A cigarette making machine fuzzy control method

Abstract

The present application relates to a cigarette making machine fuzzy control method, it includes the following steps: obtaining cigarette making machine operation parameter, operation parameter includes machine group rated production speed, tobacco filling rate, cigarette temperature and cigarette weight, calculates the parameter deviation and deviation change rate of operation parameter, calculates the self-adjusting membership function value according to the parameter deviation and deviation change rate of each operation parameter, determines the actuator and fuzzy control quantity that need to be adjusted according to the parameter deviation and deviation change rate of each operation parameter, adopts centroid method to carry out defuzzification according to membership function value and fuzzy control quantity, obtains the control value of actuator, and the actuator and corresponding control value constitute control scheme, controls the actuator according to control quantity. The present application can effectively reduce the parameter deviation of operation parameter, and the control consistency is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of cigarette production control technology, and in particular to a fuzzy control method for cigarette machines. Background Technology

[0002] Currently, in the field of cigarette machine operating parameter control, existing research presents several different technical approaches: Wu Dongjie's team uses microwave resonance detection to monitor tobacco density for the ZJ118 cigarette machine, and adjusts relevant parameters through independent control modes to optimize cigarette quality deviation; Yang Yunkai's team uses Minitab16 software to conduct DOE statistical analysis for the ZJ112 cigarette machine, optimizes parameters such as needle roller speed compensation coefficient offline, and achieves precise tuning of a single operating parameter by fitting the coefficient-weight calibration relationship through a response surface model; Qian Wen... Cong's team adopted independent control of the small fan speed and recycle amount for the PT70 cigarette machine, and solved the problem of steady-state control of a single parameter by fitting the parameter-weight deviation characteristic curve through cubic spline curve fitting; Zhu Yixiao's team proposed an adaptation solution for the ZJ17 and ZJ118 models, and controlled the proportion of short filaments by adjusting the recycle amount of the needle roller to stabilize the weight deviation, which indirectly affected the tobacco filling effect; Chen Zhiming's team optimized the fluidized bed temperature control for the ZJ116B fine cigarette making machine, and indirectly improved the temperature distribution of the cigarette and increased the whole tobacco yield by modifying the airflow. Wu Dongjie and Qian Wencong's team failed to consider the correlation between parameters, and Qian Wencong's solution showed limited effectiveness in reducing weight deviation at high speeds; Yang Yunkai's team's DOE optimization required manual parameter input, making it unable to respond to changes in operating conditions in real time, and its accuracy decreased under time-varying interference; Zhu Yixiao's team's solution was only compatible with a single model and lacked a unified control framework, resulting in significant differences in moisture content and weight deviation of tobacco strips during production, leading to poor quality consistency; Chen Zhiming's team lacked a multi-sensor dynamic weight allocation mechanism, making control deviation prone to increase when a single sensor fails, resulting in poor system robustness. These issues urgently need to be addressed. Summary of the Invention

[0003] This invention discloses a fuzzy control method for cigarette making machines, which aims to solve the technical problems existing in the prior art.

[0004] The present invention adopts the following technical solution: This invention provides a fuzzy control method for a cigarette rolling machine, which includes the following steps: Obtain the operating parameters of the cigarette rolling machine, including the rated production speed of the unit, the tobacco filling rate, the cigarette temperature, and the cigarette weight; Calculate the parameter deviation and the rate of change of the deviation of the operating parameters; Calculate the self-adjusting membership function value based on the parameter deviation and deviation change rate of each of the aforementioned operating parameters; The actuators and fuzzy control quantities that need to be adjusted are determined based on the parameter deviations and the rate of change of each of the aforementioned operating parameters. Based on the membership function value and the fuzzy control quantity, the centroid method is used to defuzzify the control value of the actuator, and the actuator and the corresponding control value constitute the control scheme. The actuator is controlled according to the control quantity.

[0005] In the fuzzy control method for cigarette machines of the present invention, the same operating parameter is obtained through multiple sensors, and the operating parameter is the weighted average of the measured values ​​of each sensor, and the weight values ​​of each sensor are added together to equal 1.

