Construction waste crushing control method and system based on load feedback

Abstract

This invention provides a method and system for controlling construction waste crushing based on load feedback. The method includes: acquiring the stator current signal of the crusher's main shaft motor; extracting the active current component representing torque; constructing a time sliding window sequence; calculating the algebraic difference between the current active current component and a preset target value to obtain the original load deviation; constructing a weighted function to correct the load deviation amplitude; obtaining the load change rate by taking the first derivative with respect to time; building a fuzzy logic controller, using a Gaussian membership function in the fuzzification stage to establish a mapping between sample entropy and the width of the Gaussian distribution, and setting the membership function width in real time; adjusting the fuzzy subset of PID parameters according to fuzzy rules; using the peak-to-mean ratio of the sliding window sequence as an impact factor to weight and correct the centroid method defuzzification results; superimposing the adjustment increment onto the PID reference parameters; and combining the corrected load deviation to calculate the control command and adjust the output frequency of the feed motor inverter. This invention enables multi-dimensional adjustment of the crusher.

Description

Technical Field

[0001] This invention belongs to the field of control, and in particular relates to a method and system for controlling the crushing of construction waste based on load feedback. Background Technology

[0002] Construction waste crushing systems utilize a crusher spindle motor to drive a high-speed rotor, feeding materials into the crushing chamber via a feeding device. To ensure the continuity and safety of the crushing operation, the control system needs to adjust the feeding speed in real time based on the load on the crusher spindle. Existing control methods are mostly based on manual experience or employ PID constant current control, which monitors the stator current amplitude of the spindle motor. When the current exceeds a set threshold, the feeding frequency is reduced; conversely, the frequency is increased to maintain the spindle load within the rated range. When processing construction waste, the mixture of materials of varying hardness and size, such as concrete blocks, bricks, steel bars, and wood, causes the crusher's load characteristics to exhibit strong time-varying and impact-driven features. PID control algorithms cannot adapt to drastic fluctuations in operating conditions: when encountering high-hardness materials or sudden impacts from large pieces, the current signal not only increases in amplitude but also changes in its fluctuation pattern and frequency characteristics. Feedback control based on current amplitude exhibits lag, easily leading to overshoot or insufficient response. Furthermore, while existing fuzzy PID control utilizes rule-based reasoning to adjust parameters, it cannot deeply analyze the statistical distribution characteristics of the load signal. The controller cannot distinguish between normal, uniform high load and abnormal, violent impact load, making it difficult to suppress mechanical impact while ensuring crushing efficiency. There is an urgent need for a control method that can deeply analyze the characteristics of the load signal and make multi-dimensional adjustments accordingly. Summary of the Invention

[0003] This addresses the problem that existing technologies cannot deeply analyze load signal characteristics and make multi-dimensional adjustments accordingly.

[0004] In the first aspect, the present invention proposes a method for controlling the crushing of construction waste based on load feedback, comprising:

[0005] The stator current signal of the crusher's main shaft motor is collected, and the active current component representing the torque is extracted through coordinate transformation. The current time sliding window sequence of the active current component is constructed. The sample entropy value and coefficient of variation of the time sliding window sequence are calculated.

[0006] Calculate the algebraic difference between the current active current component and the preset target current value to obtain the original load deviation; construct a weighting function using the coefficient of variation to correct the amplitude of the original load deviation to obtain the corrected load deviation term; calculate the first derivative of the corrected load deviation term with respect to time to obtain the load change rate term;

[0007] A fuzzy logic controller is established, with the corrected load deviation term and load change rate term as input variables. In the fuzzification stage, a Gaussian membership function is used to construct a mapping function between the sample entropy value and the Gaussian distribution width parameter. The distribution width of the Gaussian membership function at the current time is calculated and set.

[0008] Based on a preset fuzzy rule table, a fuzzy subset of PID parameter adjustment values ​​is obtained. In the defuzzification stage, the ratio of the peak value to the mean value of the time sliding window sequence is calculated as an impact factor. The output value obtained by the centroid method defuzzification calculation is post-weighted and corrected using the impact factor to obtain the adjustment increments of the proportional, integral, and derivative coefficients. The adjustment increments are superimposed on the reference parameters of the PID controller to obtain the PID parameters. The control command is calculated using the PID parameters and the corrected load deviation term to adjust the inverter output frequency of the crusher feed motor.

