An Automatic Moisture Removal Control Method for Thin Plate Drying Machines Based on Grey Prediction Model

Abstract

An automatic dehumidification control method for a thin-plate drying machine based on a grey prediction model belongs to the technical field of tobacco production methods. The calculation method includes the following steps: Step 1: Accumulate the multi-input, multi-output time series of the thin-plate drying machine; Step 2: Construct the grey differential expression of the grey prediction model of the thin-plate drying machine and discretize it; Step 3: Calculate the predicted operating state value of the thin-plate drying machine; Step 4: Establish a delay-compensated PID controller; Step 5: Optimize and tune the delay-compensated PID controller parameters using biogeography with chaotic mapping; Step 6: Implement automatic dehumidification control of the thin-plate drying machine. This invention is less affected by a small sample size; the predicted operating state value is relatively accurate; the step response during automatic dehumidification control of the thin-plate drying machine is relatively rapid; it can effectively control the outlet moisture content and the opening of the dehumidification damper of the thin-plate drying machine, and the application effect is good.

Description

Technical Field

[0001] This invention belongs to the technical field of tobacco production methods, and more specifically relates to an automatic dehumidification control method for a thin-plate drying machine based on a gray prediction model. Background Technology

[0002] The thin-plate drying machine is one of the main production equipment in a cigarette production line. During its operation, the tobacco shreds are subjected to the combined effects of hot air blowing and the temperature of the thin plates on the drying drum, causing the internal moisture to evaporate. The moisture is then expelled from the drying machine through the dehumidification system, thus achieving tobacco drying. In the drying machine, the dehumidification system is a crucial component for controlling the internal airflow balance. Activating the dehumidification system quickly removes moisture from the thin-plate drying machine, improving tobacco drying efficiency. When the dehumidification system of the thin-plate drying machine is poorly controlled, a stable airflow cannot be formed inside the drying machine, resulting in excessively high or low moisture content in the tobacco shreds. Summary of the Invention

[0003] This invention provides an automatic dehumidification control method for a thin-plate filament drying machine based on a grey prediction model. The algorithm is used to complete the parameter tuning of the controller, and the controller after parameter tuning is used to realize the automatic dehumidification control of the thin-plate filament drying machine. This solves the problems of unstable working state, slow control response, and large fluctuation of outlet moisture content in the dehumidification system of the thin-plate filament drying machine.

[0004] To achieve the above objectives, the present invention employs the following technical solution: The calculation method includes the following steps: Step 1: Based on the multi-input, multi-output time series of the thin plate drying machine, perform cumulative processing; Step 2: Construct the grey differential expression of the grey prediction model of the thin plate drying machine and discretize it; Step 3: Calculate the predicted value of the operating state of the thin plate drying machine; Step 4: Establish a delay-compensated PID controller; Step 5: Use a biogeographical optimization algorithm to tune the parameters of the delay-compensated PID controller; Step 6: Implement automatic dehumidification control of the thin plate drying machine.

[0005] Preferably, step 1: based on the multi-input, multi-output time series of the thin plate wire drying machine, the first step of the thin plate wire drying machine is calculated. The positive data for the multi-input and multi-output time series of each subsystem are:

[0006] (1)

[0007] in, These represent the first and second parts of the thin plate drying machine. The time series of multiple inputs and multiple outputs of the subsystem, among which , Indicates the total number of multiple inputs and multiple outputs. , , These represent the thin plate wire drying machine generated after mapping. Positive value data of multi-input and multi-output time series of individual subsystems;

[0008] After accumulating the results, the first result of the thin plate wire drying machine is obtained. The single-generation cumulative sequence of the multi-input and multi-output time series of each subsystem is as follows:

[0009] (2)

[0010] in, , These represent the first and second parts of the thin plate drying machine. Positive cumulative sequences of multi-input and multi-output time series of a subsystem.

[0011] Preferably, the white background value of the thin plate wire drying machine in step 1 is:

[0012] (3)

[0013] in, The white background value indicates the operation of the thin plate drying machine; This indicates that the generating coefficients range from 0 to 1.

