output internal control quantity, including:

[0073] Subtracting the actual output of the system from the preset input of the system to obtain an error parameter, mapping the error parameter and the adjustment error parameter to the predicted digital level to obtain a quantized mapped error parameter and a quantized adjustment error parameter;

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

[0075] Obtaining the control amount membership through a preset inference table according to the membership of the quantization mapping error parameter and the quantization adjustment error parameter;

[0076] Using the control amount membership as the input of the convolution layer and setting the temperature sensitivity coefficient as the weight coefficient of the convolution layer to obtain the output specific control amount membership;

[0077] The internal control quantity is obtained by calculating 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 reasoning module, a convolution layer module, a defuzzification module, and of course, may also include other modules, and the embodiments of the present invention do not impose too many restrictions on this.

[0079] Specifically applied to the embodiment of the present invention, firstly, the error parameter and the adjustment error parameter are quantized, that is, the error parameter and the adjustment error parameter are mapped to the predicted digital level, and the error parameter and the adjustment error parameter are continuously updated to obtain the quantized mapped error parameter and the quantized adjusted error parameter;

[0080] Further, after obtaining the quantization mapping error parameter and the quantization adjustment error parameter, a plurality of fuzzy subsets can be determined, and the fuzzy subsets may include: NB, NM, NS, ZO, PS, PM, PB, 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 reasoning table is established, and the control amount membership is obtained according to the preset reasoning, which can be implemented by the trapmf membership function, and the embodiment of the present invention does not impose too many restrictions on this, and then the control amount membership is used as the input of the convolution layer and the temperature sensitivity coefficient is set as the weight coefficient of the convolution layer to obtain the output specific control amount membership; the internal control amount is calculated by the specific control amount membership, and the internal control amount includes a first internal control amount, a second internal control amount and a third internal control amount, the first internal control amount is the proportional parameter adjustment amount △Kp, the second internal control amount is the integral parameter adjustment amount △Ki, and the third internal control amount is the differential parameter adjustment amount △Kd;

[0081] In a preferred embodiment of the embodiments 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 internal control quantity is obtained by calculating the membership of the specific control quantity, including: performing defuzzification processing on the membership of the specific control quantity, such as performing 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 of error quantization, error fuzzification, parameter fuzzy reasoning, defuzzification, etc. can be data processing steps set by technical personnel in this field according to actual conditions. The embodiment of the present invention does not impose too many restrictions on this. The innovation of this step is that a convolution layer is added between the membership degree and the control amount. By utilizing the characteristics of the convolution operation, more complex features are gradually extracted, the number of parameters of the fuzzy reasoning model is reduced, the calculation efficiency is improved, and the risk of overfitting is reduced by sharing weight parameters.

[0083] Specifically, the input of the convolution layer is the control quantity membership, and the temperature sensitivity coefficient is used as the weight coefficient of the convolution layer, and reciprocating iterations are performed until the loss function of the convolution layer converges, and the output is a specific control quantity membership after the convolution operation, wherein the convolution layer may include multiple convolution kernels, the convolution kernel slides on the control quantity membership, and performs local operations to extract features, the size, shape and weight coefficient (i.e., temperature sensitivity coefficient) of the convolution kernel can be carried out through the training process, on the other hand, the parameters such as the stride, padding and receptive field of the convolution layer can also be set, and the embodiments of the present invention do not impose too many restrictions on this.

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

[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 differential parameter adjustment quantity △Kd, the initial proportional parameter adjustment quantity Kpo, the initial integral parameter adjustment quantity Kio, and the initial differential 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 differential parameter adjustment quantity △Kd, respectively, to obtain the proportional parameter adjustment quantity Kp, the integral parameter adjustment quantity Ki, and the differential 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 differential parameter adjustment quantity Kd. The embodiments of the present invention do not impose excessive restrictions on this.

[0086] In an embodiment of the present 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 regulator includes: obtaining a rotor temperature observation value of a temperature sensor of the DC motor; obtaining ambient temperature data; calculating a temperature sensitivity coefficient according to the rotor temperature data and the ambient temperature data; adjusting an error parameter of the DC motor by the temperature sensitivity coefficient to obtain an adjusted error parameter; performing fuzzy convolution reasoning according to the adjusted error parameter and a preset input of the system to obtain an output internal control quantity; controlling the operation of the DC motor by the internal control quantity, gradually extracting more complex features, reducing the number of parameters of the fuzzy reasoning model, improving calculation efficiency, and reducing the risk of overfitting by sharing weight parameters, effectively reducing the uncertainty and nonlinearity of the brushless DC motor control system, and the controller in the embodiment of the present invention has better robustness.