[0009] In another aspect, the present invention also proposes a construction waste crushing control system based on load feedback, comprising the following modules:

[0010] The first calculation module is used to collect the stator current signal of the crusher main shaft motor, extract the active current component representing the torque through coordinate transformation, construct the current time sliding window sequence of the active current component, and calculate the sample entropy value and coefficient of variation of the time sliding window sequence.

[0011] The second calculation module is used to calculate the algebraic difference between the current active current component and the preset target current value to obtain the original load deviation; to construct a weighting function using the coefficient of variation to correct the amplitude of the original load deviation to obtain the corrected load deviation term; and to calculate the first derivative of the corrected load deviation term with respect to time to obtain the load change rate term.

[0012] The setting module is used to establish a fuzzy logic controller. The modified load deviation term and load change rate term are used as input variables. In the fuzzification stage, a Gaussian membership function is used to construct a mapping function between the sample entropy value and the Gaussian distribution width parameter. The Gaussian membership function distribution width at the current time is calculated and set.

[0013] The adjustment module is used to infer a fuzzy subset of PID parameter adjustment amounts based on a preset fuzzy rule table. During the defuzzification stage, the ratio of the peak value to the mean value of the time sliding window sequence is calculated as an impact factor. This impact factor is used to perform a post-weighted correction on the output value obtained from the centroid method defuzzification calculation, resulting in adjustment increments for the proportional, integral, and derivative coefficients. These adjustment increments are then superimposed onto the baseline parameters of the PID controller to obtain the PID parameters. Finally, the PID parameters and the corrected load deviation term are used to calculate control commands to adjust the inverter output frequency of the crusher feed motor.

[0014] This invention extracts the active current component of the spindle motor and constructs a time sliding window. It then uses the coefficient of variation to construct a weighting function to correct the amplitude of the original load deviation, increasing the matching degree between the control method and the drastic load fluctuations during construction waste crushing. By calculating the sample entropy value to represent the complexity of the load signal, the distribution width of the Gaussian membership function in the fuzzy controller is adjusted accordingly, thus adjusting the resolution of the fuzzy inference when the load signal has varying degrees of disorder. Using the peak-to-mean ratio (PMR) representing impact characteristics as an impact factor, the adjustment of the defuzzified PID parameters is post-weighted, enhancing the control system's adjustment strength when encountering instantaneous impacts from high-hardness materials. This prevents the crusher from overloading and stalling, improving the continuity and production efficiency of the crushing operation while ensuring safe equipment operation. Attached Figure Description

[0015] Figure 1 A flowchart of the first embodiment;

[0016] Figure 2 This is a schematic diagram showing the relationship between the coefficient of variation and the weighted function of the load deviation;

[0017] Figure 3 This is a structural diagram of the second embodiment. Detailed Implementation

[0018] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0020] In the first embodiment, the present invention proposes a method for controlling the crushing of construction waste based on load feedback, such as... Figure 1 ,include:

[0021] S1. Collect the stator current signal of the crusher main shaft motor, extract the active current component representing the torque through coordinate transformation, and construct the current time sliding window sequence of the active current component; calculate the sample entropy value and coefficient of variation of the time sliding window sequence.

[0022] Hall effect current sensors are installed on the three-phase input lines of the crusher's main shaft motor to detect the three-phase analog current signals in real time. The analog signals are converted to digital signals using an analog-to-digital converter at a sampling frequency of 1kHz to 5kHz. The Clarke transform is used to convert the three-phase stationary coordinate system current signals into two-phase stationary coordinate system current signals. Using the rotor electrical angle obtained from a rotary transformer or encoder, a Parker transform is performed to convert the two-phase stationary coordinate system current signals into two-phase rotating coordinate system current signals, where the d-axis component is the excitation current and the q-axis component is the torque current. The q-axis current component is extracted as the active current component representing the load torque. A first-in-first-out (FIFO) queue buffer of length N is allocated in the controller's memory, with N ranging from 50 to 200. The most recently calculated active current component is stored at the tail of the queue, while the oldest data at the head of the queue is removed, thus forming a real-time active current component time-sliding window sequence.