[0014] Preferably, step 2 involves discretizing the gray differential expression of the gray prediction model for the thin-plate wire drying machine; the gray differential expression of the gray prediction model for the thin-plate wire drying machine is:

[0015] (4)

[0016] in, Indicates time, To express differentiation; , represents the dimension of the coefficient matrix; , All represent coefficients. , The results are obtained by calculation using formulas (5) and (6):

[0017] (5)

[0018] (6)

[0019] In formula (5) The result is obtained by formula (7):

[0020] (7)

[0021] In the above formula, Indicates the transpose symbol;

[0022] After discretizing formula (4), the formula can be rewritten by subtracting both sides:

[0023] (8);

[0024] in, This indicates the sampling cycle of the thin plate wire drying machine.

[0025] Preferably, step 3: Calculate the predicted operating status value of the thin plate wire drying machine:

[0026] (9)

[0027] in, This indicates the predicted value representing the operating status of the thin plate wire drying machine.

[0028] Preferably, in step 4: the predicted operating status value of the thin plate drying machine is used as input to establish a delay-compensated PID controller; the closed-loop transfer function of the delay-compensated PID controller during the dehumidification operation of the thin plate drying machine is:

[0029] (10)

[0030] in, The closed-loop transfer function represents the dehumidification operation of the thin plate drying machine. This transfer function consists of a delayed component and an inertial component connected in series. , , These represent the coefficients, time constant, and time delay constant of the PID controller, respectively. Indicates the PID setpoint;

[0031] The desired transfer function of the delay-compensated PID controller for the thin plate wire drying machine is:

[0032] (11)

[0033] in, This represents the desired transfer function of the delay-compensated PID controller for controlling the thin plate wire drying machine;

[0034] After inputting the predicted operating status value of the thin plate wire drying machine calculated in step 3 into the delay-compensated PID controller, its closed-loop transfer function during dehumidification operation is rewritten as follows:

[0035] (12)

[0036] in, A mathematical model representing a thin plate wire drying machine; This indicates a delay-compensated PID controller;

[0037] When formula (12) equals formula (11), the delay-compensated PID controller The transfer function is expressed as follows:

[0038] (13)

[0039] Substituting the output and input of the delay-compensated PID controller into formula (13), where This represents the output of the delay-compensated PID controller. The input value is the predicted operating state value of the thin plate wire drying machine. The delay-compensated PID controller... The transfer function expression can be rewritten as:

[0040] (14)

[0041] in, Indicates the gain of the thin plate wire drying machine; , This represents the time constant and time lag constant of the thin plate wire drying machine; This represents the adjustment factor of the delay-compensated PID controller;

[0042] Output of delay-compensated PID controller The formula is as follows:

[0043] (15)

[0044] in, This indicates the delay compensation section for controlling the operation of the thin plate wire drying machine; This indicates a PI controller.

[0045] Preferably, step 5: tuning the delay-compensated PID controller parameters using a biogeographical optimization algorithm with chaotic mapping, including the following steps: Step 5.1: generating an initial population; Step 5.2: determining whether the habitat meets the current iteration cutoff condition; Step 5.3: calculating the immigration rate and emigration rate of each solution for the delay-compensated PID controller parameters; Step 5.4: selecting whether the population should emigrate or emigrate into the habitat; Step 5.5: performing mutation operations on the population within the habitat; Step 5.6: searching for the optimal solution within the population using a chaotic search method; Step 5.7: determining whether the optimal solution within the population meets the iteration stopping condition after the i-th search.

[0046] Preferably, the biogeographical optimization algorithm is as follows:

[0047] Step 5.1: Using formula (15) Based on this, an initial population is generated. The population size and maximum number of iterations are respectively determined by , This indicates that the maximum migration rate and emigration rate of this population were respectively determined by , It means, and The maximum mutation rate of the population is ;

[0048] (16)

[0049] Step 5.2: Calculate the fitness index of the habitat where the PID parameters are located in the initial population, and compare them pairwise. Finally, retain the habitat with the highest fitness index, and then determine whether the habitat meets the current iteration cutoff condition. If yes, output the PID parameter value corresponding to the habitat; otherwise, proceed to the next step.