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

[0088] Reference Figure 4 , shows a structural block diagram of an embodiment of a DC motor speed regulator operation monitoring control device of this embodiment, wherein the DC motor is connected to a PID controller, a temperature sensor and a position sensor; specifically, the following modules may be included:

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

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

[0091] A calculation module 303, used to calculate a temperature sensitivity coefficient according to the rotor temperature data and the ambient temperature data;

[0092] An adjustment module 304 is used to adjust the error parameter of the DC motor according to the temperature sensitivity coefficient to obtain an adjusted error parameter;

[0093] A fuzzy convolution inference module 305 is used to perform fuzzy convolution inference according to the adjustment error amount parameter and the system preset input amount to obtain an output internal control amount;

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

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

[0096] A first acquisition submodule, used to acquire first temperature data from the first temperature sensor;

[0097] A second acquisition submodule, used to acquire second temperature data from the second temperature sensor;

[0098] A third acquisition submodule, used to acquire third temperature data of the third temperature sensor;

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

[0100] Preferably, the calculation module comprises:

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

[0102] 4. The operation monitoring and control device for a DC motor speed regulator 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, and adjust the error parameter of the DC motor according to the relationship model to obtain the adjusted error parameter.

[0104] Preferably, the fuzzy convolution reasoning module includes:

[0105] A quantization mapping submodule is used to obtain an error parameter by subtracting the actual output of the system from the preset input of the system, and to map the error parameter and the adjustment error parameter to a predicted digital level to obtain a quantization mapping error parameter and a quantization adjustment error parameter;

[0106] A determination submodule, used to determine a fuzzy subset of a quantization mapping error parameter and a quantization adjustment error parameter;

[0107] An inference submodule, used for obtaining the control amount membership through a preset inference table according to the membership of the quantization mapping error parameter and the quantization adjustment error parameter;

[0108] Setting a submodule, used to use the control amount membership as the input of the convolution layer and set the temperature sensitivity coefficient as the weight coefficient of the convolution layer, to obtain the output specific control amount membership;

[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; and the calculation submodule includes:

[0111] The defuzzification processing unit is used to perform defuzzification 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 operation monitoring and control system of the DC motor speed regulator can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of the processor in the DC motor speed regulator in the form of hardware, or can be stored in the memory in the DC motor speed regulator in the form of software, so that the processor can call and execute the operations corresponding to each module.

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

[0114] In one embodiment, a DC motor speed regulator is provided, and its internal structure diagram can be shown as follows: Figure 5 As shown. The DC motor speed regulator includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the DC motor speed regulator is used to provide computing and control capabilities. The memory of the DC motor speed regulator includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the DC motor speed regulator is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for an operation monitoring control device of a DC motor speed regulator is implemented. The display screen of the DC motor speed regulator can be a liquid crystal display screen or an electronic ink display screen, and the input device of the DC motor speed regulator can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the housing of the DC motor speed regulator, or an external keyboard, touchpad or mouse, etc.

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

[0116] In one embodiment, a DC motor speed regulator is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the above embodiment are implemented:

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

[0118] Get the ambient temperature data;

[0119] Calculate the temperature sensitivity coefficient according to the rotor temperature data and the ambient temperature data;

[0120] The error parameter of the DC motor is adjusted by the temperature sensitivity coefficient to obtain an adjusted error parameter;

[0121] Perform fuzzy convolution reasoning according to the adjustment error amount parameter and the system preset input amount to obtain the output internal control amount;

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

[0123] Preferably, the temperature sensor comprises a first temperature sensor, a second temperature sensor and a third temperature sensor distributed at different positions of the motor stator; the rotor temperature data of the temperature sensor of the DC motor is obtained, including:

[0124] Acquire first temperature data of the first temperature sensor;

[0125] Acquiring second temperature data of the second temperature sensor;

[0126] Acquiring third temperature data of the third temperature sensor;

[0127] A temperature set sequence is formed by the first temperature data, the second temperature data and the third temperature data, and a threshold limiting operation is performed on the temperature set sequence and a mean value is calculated to generate a rotor temperature observation value.

