Method and device for controlling moisture content at the outlet of a cut tobacco dryer based on fuzzy algorithm

Abstract

This application discloses a method and apparatus for controlling the outlet moisture of a wire dryer based on a fuzzy algorithm. The method includes: establishing a second-order inertial hysteresis model of the material being dried by heating the drum wall; determining the current state of the wire dryer; if the wire dryer is in the main production state, then a cascaded single-closed-loop negative feedback PID control mode formed by a front-end PID moisture control loop and a rear-end PID drum wall temperature control loop is used to control the moisture of the outlet material. The optimal combination of control parameters for the front-end PID moisture control loop is determined by a fuzzy controller. This application uses cascaded PID control and a fuzzy algorithm to calculate the control parameters of the front-end PID moisture control loop, thereby controlling the moisture of the outlet material. It has good dynamic performance and control accuracy, which is beneficial for achieving rapid system response and control. Compared with traditional cascaded PID control, this application is better able to handle fuzzy, complex, and uncertain large-hysteresis systems, and has better robustness and adaptability.

Description

Technical Field

[0001] This application relates to the field of cigarette production technology, and more specifically, to a method and apparatus for controlling the outlet moisture of a tobacco drying machine based on a fuzzy algorithm. Background Technology

[0002] The common working principle of cigarette shredding and drying machines is as follows: a certain flow rate of steam passes through the inner wall of the cylinder's thin plate. The steam condenses in the steam channel, effectively transferring heat to the thin plate and onto the tobacco shreds. Simultaneously, a fan sends ambient air into the steam-heated heat exchanger to generate hot air. As the air flows through the drum, heat is transferred to the tobacco shreds through convection, and moisture is carried away. This ensures uniform drying, uniform heating, and a uniform increase in filling force, as well as a constant outlet moisture content. Furthermore, a dehumidification system is installed inside the cylinder to extract and discharge some of the moisture-containing and hot exhaust gases, thereby maintaining a constant moisture content in the material and indirectly affecting its moisture content.

[0003] Due to the highly nonlinear, uncertain, and time-dependent nature of the tobacco drying process, coupled with the unique properties of tobacco leaves themselves, moisture control during the drying process becomes extremely complex. Essentially, the moisture control of materials in tobacco drying machines is a nonlinear, time-varying system with a large time lag, such as... Figure 1 As shown, current mainstream equipment manufacturers use cascade PID control to control the moisture content of the outlet material in a wire drying machine. The control parameters of the front-stage PID moisture control loop lack self-adjustment functionality. The output value CV1 of the front-stage PID (moisture controller) is used to obtain the setpoint SP2 of the subsequent PID through a linear function f(x). This allows the cylinder wall temperature controller to adjust the cylinder wall temperature using the subsequent PID, thereby regulating the actual moisture content of the outlet material. However, in existing multi-stage PID control systems, the controller's parameter adjustment capability is poor, resulting in poor dynamic performance and control accuracy of the system. Summary of the Invention

[0004] This application provides a method and device for controlling the outlet moisture of a wire drying machine based on a fuzzy algorithm. The method uses cascade PID control and a fuzzy algorithm to calculate the control parameters of the preceding PID moisture control loop, thereby controlling the moisture content of the outlet material. It has good dynamic performance and control accuracy, which is conducive to achieving rapid response and control of the system. Compared with traditional cascade PID control, this application is better able to handle fuzzy, complex and uncertain systems with large time delays, and has better robustness and adaptability.

[0005] This application provides a method for controlling the outlet moisture of a yarn drying machine based on a fuzzy algorithm, including:

[0006] Establish a second-order inertial hysteresis model for drying materials by heating the cylinder wall;

[0007] Determine the current status of the wire drying machine;

[0008] If the drying machine is in production mode, a cascaded single closed-loop negative feedback PID control method is used to control the moisture content of the outlet material, which is formed by the front-end PID moisture control loop and the back-end PID cylinder wall temperature control loop. The optimal combination of control parameters of the front-end PID moisture control loop is determined by a fuzzy controller.

[0009] Preferably, the outlet moisture control method of the fuzzy drying machine based on fuzzy algorithm further includes optimizing the fuzzy controller through a genetic algorithm.