[0050] Step 5.3: Calculate the migration rate and migration rate for each solution of the delay-compensated PID controller parameters, and its nth... The migration rate and migration rate for each solution of the time-compensated PID controller are:

[0051]

[0052] (17)

[0053] in, , They represent the first The migration rate and migration rate for each solution of the time-delay compensated PID controller parameters. This represents the total number of parameter solutions for the delay-compensated PID controller;

[0054] Step 5.4: After obtaining the mapping probability of the initial population using the chaotic mapping method, compare it with the first... The results of the immigration and emigration rates for each solution of the delay-compensated PID controller parameters are compared to determine whether the population should migrate out of or into the habitat. The fitness value of the current population habitat is then recalculated and the population habitat is updated.

[0055] Step 5.5: Perform population variation operations within the habitat, as follows:

[0056] (18)

[0057] In the formula, This represents the numerical value of population variation; Indicates the probability of the number of species within a population; This represents the probability of the largest number of species within a population. Indicates the first Individual populations;

[0058] Step 5.6: Use chaotic search to search for the optimal solution within the population. The formula is as follows:

[0059] (19)

[0060] In the formula, Indicates the first The optimal solution within the population after the second search; This represents the optimal solution within the current population. Indicates the first Sub-chaotic mapping value;

[0061] Step 5.7: Determine the first... If the optimal solution in the population meets the iteration stopping condition after the second search, the optimal solution is saved, and this optimal solution is the optimal parameter of the delay-compensated PID controller; otherwise, the process returns to the second step and iterates again.

[0062] Preferably, step 6: Use the CBBO algorithm to complete the parameter tuning of the delay-compensated PID controller, and use the parameter-tuned delay-compensated PID controller to realize automatic dehumidification control of the thin plate drying machine.

[0063] Beneficial effects of this invention:

[0064] This invention constructs predicted operating status values ​​for a thin-plate drying machine using a grey prediction model, establishes a delay-compensated PID controller, and tunes the parameters of the delay-compensated PID controller using a biogeographical optimization algorithm with chaotic mapping. This delay-compensated PID controller is then used to achieve automatic dehumidification control of the thin-plate drying machine. It is less affected by a small sample size; the predicted operating status values ​​are relatively accurate; the step response during automatic dehumidification control of the thin-plate drying machine is relatively rapid; and it can effectively control the outlet moisture content and the opening of the dehumidification damper of the thin-plate drying machine, demonstrating good application results. Attached Figure Description

[0065] Figure 1 This is a flowchart of the present invention.

[0066] Figure 2 It is a delay-compensated PID controller structure.

[0067] Figure 3 It refers to the generalization performance of the grey prediction model.

[0068] Figure 4 This is a predicted value for the humidity operation status of the thin plate drying machine.

[0069] Figure 5 It controls the step response of the dehumidification temperature of the thin plate drying machine.

[0070] Figure 6 This is the result of controlling the opening of the dehumidification damper of the thin plate drying machine.

[0071] Figure 7 It is the peak moisture content range at the outlet of the tobacco shreds dried using a thin plate drying machine during the first stage of the experiment (limited to 4 months, the first two months).

[0072] Figure 8 In the second phase of the experiment (limited to 4 months, with the last two months), the control method of this invention was used to control the peak range of the outlet moisture content of the tobacco drying machine. Detailed Implementation

[0073] To facilitate understanding and implementation of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0074] Example 1: An automatic dehumidification control method for a thin sheet wire drying machine based on a grey prediction model, applied to the automatic control of the dehumidification system of the thin sheet wire drying machine, including the following steps:

[0075] Step 1: Based on the multi-input, multi-output time series of the thin plate wire drying machine, perform cumulative processing on it. Specifically, let... These represent the first and second parts of the thin plate drying machine. The time series of multiple inputs and multiple outputs of the subsystem, among which , Indicates the total number of multiple inputs and multiple outputs. Use... Mapping function for thin plate wire drying machine The time series data of each subsystem with multiple inputs and multiple outputs are mapped and processed to obtain the first... The positive value data of the multi-input and multi-output time series of each subsystem are expressed by the following formula:

[0076] (1)

[0077] In the above formula, , , These represent the thin plate wire drying machine generated after mapping. Positive value data from multiple input and multiple output time series of a subsystem.