[0023] The embedding dimension *m* for calculating sample entropy is set to 2, and the similarity tolerance *r* is set to 0.1 to 0.25 times the standard deviation of the time-sliding window sequence. The time-sliding window sequence is reconstructed into an m-dimensional vector set. The Chebyshev distance between the vectors is calculated. The number of vector pairs whose vector distance is less than the similarity tolerance *r* is counted. The ratio of the number of vector pairs meeting the condition is calculated for dimensions *m+1* and *m*, respectively. The negative value of the natural logarithm of this ratio is taken to obtain the sample entropy value of the time-sliding window sequence. Simultaneously, the arithmetic mean and standard deviation of all data are calculated by traversing the time-sliding window sequence. The calculated standard deviation is divided by the arithmetic mean to obtain the coefficient of variation, which reflects the degree of load fluctuation.

[0024] In an optional embodiment, the step of acquiring the stator current signal of the crusher's main shaft motor and extracting the active current component representing torque through coordinate transformation includes:

[0025] The collected three-phase stator current signal , , Perform Clarke transformation to convert the current components into a two-phase stationary coordinate system. , ;

[0026] The rotor magnetic field orientation angle of the spindle motor is obtained using a flux linkage observer or position sensor. The Park transformation matrix is ​​used to transform the current components. , Converted to direct-axis current components in a synchronous rotating coordinate system and cross-axis current components ;

[0027] The quadrature current component It is determined to be the active current component.

[0028] The current sampling frequency is set to 2kHz to 10kHz. Assume the three-phase stator currents sampled at the current moment are as follows: , , And satisfy ;

[0029] The Clarke transform is used to calculate the current in a two-phase stationary coordinate system. The formula is as follows:

[0030]

[0031]

[0032] The rotor's mechanical angle is read in real time by a photoelectric encoder installed on the motor shaft end, and the angle is determined according to the number of motor pole pairs. Calculate electrical angle ;

[0033] The current in the synchronously rotating coordinate system is calculated using the Park transformation, and the formula is as follows:

[0034]

[0035]

[0036] Output This refers to the active current component, which linearly corresponds to the electromagnetic torque of the motor under the vector control strategy.

[0037] In an optional embodiment, calculating the sample entropy value of the time-sliding window sequence includes:

[0038] Set the embedding dimension to m and the tolerance threshold to r;

[0039] Reconstruct the time-sliding window sequence into a set of vector sequences of dimension m in sequence;

[0040] Calculate the Chebyshev distance between any two vectors in the vector sequence, count the number of vector pairs whose Chebyshev distance is less than the tolerance threshold r, and calculate the ratio of this number to the total number of vectors to obtain the first probability.

[0041] Increase the embedding dimension to m + 1, repeat the above reconstruction and statistical steps, and obtain the ratio of the number of vector pairs with a distance less than the tolerance threshold r to the total number of vectors as the second probability;

[0042] Calculate the negative value of the natural logarithm of the ratio of the second probability to the first probability to obtain the sample entropy value.

[0043] Set the time sliding window length N to be 200 to 500 sampling points. For example, take N = 300. The recommended value of the embedding dimension m is 2, and the tolerance threshold r is taken as 0.1 to 0.25 times the standard deviation SD of the time sliding window sequence. For example, take r = 0.2×SD.

[0044] From the current sliding window sequence Reconstruct to obtain N - m + 1 m-dimensional vectors ;

[0045] Define two vectors and The Chebyshev distance between them is the maximum value of the absolute value of the difference between their corresponding elements, that is ;

[0046] Statistically count the number of vector pairs satisfying the distance d < r , calculate the average probability ;

[0047] Increase the dimension to m + 1, and repeat the above steps to calculate the average probability .

[0048] Calculate the sample entropy value . If the sequence is completely regular, this value is 0; if the sequence contains mutations or noise, this value increases.