[0010] Preferably, the input data of the fuzzy controller are the error between the actual moisture value of the material at the outlet of the drying machine and the set value of the outlet moisture, as well as the change in error between the current error and the previous error. The output data of the fuzzy controller are the proportional control parameters, integral control parameters, and derivative control parameters of the preceding PID moisture control.

[0011] Preferably, the outlet moisture control method of the fuzzy algorithm-based yarn drying machine further includes:

[0012] If the moisture content of the outlet material is stable under the control parameters of the current preceding PID moisture control loop, then the control parameters of the current preceding PID moisture control loop will continue to be used to control the moisture content of the outlet material.

[0013] Preferably, if the moisture content of the outlet material is unstable under the control parameters of the current upstream PID moisture control loop, a second-order inertial hysteresis model for drying the material by heating the cylinder wall is re-established, and the control parameters of the upstream PID moisture control loop are re-determined.

[0014] This application also provides an outlet moisture control device for a fuzzy algorithm-based yarn drying machine, including a drying model establishment module, a first judgment module, and a control module;

[0015] The drying model building module is used to build a second-order inertial hysteresis model for drying materials by heating the cylinder wall;

[0016] The first judgment module is used to determine the current state of the wire drying machine;

[0017] The control module is used to control the moisture content of the outlet material when the drying machine is in the production host state. It adopts a cascade single closed-loop negative feedback PID control method formed by the front-end PID moisture control loop and the back-end PID cylinder wall temperature control loop. The optimal combination of control parameters of the front-end PID moisture control loop is determined by the fuzzy controller.

[0018] Preferably, the outlet moisture control device of the fuzzy fiber drying machine based on fuzzy algorithm further includes an optimization module, which is used to optimize the fuzzy controller through a genetic algorithm.

[0019] Preferably, the input data of the fuzzy controller are the error between the actual moisture value of the material at the outlet of the drying machine and the set value of the outlet moisture, as well as the change in error between the current error and the previous error. The output data of the fuzzy controller are the proportional control parameters, integral control parameters, and derivative control parameters of the preceding PID moisture control.

[0020] Preferably, the outlet moisture control device of the fuzzy algorithm-based wire drying machine further includes a second judgment module, which is used to determine whether the outlet material moisture is stable under the control parameters of the current preceding PID moisture control loop; and,

[0021] The control module is also used to continue to control the moisture content of the outlet material by using the control parameters of the current preceding PID moisture control loop when the moisture content of the outlet material is stable under the control parameters of the current preceding PID moisture control loop.

[0022] Preferably, the drying model establishment module is also used to re-establish a second-order inertial hysteresis model for drying materials heated by the drum wall when the moisture content of the outlet material is unstable under the control parameters of the current upstream PID moisture control loop; and,

[0023] The control module is also used to redetermine the control parameters of the preceding PID moisture control loop after re-establishing the second-order inertial hysteresis model of the material being dried by heating the cylinder wall.

[0024] Other features and advantages of this application will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments of the present application and, together with their description, serve to explain the principles of the present application.

[0026] Figure 1 This is a schematic diagram illustrating the moisture control of the outlet material in the filament drying machine currently used by mainstream equipment manufacturers.

[0027] Figure 2 A flowchart of the outlet moisture control method for a fuzzy algorithm-based yarn drying machine provided in this application;

[0028] Figure 3 A time-domain schematic diagram of the positive step response followed by the negative step response provided in this application;

[0029] Figure 4 A schematic diagram of the membership functions provided in this application;

[0030] Figure 5 A schematic diagram illustrating the moisture control of the outlet material of the filament drying machine provided in this application;

[0031] Figure 6The structural diagram of the outlet moisture control device of the fuzzy algorithm-based wire drying machine provided in this application. Detailed Implementation

[0032] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present application.

[0033] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.

[0034] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0035] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0036] This application provides a method and device for controlling the outlet moisture of a wire drying machine based on a fuzzy algorithm. The method uses cascade PID control and a fuzzy algorithm to calculate the control parameters of the preceding PID moisture control loop, thereby controlling the moisture content of the outlet material. It has good dynamic performance and control accuracy, which is conducive to achieving rapid response and control of the system. Compared with traditional cascade PID control, this application is better able to handle fuzzy, complex and uncertain systems with large time delays, and has better robustness and adaptability.