[0078] The generation and accumulation process is applied to formula (1) to obtain the first result of the thin plate wire drying machine. The formula for a single-generation cumulative sequence of a multi-input and multi-output time series of a subsystem is as follows:

[0079] (2)

[0080] In the above formula, , These represent the first and second parts of the thin plate drying machine. Positive cumulative sequences of multi-input and multi-output time series of a subsystem.

[0081] Then, interpolation is performed on formula (2) to obtain the white background value of the thin plate wire drying machine during operation. The formula is as follows:

[0082] (3)

[0083] In the above formula, The white background value indicates the operation of the thin plate drying machine; This represents the generation coefficient, which takes values ​​between 0 and 1.

[0084] Step 2: Construct the gray differential expression of the gray prediction model of the thin plate wire drying machine, and discretize the gray differential expression of the gray prediction model of the thin plate wire drying machine.

[0085] Based on the results of step 1, the grey differential expression of the grey prediction model for the thin plate wire drying machine is constructed as follows:

[0086] (4)

[0087] In the above formula, Indicates time, This indicates differentiation; , represents the dimension of the coefficient matrix; , Both represent coefficients, and their calculation formulas are as follows:

[0088] (5)

[0089] (6)

[0090] (7)

[0091] In the above formula, This represents the transpose symbol.

[0092] Specifically, after discretizing formula (4), the two sides of the formula are subtracted to generate the result, and formula (4) is rewritten as:

[0093] (8)

[0094] In the above formula, This indicates the sampling cycle of the thin plate wire drying machine.

[0095] Step 3: Calculate the predicted operating status of the thin plate wire drying machine; by performing an inverse mapping of formula (8), the predicted operating status of the thin plate wire drying machine can be obtained, and its expression formula is as follows:

[0096] (9)

[0097] In the above formula, This indicates the predicted value representing the operating status of the thin plate wire drying machine.

[0098] Step 4: Use the predicted operating status value of the thin plate drying machine as input to establish a delay-compensated PID controller; specifically, in this embodiment, since the thin plate drying machine has many dynamic characteristic factors during operation, and it is impossible to accurately describe these dynamic characteristic factors, the delay of the drying machine's dehumidification is considered when controlling the automatic dehumidification of the thin plate drying machine. The closed-loop transfer function represents the dehumidification operation of the thin plate drying machine. This transfer function consists of a delayed part and an inertial part connected in series, and its formula is as follows:

[0099] (10)

[0100] In the above formula, , , These represent the coefficients, time constant, and time delay constant of the PID controller, respectively. This represents the PID tuning value.

[0101] make The desired transfer function of the delay-compensated PID controller for the thin plate wire drying machine is expressed by the following formula:

[0102] (11)

[0103] After inputting the predicted operating status value of the thin plate wire drying machine into the delay-compensated PID controller, formula (10) can be rewritten as:

[0104] (12)

[0105] In the above formula, A mathematical model representing a thin plate wire drying machine; This indicates a delay-compensated PID controller.

[0106] To achieve the same values ​​for formulas (11) and (12), the dehumidification control of the thin plate wire drying machine is realized. At this time, the delay-compensated PID controller... The transfer function is expressed as follows:

[0107] (13)

[0108] make Let represent the output of the delay-compensated PID controller, where the input value is the predicted value of the thin plate wire drying machine's operating status. Substituting the output and input of the delay-compensated PID controller into formula (13), formula (13) can be rewritten as:

[0109] (14)

[0110] In the above formula, Indicates the gain of the thin plate wire drying machine; , This represents the time constant and time lag constant of the thin plate wire drying machine; This represents the adjustment factor of the delay-compensated PID controller.

[0111] Mathematically transforming formula (14), the output of the delay-compensated PID controller is... The formula is as follows:

[0112] (15)

[0113] In the above formula, This indicates the delay compensation section for controlling the operation of the thin plate wire drying machine; This indicates a PI controller.