[0049] S2, calculate the algebraic difference between the current active current component and the preset target current value to obtain the original load deviation; use the coefficient of variation to construct a weighting function to perform amplitude correction on the original load deviation to obtain the corrected load deviation term; calculate the first derivative of the corrected load deviation term with respect to time to obtain the load change rate term;

[0050] Read the rated current value corresponding to the best working condition of the crusher preset by the controller as the target current value. Subtract the target current value from the active current component collected at the current moment to obtain the original load deviation. Construct an exponential weighting function with the coefficient of variation as the independent variable, and the output value increases exponentially as the coefficient of variation increases, such as Figure 2The original load deviation is multiplied by the output value of the weighting function to scale the deviation amplitude, thereby amplifying the effect of the deviation signal when the load fluctuates drastically, resulting in a corrected load deviation term. The corrected load deviation term at the current sampling time is subtracted from the corrected load deviation term at the previous sampling time, and the difference is divided by the length of the sampling period to obtain the load change rate term, which reflects the rate of load change.

[0051] In an optional embodiment, the step of constructing a weighting function using the coefficient of variation to correct the magnitude of the original load deviation, thereby obtaining the corrected load deviation term, includes:

[0052] The coefficient of variation is obtained by calculating the ratio of the standard deviation to the absolute value of the arithmetic mean of the time sliding window sequence.

[0053] Construct an exponential weighting function, wherein the base of the function is the natural constant e, and the exponent is the product of the coefficient of variation and a preset sensitivity factor;

[0054] Calculate the output value of the weighting function and multiply the output value by the original load deviation to obtain the corrected load deviation term.

[0055] Let the current time window sequence be X, calculate the standard deviation of the sequence. and mean Then the coefficient of variation ,in To prevent the minimum value where the denominator is zero.

[0056] Preset Sensitive Factors The recommended range is 0.5 to 2.0.

[0057] Constructing a weighted function .

[0058] Let the original load deviation be... The corrected load deviation term The calculation is as follows:

[0059] .

[0060] The above steps amplify the deviation signal through an exponential function when the load fluctuates drastically, thereby enhancing the control system's response to the operating conditions. Furthermore, compared to traditional PID proportional followers, the exponential weighted function can instantly cut off the feed before the crusher's main shaft jams, providing rapid protection for the electronic safety pin.

[0061] S3, Establish a fuzzy logic controller, using the corrected load deviation term and load change rate term as input variables, and adopt a Gaussian membership function in the fuzzification stage to construct a mapping function between the sample entropy value and the Gaussian distribution width parameter, and calculate and set the Gaussian membership function distribution width at the current time.

[0062] The structure of a fuzzy logic controller is defined in the controller. Two input variables are set: the corrected load deviation term and the load change rate term. Three output variables are set: the proportional coefficient increment, the integral coefficient increment, and the derivative coefficient increment. The universe of discourse of the input variables is mapped to a standardized interval of -6 to +6 using a quantization factor. The input variables are divided into seven fuzzy language levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. A Gaussian function is selected as the membership function for each language level. A linear function or inverse proportional function is constructed as a negative correlation mapping function. The input of this function is the sample entropy value, and the output is the width parameter σ of the Gaussian function. The maximum width parameter is defined when the sample entropy value is zero, and the minimum width parameter is defined when the sample entropy value reaches a preset maximum value. The sample entropy value calculated in the current step is substituted into the mapping function to calculate the current width parameter σ value. The shape of all Gaussian membership functions in the fuzzy controller is updated in real time using the σ value, so that the membership functions become narrower when the sample entropy is large (i.e., the load complexity is high), thereby improving control sensitivity.

[0063] In an optional embodiment, constructing the mapping function between the sample entropy value and the Gaussian distribution width parameter includes:

[0064] Set the baseline value and adjustment coefficient for the width parameter of the Gaussian membership function;

[0065] Establish a mapping function, wherein the width of the Gaussian membership function distribution at the current time is equal to the baseline value of the width parameter divided by the adjustment denominator, and the adjustment denominator is 1 plus the product of the adjustment coefficient and the sample entropy value;

[0066] The calculated distribution width is used to update the parameters of the membership function of the input variables in the fuzzy logic controller.

[0067] In a fuzzy controller, the linguistic variables of the input variables, such as NB, NM, NS, Z, PS, PM, and PB, are assigned Gaussian membership functions. .

[0068] Set the baseline value for the width parameter The adjustment coefficient is between 0.8 and 1.5. The range is from 1.0 to 3.0.

[0069] The negative correlation mapping function is constructed as follows: .

