A real-time feedback-based control method and system for the rotational speed of an internal mixer

Abstract

The application relates to the technical field of mixer rotating speed control, and relates to a mixer rotating speed control method and system based on real-time feedback, which comprises the following steps: for each sampling period in a sampling period set, the following operations are performed: data of a mixer to be controlled is collected to obtain a current pulse value, an adjacent previous pulse value is obtained, a mixer rotating speed value is calculated based on a pulse difference value and the sampling period, fuzzy calculation is performed based on the mixer rotating speed value and a target rotating speed value to obtain a fuzzy rotating speed error and a fuzzy error change rate, a PID control amount is calculated based on the fuzzy rotating speed error and the fuzzy error change rate, rotating speed control is performed on the mixer to be controlled to obtain a controlled mixer, the controlled mixers are integrated to obtain a rotating speed control mixer, and the mixer based on the rotating speed control is used to complete the mixer rotating speed control based on the real-time feedback. The application can realize automatic adaptation of control parameters to working condition changes.

Description

Technical Field

[0001] This invention relates to the field of internal mixer speed control technology, and in particular to a method and system for internal mixer speed control based on real-time feedback. Background Technology

[0002] An internal mixer, also known as a closed-loop rubber mixing mill, is an intermittent plasticizing and mixing equipment with a rotor of a specific shape. It is mainly used for the mixing and processing of polymer materials such as rubber and plastics. Internal mixer speed control refers to the precise control of the rotor's rotation speed by adjusting the speed of the drive motor to meet the mixing requirements of different rubber compound formulations and different process stages.

[0003] Early internal mixers used fixed-speed motors, with fixed and non-adjustable speeds, making it difficult to adapt to the process requirements of different rubber compound formulations. With the development of frequency converter technology, modern internal mixers generally use frequency converters to achieve stepless speed regulation, significantly improving process adaptability. However, existing speed control methods still have the following technical shortcomings: First, most systems use open-loop control or conventional PID closed-loop control, with long sampling periods, failing to respond in real time to sudden load changes during material feeding, resulting in significant speed drops and excessively long recovery times. Second, fixed PID parameters are difficult to adapt to the nonlinear and time-varying characteristics of the internal mixer at different stages such as feeding, mixing, and discharging. When the rubber compound formulation or operating conditions change, parameters need to be manually readjusted, which is complex and results in unstable control performance. Therefore, how to achieve automatic adaptation of control parameters to changes in operating conditions is an urgent technical problem to be solved. Summary of the Invention

[0004] This invention provides a method for controlling the speed of an internal mixer based on real-time feedback and a computer-readable storage medium. Its main purpose is to achieve automatic adaptation of control parameters to changes in operating conditions.

[0005] To achieve the above objectives, the present invention provides a method for controlling the speed of an internal mixer based on real-time feedback, comprising:

[0006] Once the internal mixer to be controlled is identified, the sampling period set and target speed value are set. For each sampling period in the sampling period set, the following operations are performed:

[0007] Data is collected from the internal mixer to be controlled according to the sampling period to obtain the current pulse value, acquire the adjacent preceding pulse value, and calculate the pulse difference based on the current pulse value and the adjacent preceding pulse value.

[0008] The internal mixer speed value is calculated based on the pulse difference and sampling period. Fuzzy calculation is performed based on the internal mixer speed value and the target speed value to obtain the fuzzy speed error and the fuzzy error change rate.

[0009] PID control input is calculated based on fuzzy rotational speed error and fuzzy error change rate.

[0010] The control increment is calculated based on the PID control quantity and the preset pre-cycle control quantity, and the frequency converter adjustment command is sent based on the control increment.

[0011] The inverter adjustment command is sent to the pre-built inverter to obtain the adjusted output frequency, and the actual motor speed is calculated based on the adjusted output frequency.

[0012] Based on the actual speed of the motor, the speed of the internal mixer to be controlled is controlled to obtain the regulated internal mixer;

[0013] Take the already regulated internal mixer as the internal mixer to be controlled, and return to the step of collecting data from the internal mixer to be controlled according to the sampling period, until all the sampling periods in the sampling period set have been completed.

[0014] By integrating the already regulated internal mixer, a speed-controlled internal mixer is obtained, and speed control of the internal mixer based on real-time feedback is completed based on the speed-controlled internal mixer.

[0015] Optionally, the step of performing fuzzification calculation based on the internal mixer speed value and the target speed value to obtain the fuzzified speed error and the fuzzification error change rate includes:

[0016] The difference between the target speed value and the internal mixer speed value is calculated to obtain the speed error value;

[0017] Obtain adjacent preceding error values, and calculate the error change rate based on adjacent preceding error values ​​and speed error values;

[0018] Set the error quantization factor and the error rate of change quantization factor;

[0019] The fuzzy speed error is obtained by multiplying the speed error value and the error quantization factor. The fuzzy error change rate is obtained by multiplying the error change rate and the error change rate quantization factor.

[0020] Optionally, the setting of the error quantization factor and the error change rate quantization factor includes:

[0021] Obtain a fuzzy set, and establish a fuzzy control rule set based on the fuzzy set, wherein the fuzzy set includes multiple linguistic variables;

[0022] The number of linguistic variables in the fuzzy set is counted to obtain the upper limit of the actual rotational speed error and the upper limit of the actual error change rate.

[0023] The error quantification factor is obtained by dividing the number of linguistic variables by the upper limit of the actual rotational speed error, and the error change rate quantification factor is obtained by dividing the number of linguistic variables by the upper limit of the actual error change rate.

[0024] Optionally, the step of establishing a fuzzy control rule set based on fuzzy sets includes:

[0025] The fuzzy set central value parameter is obtained by assigning a central value to each linguistic variable in the fuzzy set. The fuzzy set central value parameter includes multiple linguistic variable central values, and there is a one-to-one correspondence between the linguistic variable and the linguistic variable central value.

[0026] The rule form is determined based on the fuzzy rotational speed error and the rate of change of fuzzy error, and a fuzzy control rule set is established based on the rule form and the central values ​​of multiple linguistic variables.

[0027] Optionally, the calculation of the PID control quantity based on the fuzzy speed error and the rate of change of the fuzzy error includes:

[0028] Fuzzy inference is performed based on the fuzzy rotation speed error and the rate of change of fuzzy error to obtain the set of activation rule conclusion parameters;

[0029] The precise output values ​​of the proportional coefficient correction, integral coefficient correction, and differential coefficient correction are calculated based on the parameter set of the activation rule conclusion.

[0030] The pre-constructed proportional-integral-derivative control algorithm is updated with the precise output values ​​of the proportional coefficient correction, integral coefficient correction, and derivative coefficient correction, respectively, to obtain updated control parameters. The updated control parameters include the updated proportional coefficient, updated integral coefficient, and updated derivative coefficient.

[0031] The control parameters are updated to obtain the current set of control coefficients, which includes proportional control coefficients, integral control coefficients, and derivative control coefficients.

[0032] Calculate the PID control input based on the current set of control coefficients.

[0033] Optionally, the fuzzy inference operation based on the fuzzy rotation speed error and the rate of change of the fuzzy error to obtain the activation rule conclusion parameter set includes:

[0034] Linguistic variables are extracted sequentially from the fuzzy set. Based on the extracted linguistic variables, the center value of the target linguistic variable is determined from the center value parameter of the fuzzy set. The error membership degree is calculated based on the center value of the target linguistic variable and the fuzzy rotation speed error.

[0035] Calculate the membership degree of the error change rate based on the central value of the target language variable and the fuzzification error change rate;

[0036] Summarize the error membership degree and the error change rate membership degree respectively to obtain the error membership degree set and the error change rate membership degree set;

[0037] By pairwise combining the error membership set and the error change rate membership set, we obtain the membership set;

[0038] An activation control rule set is identified from the fuzzy control rule set based on the membership degree set. The activation control rule set includes multiple activation control rules, among which the activation control rules include: activation membership degree sets.

[0039] Calculate the rule conclusion value for each activation control rule in the activation control rule set to obtain the activation rule conclusion parameter set.

[0040] Optionally, the formula for calculating the error membership degree is as follows:

[0041]

[0042] in, Indicates the membership degree of the error. Indicates the fuzzy rotation speed error. Represents the central value of the target language variable. This represents the preset membership function span. This represents a function that takes the maximum value.

[0043] Optionally, the step of calculating the rule conclusion value for each activation control rule in the activation control rule set to obtain the activation rule conclusion parameter set includes:

[0044] Activation control rules are extracted sequentially from the activation control rule set. Based on the extracted activation control rules, the rule output parameters are queried from the pre-constructed proportional coefficient correction rule matrix, integral coefficient correction rule matrix, and differential coefficient correction rule matrix, respectively. The rule output parameters include: proportional coefficient correction output value, integral coefficient correction output value, and differential coefficient correction output value.