[0037] like Figure 2 As shown, the outlet moisture control method for a yarn drying machine based on fuzzy algorithm provided in this application includes:

[0038] S210: Establish a mathematical model for drying materials by heating the cylinder wall.

[0039] Specifically, the transfer function of drying material by heating the cylinder wall is fitted into a second-order inertial hysteresis model, and the transfer function is as follows:

[0040]

[0041] Where Φ(s) is the transfer function, s is the Laplace operator, K is the static amplification factor, T1 and T2 are the second-order inertial element coefficients, and the constant τ is the lag time.

[0042] By identifying the model of the actual controlled object (the mathematical model of the material drying in the cylinder wall), the overshoot and steady-state error are reduced, and the settling time is shortened, thereby improving the stability, convergence and robustness of the material moisture control model of the filament drying machine.

[0043] Preferably, a positive step response followed by a negative step response is used for model identification. This positive-then-negative step response has less impact on the material. Through calculation, the mathematical model for drying the material by heating it against the cylinder wall can be identified, such as... Figure 3 As shown.

[0044] like Figure 5 As shown, this application uses a series multi-stage PID control method to control the moisture content of the outlet material, namely the front-stage PID moisture control loop ( Figure 5 (Shown as implemented through a moisture controller) and subsequent PID cylinder wall temperature control loop ( Figure 5 The system is implemented via a cylinder wall temperature controller. The output value CV1 of the preceding PID controller (moisture controller) is used to obtain the setpoint SP2 of the subsequent PID controller via a linear function f(x), thus allowing the cylinder wall temperature to be adjusted by the subsequent PID controller. Furthermore, the multi-stage PID control loop is connected in series with an established second-order inertial lag model, and a closed-loop feedback channel with a feedback coefficient of 1 is set to achieve control and automatic adjustment of the moisture content of the outlet material.

[0045] S220: Determine the current state of the wire drying machine. If the wire drying machine is in the main production state, execute S230; otherwise, continue waiting until the main production state conditions are met for direct normal wire drying process production. Furthermore, other stages of the wire drying machine, such as preheating, standby, and residual material output, are controlled according to existing equipment control methods.

[0046] S230: A cascaded single-closed-loop negative feedback PID control method, formed by a front-stage PID moisture control loop and a rear-stage PID cylinder wall temperature control loop, is used to control the moisture content of the outlet material. Figure 5 As shown.

[0047] The output value u1(k) of the preceding PID moisture control loop is:

[0048]

[0049] The setpoint SP2(k) for the cylinder wall temperature control loop of the subsequent PID cylinder wall temperature control is:

[0050]

[0051] Among them, Kp * Ki * Kd *For the optimal combination of control parameters of the preceding PID moisture control loop, e1(k) is the deviation between the setpoint SP1(k) and the process value PV1(k) of the preceding PID moisture control loop, ΔSP2(k) represents the cylinder wall temperature correction value, f(x) represents the proportional coefficient function of the outlet material moisture deviation and the cylinder wall temperature deviation, and k and k-1 represent different times.

[0052] The optimal combination of control parameters for the upstream PID moisture control loop is determined by the fuzzy controller using a fuzzy algorithm.

[0053] The input data of the fuzzy controller are the error between the actual moisture value of the material at the outlet of the drying machine and the setpoint of the outlet moisture value, as well as the error change ec1 between the current error e1 and the previous error. The output data Y of the fuzzy controller are the proportional control parameter Kp, integral control parameter Ki, and derivative control parameter Kd of the preceding PID moisture control.

[0054] Specifically, constructing a fuzzy controller includes the following steps:

[0055] P1: Subset Partitioning. Before fuzzifying the input data of the fuzzy controller, the input data is first partitioned. The error e1 between the actual moisture value of the material at the outlet of the drying machine and the setpoint moisture value, as well as the change in error ec1 between the current error and the previous error, are divided into seven universe subsets, namely {NB, NM, NS, ZO, PS, PM, PB}, corresponding to {very small, small, relatively small, zero, relatively large, large, very large}, and encoded using integers {1, 2, 3, 4, 5, 6, 7}. The output data (the proportional control parameter Kp, integral control parameter Ki, and derivative control parameter Kd of the preceding PID moisture control) are taken as integers within [1, 7], which serve as the gene variation range in the genetic algorithm, corresponding to the membership values ​​of the fuzzy subsets (see the following description of the genetic algorithm for details).