[0128] Preferably, the temperature sensitivity coefficient is calculated according to the rotor temperature observation value and the ambient temperature data, including:

[0129] The rotor temperature observation value is corrected by the ambient temperature data in a linear regression manner to obtain a temperature sensitivity coefficient.

[0130] Preferably, the step of adjusting the error parameter of the DC motor by the temperature sensitivity coefficient to obtain the adjusted error parameter comprises:

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

[0132] Preferably, the fuzzy convolution reasoning is performed according to the adjustment error amount parameter and the system preset input amount to obtain the output internal control amount, including:

[0133] Subtracting the actual output of the system from the preset input of the system to obtain an error parameter, mapping the error parameter and the adjustment error parameter to the predicted digital level to obtain a quantized mapped error parameter and a quantized adjustment error parameter;

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

[0135] Obtaining the control amount membership through a preset inference table according to the membership of the quantization mapping error parameter and the quantization adjustment error parameter;

[0136] Using the control amount membership as the input of the convolution layer and setting the temperature sensitivity coefficient as the weight coefficient of the convolution layer to obtain the output specific control amount membership;

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

[0138] Preferably, the internal control amount includes a first internal control amount, a second internal control amount and a third internal control amount; the internal control amount is obtained by calculating the membership degree of the specific control amount, including:

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

[0140] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above embodiment are implemented:

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

[0142] Get the ambient temperature data;

[0143] Calculate the temperature sensitivity coefficient according to the rotor temperature data and the ambient temperature data;

[0144] The error parameter of the DC motor is adjusted by the temperature sensitivity coefficient to obtain an adjusted error parameter;

[0145] Perform fuzzy convolution reasoning according to the adjustment error amount parameter and the system preset input amount to obtain the output internal control amount;

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

[0147] Preferably, the temperature sensor comprises a first temperature sensor, a second temperature sensor and a third temperature sensor distributed at different positions of the motor stator; the rotor temperature data of the temperature sensor of the DC motor is obtained, including:

[0148] Acquire first temperature data of the first temperature sensor;

[0149] Acquiring second temperature data of the second temperature sensor;

[0150] Acquiring third temperature data of the third temperature sensor;

[0151] A temperature set sequence is formed by the first temperature data, the second temperature data and the third temperature data, and a threshold limiting operation is performed on the temperature set sequence and a mean value is calculated to generate a rotor temperature observation value.

[0152] Preferably, the temperature sensitivity coefficient is calculated according to the rotor temperature observation value and the ambient temperature data, including:

[0153] The rotor temperature observation value is corrected by the ambient temperature data in a linear regression manner to obtain a temperature sensitivity coefficient.

[0154] Preferably, the step of adjusting the error parameter of the DC motor by the temperature sensitivity coefficient to obtain the adjusted error parameter comprises:

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

[0156] Preferably, the fuzzy convolution reasoning is performed according to the adjustment error amount parameter and the system preset input amount to obtain the output internal control amount, including:

[0157] Subtracting the actual output of the system from the preset input of the system to obtain an error parameter, mapping the error parameter and the adjustment error parameter to the predicted digital level to obtain a quantized mapped error parameter and a quantized adjustment error parameter;

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

[0159] Obtaining the control amount membership through a preset inference table according to the membership of the quantization mapping error parameter and the quantization adjustment error parameter;

[0160] Using the control amount membership as the input of the convolution layer and setting the temperature sensitivity coefficient as the weight coefficient of the convolution layer to obtain the output specific control amount membership;

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

[0162] Preferably, the internal control amount includes a first internal control amount, a second internal control amount and a third internal control amount; the internal control amount is obtained by calculating the membership degree of the specific control amount, including:

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

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

[0165] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product 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 codes.