[0045] Summarize the rule output parameters to obtain the rule output parameter set. Perform a minimum membership value extraction operation on each activation control rule in the activation control rule set to obtain the rule activation intensity value set.

[0046] The rule activation intensity values ​​are extracted from the rule activation intensity value set. Based on the extracted rule activation intensity values, the target rule output parameters are identified from the rule output parameter set. Based on the target rule output parameters and the extracted rule activation intensity values, the activation rule conclusion parameters are calculated. The activation rule conclusion parameters include: the conclusion values ​​of the proportional coefficient correction, the integral coefficient correction, and the differential coefficient correction.

[0047] Summarize the activation rule conclusion parameters to obtain the activation rule conclusion parameter set.

[0048] Optionally, the step of performing a parameter update operation on the updated control parameters to obtain the current set of control coefficients includes:

[0049] The target control coefficients are extracted sequentially from the updated control parameters, where the target control coefficients are the updated proportional coefficient, the updated integral coefficient, or the updated derivative coefficient.

[0050] Based on the target control coefficient, the upper and lower limits of the control coefficient are determined.

[0051] If the target control coefficient is greater than the upper limit of the control coefficient, then the upper limit of the control coefficient shall be used as the target control coefficient.

[0052] If the target control coefficient is less than the lower limit of the control coefficient, then the lower limit of the control coefficient shall be used as the target control coefficient.

[0053] If the target control coefficient is not greater than the upper limit of the control coefficient and not less than the lower limit of the control coefficient, then the target control coefficient shall be used as the normal control coefficient.

[0054] Based on the target control coefficient or normal control coefficient, the current control coefficient is determined, and the current control coefficients are summarized to obtain the current control coefficient set.

[0055] To achieve the above objectives, the present invention also provides a real-time feedback-based internal mixer speed control system, comprising:

[0056] The internal mixer speed acquisition module is used to identify the internal mixer to be controlled, set the sampling period set and the target speed value, and perform the following operations for each sampling period in the sampling period set: collect data from the internal mixer to be controlled according to the sampling period to obtain the current pulse value, obtain the adjacent preceding pulse value, calculate the pulse difference based on the current pulse value and the adjacent preceding pulse value, calculate the internal mixer speed value based on the pulse difference and the sampling period, and perform fuzzification calculation based on the internal mixer speed value and the target speed value to obtain the fuzzified speed error and the fuzzification error change rate;

[0057] The fuzzy PID decision module is used to calculate the PID control quantity based on the fuzzy speed error and the fuzzy error change rate, calculate the control increment based on the PID control quantity and the preset pre-cycle control quantity, and send the frequency converter adjustment command based on the control increment.

[0058] The internal mixer speed control module is used to send the frequency converter adjustment command to the pre-built frequency converter to obtain the adjustment output frequency, calculate the actual motor speed based on the adjustment output frequency, and perform speed control on the internal mixer to be controlled based on the actual motor speed to obtain the regulated internal mixer.

[0059] The internal mixer cycle control module is used to take the regulated internal mixer as the internal mixer to be controlled, return to the step of collecting data of the internal mixer to be controlled according to the sampling period, until all sampling periods in the sampling period set have been completed, integrate the regulated internal mixers to obtain the speed control internal mixer, and complete the internal mixer speed control based on real-time feedback based on the speed control internal mixer.

[0060] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0061] Memory, storing at least one instruction;

[0062] The processor executes the instructions stored in the memory to implement the above-described method for controlling the speed of a mixer based on real-time feedback.

[0063] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned method for controlling the speed of a mixer based on real-time feedback.

[0064] To address the problems described in the background art, this invention identifies the internal mixer to be controlled, sets a sampling period set and a target speed value, and performs the following operations for each sampling period in the sampling period set: Data is collected from the internal mixer to be controlled according to the sampling period to obtain the current pulse value, acquire the adjacent preceding pulse value, and calculate the pulse difference based on the current pulse value and the adjacent preceding pulse value. This invention achieves real-time acquisition of the internal mixer's operating status, ensuring the authenticity and timeliness of the feedback data. By calculating the pulse difference, cumulative pulse errors can be eliminated, highlighting the speed change within a unit period and improving the accuracy of speed detection. The internal mixer speed value is calculated based on the pulse difference and the sampling period. This invention performs fuzzy calculations on the internal mixer's rotational speed and target rotational speed to obtain the fuzzy rotational speed error and its rate of change. It transforms physical pulse signals into intuitive speed control quantities, establishing a mapping relationship between the detection signal and the control target. By using fuzzy processing for the rotational speed error and its rate of change, it weakens system nonlinearity, hysteresis, and noise interference, improving the robustness of the control algorithm. Based on the fuzzy rotational speed error and its rate of change, it calculates the PID control quantity. This invention combines the advantages of fuzzy control and PID control, optimizing dynamic response performance while ensuring steady-state control accuracy. It solves the problem that traditional PID parameters are difficult to adapt to the complex operating conditions of the internal mixer, thus improving... The stability of speed control is achieved by calculating the control increment based on the PID control quantity and the preset pre-cycle control quantity. Based on this control increment, a frequency converter adjustment command is sent. This invention uses control increments instead of directly outputting control quantities, avoiding the impact of sudden control signal changes on the frequency converter and motor, reducing equipment stress and losses. The frequency converter adjustment command is sent to the pre-built frequency converter to obtain the adjusted output frequency. The actual motor speed is calculated based on the adjusted output frequency. This invention uses the output frequency to infer the actual motor speed, forming a closed-loop feedback link, providing a basis for subsequent corrective control. Based on the actual motor speed, speed control is performed on the internal mixer to be controlled, resulting in a regulated internal mixer. This invention provides real-time... The actual rotational speed is used as the basis for closed-loop adjustment, which can promptly correct the deviation between theoretical control commands and actual operation, ensuring that the internal mixer speed approaches the target speed and improving control accuracy. The adjusted internal mixer is then used as the target internal mixer, and the process returns to the step of collecting data from the target internal mixer according to the sampling period, until all sampling periods within the sampling period set have been completed. The adjusted internal mixers are then integrated to obtain the speed-controlled internal mixer. Based on the speed-controlled internal mixer, real-time feedback-based internal mixer speed control is achieved. This invention, through real-time feedback closed-loop control, significantly improves the accuracy, stability, and reliability of internal mixer speed control, meeting the high-precision speed control requirements of the internal mixing process. Therefore, this invention can achieve automatic adaptation of control parameters to changes in operating conditions. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating a real-time feedback-based internal mixer speed control method according to an embodiment of the present invention.

[0066] Figure 2 A functional block diagram of a real-time feedback-based internal mixer speed control system provided in an embodiment of the present invention;

[0067] Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the real-time feedback-based internal mixer speed control method according to an embodiment of the present invention.

[0068] Explanation of reference numerals in the attached figures:

[0069] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0070] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0071] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0072] This application provides a method for controlling the speed of an internal mixer based on real-time feedback. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for controlling the speed of an internal mixer based on real-time feedback can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0073] Reference Figure 1 The diagram shown is a flowchart illustrating a real-time feedback-based internal mixer speed control method according to an embodiment of the present invention. In this embodiment, the real-time feedback-based internal mixer speed control method includes:

[0074] S1. Identify the internal mixer to be controlled, set the sampling period set and target speed value, and perform the following operations for each sampling period in the sampling period set.

[0075] It should be explained that the internal mixer to be controlled is the internal mixer for which closed-loop speed regulation is required in this invention. The sampling period set is a collection of multiple identical sampling time intervals. The sampling period is the time interval for one acquisition, calculation, and output control of the internal mixer speed.

[0076] S2. Collect data from the internal mixer to be controlled according to the sampling period to obtain the current pulse value, acquire the adjacent preceding pulse value, and calculate the pulse difference based on the current pulse value and the adjacent preceding pulse value.

[0077] It should be explained that the data acquisition of the internal mixer under control according to the sampling period refers to the operation of acquiring the pulse signals generated by the rotation of the main shaft of the internal mixer under control through a pulse detection device (such as a magnetoelectric incremental rotary encoder) according to the sampling period. The current pulse value is the value obtained from the pulse detection device at the current sampling time, which reflects the rotational position of the internal mixer shaft. The adjacent preceding pulse value is the real-time count value obtained at the previous adjacent sampling time, which reflects the rotational position of the internal mixer shaft. The pulse difference is the difference between the current pulse value and the adjacent preceding pulse value.

[0078] S3. Calculate the internal mixer speed value based on the pulse difference and sampling period. Perform fuzzification calculation based on the internal mixer speed value and the target speed value to obtain the fuzzified speed error and the fuzzification error change rate.