[0070] Assuming the current sample entropy SampEn = 0.5, the current Gaussian distribution width is updated to... .

[0071] This results in a narrower membership function when the entropy value is high, improving the resolution of fuzzy inference and thus enabling the detection of subtle changes in the input variables.

[0072] S4. Based on the preset fuzzy rule table, reasoning is performed to obtain a fuzzy subset of the PID parameter adjustment amount; in the defuzzification stage, the ratio of the peak value to the mean value of the time sliding window sequence is calculated as an impact factor, and the output value obtained by the centroid method defuzzification calculation is post-weighted and corrected using the impact factor to obtain the adjustment increments of the proportional, integral and derivative coefficients; the adjustment increments are superimposed on the reference parameters of the PID controller to obtain the PID parameters; the control command is calculated using the PID parameters and the corrected load deviation term to adjust the inverter output frequency of the crusher feed motor.

[0073] A pre-defined fuzzy control rule table is stored in the controller. This rule table contains logic entries that reduce the proportional coefficient when the deviation is large and the rate of change is large. Optionally, in the fuzzy rule table, at the corner of the universe of discourse representing a large load impact, such as a load deviation of large positive PB, the fuzzy subset of the integral adjustment is set to negative large (NB) or negative medium (NM). After defuzzification, multiplying by an impact factor greater than 1 can amplify this negative adjustment by a factor of several times, thereby attenuating or even reducing the current integral coefficient to zero extremely rapidly at the moment of a severe impact. Based on the currently input fuzzy linguistic variables, the applicable rules in the rule table are matched, and fuzzy implication operations are performed using the Mamdani inference method to obtain a fuzzy subset containing proportional, integral, and derivative parameter adjustments. The centroid abscissa of the area enclosed by the fuzzy subset is calculated using the centroid method to obtain the initial output value. The maximum current value in the current time window sequence is searched. The impact factor is calculated by dividing the maximum current value by the arithmetic mean of the sequence. A correction formula is constructed by multiplying the initial output value by the impact factor, or by a power function value with the impact factor as the base. This multiplication operation is then performed to perform a secondary correction on the PID parameter adjustments, resulting in a larger output adjustment when an instantaneous impact load is detected. This yields the proportional coefficient adjustment increment, integral coefficient adjustment increment, and derivative coefficient adjustment increment.

[0074] The system reads the preset reference proportional coefficient, reference integral coefficient, and reference derivative coefficient of the PID controller. The calculated increments of the proportional, integral, and derivative coefficients are added to their corresponding reference coefficients to obtain the PID parameters for the current control cycle. The corrected load deviation term is used as input and substituted into the positional or incremental PID control algorithm formula containing the PID parameters to perform proportional, integral, and derivative operations, calculating the control output value. This control output value is linearly mapped to a 4-20 mA analog current signal or a 0-10 V analog voltage signal via a digital-to-analog converter interface. This analog signal is transmitted to the control terminal of the feed motor inverter, controlling the inverter to change the frequency of the output power supply. When the load is too high, the frequency is reduced to slow down the feeding speed; when the load is too low, the frequency is increased to speed up the feeding speed, thus achieving closed-loop constant power crushing control.

[0075] In an optional embodiment, during the defuzzification stage, the ratio of the peak value to the mean value of the time sliding window sequence is calculated as an impact factor. The output value obtained from the centroid method defuzzification calculation is then post-weighted and corrected using this impact factor to obtain the adjustment increments for the proportional, integral, and differential coefficients. This includes:

[0076] The maximum current value is searched in the time sliding window sequence as the peak value, the arithmetic mean of all data in the sequence is calculated as the mean, and the quotient of the peak value divided by the mean is calculated to obtain the impact factor.

[0077] Obtain the initial PID adjustment calculated using the center of gravity method;

[0078] A correction formula is set, wherein the adjustment amount is defined as equal to the initial PID adjustment amount multiplied by the correction coefficient, and the correction coefficient is 1 plus the product of the impact factor and the preset weight ratio.

[0079] The adjustment increments of the proportional, integral, and differential coefficients are calculated using the aforementioned correction formula.

[0080] Let the maximum current value in the time sliding window sequence be... The average current value is Calculate the impact factor Under normal load, S is close to 1; when subjected to impact from a large, hard material, S is greater than 1.