[0114] Using formula (15), output parameters such as steam regulation amount and valve opening during automatic dehumidification of the thin plate drying machine, and realize automatic dehumidification control of the thin plate drying machine based on these parameters.

[0115] Step 5: Tune the delay-compensated PID controller parameters using a biogeographical optimization algorithm with chaotic mapping;

[0116] Specifically, the parameter tuning process is as follows:

[0117] Step 1: Based on the result of formula (15), generate the initial population. The population size and maximum number of iterations are respectively determined by , Indicated. The maximum migration rate and emigration rate of this population are respectively determined by , It means, and The maximum mutation rate of the population is .

[0118] Step 2: Calculate the fitness index of the habitats containing the PID parameters within the initial population, perform pairwise comparisons, and finally retain the habitat with the highest fitness index. Then determine whether the habitat meets the current iteration cutoff condition. If yes, output the PID parameter value corresponding to the habitat; otherwise, proceed to the next step.

[0119] Step 3: Order , They represent the first The migration rate and migration rate for each solution of the time-delay compensated PID controller parameters are expressed by the following formulas:

[0120] (16)

[0121] (17)

[0122] In the above formula, This represents the total number of parameter solutions for the delay-compensated PID controller.

[0123] Step 4: After obtaining the initial population mapping probability using the chaotic mapping method, compare the GIA probability value with the results of formulas (16) and (17) to determine whether the population should migrate out of or into the habitat. Then recalculate the current population habitat suitability value and update the population habitat.

[0124] Step 5: Use the following formula to perform mutation operations on the population in the current habitat:

[0125] (18)

[0126] In the above formula, This represents the numerical value of population variation; Indicates the probability of the number of species within a population; This represents the probability of the largest number of species within a population. Indicates the first Individual populations.

[0127] Step 6: Use chaotic search to search for the optimal solution within the population. The formula is as follows:

[0128] (19)

[0129] In the above formula, Indicates the first The optimal solution within the population after the second search; This represents the optimal solution within the current population. Indicates the first Sub-chaotic mapping value.

[0130] Step 7: Determine whether the result of formula (19) meets the iteration stopping condition. If it does, save the best solution, which is the optimal parameter of the delay-compensated PID controller. Otherwise, return to Step 2 to iterate again.

[0131] Step 6: Use the time-compensated PID controller with adjusted parameters to achieve automatic dehumidification control of the thin plate drying machine.

[0132] Specifically, the operation is as follows:

[0133] The CBBO algorithm is used to complete the parameter tuning of the delay-compensated PID controller, and the delayed-compensated PID controller after parameter tuning is used to realize the automatic dehumidification control of the thin plate drying machine.

[0134] like Figure 2 As shown, in this embodiment, the predicted operating status value of the thin plate drying machine is used as input, and a delay-compensated PID controller is designed to realize the automatic dehumidification control of the thin plate drying machine.

[0135] like Figure 3 As shown, the precision and recall values ​​of the grey model of this method both show a slight downward trend. The intersection point of the recall and precision curves, i.e. the equilibrium point, is located at about 0.91 of the precision value and about 0.94 of the recall value. This value indicates that the predicted value of the thin plate wire drying machine operation status output by the method of this embodiment is relatively accurate and has good generalization ability.

[0136] like Figure 4 As shown, the humidity inside the thin-plate tobacco drying machine decreases over time, indicating that the moisture content of the tobacco shreds is gradually decreasing and the tobacco is being dried. When using the method described in this paper to predict the humidity operating status of the thin-plate tobacco drying machine, the predicted and actual values ​​almost perfectly match. This result demonstrates that the method described in this paper accurately predicts the operating status of the drying machine, and also indirectly indicates that the method of this invention has a good effect on moisture control in the thin-plate tobacco drying machine.

[0137] like Figure 5 As shown, when the thin plate drying machine experiences dehumidification abnormality, the method of the present invention controls the temperature follow-up time of the thin plate drying machine during dehumidification to about 8 seconds, and it can quickly restore the internal temperature of the thin plate drying machine to the given temperature value. Moreover, when the internal temperature of the thin plate drying machine reaches the given temperature value, the overshoot value is small, the response is rapid, and the adjustment time is short.