[0079] It should be explained that the formula for calculating the internal mixer speed value in the step of calculating the internal mixer speed value based on the pulse difference and sampling period is as follows:

[0080]

[0081] in, This indicates the rotational speed of the internal mixer. Indicates the pulse difference. Indicates the sampling period. This refers to the encoder resolution. Encoder resolution is the total number of pulses output by the encoder (such as a magnetoelectric incremental rotary encoder) per revolution of the internal mixer spindle. The formula for calculating the internal mixer speed value in this invention, combined with the encoder resolution, determines the actual number of spindle revolutions. Then, the sampling period is converted into a time base, and finally, the speed value expressed in standard units (i.e., the internal mixer speed value) is obtained through proportional calculation (i.e., the formula for calculating the internal mixer speed value). The numerator of the formula is composed of the pulse difference multiplied by 60, which is used to convert the pulse quantity per unit time into the corresponding total number of pulses per minute. The denominator is composed of the encoder resolution multiplied by the sampling period, which is used to convert the pulse quantity into the actual number of spindle revolutions. The overall calculation can accurately obtain the actual number of revolutions per minute of the internal mixer spindle.

[0082] In detail, the fuzzification calculation based on the internal mixer speed value and the target speed value to obtain the fuzzified speed error and the fuzzification error change rate includes:

[0083] The difference between the target speed value and the internal mixer speed value is calculated to obtain the speed error value;

[0084] Obtain adjacent preceding error values, and calculate the error change rate based on adjacent preceding error values ​​and speed error values;

[0085] Set the error quantization factor and the error rate of change quantization factor;

[0086] The fuzzy speed error is obtained by multiplying the speed error value and the error quantization factor. The fuzzy error change rate is obtained by multiplying the error change rate and the error change rate quantization factor.

[0087] It should be explained that the target speed is the pre-set speed value that the internal mixer is expected to reach and operate stably. The speed error value is the difference obtained by subtracting the target speed value from the internal mixer speed value. The adjacent preceding error is the speed error value calculated in the previous adjacent sampling period. The step of calculating the error change rate based on the adjacent preceding error value and the speed error value is as follows: subtract the adjacent preceding error value from the speed error value, and then divide the difference by the sampling period to obtain the error change rate. The detailed steps for setting the error quantization factor and the error change rate quantization factor will be given later. The fuzzy speed error is the value obtained by multiplying the speed error value and the error quantization factor. The fuzzy error change rate is the value obtained by multiplying the error change rate and the error change rate quantization factor.

[0088] It should be noted that, in order to solve the problems that the actual physical speed deviation is inconsistent with the fixed numerical range required by the fuzzy domain and cannot be directly used for fuzzy inference, and that a single error cannot reflect the speed fluctuation trend, the present invention first needs to clarify the degree of deviation between the internal mixer speed value to be controlled and the target speed value. Therefore, by performing a difference calculation between the internal mixer speed value to be controlled and the target speed value, the actual deviation of the current speed is reflected, and the speed error value is obtained. Meanwhile, to further understand the dynamic change pattern of the speed deviation and avoid the drawback of relying solely on instantaneous errors to determine whether the deviation is expanding, shrinking, or stabilizing, it is necessary to obtain the adjacent preceding error value calculated at the previous sampling moment. Based on this historical error data and the current speed error value, the difference is calculated again to characterize the rate of change and development trend of the speed deviation, thereby obtaining the error change rate. Since the speed error value and error change rate in actual engineering scenarios are actual engineering values ​​with physical dimensions, their numerical range does not match the fixed numerical range used by fuzzy inference. They cannot be directly substituted into the fuzzy control algorithm for calculation, which would lead to the fuzzy control logic failing to operate normally or a decrease in adjustment accuracy. Therefore, it is necessary to first obtain the error quantization factor and error change rate quantization factor adapted to fuzzy inference, and then multiply the speed error value with the error quantization factor, while simultaneously quantizing the error change rate. Factors are multiplied accordingly, and through the scaling effect of quantization factors, the actual physical deviation and deviation change are uniformly normalized and mapped to the standard domain of discourse that the fuzzy control algorithm can recognize and process. Finally, the fuzzy speed error and fuzzy error change rate that adapt to the input requirements of the fuzzy control algorithm are obtained. This invention uses instantaneous speed difference calculation to locate the static deviation between the internal mixer speed value and the target speed, and uses adjacent time error difference calculation to capture the dynamic change trend of speed deviation, taking into account both static deviation and dynamic trend dimensions. Then, through error quantization factors and error change rate quantization factors, the actual engineering physical quantity and the fuzzy control input quantity are accurately matched, avoiding the control distortion problem caused by the mismatch between the actual engineering quantity and the fuzzy domain. This can not only improve the adaptability of the fuzzy control algorithm to the internal mixer, but also optimize the accuracy and response rationality of speed adjustment, effectively ensuring the control accuracy of the internal mixer speed control process.

[0089] For example, the numerical range of the actual rotational speed error is [-30 rpm, +30 rpm], and the fixed numerical range of the fuzzy universe of discourse is [-6, +6].

[0090] Specifically, the setting of the error quantization factor and the error change rate quantization factor includes:

[0091] Obtain a fuzzy set, and establish a fuzzy control rule set based on the fuzzy set, wherein the fuzzy set includes multiple linguistic variables;

[0092] The number of linguistic variables in the fuzzy set is counted to obtain the upper limit of the actual rotational speed error and the upper limit of the actual error change rate.

[0093] The error quantification factor is obtained by dividing the number of linguistic variables by the upper limit of the actual rotational speed error, and the error change rate quantification factor is obtained by dividing the number of linguistic variables by the upper limit of the actual error change rate.

[0094] It should be explained that linguistic variables are variables whose states are described using words or sentences in natural language, used to convert precise numerical values ​​into linguistic values ​​that fuzzy logic can recognize. For example, a fuzzy set might be {NB, NM, NS, ZO, PS, PM, PB}, where NB represents negative large (negative large means the described linguistic variable is in a negative direction and has a large absolute value), NM represents negative medium (negative medium means the described linguistic variable is in a negative direction and has a medium absolute value), NS represents negative small (negative small means the described linguistic variable is in a negative direction and has a small absolute value), ZO represents zero, PS represents positive small (positive small means the described linguistic variable is in a positive direction and has a small absolute value), PM represents positive medium (positive medium means the described linguistic variable is in a positive direction and has a medium absolute value), and PB represents positive large (positive large means the described linguistic variable is in a positive direction and has a large absolute value). For example, if the target speed is 40 r / min, and the internal mixer speed reaches 50 r / min or higher, with a speed error of -10 r / min, then the linguistic variable is in a negative large state. If the target speed is 40 r / min, and the internal mixer speed is between 43 and 45 r / min, with a speed error of -3 to -5 r / min, then the linguistic variable is in a negative medium state. If the target speed is 40 r / min, and the internal mixer speed is between 40.5 and 42 r / min, with a speed error of -0.5 to -2 r / min, then the linguistic variable is in a negative small state. If the target speed is 40 r / min, and the internal mixer speed is between 38-39.5 r / min with a speed error of +0.5 to +2 r / min, then the linguistic variable is in a small positive state. If the target speed is 40 r / min, and the internal mixer speed is between 35-37 r / min with a speed error of +3 to +5 r / min, then the linguistic variable is in a medium positive state. If the target speed is 40 r / min, and the internal mixer speed is below 30 r / min with a speed error above +10 r / min, then the linguistic variable is in a large positive state. The number of linguistic variables is the number of linguistic variables in the fuzzy set obtained by fuzzy partitioning the fuzzy speed error and the fuzzy error change rate. For example, if the fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, then the number of linguistic variables is 7. The upper limit of the actual speed error is the maximum deviation between the actual speed and the target speed of the internal mixer during operation. The upper limit of the actual error rate of change is the maximum rate of change of the rotational speed error over time. The error quantization factor is a proportionality coefficient calculated based on the number of linguistic variables and the upper limit of the actual rotational speed error; it is used to map the actual rotational speed error to the fuzzy domain. The error rate of change quantization factor is also a proportionality coefficient calculated based on the number of linguistic variables and the upper limit of the actual error rate of change; it is used to map the actual error rate of change to the fuzzy domain.The method for obtaining the upper limit of the actual speed error and the upper limit of the actual error change rate is as follows: under various typical working conditions such as no-load, feeding, and normal mixing of the internal mixer, the actual speed and the target speed are collected in real time, the speed error at each moment is calculated, the maximum value of the speed error that occurs during the operation is recorded, and this maximum value is taken as the upper limit of the actual speed error. Under the above-mentioned typical working conditions, the speed error at adjacent sampling moments is collected continuously, the ratio of the difference between adjacent errors to the sampling period is calculated, the error change rate at each moment is obtained, the maximum value of the error change rate that occurs during the operation is recorded, and this maximum value is taken as the upper limit of the actual error change rate.