[0081] Let the initial value of the PID parameter adjustment calculated by fuzzy inference using the centroid method be... , respectively corresponding , , ;

[0082] Set weight ratio The recommended range is 0.1 to 0.5;

[0083] Calculate the correction factor ;

[0084] Adjust the increment as follows: ;

[0085] The above process ensures that when a mechanical shock is detected, the adjustment range of the PID parameters is forcibly amplified to quickly respond to sudden load changes.

[0086] In an optional embodiment, the step of calculating control commands using the PID parameters and the corrected load deviation term to adjust the inverter output frequency of the crusher feed motor includes:

[0087] The adjustment increments of the proportional, integral, and derivative coefficients are added to the pre-set PID baseline parameters to obtain the proportional coefficient at the current moment. Integral coefficient and differential coefficients ;

[0088] Using the aforementioned proportionality coefficient Multiply by the corrected load deviation term at the current moment to obtain the proportional control component;

[0089] Using the integral coefficient Multiply by the corrected load deviation term at the current moment, and then multiply by the sampling period T to obtain the current integral increment. Add the integral increment to the integral control component at the previous moment to obtain the current integral control component.

[0090] Calculate the difference between the corrected load deviation term at the current time and the previous sampling time, using the differential coefficients. Multiply by the difference and divide by the sampling period T to obtain the differential control component;

[0091] The frequency adjustment command of the frequency converter is obtained by summing the proportional control component, the current integral control component and the derivative control component, and then sent to the feed motor frequency converter through the digital-to-analog conversion interface.

[0092] Let the PID reference parameters be , , The adjustment increment of the fuzzy controller output is , , .

[0093] The parameters of k at the current time are:

[0094]

[0095]

[0096]

[0097] Let the corrected load deviation at the current moment be e(k), the previous moment be e(k-1), and the control sampling period be T.

[0098] Proportional control items: ;

[0099] Integral control items: ;

[0100] Differential control terms: ;

[0101] Total control output: .

[0102] Will The normalized mapping is converted into the frequency command of the frequency converter and sent to the frequency converter through the 4-20mA analog interface; if the frequency corresponding to the calculation result exceeds the preset safety range, the amplitude is limited.

[0103] In the second embodiment, as Figure 3 The diagram shows a construction waste crushing control system based on load feedback, comprising the following modules:

[0104] The first calculation module is used to collect the stator current signal of the crusher main shaft motor, extract the active current component representing the torque through coordinate transformation, construct the current time sliding window sequence of the active current component, and calculate the sample entropy value and coefficient of variation of the time sliding window sequence.

[0105] The second calculation module is used to calculate the algebraic difference between the current active current component and the preset target current value to obtain the original load deviation; to construct a weighting function using the coefficient of variation to correct the amplitude of the original load deviation to obtain the corrected load deviation term; and to calculate the first derivative of the corrected load deviation term with respect to time to obtain the load change rate term.

[0106] The setting module is used to establish a fuzzy logic controller. The modified load deviation term and load change rate term are used as input variables. In the fuzzification stage, a Gaussian membership function is used to construct a mapping function between the sample entropy value and the Gaussian distribution width parameter. The Gaussian membership function distribution width at the current time is calculated and set.

[0107] The adjustment module is used to infer a fuzzy subset of PID parameter adjustment amounts based on a preset fuzzy rule table. During the defuzzification stage, the ratio of the peak value to the mean value of the time sliding window sequence is calculated as an impact factor. This impact factor is used to perform a post-weighted correction on the output value obtained from the centroid method defuzzification calculation, resulting in adjustment increments for the proportional, integral, and derivative coefficients. These adjustment increments are then superimposed onto the baseline parameters of the PID controller to obtain the PID parameters. Finally, the PID parameters and the corrected load deviation term are used to calculate control commands to adjust the inverter output frequency of the crusher feed motor.