[0006] The fuzzy control method for cigarette making machines of the present invention further includes determining whether the deviation of the measured values ​​of the plurality of sensors exceeds a preset threshold. If so, then the weight value of the sensor that exceeds the preset threshold will be reduced, and the weight values ​​of the remaining sensors will be increased, and the sum of the increased values ​​will equal the reduced value.

[0007] In the fuzzy control method for cigarette machines of the present invention, the weight values ​​of the remaining sensors are increased by the same amount.

[0008] The fuzzy control method for cigarette machines of the present invention further includes performing Kalman filtering and sliding window mean processing on the measured values ​​of the operating parameters, and calculating the parameter deviation and the deviation change rate based on the processed measured values ​​of the operating parameters.

[0009] In the fuzzy control method for cigarette machines of the present invention, the self-adjusting membership function value , wherein The parameter deviation or rate of change of deviation for each normalized operating parameter is... As the center position of the membership function, the For the width parameter, the It is a natural constant.

[0010] In the fuzzy control method for cigarette making machine of the present invention, when the absolute value of the parameter deviation or the rate of change of deviation is greater than 2, the σ is 1.8; when the absolute value of the parameter deviation or the rate of change of deviation is less than 0.5, the σ is 0.8; and when the absolute value of the parameter deviation or the rate of change of deviation is greater than or equal to 0.5 and less than or equal to 2, the σ is 1.

[0011] In the fuzzy control method for cigarette machines of the present invention, the step of determining the actuator to be adjusted based on the parameter deviation and the rate of change of each of the operating parameters includes: The parameter deviation and the rate of change of the deviation are normalized to a universe of discourse in the range of [-3.5, 3.5]. The domain is divided into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, with each level having a length of 1. The actuator that needs to be adjusted is determined by a preset level fuzzy control scheme mapping rule corresponding to the level of the normalized parameter deviation and the level of the deviation change rate.

[0012] The fuzzy control method for cigarette machines of the present invention further includes a step of optimizing the preset hierarchical fuzzy control scheme mapping rules using the improved integral time absolute error as a performance evaluation index; the optimization objective is to minimize the improved integral time absolute error value.

[0013] The fuzzy control method for cigarette machines of the present invention further includes a step of adjusting and updating the weight coefficients in the improved integral time absolute error formula, including: Updated formula: in, The adaptive gain matrix is ​​set to diag[5,5,5,5]. The output value vector of the self-adjusting membership function of the running parameters; The attenuation coefficient is set to 0.002. A four-dimensional vector composed of the deviations of the operating parameters; This is the rule weight matrix.

[0014] The fuzzy control method for cigarette machines of the present invention further includes a stability evaluation step for the control scheme, comprising: If the control scheme meets the preset requirements, then the control scheme is stable and the adjustment ends; If the control scheme does not meet the preset requirements, the step of obtaining control values ​​is repeated until the control scheme meets the preset requirements.

[0015] The technical solution adopted in this invention can achieve the following beneficial effects: This invention mainly provides a fuzzy control method for cigarette making machines. Based on the operating parameters as input, the method obtains the parameter deviation and the rate of change of deviation. Then, based on the parameter deviation and the rate of change of deviation, it determines the self-adjusting membership function value, the fuzzy control quantity, and the controller that needs to be adjusted. Finally, the control quantity of the final actuator is obtained using the membership function value and the fuzzy control quantity. This method can improve the stability of the control of operating parameters, effectively reduce the parameter deviation of operating parameters, and significantly improve the control consistency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below, forming part of the present invention. The illustrative embodiments of the present invention and their descriptions explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1This is a flowchart of a fuzzy control method for a cigarette machine according to the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. In the description of this invention, it should be noted that the term "or" is generally used to include the meaning of "and / or," unless otherwise expressly indicated.

[0018] Unless explicitly stated otherwise, the numerical parameters in this specification and the appended claims may be approximate values ​​and can be varied according to the desired characteristics obtained from the content of this invention. Specifically, all figures used in the specification and claims to indicate the content of composition, reaction conditions, etc., should be understood to be modified by the term "about" in all cases. Generally, this means that there may be variations of ±10% in some embodiments, ±5% in some embodiments, ±1% in some embodiments, and ±0.5% in some embodiments.