[0095] In detail, the establishment of a fuzzy control rule set based on fuzzy sets includes:

[0096] The fuzzy set central value parameter is obtained by assigning a central value to each linguistic variable in the fuzzy set. The fuzzy set central value parameter includes multiple linguistic variable central values, and there is a one-to-one correspondence between the linguistic variable and the linguistic variable central value.

[0097] The rule form is determined based on the fuzzy rotational speed error and the rate of change of fuzzy error, and a fuzzy control rule set is established based on the rule form and the central values ​​of multiple linguistic variables.

[0098] It should be explained that the step of assigning center values ​​to each linguistic variable in the fuzzy set to obtain the fuzzy set center value parameter is as follows: determine the universe of discourse of the fuzzified input variable (such as fuzzification speed error and fuzzification error change rate), divide the entire universe of discourse interval into uniform intervals according to the determined universe of discourse interval and the number of linguistic variables, calculate the corresponding value in each interval, and the value is the linguistic variable center value. Summarize the linguistic variable center values ​​to obtain the fuzzy set center value parameter.

[0099] For example, the universe of discourse range is [-3, 3]. Based on the universe of discourse range, the total length of the universe is determined to be 6, the left boundary of the universe is -3, and the right boundary of the universe is 3. Based on the fuzzy set, the number of linguistic variables is determined to be 7. When using a uniform partitioning method, the central values ​​of the linguistic variables are evenly distributed across the universe of discourse, and the distance between adjacent central values ​​is 6 / (7-1) = 1. Then, starting from the left boundary of the universe of discourse at -3, the distance between {NB, NM, NS, ZO, PS, PM, PB} increases sequentially, resulting in:

[0100] The central value of the language variable NB is -3;

[0101] The center value of a linguistic variable with the value NM is the left boundary of the universe of discourse plus 1, multiplied by the distance between adjacent center values, which is -2.

[0102] The center value of a linguistic variable with the value NS is the left boundary of the universe of discourse plus 2 times the distance between adjacent center values, and is -1.

[0103] The linguistic variable ZO has a center value of the left boundary of the universe of discourse plus 3 times the distance between adjacent center values, and is 0.

[0104] The linguistic variable PS has its center value as the left boundary of the universe of discourse plus 4 times the distance between adjacent center values, which is 1.

[0105] The linguistic variable PM has its center value as the left boundary of the universe of discourse plus 5 multiplied by the distance between adjacent center values, which is 2.

[0106] The linguistic variable PB has a center value of the left boundary of the universe of discourse plus 6 multiplied by the distance between adjacent center values ​​of 3.

[0107] Understandably, the central value of a linguistic variable is a numerical value calculated for each linguistic variable used to describe the deviation state within the fuzzy universe of discourse. The central value of a linguistic variable is used to determine the position of that linguistic variable within the entire fuzzy universe of discourse. The method for assigning central values ​​to each linguistic variable in the fuzzy set described in this invention is prior art and will not be elaborated here. The rule form determined based on the fuzzy rotational speed error and the fuzzy error change rate uses the rotational speed error as the antecedent of the fuzzy control rule and the error change rate as the consequent. It adopts a multi-input single-output or multi-input multi-output rule structure to establish a conditional judgment form (i.e., rule form) that states "if the rotational speed error is a certain linguistic variable and the error change rate is a certain linguistic variable, then the output is a certain correction amount." The steps for establishing a fuzzy control rule set based on the rule form and the central values ​​of multiple linguistic variables are as follows: First, determine the total number of rules based on the number of linguistic variables for the input variables fuzzy speed error and fuzzy error change rate. A 7×7 matrix of 49 rules is used, constructed as a 7x7 matrix. The 7 rows correspond to the 7 linguistic variables for the fuzzy speed error (NB, NM, NS, ZO, PS, PM, PB), and the 7 columns correspond to the 7 linguistic variables for the fuzzy error change rate. Both rows and columns are arranged in ascending order of the linguistic variable central values. Then, determine the control trend based on the linguistic variable central values. Specifically, determine the error state (negative large, negative medium, negative small, zero, positive small, positive medium, positive large) based on the central value of the fuzzy error error, and determine the error change rate state based on the central value of the fuzzy error change rate. Finally, combining the control law of speed closed-loop regulation with engineering experience, assign output linguistic variables to each input combination, including proportional coefficient correction, integral coefficient correction, and derivative coefficient correction. Each output is also represented by 7 linguistic variables. When allocating outputs, a pre-defined basic control law is followed. For example, when the error is large and the error change rate is also large, the proportional coefficient needs to be significantly increased to quickly reduce the error; therefore, the proportional coefficient correction is set to a large positive value. When the error is zero and the error change rate is zero, the parameter remains unchanged, and the proportional coefficient correction is set to zero. When the error is large and the error change rate is also large, the proportional coefficient needs to be significantly decreased, and the proportional coefficient correction is set to a large negative value. The allocation trend of the integral coefficient correction is usually opposite to that of the proportional coefficient correction, while the differential coefficient correction focuses on the influence of the error change rate. The above allocation results are filled into each cell of a 7×7 matrix, forming three rule matrices: the proportional coefficient correction rule matrix, the integral coefficient correction rule matrix, and the differential coefficient correction rule matrix. The values ​​1 to 7 in the matrix represent the output linguistic variables NB to PB and their corresponding center values ​​-3 to 3, respectively.Finally, each cell is converted into a complete fuzzy rule statement, namely, "If the speed error value is a certain linguistic variable and the error change rate is a certain linguistic variable, then the output is a certain correction amount." The completeness, continuity, and symmetry of the fuzzy control rule set are verified to ensure that all input combinations have corresponding rule outputs, adjacent rule outputs transition smoothly, and the rules are symmetrical about the zero point. The verified fuzzy control rule set is stored in the controller as an array for subsequent fuzzy inference. The method for establishing a fuzzy control rule set described above has been widely used in the field of industrial process control and is a conventional technique well-known to those skilled in the art; therefore, it will not be elaborated further here.

[0108] S4. Calculate the PID control quantity based on the fuzzy speed error and the rate of change of fuzzy error.

[0109] In detail, the calculation of the PID control quantity based on the fuzzy speed error and the rate of change of the fuzzy error includes:

[0110] Fuzzy inference is performed based on the fuzzy rotation speed error and the rate of change of fuzzy error to obtain the set of activation rule conclusion parameters;

[0111] The precise output values ​​of the proportional coefficient correction, integral coefficient correction, and differential coefficient correction are calculated based on the parameter set of the activation rule conclusion.

[0112] The pre-constructed proportional-integral-derivative control algorithm is updated with the precise output values ​​of the proportional coefficient correction, integral coefficient correction, and derivative coefficient correction, respectively, to obtain updated control parameters. The updated control parameters include the updated proportional coefficient, updated integral coefficient, and updated derivative coefficient.

[0113] The control parameters are updated to obtain the current set of control coefficients, which includes proportional control coefficients, integral control coefficients, and derivative control coefficients.

[0114] Calculate the PID control input based on the current set of control coefficients.

[0115] It should be explained that the detailed steps for performing fuzzy inference based on the fuzzy speed error and the rate of change of the fuzzy error to obtain the activation rule conclusion parameter set will be given later. The steps for calculating the precise output values ​​of the proportional coefficient correction, integral coefficient correction, and derivative coefficient correction based on the activation rule conclusion parameter set are as follows: extract all correction conclusion values ​​and their corresponding rule activation intensity values ​​from the activation rule conclusion parameter set; multiply each correction conclusion value by its corresponding rule activation intensity value and sum them; then divide the sum by the sum of all rule activation intensity values ​​to obtain the precise output values ​​of the proportional coefficient correction, integral coefficient correction, and derivative coefficient correction in sequence. The proportional-integral-derivative (PID) control algorithm is a control algorithm that achieves closed-loop speed control based on the proportional, integral, and derivative adjustment actions. It reduces the deviation between the internal mixer speed and the target speed by dynamically adjusting the speed error value.

[0116] It should be noted that the proportional-integral-derivative (PID) control algorithm includes an initial proportional coefficient, an initial integral coefficient, and an initial derivative coefficient. These initial proportional, integral, and derivative coefficients are pre-set fixed parameters, not corrected by fuzzy inference, and used only for startup or initial operation. The step of updating the pre-constructed PID control algorithm parameters using the precise output values ​​of the proportional coefficient correction, integral coefficient correction, and derivative coefficient correction to obtain updated control parameters is as follows: multiply the precise output value of the proportional coefficient correction by a preset proportional correction coefficient (e.g., 0.1), add it to the initial proportional coefficient to obtain the updated proportional coefficient; multiply the precise output value of the integral coefficient correction by a preset integral correction coefficient (e.g., 0.5), add it to the initial integral coefficient to obtain the updated integral coefficient; and multiply the precise output value of the derivative coefficient correction by a preset derivative correction coefficient (e.g., 0.05), add it to the initial derivative coefficient to obtain the updated derivative coefficient. Detailed steps for performing parameter update operations to obtain the current control coefficient set will be given later. The calculation formula for the PID control quantity based on the current control coefficient set is as follows:

[0117]

[0118] in, This represents the PID control input. This represents the proportional control coefficient. This indicates the speed error value. Represents the integral control coefficient. This indicates the historical speed error value. Represents the differential control coefficient. This indicates the adjacent preceding error. The historical speed error value is the speed error value collected in the past sampling period.