[0166] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A 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 device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0169] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0170] Finally, it should be noted that, in this article, 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 any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0171] The above is a detailed introduction to an operation monitoring and control method for a DC motor speed regulator, an operation monitoring and control device for a DC motor speed regulator, a DC motor speed regulator and a computer-readable storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for a general technician in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for monitoring and controlling the operation of a DC motor speed regulator, characterized in that: The DC motor is connected to a PID controller, a temperature sensor and a position sensor; the method comprises: Obtain the rotor temperature observation value of the temperature sensor of the DC motor; Get the ambient temperature data; Calculate the temperature sensitivity coefficient according to the rotor temperature data and the ambient temperature data; The error parameter of the DC motor is adjusted by the temperature sensitivity coefficient to obtain an adjusted error parameter; Perform fuzzy convolution reasoning according to the adjustment error amount parameter and the system preset input amount to obtain the output internal control amount; The operation of the DC motor is controlled by the internal control variable.

2. The operation monitoring and control method of the DC motor speed regulator according to claim 1 is characterized in that: The temperature sensor includes a first temperature sensor, a second temperature sensor and a third temperature sensor distributed at different positions of the motor stator; the rotor temperature data of the temperature sensor of the DC motor is obtained, including: Acquire first temperature data of the first temperature sensor; Acquiring second temperature data of the second temperature sensor; Acquiring third temperature data of the third temperature sensor; A temperature set sequence is formed by the first temperature data, the second temperature data and the third temperature data, and a threshold limiting operation is performed on the temperature set sequence and a mean value is calculated to generate a rotor temperature observation value.

3. The operation monitoring and control method of the DC motor speed regulator according to claim 2 is characterized in that: The temperature sensitivity coefficient is calculated according to the rotor temperature observation value and the ambient temperature data, including: The rotor temperature observation value is corrected by the ambient temperature data in a linear regression manner to obtain a temperature sensitivity coefficient.

4. The operation monitoring and control method of the DC motor speed regulator according to claim 3 is characterized in that: The step of adjusting the error parameter of the DC motor by using the temperature sensitivity coefficient to obtain the adjusted error parameter includes: A relationship model between the temperature sensitivity coefficient and the error parameter is established, and the error parameter of the DC motor is adjusted according to the relationship model to obtain the adjusted error parameter.

5. The operation monitoring and control method of a DC motor speed regulator according to claim 2, characterized in that: The step of performing fuzzy convolution reasoning according to the adjustment error amount parameter and the system preset input amount to obtain the output internal control amount includes: Subtracting the actual output of the system from the preset input of the system to obtain an error parameter, mapping the error parameter and the adjustment error parameter to the predicted digital level to obtain a quantized mapped error parameter and a quantized adjustment error parameter; Determine the fuzzy subsets of the quantization mapping error amount parameters and the quantization adjustment error amount parameters; Obtaining the control amount membership through a preset inference table according to the membership of the quantization mapping error parameter and the quantization adjustment error parameter; Using the control amount membership as the input of the convolution layer and setting the temperature sensitivity coefficient as the weight coefficient of the convolution layer to obtain the output specific control amount membership; The internal control quantity is obtained by calculating the membership degree of the specific control quantity.

6. The operation monitoring and control method of the DC motor speed regulator according to claim 5, characterized in that: The internal control amount includes a first internal control amount, a second internal control amount and a third internal control amount; the internal control amount obtained by calculating the membership degree of the specific control amount includes: Defuzzification processing is performed on the membership degree of the specific control quantity to determine the first internal control quantity, the second internal control quantity and the third internal control quantity.

7. An operation monitoring and control device for a DC motor speed regulator, characterized in that: The DC motor is connected to a PID controller, a temperature sensor and a position sensor; the device comprises: A first acquisition module is used to acquire a rotor temperature observation value of a temperature sensor of the DC motor; The second acquisition module is used to acquire ambient temperature data; A calculation module, used for calculating a temperature sensitivity coefficient according to the rotor temperature data and the ambient temperature data; An adjustment module, used for adjusting an error parameter of the DC motor by using the temperature sensitivity coefficient to obtain an adjusted error parameter; A fuzzy convolution inference module, used for performing fuzzy convolution inference according to the adjustment error amount parameter and the system preset input amount to obtain an output internal control amount; The control module is used to control the operation of the DC motor through the internal control quantity.

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

9. A DC motor speed regulator, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the operation monitoring and control method of the DC motor speed regulator described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the operation monitoring and control method of the DC motor speed regulator according to any one of claims 1 to 6 are implemented.

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