[0019] Furthermore, the word "comprising" does not exclude the presence of materials or steps not listed in the claims. The ordinal numbers used in the specification and claims, such as "first," "second," "third," and Arabic numerals and letters, to modify corresponding elements or steps, do not in themselves imply an order of manufacturing process; their use is solely to ensure clear distinction between steps.

[0020] Furthermore, unless specifically described or required to occur in a specific order, the order of the above steps is not limited to those listed above and can be varied or rearranged according to the desired design. Moreover, the above embodiments can be used in combination with each other or with other embodiments based on design and reliability considerations; that is, technical features from different embodiments can be freely combined to form more embodiments.

[0021] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] To address the problems existing in the prior art, this application provides a fuzzy control method for cigarette making machines.

[0023] A fuzzy control method for cigarette making machines, such as Figure 1 As shown, it includes the following steps: Obtain the operating parameters and quality parameters of the cigarette making machine. The operating parameters include the rated production speed of the unit, the tobacco filling rate, the cigarette temperature, and the cigarette weight. The quality parameters include the cigarette pack rejection rate and the empty cigarette rate. Calculate the parameter deviation and rate of change of the operating parameters. = Actual value - Target value, Rate of change of deviation ; The self-adjusting membership function value is calculated based on the parameter deviation and the rate of change of each operating parameter. Specifically, the membership function values ​​corresponding to the parameter deviation and the rate of change of deviation for each operating parameter are calculated respectively. The actuators and fuzzy control quantities that need to be adjusted are determined based on the parameter deviations and deviation change rates of each operating parameter; Based on the membership function value and the fuzzy control quantity, the centroid method is used to defuzzify the control value of the actuator. The actuator and the corresponding control value constitute the control scheme. The actuator is controlled based on the control value.

[0024] The present invention discloses a fuzzy control method for a cigarette machine. Based on the input of operating parameters, the method obtains parameter deviation and deviation change rate, and determines the self-adjusting membership function value, fuzzy control quantity, and controller to be adjusted based on the parameter deviation and deviation change rate. Finally, the control quantity of the final actuator is obtained using the membership function value and fuzzy control quantity. This method can improve the stability of operating parameter control, effectively reduce the parameter deviation of operating parameters, and significantly improve control consistency.

[0025] In some preferred embodiments, two membership function values ​​are obtained based on the parameter deviation and the rate of change of the deviation of the operating parameters. The smaller of the two membership function values ​​is taken as the final membership function value. Based on this, a conservative decision is made; control overshoot and oscillation are suppressed; the computational complexity is reduced; and the design of fuzzy rules is facilitated.

[0026] In some preferred embodiments, the same operating parameter is acquired by multiple sensors, and the operating parameter is the weighted average of the measurements from each sensor, with the weight values ​​of each sensor added together to equal 1; based on this, the impact of sensor failure is reduced.

[0027] Specifically, the formula for the weighted average method is as follows: ; in, It is a comprehensive value of operating parameters after fusion of multiple sensors, which can eliminate the bias of data from a single sensor; Let be the weighting coefficient for the i-th sensor, dynamically allocated based on the sensor's accuracy. Specifically, higher accuracy results in a larger weight, and this allocation can be determined according to actual working conditions; no specific limitation is imposed here. Let be the test value of the i-th sensor.

[0028] Preferably, the number of sensors is 3.

[0029] In some preferred embodiments, the method further includes determining whether the deviation of the measurement values ​​from multiple sensors exceeds a preset threshold. If so, then the weight values ​​of sensors exceeding the preset threshold are reduced, and the weight values ​​of the remaining sensors are increased, with the sum of the increases equal to the reduction. Based on this, data distortion from faulty sensors can be approximately compensated, preventing the control deviation of actuators controlling operating parameters from expanding; it can enhance sensor fault tolerance and robustness. Through dynamic adjustment of sensor weights and fuzzy approximation compensation, the increase in control deviation is ≤3% when a single operating parameter sensor fails, and the operating parameters can still be kept stable even when actuator performance is lost, showing improved robustness compared to traditional solutions.

[0030] Preferably, the preset threshold can be: cigarette weight deviation > 2mg, tobacco filling rate deviation > 1%, cigarette temperature deviation > 0.5℃, rated production speed deviation > 5 cigarettes / minute; that is, the difference between the deviation of a certain sensor measurement value and the weighted value of the deviation of multiple sensor measurements.