[0119] Importantly, in the above steps of this invention, the actual operating condition deviation is first matched according to the fuzzy control rule set to select effective fuzzy control rules (i.e., activation control rules) that are compatible with the current speed error and error change rate state, while fuzzy control rules that are irrelevant to the current operating condition are ignored, thereby avoiding indiscriminate calculations and improving the pertinence of inference. Then, based on the parameter set of the activation rule conclusion, the precise output values ​​of the proportional, integral, and derivative coefficient correction quantities are calculated respectively. By weighted summation and averaging of the results of each rule, the multiple results obtained from fuzzy inference are merged into a single value that can be directly used for parameter adjustment, thus solving the problem that fuzzy results cannot be directly used for parameter adjustment and ensuring the accuracy and practicality of the correction quantity. Next, the precise output values ​​of the three coefficient correction quantities are used to update the proportional-integral-derivative control algorithm with fixed parameters to obtain the corresponding updated proportional coefficient, updated integral coefficient, and updated derivative coefficient, thereby breaking the limitation of poor adaptability of traditional PID fixed parameters and enabling the parameters to be adjusted in real time according to the speed error and error change rate, adapting to the speed fluctuation requirements of the internal mixer under different operating conditions. Then, a limit-checking parameter update operation (i.e., performing parameter update operations on the updated control parameters to obtain the current control coefficient set) is performed on the updated control parameters to remove parameters that exceed the safe operating range. This results in a current control coefficient set containing effective proportional, integral, and derivative control coefficients. This prevents problems such as system oscillation and adjustment failure caused by excessively large or small parameters, ensuring the stability and reliability of the control process and avoiding equipment operation risks. Finally, the PID control quantity is calculated based on the verified current control coefficient set. The adaptively tuned parameters are substituted into the proportional-integral-derivative control algorithm to output the final control quantity. This process combines the adaptive adjustment advantages of fuzzy control with the stability of PID control. It can achieve dynamic optimization of parameters according to operating conditions and ensure control safety through parameter limit, thereby effectively improving the accuracy and dynamic response capability of the internal mixer speed control and solving the shortcomings of traditional fixed-parameter PID that is difficult to adapt to complex operating conditions.

[0120] In detail, the fuzzy inference operation based on the fuzzy rotation speed error and the rate of change of the fuzzy error yields a set of activation rule conclusion parameters, including:

[0121] Linguistic variables are extracted sequentially from the fuzzy set. Based on the extracted linguistic variables, the center value of the target linguistic variable is determined from the center value parameter of the fuzzy set. The error membership degree is calculated based on the center value of the target linguistic variable and the fuzzy rotation speed error.

[0122] Calculate the membership degree of the error change rate based on the central value of the target language variable and the fuzzification error change rate;

[0123] Summarize the error membership degree and the error change rate membership degree respectively to obtain the error membership degree set and the error change rate membership degree set;

[0124] By pairwise combining the error membership set and the error change rate membership set, we obtain the membership set;

[0125] An activation control rule set is identified from the fuzzy control rule set based on the membership degree set. The activation control rule set includes multiple activation control rules, among which the activation control rules include: activation membership degree sets.

[0126] Calculate the rule conclusion value for each activation control rule in the activation control rule set to obtain the activation rule conclusion parameter set.

[0127] It should be explained that the detailed steps for establishing the fuzzy control rule set will be given later. The target linguistic variable center value is the center value of the extracted linguistic variable corresponding to the fuzzy set center value parameter. Error membership degree is used to characterize the degree to which the fuzzy rotational speed error belongs to the corresponding linguistic variable. Error change rate membership degree is used to characterize the degree to which the fuzzy error change rate belongs to the corresponding linguistic variable. The method for calculating the error change rate membership degree based on the target linguistic variable center value and the fuzzy error change rate is the same as the method for calculating the error membership degree based on the target linguistic variable center value and the fuzzy rotational speed error, and will not be repeated here. The error membership degree set is a set composed of error membership degrees. The error change rate membership degree set is a set composed of error change rate membership degrees. The pairwise combination of the error membership degree set and the error change rate membership degree set is the operation of combining each error membership degree in the error membership degree set with each error change rate membership degree in the error change rate membership degree set. A membership set is a collection of membership groups, where each membership group includes an error membership degree and an error change rate membership degree. An activation control rule set is a collection of activation control rules. An activation control rule is a single, valid rule in the activation control rule set. An activation membership group is the combination of error membership degree and error change rate membership degree corresponding to each activation control rule. The detailed steps for calculating the rule conclusion value for each activation control rule in the activation control rule set to obtain the activation rule conclusion parameter set will be given later.

[0128] For example, the fuzzy rotation speed error is 2.1, the fuzzy error change rate is 3.0, and the membership set is {membership group A: (0.9, 1.0), membership group B: (0.1, 1.0), membership group C: (0.9, 0), membership group D: (0.1, 0)}, where 0.9 in membership group A represents the center, indicating that the fuzzy rotation speed error has a membership of 0.9 to the center, and 1.0 in membership group A represents the large, indicating that the fuzzy error change rate has a membership of 0.9 to the large.

[0129] In membership group B, 0.1 represents positive, indicating that the membership degree of the fuzzy rotation speed error to positive is 0.1. In membership group B, 1.0 represents positive, indicating that the membership degree of the fuzzy error change rate to positive is 1.0.

[0130] In membership group C, 0.9 represents the center, indicating that the fuzzy rotation speed error has a membership degree of 0.9 to the center. In membership group A, 0 indicates that the fuzzy error rate of change does not belong to the linguistic variable.

[0131] In membership group D, 0.1 indicates the center, and the membership degree of the fuzzy rotation speed error to the center is 0.9. In membership group A, 0 indicates that the fuzzy error change rate is not a linguistic variable.

[0132] Based on the membership set, a query is performed from the fuzzy control rule set. If membership set A: (0.9, 1.0) and membership set B: (0.1, 1.0) are activated, then membership set A and membership set B are used as the activation control rule set.

[0133] In detail, the formula for calculating the error membership degree is as follows:

[0134]

[0135] in, Indicates the membership degree of the error. Indicates the fuzzy rotation speed error. Represents the central value of the target language variable. This represents the preset membership function span. This represents a function that takes the maximum value.

[0136] It should be explained that the membership function span is a pre-defined value used to characterize the length of the interval covered by a single linguistic variable in the fuzzy universe of discourse. The membership function span is set as follows: First, determine the overall universe of discourse interval for the fuzzification speed error and the rate of change of the fuzzification error based on requirements (the overall universe of discourse interval is the range of values ​​limited by the fuzzification speed error and the rate of change of the fuzzification error after scaling by the quantization factor). Then, according to the control accuracy requirements, evenly divide the overall universe of discourse interval into several fuzzy subsets. Each fuzzy subset corresponds to a linguistic variable and a target linguistic variable's center value. The absolute value of the difference between two adjacent target linguistic variable center values ​​is used as the range of the interval for a single fuzzy subset. To ensure that adjacent fuzzy subsets can overlap normally for a smooth transition, half of this interval's range is set as the membership function span of the corresponding fuzzy subset; that is, the membership function span is equal to half the difference between the center values ​​of adjacent target linguistic variables. For example, if the center values ​​of two adjacent target language variables are -6 and -4, then the absolute value of the difference between the center values ​​of the two adjacent target language variables is 2. Then, the value 1 obtained by dividing 2 by one half is used as the span of the membership function.

[0137] In detail, the step of calculating the rule conclusion value for each activation control rule in the activation control rule set to obtain the activation rule conclusion parameter set includes:

[0138] Activation control rules are extracted sequentially from the activation control rule set. Based on the extracted activation control rules, the rule output parameters are queried from the pre-constructed proportional coefficient correction rule matrix, integral coefficient correction rule matrix, and differential coefficient correction rule matrix, respectively. The rule output parameters include: proportional coefficient correction output value, integral coefficient correction output value, and differential coefficient correction output value.

[0139] Summarize the rule output parameters to obtain the rule output parameter set. Perform a minimum membership value extraction operation on each activation control rule in the activation control rule set to obtain the rule activation intensity value set.

[0140] The rule activation intensity values ​​are extracted from the rule activation intensity value set. Based on the extracted rule activation intensity values, the target rule output parameters are identified from the rule output parameter set. Based on the target rule output parameters and the extracted rule activation intensity values, the activation rule conclusion parameters are calculated. The activation rule conclusion parameters include: the conclusion values ​​of the proportional coefficient correction, the integral coefficient correction, and the differential coefficient correction.