[0056] P2: Perform fuzzification on the input data, that is, construct membership functions to fuzzify the input quantities.

[0057] As an example, such as Figure 4 As shown, a membership function combining trigonometric functions (see the function curves corresponding to NM, NS, ZO, PS, PM) and Gaussian functions (see the function curves corresponding to NB and PB) is used as the membership function for the error e1(k) and the error change ec1(k). The membership degree of any element in the fuzzy set is determined based on the membership function, and fuzzification is performed to convert the precise numerical value into a fuzzy value.

[0058] P3: Establishing Fuzzy Rules. Based on the impact of the preceding PID moisture control parameters on system performance and the automatic adjustment principle of PID parameters at different stages of the system's dynamic response, and according to the parameter adjustment experience obtained from actual testing, fuzzy PID control parameter tuning rules that enable the system to achieve optimal time-domain response performance are obtained. These rules are then set based on expert experience, resulting in the fuzzy control rules for control parameters Kp, Ki, and Kd, as shown in the table below:

[0059]

[0060]

[0061] P4: Defuzzification. The parameters Kp, Ki, and Kd obtained from the fuzzy inference in P2 are transformed to obtain accurate inferred values, completing the construction of the fuzzy controller. The construction formula is:

[0062]

[0063] Where y represents the determined value of parameters Kp, Ki, or Kd calculated based on the adjusted fuzzy subset, G(i) represents the fuzzy subset (i.e., NB, NM, NS, ZO, PS, PM, or PB) to which parameters Kp, Ki, or Kd belong after fuzzy inference, and u G (i) represents the membership value of G(i), and P represents the number of elements in G(i).

[0064] Preferably, step S230 further includes optimizing the fuzzy controller using a genetic algorithm, such as optimizing the membership function and fuzzy inference rules of the fuzzy controller. Figure 5 As shown. It is preferable to execute the genetic algorithm offline.

[0065] Specifically, the fuzzy controller is optimized using a genetic algorithm, including:

[0066] Q1: Determine the initialization parameters and encoding format of the genetic algorithm.

[0067] Initialization parameters include population size S, crossover probability Pc, mutation probability Pm, and maximum number of generations Gm. Genetic encoding is performed on the membership function of the fuzzy controller to determine the initial population. The vertices of the membership function, composed of five mixed variables, are arranged sequentially in the following order: error e1, error change ec1, proportional control parameter Kp, integral control parameter Ki, and derivative control parameter Kd form a vector, which is then encoded with real numbers. The membership function and fuzzy control rule table of the fuzzy controller are converted into numerical descriptions. The membership function and fuzzy rule table are encoded in decimal, with the rule table using numbers 1 to 7 corresponding to NB, NS, NM, ZO, PS, PM, and PB, respectively. The fuzzy rule table is then converted into a two-dimensional matrix and arranged vertically to represent the individual chromosomes in the population.

[0068] Q2: Construct the fitness function.

[0069] Considering the dynamic and steady-state characteristics of the system, the time integral function J of the absolute value of the construction error is selected as the fitness function to comprehensively evaluate the system's stability and accuracy, as shown below:

[0070]

[0071] Where e1(t) represents the difference between the actual and expected moisture content of the material exiting the drying machine, u1(t) is the output value of the upstream PID moisture controller, and t u1 The rise time of the output time domain response of the upstream PID moisture controller is denoted by α, which serves as a regularization term to reduce overfitting and the risk of prematurely falling into local optimization. Here, w1, w2, w3, and w4 are weighting coefficients.

[0072] Q3: Construct intersection and mutation operators.

[0073] When performing crossover and mutation operations on individuals in the genetic algorithm population, a multi-point crossover method is used to generate new individuals during the crossover process. Simultaneously, the start and end segments of multiple selected chromosome positions are copied to the offspring according to their positions, and the corresponding genes in the parent generation are deleted, inheriting sequentially to the offspring. The mutation process largely avoids premature convergence. First, the random number and mutation probability are compared to decide whether to mutate. Then, the mutated value is selected. To improve global search capability and avoid prematurely falling into local optima, an adaptive adjustment method is used to adjust the crossover probability P. C and adaptive mutation probability P m as follows:

[0074]

[0075]

[0076] Among them, f max f is the maximum fitness value in the population. avg f is the mean fitness level in the group; c The larger Fitness value is taken from the two individuals at the intersection; f m To assign a value to the Fitness of the individual to be mutated; P C1 and P C2 P represents the upper and lower bounds of the crossover probability; m1 and P m2 These represent the upper and lower limits of the mutation probability.