[0031] Preferably, the weight values ​​of the remaining sensors are increased by the same amount.

[0032] Preferably, when the deviation of a certain sensor exceeds a threshold, its weight is reduced by 20%. Distribute equally to other positive For constant sensors, the weight adjustment formula is: in, , Adjusted weight values The weights are the values ​​before adjustment, and n is the number of sensors. Based on this, progressive fault isolation can be achieved, ensuring that the increase in control deviation is within a controllable range when a single sensor fails, and with high computational efficiency.

[0033] In some preferred embodiments, the method further includes performing Kalman filtering and sliding window averaging on the measured values ​​of the operating parameters, and calculating the parameter deviation and the rate of change of deviation from the processed measured values ​​of the operating parameters; that is, the sensor test values The measured values ​​were obtained by performing Kalman filtering and sliding window averaging. Kalman filtering first removed most of the Gaussian white noise, and sliding window averaging then eliminated the residual pulse spikes and non-Gaussian noise, resulting in better final measurements.

[0034] Preferably, the Kalman filter coefficient is set to 0.85, and the filtering iteration formula is as follows: in, The filtered operating parameter values ​​at time k; The sensor readings at time k are: A=1, B=0, H=1; The control input of the actuator at time k-1; process noise. Measurement noise All are set to 0.01; In the sliding window mean smoothing, the cigarette weight and filling rate data are smoothed using the 5s sliding window mean method, with the window step size set to 1s. For outliers that deviate from the mean within the window by 3 times the standard value, the mean of the adjacent window is used to replace them to ensure data continuity.

[0035] In some preferred embodiments, the self-adjusting membership function value ,in, This represents the parameter deviation or rate of change of deviation for each normalized operating parameter. The location of the center of the membership function. For width parameter, It is a natural constant.

[0036] Preferably, The value of the membership function center position can be -3, -2, -1, 0, 1, 2, or 3, depending on the specific value. To determine the normalized levels.

[0037] Preferably, when the absolute value of the parameter deviation or the rate of change of deviation is greater than 2, A value of 1.8 allows for wider coverage; when the absolute value of the parameter deviation or the rate of change of deviation is less than 0.5, A value of 0.8 can increase the density of small deviation intervals and improve the sensitivity of operating parameter control; when the absolute value of the parameter deviation or the rate of change of deviation is greater than or equal to 0.5 and less than or equal to 2, The value is 1.

[0038] Preferably, it also includes real-time monitoring of raw material moisture and tobacco looseness; when fluctuations exceed ±3%, the membership function density of the small deviation interval is increased, that is... The value is set to 0.8 to adapt to the impact of changes in raw material characteristics on the filling rate and weight deviation; the system has a short stabilization time when the raw material humidity fluctuates, effectively dealing with the impact of complex working conditions in industrial sites on the winding operation parameters.

[0039] In some preferred embodiments, the step of determining the actuators to be adjusted based on the parameter deviations and rates of change of each operating parameter includes: The parameter deviation and the rate of change of deviation are normalized to a universe of discourse in the range of [-3.5, 3.5]. The domain is divided into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, with each level having a length of 1. The actuators and fuzzy control quantities that need to be adjusted are determined by the preset level fuzzy control scheme mapping rules corresponding to the levels of the normalized parameter deviation and the deviation change rate.

[0040] Preferably, the preset hierarchical fuzzy control scheme further includes a step of determining the fuzzy control quantity based on the preset level corresponding to the level of the normalized parameter deviation and the deviation change rate, including: When the deviation of the operating parameters is large, the output fuzzy control quantity is of medium amplitude, balancing convergence speed and overshoot suppression; when the deviation of the operating parameters is small, the output fuzzy control quantity is of small amplitude, ensuring steady-state control accuracy; when the operating parameters are zero, the output fuzzy control quantity is zero-adjustment, avoiding frequent actuator operation under steady-state conditions.