[0141] Summarize the activation rule conclusion parameters to obtain the activation rule conclusion parameter set.

[0142] It should be explained that the proportional coefficient correction rule matrix, integral coefficient correction rule matrix, and derivative coefficient correction rule matrix are pre-constructed and stored two-dimensional matrices, corresponding to the rule output lookup tables for proportional, integral, and derivative coefficient corrections, respectively. Each element in the matrix is ​​a correction output value corresponding to different errors and fuzzy states of error change rate. It should be noted that the construction methods of the proportional, integral, and derivative coefficient correction rule matrices have been given above and will not be repeated here. The proportional coefficient correction output value is the output value of the activated control rule obtained from the proportional coefficient correction rule matrix. The integral coefficient correction output value is the output value of the activated control rule obtained from the integral coefficient correction rule matrix. The derivative coefficient correction output value is the output value of the activated control rule obtained from the derivative coefficient correction rule matrix. The proportional coefficient correction output value is used to adaptively adjust the intensity of the PID proportional action based on the real-time speed deviation and deviation change trend, compensating for the inability of a fixed proportional coefficient to adapt to operating condition fluctuations. Its value directly determines the increase or decrease in the proportional control action. The integral coefficient correction output value is used to eliminate the constant deviation between the actual value and the set target value of the controlled physical quantity after the internal mixer speed control enters a stable operating state. This avoids long-term deviations between the actual and target speeds caused by load changes and equipment interference. This correction value can dynamically adjust the accumulation rate of the integral, preventing integral saturation and quickly smoothing out state-of-the-art deviations. The derivative coefficient correction output value is used to predict the changing trend of the internal mixer speed deviation, suppressing dynamic fluctuations and overshoot in advance. For conditions such as sudden speed changes and disturbances, dynamic correction through derivative adjustment accelerates the response speed and improves the stability of the control process. This correction value can adjust the strength of the derivative action in real time according to the rate of change of deviation.

[0143] Understandably, the rule output parameter set is a collection of rule output parameters. The step of extracting the minimum membership value for each activation control rule in the activation control rule set is as follows: for each activation control rule in the activation control rule set, the smaller of its error membership degree and error change rate membership degree is taken as the rule activation intensity value. The rule activation intensity values ​​are then summarized to obtain the rule activation intensity value set. The target rule output parameter is the rule output parameter corresponding to the current rule identified from the rule output parameter set based on the rule activation intensity value. The step of calculating the activation rule conclusion parameter based on the target rule output parameter and the extracted rule activation intensity value is as follows: the extracted rule activation intensity value is multiplied by the corresponding proportional coefficient correction output value, integral coefficient correction output value, and differential coefficient correction output value in the target rule output parameter, respectively, to obtain the proportional coefficient correction conclusion value, integral coefficient correction conclusion value, and differential coefficient correction conclusion value. The activation rule conclusion parameter set is a collection of activation rule conclusion parameters.

[0144] It should be noted that in the above steps of this invention, for each activation control rule, the error membership degree and error change rate membership degree corresponding to the rule are obtained respectively, and the minimum value operation is performed on these two membership degrees. The smaller value is taken as the effective activation weight of the activation control rule. This process follows the logical AND operation principle in fuzzy control, that is, the rule can only be effective when the speed error and error change rate simultaneously meet the rule conditions. Therefore, using the lower value of the two matching degrees as the overall effectiveness of the rule can more rigorously and reasonably reflect the matching degree between the current working condition and the activation control rule, ensuring the accuracy and reliability of the fuzzy inference process. Specifically, the parameter update operation of the updated control parameters to obtain the current control coefficient set includes:

[0145] The target control coefficients are extracted sequentially from the updated control parameters, where the target control coefficients are the updated proportional coefficient, the updated integral coefficient, or the updated derivative coefficient.

[0146] Based on the target control coefficient, the upper and lower limits of the control coefficient are determined.

[0147] If the target control coefficient is greater than the upper limit of the control coefficient, then the upper limit of the control coefficient shall be used as the target control coefficient.

[0148] If the target control coefficient is less than the lower limit of the control coefficient, then the lower limit of the control coefficient shall be used as the target control coefficient.

[0149] If the target control coefficient is not greater than the upper limit of the control coefficient and not less than the lower limit of the control coefficient, then the target control coefficient shall be used as the normal control coefficient.

[0150] Based on the target control coefficient or normal control coefficient, the current control coefficient is determined, and the current control coefficients are summarized to obtain the current control coefficient set.

[0151] It should be explained that the upper and lower limits of the control coefficient are the maximum and minimum values ​​of the proportional, integral, or derivative coefficients, respectively, preset based on the operating characteristics, drive capability, and control stability requirements of the internal mixer speed control system. If the target control coefficient is greater than the upper limit, it indicates that the target control coefficient exceeds the maximum safe value; direct use of this value could easily lead to excessive control input or equipment overload. If the target control coefficient is less than the lower limit, it indicates that the target control coefficient is below the minimum effective value that can be normally adjusted; direct use of this value could result in insufficient adjustment capability, slow response, or inability to eliminate steady-state error. If the target control coefficient is neither greater than the upper limit nor less than the lower limit, it indicates that the target control coefficient is within a safe and effective reasonable range and can be directly used in control calculations. The target control coefficient is the updated proportional, integral, or derivative coefficient extracted from the updated control parameters and is to be verified. The normal control coefficient is the target control coefficient that is directly usable without amplitude limiting correction when the target control coefficient is between the upper and lower limits. The current control coefficient is either the target control coefficient or the normal control coefficient. The current control coefficient set is a collection of current control coefficients.

[0152] S5. Calculate the control increment based on the PID control quantity and the preset pre-cycle control quantity, and send the frequency converter adjustment command based on the control increment.

[0153] It should be explained that the preceding cycle control quantity is the PID control quantity output to the inverter in the previous control cycle. The control increment is the difference obtained by subtracting the preceding cycle control quantity from the PID control quantity. The control increment is used to characterize the magnitude of the adjustment required for this control. The inverter adjustment command is the instruction used to instruct the inverter to perform frequency adjustment operations.

[0154] S6. Send the inverter adjustment command to the pre-built inverter to obtain the adjusted output frequency, and calculate the actual motor speed based on the adjusted output frequency.

[0155] It should be explained that a frequency converter is a speed-regulating actuator used to drive the motor of an internal mixer. This speed-regulating actuator can change the output frequency according to the received frequency converter adjustment command. The calculation formula for adjusting the output frequency in the step of sending the frequency converter adjustment command to the pre-built frequency converter to obtain the adjusted output frequency is as follows:

[0156]

[0157] in, This indicates adjusting the output frequency. This represents the PID control input. This indicates the preset maximum control value. This indicates the preset maximum output frequency of the frequency converter. The maximum output frequency of the frequency converter is the highest power frequency value that the frequency converter can output. The maximum output frequency of the frequency converter is obtained from the frequency converter's design manual. The steps for calculating the actual motor speed based on the adjusted output frequency are as follows: multiply 60 by the adjusted output frequency and divide by the number of motor pole pairs to obtain the actual motor speed. The number of motor pole pairs is the number of magnetic pole pairs in the motor.

[0158] S7. Based on the actual speed of the motor, control the speed of the internal mixer to be controlled to obtain the controlled internal mixer.

[0159] It should be explained that speed control is a closed-loop control process that adjusts the actual speed of the motor to make the internal mixer speed track the target speed. The internal mixer that has been regulated is the one to be controlled that is in the corresponding operating state after the speed adjustment of the current cycle.

[0160] S8. Take the controlled internal mixer as the internal mixer to be controlled, and return to the step of collecting data of the internal mixer to be controlled according to the sampling period, until all sampling periods in the sampling period set have been completed.

[0161] It should be explained that the completion of all sampling cycles in the sampling cycle set indicates that all sampling cycles have completed the complete process of data acquisition, calculation, adjustment and feedback, and no new round of cyclic control will be performed.

[0162] S9. Integrate the already regulated internal mixer to obtain a speed-controlled internal mixer, and complete the speed control of the internal mixer based on real-time feedback based on the speed-controlled internal mixer.

[0163] It should be explained that the integrated controlled internal mixer refers to the operation of uniformly summarizing the adjustment process and operating status of the controlled internal mixer under each sampling period to form a continuous and stable control result. The speed-controlled internal mixer is a controlled internal mixer whose speed meets the process requirements after full-process closed-loop adjustment.