[0077] Q4: Genetic decoding and optimal value calculation.

[0078] Chromosomes in the population are decoded one by one, and their fitness values ​​are calculated. For each chromosome, real-valued decoding is performed, and the resulting vector is used as a parameter for the mixed membership function. A membership function is then established based on this parameter, and a fuzzy control model is constructed. Subsequently, moisture data at the outlet of the drying machine is collected using a moisture meter. The established fuzzy control model is used to control the correction value of the cylinder wall temperature setpoint, and the model's control effect is verified. The error e between the drying machine outlet moisture and the setpoint, as well as the error change ec, during the detection period are input into the fuzzy control model, and its output is used as the optimal combination of control parameters Kp. * Ki * Kd * .

[0079] Q5: Construct a selection strategy.

[0080] The selection process is performed on individual chromosomes in the population based on their fitness, to determine the quality of individuals and update the population accordingly.

[0081] This application preferably uses the sequential selection method (an improvement on the roulette wheel selection method) as the selection strategy, which is more suitable for the optimization of offline PID parameters and reduces the amount of computation.

[0082] If NP individuals are sorted from best to worst using the sequential selection method, then the probability of selecting the j-th individual is:

[0083] P(j)=q(1-q) j-1 (8)

[0084] Where q is the probability of choosing the best individual.

[0085] Q6: Termination of heredity and convergence of control.

[0086] Determine if the number of genetic iterations has reached the maximum number of evolutions, Gm. If so, end the process and output the fuzzy control model corresponding to the individual with the maximum Fitness function J value, which is the optimized fuzzy control model (fuzzy controller). Otherwise, return to step Q3 to recalculate the intersection and mutation. Matrix operations can be used to verify that the control system converges within a reasonable range.

[0087] After obtaining the optimized fuzzy controller, the optimal combination of control parameters is obtained using the optimized fuzzy controller. Figure 5 (The value shown in the middle is the optimal parameter set) Kp * Ki * Kd * The optimal combination is then assigned to the proportional, integral, and derivative parameters in the preceding PID moisture control loop to achieve control of the moisture content of the outlet material.

[0088] Preferably, each time the wire drying machine enters the main production state, it performs a search for the optimal combination of control parameters based on a genetic algorithm.

[0089] Preferably, after step S230, the method further includes:

[0090] S240: Determine whether the moisture content of the outlet material is stable under the control parameters of the current upstream PID moisture control loop. If yes, return to S230; otherwise, return to S210, re-establish the second-order inertial hysteresis model for drying the material by heating the drum wall, and redetermine the control parameters of the upstream PID moisture control loop (S230, at which point the drying machine is in the production host state). If the drying machine switches from the production host state to the non-production stage, it switches to the existing host control mode for control.

[0091] Based on the above-mentioned fuzzy algorithm-based method for controlling the outlet moisture of a wire drying machine, this application also provides a fuzzy algorithm-based device for controlling the outlet moisture of a wire drying machine. For example... Figure 6 As shown, the outlet moisture control device of the fuzzy algorithm-based yarn drying machine includes a drying model establishment module 610, a first judgment module 620, and a control module 630.

[0092] The drying model establishment module 610 is used to establish a second-order inertial hysteresis model for drying materials by heating the cylinder wall.

[0093] The first judgment module 620 is used to determine the current state of the wire drying machine.

[0094] The control module 630 is used to control the moisture content of the outlet material by adopting a cascade single closed-loop negative feedback PID control method formed by the front-end PID moisture control loop and the rear-end PID cylinder wall temperature control loop when the drying machine is in the production host state. The optimal combination of control parameters of the front-end PID moisture control loop is determined by the fuzzy controller.

[0095] Specifically, the input data of the fuzzy controller are the error between the actual moisture value of the material at the outlet of the drying machine and the setpoint of the outlet moisture value, as well as the change in error between the current error and the previous error. The output data of the fuzzy controller are the proportional control parameters, integral control parameters, and derivative control parameters of the preceding PID moisture control.