[0041] Preferably, the preset hierarchical fuzzy control scheme includes: If the weight deviation of the cigarette is PB (positive large) and the tobacco filling rate deviation is NS (negative small), then the output tobacco filling adjustment mechanism moves the tobacco flattener by PS (positive small) and the speed of the pressure wheel servo motor of the weight compensation actuator is NM (negative medium). If the rated speed deviation of the unit is PB (positive large) and the cigarette temperature deviation is PB (positive large), then the output speed of the speed regulating servo motor is NS (negative small) and the power of the cigarette temperature control module is NM (negative medium). If the tobacco filling rate deviation is PB (positive large) and the unit's rated speed deviation is PB (positive large), then the output filling adjustment mechanism will move the tobacco leveler by NS (negative small) and the speed of the speed-regulating servo motor will be NS (negative small). The above three levels of fuzzy control schemes are for illustrative purposes only; specific settings can be adjusted according to actual control requirements.

[0042] Specifically, the control quantity of each actuator can be obtained by comparing the parameter deviation value with the range. For example, the control quantity adjustment range can be divided into 7 levels, that is, the adjustment range can be divided into 7 equal parts. Specifically, the deviation range of each operating parameter and the corresponding actuator control quantity adjustment range are as follows: The rated deviation range of the unit is [-140, +140] units / minute; corresponding to the speed adjustment range of the speed-regulating servo motor is [-14, +14] revolutions / minute. The weight deviation range of the cigarette stick is [-70, +70] mg; the speed adjustment range of the servo motor of the pressing wheel is [-14, +14] rpm; The tobacco filling rate deviation range is [-7%, +7%]; the vertical movement range of the tobacco leveler is [-1.05, +1.05] mm. The temperature deviation range of the smoke bar is [-3.5°, +3.5°]; the operating power adjustment range of the temperature module is [-175, +175]W; that is, the control quantity is adjusted linearly according to the deviation value.

[0043] In some preferred embodiments, the method further includes a step of optimizing the preset hierarchical fuzzy control scheme mapping rules using the improved integral time absolute error as a performance evaluation index; the optimization objective is to minimize the improved integral time absolute error value.

[0044] Specifically, Optimization includes the following steps: 1. Generate an initial expert rule base; 2. Iterative optimization based on historical data offline, iterating to... The rate of change of the value is stable; 3. Check all rules one by one and correct those that do not conform to the actual operating logic (e.g., continuing to increase the cigarette filling amount when the cigarette weight is too large): After downloading the optimized fuzzy rules to the PLC, small-batch trial production was carried out. Record actual operating data and calculate the fuzzy control effect. The control effect is reflected in the following two aspects: First, the deviation of four operating parameters (weight deviation ≤ 1mg; filling rate deviation ≤ 0.5%; temperature deviation ≤ 0.3°; unit speed deviation ≤ 3 pieces / minute). Second, stability index, i.e. step response index: sudden change of one of the four operating parameters, specifically ±10%, with an overshoot of <3%, and steady-state errors of weight deviation ≤ 1mg; filling rate deviation ≤ 0.5%; temperature deviation ≤ 0.3°; unit speed deviation ≤ 3 pieces / minute. Long-term operating index: deviation change rate over 8 hours of continuous operation, with the deviation change rate of the three parameters other than temperature required to be ≤ 5%, temperature change ≤ 10%, and continuous operation for 72 hours without failure. Based on the trial production results, adjustments were made to the fuzzy rules that performed poorly; for example, the control quantity of the actuator was corrected. In actual control, after outputting an opening of +4%, the filling rate only increased by 1.75% (it should be 2%), which did not reach the target. Therefore, the control quantity of the corresponding actuator was changed from +0.6mm to +0.6mm plus the corresponding calibration value, which became 0.686. The increment is the correction calibration value.

[0045] Repeat the trial production and adjustment process until all indicators meet the production requirements; 4. Convert the fuzzy rules into a mapping table that the PLC can directly execute, which corresponds to the range of actual actuator control quantities mentioned above.

[0046] Preferably, the improved formula for the absolute error value of the integration time is: in, The time variable is for integration, and the unit is seconds. The control period is measured in seconds. , , , The weighting coefficients for tobacco stick weight, tobacco filling rate, tobacco stick temperature, and rated speed of the unit are initially set to 0.4, 0.25, 0.2, and 0.15, respectively. , , , These are the deviations in tobacco stick weight, tobacco filling rate, tobacco stick temperature, and rated speed of the unit.