[0138] like Figure 6 As shown, during the operation of the thin plate drying machine, after controlling its dehumidification using the method of the present invention, the opening of its damper can be controlled for a period of time to keep the damper opening at a constant value, so as to adjust the temperature and humidity inside the thin plate drying machine and better improve the moisture content and whole-shred rate of tobacco.

[0139] like Figure 7 , Figure 8As shown, in the first stage, the moisture content at the outlet of the thin plate drying machine was between 12.60% and 12.90%, with a relatively high frequency of moisture content values ​​between 12.70% and 12.75%. However, after using the method of this invention to control the moisture content of the thin plate drying machine, its outlet moisture content ranged from 12.55% to 12.75%, with a relatively high frequency of moisture content values ​​between 12.60% and 12.65%. This indicates that after applying the method of this embodiment, the moisture content at the outlet of the thin plate drying machine is more stable, effectively improving the process quality of the thin plate drying machine.

[0140] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automatic dehumidification control method for a thin-plate wire drying machine based on a grey prediction model, characterized in that: Includes the following steps: Step 1: Based on the multi-input, multi-output time series of the thin plate wire drying machine, perform cumulative processing on it; Step 2: Construct the grey differential expression of the grey prediction model for the thin plate drying machine and discretize it; Step 3: Calculate the predicted operating status of the thin plate drying machine; Step 4: Establish a delay-compensated PID controller; Step 5: Use a biogeographical optimization algorithm with chaotic mapping to tune the parameters of the delay-compensated PID controller; Step 6: Implement automatic dehumidification control for the thin plate drying machine; Step 1: Based on the multi-input, multi-output time series of the thin plate wire drying machine, calculate the first... The positive data for the multi-input and multi-output time series of each subsystem are: （1） in, These represent the first and second parts of the thin plate drying machine. The time series of multiple inputs and multiple outputs of the subsystem, among which , Indicates the total number of multiple inputs and multiple outputs. , These represent the thin plate wire drying machine generated after mapping. Positive value data of multi-input and multi-output time series of individual subsystems; After accumulating the results, the first result of the thin plate wire drying machine is obtained. The single-generation cumulative sequence of the multi-input and multi-output time series of each subsystem is as follows: （2） in, , These represent the first and second parts of the thin plate drying machine. Positive cumulative sequences of multi-input and multi-output time series of each subsystem; The white background value for the thin plate wire drying machine described in step 1 is: （3） in, The white background value indicates the operation of the thin plate drying machine; This indicates that the generating coefficients take values ​​between 0 and 1; Step 2: Discretize the gray differential expression of the gray prediction model for the thin plate wire drying machine; the gray differential expression of the gray prediction model for the thin plate wire drying machine is: （4） in, Indicates time, To express differentiation; , represents the dimension of the coefficient matrix; , All represent coefficients. , The results are obtained by calculation using formulas (5) and (6): （5） （6） In formula (5) The result is obtained by formula (7): （7） In the above formula, Indicates the transpose symbol; After discretizing formula (4), the formula can be rewritten by subtracting both sides: （8）； in, Indicates the sampling cycle of the thin plate wire drying machine; Step 3: Calculate the predicted operating status of the thin plate wire drying machine. （9） in, Predicted values ​​indicating the operating status of the thin plate wire drying machine; Step 4: Using the predicted operating status value of the thin plate drying machine as input, a delay-compensated PID controller is established; the closed-loop transfer function of the delay-compensated PID controller controlling the dehumidification operation of the thin plate drying machine is: （10） in, The closed-loop transfer function represents the dehumidification operation of the thin plate drying machine. This transfer function consists of a delayed component and an inertial component connected in series. These represent the coefficients, time constant, and time delay constant of the PID controller, respectively. Indicates the PID setpoint; The desired transfer function of the delay-compensated PID controller for the thin plate wire drying machine is: （11） in, This represents the desired transfer function of the delay-compensated PID controller for controlling the thin plate wire drying machine; After inputting the predicted operating status value of the thin plate wire drying machine calculated in step 3 into the delay-compensated PID controller, its closed-loop transfer function during dehumidification operation is rewritten as follows: （12） in, A mathematical model representing a thin plate wire drying machine; This indicates a delay-compensated PID controller; When formula (12) equals formula (11), the delay-compensated PID controller The transfer function is expressed as follows: （13） Substituting the output and input of the delay-compensated PID controller into formula (13), where This represents the output of the delay-compensated PID controller. The input value is the predicted operating state value of the thin plate wire drying machine. The delay-compensated PID controller... The transfer function expression can be rewritten as: （14） in, Indicates the gain of the thin plate wire drying machine; This represents the time constant and time lag constant of the thin plate wire drying machine; This represents the adjustment factor of the delay-compensated PID controller; Output of delay-compensated PID controller The formula is as follows: （15） in, This indicates the delay compensation section for controlling the operation of the thin plate wire drying machine; This indicates a PI controller.