[0108] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0109] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for controlling the crushing of construction waste based on load feedback, characterized in that, Includes the following steps: The stator current signal of the crusher spindle motor is collected, and the active current component representing the torque is extracted through coordinate transformation. The current time sliding window sequence of the active current component is then constructed. Calculate the sample entropy and coefficient of variation of the time-sliding window sequence; Calculate the algebraic difference between the current active current component and the preset target current value to obtain the original load deviation; The original load deviation is corrected by constructing a weighting function using the coefficient of variation to obtain the corrected load deviation term; the first derivative of the corrected load deviation term with respect to time is calculated to obtain the load change rate term. A fuzzy logic controller is established, with the corrected load deviation term and load change rate term as input variables. In the fuzzification stage, a Gaussian membership function is used to construct a mapping function between the sample entropy value and the Gaussian distribution width parameter. The distribution width of the Gaussian membership function at the current time is calculated and set. Based on the preset fuzzy rule table, a fuzzy subset of PID parameter adjustment values ​​is obtained through reasoning. In the defuzzification stage, the ratio of the peak value to the mean value of the time sliding window sequence is calculated as an impact factor. The output value obtained by the centroid method defuzzification calculation is post-weighted and corrected using the impact factor to obtain the adjustment increment of the proportional, integral and differential coefficients. The adjustment increment is superimposed on the reference parameters of the PID controller to obtain the PID parameters; the control command is calculated using the PID parameters and the corrected load deviation term to adjust the inverter output frequency of the crusher feed motor.

2. The method according to claim 1, characterized in that, The process of acquiring the stator current signal of the crusher's main shaft motor, extracting the active current component representing torque through coordinate transformation, includes: The collected three-phase stator current signal , , Perform Clarke transformation to convert the current components into a two-phase stationary coordinate system. , ; The rotor magnetic field orientation angle of the spindle motor is obtained using a flux linkage observer or position sensor. The Park transformation matrix is ​​used to transform the current components. , Converted to direct-axis current components in a synchronous rotating coordinate system and cross-axis current components ; The quadrature current component It is determined to be the active current component.

3. The method according to claim 1, characterized in that, The calculation of the sample entropy value of the time-sliding window sequence includes: Set the embedding dimension to m and the tolerance threshold to r; Reconstruct the time-sliding window sequence into a set of vector sequences of dimension m in sequence; Calculate the Chebyshev distance between any two vectors in the vector sequence, count the number of vector pairs whose Chebyshev distance is less than the tolerance threshold r, and calculate the ratio of this number to the total number of vectors to obtain the first probability. Increase the embedding dimension to m+1, repeat the above reconstruction and statistical steps, and obtain the ratio of the number of vector logs with a distance less than the tolerance threshold r to the total number of vectors as the second probability; The sample entropy value is obtained by calculating the negative of the natural logarithm of the ratio of the second probability to the first probability.

4. The method according to claim 2, characterized in that, The step of constructing a weighting function using the coefficient of variation to correct the magnitude of the original load deviation, resulting in a corrected load deviation term, includes: The coefficient of variation is obtained by calculating the ratio of the standard deviation to the absolute value of the arithmetic mean of the time sliding window sequence. Construct an exponential weighting function, wherein the base of the function is the natural constant e, and the exponent is the product of the coefficient of variation and a preset sensitivity factor; Calculate the output value of the weighting function and multiply the output value by the original load deviation to obtain the corrected load deviation term.

5. The method according to claim 1, characterized in that, The mapping function for constructing the sample entropy value and the Gaussian distribution width parameter includes: Set the baseline value and adjustment coefficient for the width parameter of the Gaussian membership function; Establish a mapping function, wherein the width of the Gaussian membership function distribution at the current time is equal to the baseline value of the width parameter divided by the adjustment denominator, and the adjustment denominator is 1 plus the product of the adjustment coefficient and the sample entropy value; The calculated distribution width is used to update the parameters of the membership function of the input variables in the fuzzy logic controller.

6. The method according to claim 1, characterized in that, In the defuzzification stage, the ratio of the peak value to the mean value of the time sliding window sequence is calculated as an impact factor. This impact factor is then used to perform a post-weighted correction on the output value obtained from the centroid method defuzzification calculation, resulting in adjustment increments for the proportional, integral, and differential coefficients. This includes: The maximum current value is searched in the time sliding window sequence as the peak value, the arithmetic mean of all data in the sequence is calculated as the mean, and the quotient of the peak value divided by the mean is calculated to obtain the impact factor. Obtain the initial PID adjustment calculated using the center of gravity method; A correction formula is set, wherein the adjustment amount is defined as equal to the initial PID adjustment amount multiplied by the correction coefficient, and the correction coefficient is 1 plus the product of the impact factor and the preset weight ratio. The adjustment increments of the proportional, integral, and differential coefficients are calculated using the aforementioned correction formula.