[0164] To address the problems described in the background art, this invention identifies the internal mixer to be controlled, sets a sampling period set and a target speed value, and performs the following operations for each sampling period in the sampling period set: Data is collected from the internal mixer to be controlled according to the sampling period to obtain the current pulse value, acquire the adjacent preceding pulse value, and calculate the pulse difference based on the current pulse value and the adjacent preceding pulse value. This invention achieves real-time acquisition of the internal mixer's operating status, ensuring the authenticity and timeliness of the feedback data. By calculating the pulse difference, cumulative pulse errors can be eliminated, highlighting the speed change within a unit period and improving the accuracy of speed detection. The internal mixer speed value is calculated based on the pulse difference and the sampling period. This invention performs fuzzy calculations on the internal mixer's rotational speed and target rotational speed to obtain the fuzzy rotational speed error and its rate of change. It transforms physical pulse signals into intuitive speed control quantities, establishing a mapping relationship between the detection signal and the control target. By using fuzzy processing for the rotational speed error and its rate of change, it weakens system nonlinearity, hysteresis, and noise interference, improving the robustness of the control algorithm. Based on the fuzzy rotational speed error and its rate of change, it calculates the PID control quantity. This invention combines the advantages of fuzzy control and PID control, optimizing dynamic response performance while ensuring steady-state control accuracy. It solves the problem that traditional PID parameters are difficult to adapt to the complex operating conditions of the internal mixer, thus improving... The stability of speed control is achieved by calculating the control increment based on the PID control quantity and the preset pre-cycle control quantity. Based on this control increment, a frequency converter adjustment command is sent. This invention uses control increments instead of directly outputting control quantities, avoiding the impact of sudden control signal changes on the frequency converter and motor, reducing equipment stress and losses. The frequency converter adjustment command is sent to the pre-built frequency converter to obtain the adjusted output frequency. The actual motor speed is calculated based on the adjusted output frequency. This invention uses the output frequency to infer the actual motor speed, forming a closed-loop feedback link, providing a basis for subsequent corrective control. Based on the actual motor speed, speed control is performed on the internal mixer to be controlled, resulting in a regulated internal mixer. This invention provides real-time... The actual rotational speed is used as the basis for closed-loop adjustment, which can promptly correct the deviation between theoretical control commands and actual operation, ensuring that the internal mixer speed approaches the target speed and improving control accuracy. The adjusted internal mixer is then used as the target internal mixer, and the process returns to the step of collecting data from the target internal mixer according to the sampling period, until all sampling periods within the sampling period set have been completed. The adjusted internal mixers are then integrated to obtain the speed-controlled internal mixer. Based on the speed-controlled internal mixer, real-time feedback-based internal mixer speed control is achieved. This invention, through real-time feedback closed-loop control, significantly improves the accuracy, stability, and reliability of internal mixer speed control, meeting the high-precision speed control requirements of the internal mixing process. Therefore, this invention can achieve automatic adaptation of control parameters to changes in operating conditions.

[0165] like Figure 2 The diagram shown is a functional block diagram of a real-time feedback-based internal mixer speed control system provided in an embodiment of the present invention.

[0166] The real-time feedback-based internal mixer speed control system 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the real-time feedback-based internal mixer speed control system 100 may include an internal mixer speed acquisition module 101, a fuzzy PID decision module 102, an internal mixer speed control module 103, and an internal mixer cycle control module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0167] The internal mixer speed acquisition module 101 is used to identify the internal mixer to be controlled, set a sampling period set and a target speed value, and perform the following operations for each sampling period in the sampling period set: collect data from the internal mixer to be controlled according to the sampling period to obtain the current pulse value, obtain the adjacent preceding pulse value, calculate the pulse difference based on the current pulse value and the adjacent preceding pulse value, calculate the internal mixer speed value based on the pulse difference and the sampling period, and perform fuzzification calculation based on the internal mixer speed value and the target speed value to obtain the fuzzified speed error and the fuzzification error change rate;

[0168] The fuzzy PID decision module 102 is used to calculate the PID control quantity based on the fuzzy speed error and the fuzzy error change rate, calculate the control increment based on the PID control quantity and the preset pre-cycle control quantity, and send the frequency converter adjustment command based on the control increment.

[0169] The internal mixer speed control module 103 is used to send the frequency converter adjustment command to the pre-built frequency converter to obtain the adjustment output frequency, calculate the actual motor speed based on the adjustment output frequency, and perform speed control on the internal mixer to be controlled based on the actual motor speed to obtain the regulated internal mixer.

[0170] The internal mixer cycle control module 104 is used to take the regulated internal mixer as the internal mixer to be controlled, return to the step of collecting data from the internal mixer to be controlled according to the sampling period, until all sampling periods in the sampling period set have been completed, integrate the regulated internal mixers to obtain the speed-controlled internal mixer, and complete the internal mixer speed control based on real-time feedback based on the speed-controlled internal mixer. Specifically, in this embodiment of the invention, the modules in the real-time feedback-based internal mixer speed control system 100 adopt the same methods as described above. Figure 1 The method used is the same as the real-time feedback-based internal mixer speed control method described in the article, and can produce the same technical effect, so it will not be repeated here.

[0171] like Figure 3 The diagram shown is a structural schematic of an electronic device for implementing a real-time feedback-based internal mixer speed control method according to an embodiment of the present invention.

[0172] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for controlling the speed of a mixer based on real-time feedback.

[0173] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a real-time feedback-based internal mixer speed control method program, but also to temporarily store data that has been output or will be output.

[0174] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a real-time feedback-based internal mixer speed control method program) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0175] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0176] Figure 3 Only electronic devices with components are shown; those skilled in the art will understand that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0177] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0178] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0179] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0180] The internal mixer speed control method program based on real-time feedback stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0181] Once the internal mixer to be controlled is identified, the sampling period set and target speed value are set. For each sampling period in the sampling period set, the following operations are performed:

[0182] Data is collected from the internal mixer to be controlled according to the sampling period to obtain the current pulse value, acquire the adjacent preceding pulse value, and calculate the pulse difference based on the current pulse value and the adjacent preceding pulse value.

[0183] The internal mixer speed value is calculated based on the pulse difference and sampling period. Fuzzy calculation is performed based on the internal mixer speed value and the target speed value to obtain the fuzzy speed error and the fuzzy error change rate.

[0184] PID control input is calculated based on fuzzy rotational speed error and fuzzy error change rate.

[0185] The control increment is calculated based on the PID control quantity and the preset pre-cycle control quantity, and the frequency converter adjustment command is sent based on the control increment.

[0186] The inverter adjustment command is sent to the pre-built inverter to obtain the adjusted output frequency, and the actual motor speed is calculated based on the adjusted output frequency.

[0187] Based on the actual speed of the motor, the speed of the internal mixer to be controlled is controlled to obtain the regulated internal mixer;

[0188] Take the already regulated internal mixer as the internal mixer to be controlled, and return to the step of collecting data from the internal mixer to be controlled according to the sampling period, until all the sampling periods in the sampling period set have been completed.

[0189] By integrating the already regulated internal mixer, a speed-controlled internal mixer is obtained, and speed control of the internal mixer based on real-time feedback is completed based on the speed-controlled internal mixer.

[0190] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0191] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0192] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0193] Once the internal mixer to be controlled is identified, the sampling period set and target speed value are set. For each sampling period in the sampling period set, the following operations are performed:

[0194] Data is collected from the internal mixer to be controlled according to the sampling period to obtain the current pulse value, acquire the adjacent preceding pulse value, and calculate the pulse difference based on the current pulse value and the adjacent preceding pulse value.

[0195] The internal mixer speed value is calculated based on the pulse difference and sampling period. Fuzzy calculation is performed based on the internal mixer speed value and the target speed value to obtain the fuzzy speed error and the fuzzy error change rate.

[0196] PID control input is calculated based on fuzzy rotational speed error and fuzzy error change rate.

[0197] The control increment is calculated based on the PID control quantity and the preset pre-cycle control quantity, and the frequency converter adjustment command is sent based on the control increment.

[0198] The inverter adjustment command is sent to the pre-built inverter to obtain the adjusted output frequency, and the actual motor speed is calculated based on the adjusted output frequency.

[0199] Based on the actual speed of the motor, the speed of the internal mixer to be controlled is controlled to obtain the regulated internal mixer;

[0200] Take the already regulated internal mixer as the internal mixer to be controlled, and return to the step of collecting data from the internal mixer to be controlled according to the sampling period, until all the sampling periods in the sampling period set have been completed.

[0201] By integrating the already regulated internal mixer, a speed-controlled internal mixer is obtained, and speed control of the internal mixer based on real-time feedback is completed based on the speed-controlled internal mixer.