2. The automatic dehumidification control method for a thin plate filament drying machine based on a grey prediction model according to claim 1, characterized in that: Step 5: Tuning the delay-compensated PID controller parameters using a biogeographical optimization algorithm with chaotic mapping, including the following steps: Step 5.1: Generating an initial population; Step 5.2: Determining whether the habitat meets the current iteration cutoff condition; Step 5.3: Calculating the immigration and emigration rates for each solution of the delay-compensated PID controller parameters; Step 5.4: Selecting whether the population should emigrate or enter the habitat; Step 5.5: Performing mutation operations on the population within the habitat; Step 5.6: Searching for the optimal solution within the population using a chaotic search method; Step 5.7: Determining the... Does the optimal solution within the population satisfy the iteration stopping condition after the second search? 3. The automatic dehumidification control method for a thin-plate wire drying machine based on a grey prediction model according to any one of claims 1 or 2, characterized in that: The biogeographical optimization algorithm with chaotic mapping: Step 5.1: Using formula (15) Based on this, an initial population is generated. The population size and maximum number of iterations are respectively determined by , This indicates that the maximum migration rate and emigration rate of this population were respectively determined by , It means, and The maximum mutation rate of the population is ; （16）； Step 5.2: Calculate the fitness index of the habitat where the PID parameters are located in the initial population, and compare them pairwise. Finally, retain the habitat with the highest fitness index, and then determine whether the habitat meets the current iteration cutoff condition. If yes, output the PID parameter value corresponding to the habitat; otherwise, proceed to the next step. Step 5.3: Calculate the migration rate and migration rate for each solution of the delay-compensated PID controller parameters, and its nth... The migration rate and migration rate for each solution of the time-compensated PID controller are: （17）； in, , They represent the first The migration rate and migration rate for each solution of the time-delay compensated PID controller parameters. This represents the total number of parameter solutions for the delay-compensated PID controller; Step 5.4: After obtaining the mapping probability of the initial population using the chaotic mapping method, compare it with the first... The results of the immigration and emigration rates for each solution of the delay-compensated PID controller parameters are compared to determine whether the population should migrate out of or into the habitat. The suitability value of the current population habitat is then recalculated and the population habitat is updated. Step 5.5: Perform population variation operations within the habitat, as follows: （18）； In the formula, This represents the numerical value of population variation; Indicates the probability of the number of species within a population; This represents the probability of the largest number of species within a population. Indicates the first Individual populations; Step 5.6: Use chaotic search to search for the optimal solution within the population. The formula is as follows: （19）； In the formula, Indicates the first The optimal solution within the population after the second search; This represents the optimal solution within the current population. Indicates the first Sub-chaotic mapping value; Step 5.7: Determine the first... If the optimal solution in the population meets the iteration stopping condition after the second search, the optimal solution is saved, and this optimal solution is the optimal parameter of the delay-compensated PID controller; otherwise, the process returns to the second step and iterates again.

4. The automatic dehumidification control method for a thin plate wire drying machine based on a grey prediction model according to claim 1, characterized in that: Step 6: Use the CBBO algorithm to complete the parameter tuning of the delay-compensated PID controller, and use the parameter-tuned delay-compensated PID controller to realize the automatic dehumidification control of the thin plate drying machine.

Images