7. The method according to claim 5, characterized in that, The step of calculating control commands using the PID parameters and the corrected load deviation term to adjust the inverter output frequency of the crusher feed motor includes: The adjustment increments of the proportional, integral, and derivative coefficients are added to the pre-set PID baseline parameters to obtain the proportional coefficient at the current moment. Integral coefficient and differential coefficients ; Using the aforementioned proportionality coefficient Multiply by the corrected load deviation term at the current moment to obtain the proportional control component; Using the integral coefficient Multiply by the corrected load deviation term at the current moment, and then multiply by the sampling period T to obtain the current integral increment. Add the integral increment to the integral control component at the previous moment to obtain the current integral control component. Calculate the difference between the corrected load deviation term at the current time and the previous sampling time, using the differential coefficients. Multiply by the difference and divide by the sampling period T to obtain the differential control component; The frequency adjustment command of the frequency converter is obtained by summing the proportional control component, the current integral control component and the derivative control component, and then sent to the feed motor frequency converter through the digital-to-analog conversion interface.

8. A construction waste crushing control system based on load feedback, characterized in that, Includes the following modules: The first calculation module is used to collect the stator current signal of the crusher main shaft motor, extract the active current component representing the torque through coordinate transformation, and construct the current time sliding window sequence of the active current component. Calculate the sample entropy and coefficient of variation of the time-sliding window sequence; The second calculation module is used to calculate the algebraic difference between the current active current component and the preset target current value to obtain the original load deviation. The original load deviation is corrected by constructing a weighting function using the coefficient of variation to obtain the corrected load deviation term; the first derivative of the corrected load deviation term with respect to time is calculated to obtain the load change rate term. The setting module is used to establish a fuzzy logic controller. The modified load deviation term and load change rate term are used as input variables. In the fuzzification stage, a Gaussian membership function is used to construct a mapping function between the sample entropy value and the Gaussian distribution width parameter. The Gaussian membership function distribution width at the current time is calculated and set. The adjustment module is used to perform inference based on a preset fuzzy rule table to obtain a fuzzy subset of the PID parameter adjustment amount; In the defuzzification stage, the ratio of the peak value to the mean value of the time sliding window sequence is calculated as an impact factor. The output value obtained by the centroid method defuzzification calculation is post-weighted and corrected using the impact factor to obtain the adjustment increment of the proportional, integral and differential coefficients. The adjustment increment is superimposed on the reference parameters of the PID controller to obtain the PID parameters; the control command is calculated using the PID parameters and the corrected load deviation term to adjust the inverter output frequency of the crusher feed motor.

9. The system according to claim 8, characterized in that, The process of acquiring the stator current signal of the crusher's main shaft motor, extracting the active current component representing torque through coordinate transformation, includes: The collected three-phase stator current signal , , Perform Clarke transformation to convert the current components into a two-phase stationary coordinate system. , ; The rotor magnetic field orientation angle of the spindle motor is obtained using a flux linkage observer or position sensor. The Park transformation matrix is ​​used to transform the current components. , Converted to direct-axis current components in a synchronous rotating coordinate system and cross-axis current components ; The quadrature current component It is determined to be the active current component.

10. The system according to claim 8, characterized in that, The calculation of the sample entropy value of the time-sliding window sequence includes: Set the embedding dimension to m and the tolerance threshold to r; Reconstruct the time-sliding window sequence into a set of vector sequences of dimension m in sequence; Calculate the Chebyshev distance between any two vectors in the vector sequence, count the number of vector pairs whose Chebyshev distance is less than the tolerance threshold r, and calculate the ratio of this number to the total number of vectors to obtain the first probability. Increase the embedding dimension to m+1, repeat the above reconstruction and statistical steps, and obtain the ratio of the number of vector logs with a distance less than the tolerance threshold r to the total number of vectors as the second probability; The sample entropy value is obtained by calculating the negative of the natural logarithm of the ratio of the second probability to the first probability.

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