[0047] Preferably, the method further includes a step of adjusting and updating the weighting coefficients in the improved integral time absolute error formula, including: updating the formula: in, The adaptive gain matrix is ​​set to diag[5,5,5,5]. The output value vector of the self-adjusting membership function of the running parameters; The attenuation coefficient is set to 0.002. A four-dimensional vector composed of the parameter deviations of the operating parameters; The weight matrix is ​​a rule-based matrix with initial values ​​set based on historical equipment data, such as 0.4, 0.25, 0.2, and 0.15. The weight coefficients are adjusted and updated based on online conditions to adapt to the priority requirements of control parameters under different operating conditions. This can improve the robustness (stability) and anti-interference ability of the system and achieve dynamic balance of multi-objective control.

[0048] Preferably, the method further includes a step of dynamically adjusting the weight of the temperature-related fuzzy rule to 0.3 when the temperature lag of the tobacco stick exceeds the limit, such as when the temperature lag time is >2 seconds or the change in unit speed is 20 > sticks / minute. That is, the weight coefficient of the tobacco stick temperature is adjusted to 0.3 to improve the lag compensation capability, ensure the response speed of temperature and speed coordinated control, and design a dedicated weight adjustment strategy in conjunction with the dynamic adaptation capability of the self-adjusting membership function. The response speed of the operating parameters is improved, and the precise adaptation of the core rolling components can be achieved without modifying the equipment, thereby reducing the equipment upgrade cost.

[0049] In some preferred embodiments, a stability evaluation step for the control scheme is also included, including: If the control scheme meets the preset requirements, then the control scheme is stable and the adjustment ends; If the control scheme does not meet the preset requirements, the control value acquisition step is repeated until the control scheme meets the preset requirements. Based on this, the rule design strictly avoids control actions with excessive overshoot and excessive adjustment frequency, ensuring that the closed-loop system always maintains semi-global consistency and eventual boundedness during rule execution, and that the deviation of operating parameters can be converged. This avoids the risk of actuator oscillation and divergence from the rule design level. The control logic is simple, the number of parameters is reduced compared to traditional hybrid control schemes, the computational complexity is reduced, and it supports direct deployment on existing PLCs of the ZJ116B model. It focuses on the parameter control of core winding components and is highly practical.

[0050] Preferably, the preset requirement can be: This improves the convergence speed of operating parameter deviations and enhances control response speed. is a bounded constant with a value range of [0, 0.01], and e is the parameter deviation of the operating parameters obtained after the actuator executes the control scheme. Let e ​​be the 2-norm; If the above inequality holds, then the control scheme is stable and the adjustment ends. If the above inequality does not hold, then repeat the steps of obtaining the control scheme until the preset requirements are met.

[0051] Preferably, V'=0.

[0052] Preferably, the preset requirement can be: The value is limited to ≤0.045 and used as the evaluation criterion for judging the stability of the control scheme.

[0053] In some preferred embodiments, the step of transmitting the control quantity to the actuator after Butterworth filtering is also included; The transfer function of the Butterworth filter is: In the formula, The complex frequency domain transfer function of a second-order Butterworth low-pass filter; For the Laplace operator, ,in, The imaginary unit, The signal angular frequency is set to 1. Based on this, the filter has the maximum flat amplitude-frequency characteristic in the passband, which can effectively filter out high-frequency noise in the control quantity, break the delay-free algebraic loop of the fuzzy control closed loop, avoid the algebraic loop problem in PLC operation, avoid control quantity oscillation and non-convergence of solution, filter out high-frequency noise and glitches in the control quantity, smooth the output, avoid frequent actuator operation, and at the same time ensure the stability of cigarette production parameters, enhance the stability of the closed-loop system, suppress high-frequency interference, and do not introduce steady-state control error. For details, please refer to the existing technology, which will not be elaborated here.

[0054] Preferably, the control quantity After being filtered by Butterworth, the signal is output to the corresponding actuator of the core winding component of the ZJ116B model via the EtherCAT bus. The control cycle is ≤10ms, and the actuator feedback and actual operating parameter values ​​are collected in real time to form a closed-loop regulation.