[0096] Preferably, the outlet moisture control device of the fuzzy algorithm-based filament drying machine further includes an optimization module 640, which is used to optimize the fuzzy controller through a genetic algorithm.

[0097] Preferably, the outlet moisture control device of the fuzzy algorithm-based wire drying machine further includes a second judgment module 650, which is used to determine whether the outlet material moisture is stable under the control parameters of the current preceding PID moisture control loop. Furthermore, the control module 630 is also used to continue using the control parameters of the current preceding PID moisture control loop to control the outlet material moisture when the outlet material moisture is stable under the control parameters of the current preceding PID moisture control loop.

[0098] Preferably, the drying model establishment module 610 is also used to re-establish a second-order inertial hysteresis model of the material dried by heating the drum wall when the moisture content of the outlet material is unstable under the control parameters of the current upstream PID moisture control loop. Furthermore, the control module 630 is also used to redetermine the control parameters of the upstream PID moisture control loop after re-establishing the second-order inertial hysteresis model of the material dried by heating the drum wall.

[0099] The beneficial effects of this application include at least the following:

[0100] 1. This application uses cascade PID control and fuzzy algorithms to calculate the control parameters of the upstream PID moisture control loop. Compared with traditional cascade PID control in silk drying machines, it is better able to handle fuzzy, complex, and uncertain systems with large time delays, exhibiting better robustness and adaptability. It also demonstrates good dynamic performance and control accuracy. The combination of cascade PID and fuzzy algorithms utilizes fuzzy logic and fuzzy inference to achieve optimized dynamic performance and control accuracy, enabling better response and control when the system changes rapidly.

[0101] 2. This application defines the fuzzy PID control stage and the genetic algorithm calculation stage in the form of the main operating state and operating stage of the drying machine, effectively identifying the application scenarios of the control method of this application and the prerequisite requirements for switching the control algorithm.

[0102] 3. This application utilizes a combination of offline genetic algorithm and fuzzy algorithm to find the optimal PID parameter set. By performing a global search across the entire search space, it can find potential global optima, exhibiting strong global search capabilities. Furthermore, it can automatically adjust the search strategy based on feedback information from the evaluation function, improving search efficiency and gradually converging to a better solution, demonstrating strong adaptability. It also exhibits a certain degree of resistance to local extrema and noise in the parameter space, avoiding getting trapped in local optima, thus demonstrating strong robustness. The genetic search strategy and operational parameters based on this application can be adjusted and optimized, allowing for settings tailored to specific circumstances, providing good flexibility and adjustability. This application explains the parameter search process and results through the operation process and parameters of the genetic algorithm, improving the interpretability of the system.

[0103] 4. The offline genetic algorithm in this application, by employing the time integral function of the absolute error to construct the Fitness function, can comprehensively evaluate dynamic performance and steady-state accuracy. It considers both the absolute value of the error and the time integral, enabling a comprehensive evaluation of the system's dynamic response and control accuracy, while also balancing fast response and steady-state accuracy. Furthermore, since the time integral function of the absolute error does not require a specific system model, it can be used to evaluate the performance of nonlinear systems and find the optimal parameter combination through the genetic algorithm. Additionally, a regularization term is included in the function to prevent the system from prematurely falling into local solution traps.

[0104] 5. The offline genetic algorithm of this application employs sequential selection, a selection strategy more suitable for offline algorithms. Compared to roulette wheel selection, sequential selection is simpler to implement, reducing computation. It only requires selecting individuals in descending order of fitness, without complex calculations. Furthermore, sequential selection has a higher probability of retaining the optimal solution, as individuals with better fitness are more likely to be selected. Therefore, compared to roulette wheel selection, sequential selection has a higher probability of retaining the optimal solution in each generation.

[0105] While specific embodiments of this application have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of this application. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this application. The scope of this application is defined by the appended claims.