[0202] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0203] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0204] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0205] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for controlling the speed of an internal mixer based on real-time feedback, characterized in that, The method includes: Once the internal mixer to be controlled is identified, the sampling period set and target speed value are set. For each sampling period in the sampling period set, the following operations are performed: Data is collected from the internal mixer to be controlled according to the sampling period to obtain the current pulse value, acquire the adjacent preceding pulse value, and calculate the pulse difference based on the current pulse value and the adjacent preceding pulse value. The internal mixer speed value is calculated based on the pulse difference and sampling period. Fuzzy calculation is performed based on the internal mixer speed value and the target speed value to obtain the fuzzy speed error and the fuzzy error change rate. PID control input is calculated based on fuzzy rotational speed error and fuzzy error change rate. The control increment is calculated based on the PID control quantity and the preset pre-cycle control quantity, and the frequency converter adjustment command is sent based on the control increment. The inverter adjustment command is sent to the pre-built inverter to obtain the adjusted output frequency, and the actual motor speed is calculated based on the adjusted output frequency. Based on the actual speed of the motor, the speed of the internal mixer to be controlled is controlled to obtain the regulated internal mixer; Take the already regulated internal mixer as the internal mixer to be controlled, and return to the step of collecting data from the internal mixer to be controlled according to the sampling period, until all the sampling periods in the sampling period set have been completed. By integrating the already regulated internal mixer, a speed-controlled internal mixer is obtained, and speed control of the internal mixer based on real-time feedback is completed based on the speed-controlled internal mixer.

2. The internal mixer speed control method based on real-time feedback as described in claim 1, characterized in that, The fuzzification calculation based on the internal mixer speed value and the target speed value yields the fuzzified speed error and the fuzzification error change rate, including: The difference between the target speed value and the internal mixer speed value is calculated to obtain the speed error value; Obtain adjacent preceding error values, and calculate the error change rate based on adjacent preceding error values ​​and speed error values; Set the error quantization factor and the error rate of change quantization factor; The fuzzy speed error is obtained by multiplying the speed error value and the error quantization factor. The fuzzy error change rate is obtained by multiplying the error change rate and the error change rate quantization factor.

3. The internal mixer speed control method based on real-time feedback as described in claim 2, characterized in that, The set error quantization factor and error change rate quantization factor include: Obtain a fuzzy set, and establish a fuzzy control rule set based on the fuzzy set, wherein the fuzzy set includes multiple linguistic variables; The number of linguistic variables in the fuzzy set is counted to obtain the upper limit of the actual rotational speed error and the upper limit of the actual error change rate. The error quantification factor is obtained by dividing the number of linguistic variables by the upper limit of the actual rotational speed error, and the error change rate quantification factor is obtained by dividing the number of linguistic variables by the upper limit of the actual error change rate.

4. The internal mixer speed control method based on real-time feedback as described in claim 3, characterized in that, The establishment of a fuzzy control rule set based on fuzzy sets includes: The fuzzy set central value parameter is obtained by assigning a central value to each linguistic variable in the fuzzy set. The fuzzy set central value parameter includes multiple linguistic variable central values, and there is a one-to-one correspondence between the linguistic variable and the linguistic variable central value. The rule form is determined based on the fuzzy rotational speed error and the rate of change of fuzzy error, and a fuzzy control rule set is established based on the rule form and the central values ​​of multiple linguistic variables.

5. The internal mixer speed control method based on real-time feedback as described in claim 4, characterized in that, The calculation of the PID control quantity based on the fuzzy speed error and the rate of change of the fuzzy error includes: Fuzzy inference is performed based on the fuzzy rotation speed error and the rate of change of fuzzy error to obtain the set of activation rule conclusion parameters; The precise output values ​​of the proportional coefficient correction, integral coefficient correction, and differential coefficient correction are calculated based on the parameter set of the activation rule conclusion. The pre-constructed proportional-integral-derivative control algorithm is updated with the precise output values ​​of the proportional coefficient correction, integral coefficient correction, and derivative coefficient correction, respectively, to obtain updated control parameters. The updated control parameters include the updated proportional coefficient, updated integral coefficient, and updated derivative coefficient. The control parameters are updated to obtain the current set of control coefficients, which includes proportional control coefficients, integral control coefficients, and derivative control coefficients. Calculate the PID control input based on the current set of control coefficients.

6. The internal mixer speed control method based on real-time feedback as described in claim 5, characterized in that, The fuzzy inference operation based on the fuzzy rotation speed error and the rate of change of the fuzzy error yields a set of activation rule conclusion parameters, including: Linguistic variables are extracted sequentially from the fuzzy set. Based on the extracted linguistic variables, the center value of the target linguistic variable is determined from the center value parameter of the fuzzy set. The error membership degree is calculated based on the center value of the target linguistic variable and the fuzzy rotation speed error. Calculate the membership degree of the error change rate based on the central value of the target language variable and the fuzzification error change rate; Summarize the error membership degree and the error change rate membership degree respectively to obtain the error membership degree set and the error change rate membership degree set; By pairwise combining the error membership set and the error change rate membership set, we obtain the membership set; An activation control rule set is identified from the fuzzy control rule set based on the membership degree set. The activation control rule set includes multiple activation control rules, among which the activation control rules include: activation membership degree sets. Calculate the rule conclusion value for each activation control rule in the activation control rule set to obtain the activation rule conclusion parameter set.

7. The internal mixer speed control method based on real-time feedback as described in claim 6, characterized in that, The formula for calculating the error membership degree is as follows: in, Indicates the membership degree of the error. Indicates the fuzzy rotation speed error. Represents the central value of the target language variable. This represents the preset membership function span. This represents a function that takes the maximum value.

8. The internal mixer speed control method based on real-time feedback as described in claim 7, characterized in that, The step of calculating the rule conclusion value for each activation control rule in the activation control rule set yields an activation rule conclusion parameter set, including: Activation control rules are extracted sequentially from the activation control rule set. Based on the extracted activation control rules, the rule output parameters are queried from the pre-constructed proportional coefficient correction rule matrix, integral coefficient correction rule matrix, and differential coefficient correction rule matrix, respectively. The rule output parameters include: proportional coefficient correction output value, integral coefficient correction output value, and differential coefficient correction output value. Summarize the rule output parameters to obtain the rule output parameter set. Perform a minimum membership value extraction operation on each activation control rule in the activation control rule set to obtain the rule activation intensity value set. The rule activation intensity values ​​are extracted from the rule activation intensity value set. Based on the extracted rule activation intensity values, the target rule output parameters are identified from the rule output parameter set. Based on the target rule output parameters and the extracted rule activation intensity values, the activation rule conclusion parameters are calculated. The activation rule conclusion parameters include: the conclusion values ​​of the proportional coefficient correction, the integral coefficient correction, and the differential coefficient correction. Summarize the activation rule conclusion parameters to obtain the activation rule conclusion parameter set.

9. The internal mixer speed control method based on real-time feedback as described in claim 8, characterized in that, The step of performing parameter update operations on the updated control parameters to obtain the current set of control coefficients includes: The target control coefficients are extracted sequentially from the updated control parameters, where the target control coefficients are the updated proportional coefficient, the updated integral coefficient, or the updated derivative coefficient. Based on the target control coefficient, the upper and lower limits of the control coefficient are determined. If the target control coefficient is greater than the upper limit of the control coefficient, then the upper limit of the control coefficient shall be used as the target control coefficient. If the target control coefficient is less than the lower limit of the control coefficient, then the lower limit of the control coefficient shall be used as the target control coefficient. If the target control coefficient is not greater than the upper limit of the control coefficient and not less than the lower limit of the control coefficient, then the target control coefficient shall be used as the normal control coefficient. Based on the target control coefficient or normal control coefficient, the current control coefficient is determined, and the current control coefficients are summarized to obtain the current control coefficient set.

10. A speed control system for an internal mixer based on real-time feedback, characterized in that, The system includes: The internal mixer speed acquisition module is used to identify the internal mixer to be controlled, set the sampling period set and the target speed value, and perform the following operations for each sampling period in the sampling period set: collect data from the internal mixer to be controlled according to the sampling period to obtain the current pulse value, obtain the adjacent preceding pulse value, calculate the pulse difference based on the current pulse value and the adjacent preceding pulse value, calculate the internal mixer speed value based on the pulse difference and the sampling period, and perform fuzzification calculation based on the internal mixer speed value and the target speed value to obtain the fuzzified speed error and the fuzzification error change rate; The fuzzy PID decision module is used to calculate the PID control quantity based on the fuzzy speed error and the fuzzy error change rate, calculate the control increment based on the PID control quantity and the preset pre-cycle control quantity, and send the frequency converter adjustment command based on the control increment. The internal mixer speed control module is used to send the frequency converter adjustment command to the pre-built frequency converter to obtain the adjustment output frequency, calculate the actual motor speed based on the adjustment output frequency, and perform speed control on the internal mixer to be controlled based on the actual motor speed to obtain the regulated internal mixer. The internal mixer cycle control module is used to take the regulated internal mixer as the internal mixer to be controlled, return to the step of collecting data of the internal mixer to be controlled according to the sampling period, until all sampling periods in the sampling period set have been completed, integrate the regulated internal mixers to obtain the speed control internal mixer, and complete the internal mixer speed control based on real-time feedback based on the speed control internal mixer.

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