Claims

1. A method for controlling the moisture content at the outlet of a cut tobacco machine based on a fuzzy algorithm, characterized in that, include: Establish a second-order inertial hysteresis model for drying materials by heating the cylinder wall: Fit the transfer function of the material by heating the cylinder wall into a second-order inertial hysteresis model, and use a positive step followed by a negative step method for model identification. Determine the current status of the wire drying machine; If the drying machine is in the main production state, the moisture content of the outlet material is controlled by a cascade closed-loop negative feedback PID control method formed by the front-stage PID moisture control loop and the rear-stage PID cylinder wall temperature control loop. The optimal combination of control parameters of the front-stage PID moisture control loop is determined by the fuzzy controller. The fuzzy controller is optimized using a genetic algorithm, specifically including the following steps: determining the initialization parameters and encoding form of the genetic algorithm; constructing the fitness function: selecting the time integral function of the absolute value of the construction error as the fitness function and introducing a regularization term; constructing crossover and mutation operators by adaptively adjusting the crossover and mutation probabilities; genetic decoding and optimal value calculation; constructing the selection strategy; genetic termination and control convergence; Furthermore, the moisture control method further includes: if the moisture content of the outlet material is unstable under the control parameters of the current upstream PID moisture control loop, then a second-order inertial hysteresis model of the material being dried by heating the cylinder wall is re-established, and the control parameters of the upstream PID moisture control loop are re-determined.

2. The method of moisture control at the outlet of a cut tobacco making machine based on a fuzzy algorithm according to claim 1, characterized in that, The input data of the fuzzy controller are the error between the actual moisture value of the material at the outlet of the drying machine and the set value of the outlet moisture, as well as the change in error between the current error and the previous error. The output data of the fuzzy controller are the proportional control parameters, integral control parameters, and derivative control parameters of the preceding PID moisture control.

3. The method for controlling the outlet moisture of a wire drying machine based on fuzzy algorithm according to claim 1, characterized in that, Also includes: If the moisture content of the outlet material is stable under the control parameters of the current preceding PID moisture control loop, then the control parameters of the current preceding PID moisture control loop will continue to be used to control the moisture content of the outlet material.

4. A moisture control device for the outlet of a cut tobacco machine based on fuzzy algorithm, characterized in that, It includes a drying model establishment module, a first judgment module, and a control module; The drying model establishment module is used to establish a second-order inertial hysteresis model for drying materials by heating the cylinder wall: the transfer function of the material dried by heating the cylinder wall is fitted into a second-order inertial hysteresis model, and the model is identified by first positive step and then negative step. The first judgment module is used to determine the current state of the wire drying machine; The control module is used to control the moisture content of the outlet material using a cascade closed-loop negative feedback PID control method formed by a front-end PID moisture control loop and a rear-end PID cylinder wall temperature control loop when the drying machine is in production mode. The optimal combination of control parameters for the front-end PID moisture control loop is determined by a fuzzy controller. The optimization module is used to optimize the fuzzy controller using a genetic algorithm, specifically including the following steps: determining the initialization parameters and encoding form of the genetic algorithm; constructing the fitness function: selecting the time integral function of the absolute value of the construction error as the fitness function and introducing a regularization term; constructing crossover and mutation operators by adaptively adjusting the crossover probability and mutation probability; genetic decoding and optimal value calculation; constructing the selection strategy; genetic termination and control convergence; The second judgment module is used to determine whether the moisture content of the outlet material is stable under the control parameters of the current front-end PID moisture control loop. The drying model establishment module is also used to re-establish a second-order inertial hysteresis model for drying materials heated by the drum wall when the moisture content of the outlet material is unstable under the control parameters of the current upstream PID moisture control loop; and... The control module is also used to redetermine the control parameters of the preceding PID moisture control loop after re-establishing the second-order inertial hysteresis model of the material being heated and dried on the cylinder wall.

5. The moisture control device at the outlet of a cut tobacco making machine based on fuzzy algorithm according to claim 4, characterized in that, The input data of the fuzzy controller are the error between the actual moisture value of the material at the outlet of the drying machine and the set value of the outlet moisture, as well as the change in error between the current error and the previous error. The output data of the fuzzy controller are the proportional control parameters, integral control parameters, and derivative control parameters of the preceding PID moisture control.

6. The outlet moisture control device for a filament drying machine based on fuzzy algorithm according to claim 4, characterized in that, The control module is also used to continue to control the moisture content of the outlet material by using the control parameters of the current preceding PID moisture control loop when the moisture content of the outlet material is stable under the control parameters of the current preceding PID moisture control loop.

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