[0055] In some preferred embodiments, the step of using the centroid method to defuzzify based on the membership function value and the fuzzy control quantity to obtain the control value of the actuator includes: The centroid method is used to defuzzify the fuzzy inference results, directly obtaining the actuator control quantity, as shown in the following formula: ; in, The final control quantities after defuzzification (such as the speed range of the speed regulating motor, the opening degree of the filling mechanism, and the power of the temperature control module); is the fuzzy set membership degree value corresponding to the self-adjusting membership function, that is, the self-adjusting membership function obtained by parameter deviation or deviation change rate respectively; x is the fuzzy control quantity; Preferably, after determining the level of parameter deviation or the normalized level of deviation change rate, the fuzzy control scheme, i.e., the fuzzy control value, corresponding to the level adjacent to that level is obtained respectively; the fuzzy set membership value is calculated by taking the c corresponding to that level and the c adjacent to that level, and forming the fuzzy set membership value; based on this, while ensuring the control effect, the calculation efficiency can be improved, the robustness can be enhanced, the difficulty of PLC program implementation can be simplified, and the code complexity and debugging difficulty can be reduced.

[0056] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. A fuzzy control method for a cigarette rolling machine, characterized in that, Includes the following steps: Obtain the operating parameters of the cigarette rolling machine, including the rated production speed of the unit, the tobacco filling rate, the cigarette temperature, and the cigarette weight; Calculate the parameter deviation and the rate of change of the deviation of the operating parameters; Calculate the self-adjusting membership function value based on the parameter deviation and deviation change rate of each of the aforementioned operating parameters; The actuators and fuzzy control quantities that need to be adjusted are determined based on the parameter deviations and the rate of change of each of the aforementioned operating parameters. Based on the membership function value and the fuzzy control quantity, the centroid method is used to defuzzify the control value of the actuator, and the actuator and the corresponding control value constitute the control scheme. The actuator is controlled according to the control quantity.

2. The fuzzy control method for a cigarette rolling machine according to claim 1, characterized in that, The same operating parameter is acquired through multiple sensors. The operating parameter is a weighted average of the measurements from each sensor, and the weights of each sensor are summed to equal 1.

3. The fuzzy control method for a cigarette rolling machine according to claim 2, characterized in that, It also includes determining whether the deviation of the measurement values ​​from multiple sensors exceeds a preset threshold. If so, then the weight value of the sensor that exceeds the preset threshold will be reduced, and the weight values ​​of the remaining sensors will be increased, and the sum of the increased values ​​will equal the reduced value.

4. The fuzzy control method for a cigarette rolling machine according to claim 3, characterized in that, The weight values ​​of the remaining sensors are increased by the same amount.

5. The fuzzy control method for a cigarette rolling machine according to claim 1, characterized in that, It also includes performing Kalman filtering and sliding window averaging on the measured values ​​of the operating parameters, and calculating the parameter deviation and the rate of change of the deviation based on the processed measured values ​​of the operating parameters.

6. The fuzzy control method for a cigarette rolling machine according to claim 1, characterized in that, The self-adjusting membership function value , wherein The parameter deviation or rate of change of deviation for each normalized operating parameter is... As the center position of the membership function, the For the width parameter, the It is a natural constant.

7. The fuzzy control method for a cigarette rolling machine according to claim 6, characterized in that, When the absolute value of the parameter deviation or the rate of change of deviation is greater than 2, σ is 1.8; when the absolute value of the parameter deviation or the rate of change of deviation is less than 0.5, σ is 0.8; when the absolute value of the parameter deviation or the rate of change of deviation is greater than or equal to 0.5 and less than or equal to 2, σ is 1.

8. The fuzzy control method for a cigarette rolling machine according to claim 1, characterized in that, The step of determining the actuator that needs adjustment based on the parameter deviation and the rate of change of each of the operating parameters includes: The parameter deviation and the rate of change of the deviation are normalized to a universe of discourse in the range of [-3.5, 3.5]. The domain is divided into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, with each level having a length of 1. The actuator that needs to be adjusted is determined by a preset level fuzzy control scheme mapping rule corresponding to the level of the normalized parameter deviation and the level of the deviation change rate.

9. The fuzzy control method for a cigarette rolling machine according to claim 8, characterized in that, It also includes the step of optimizing the preset hierarchical fuzzy control scheme mapping rules using the improved integral time absolute error as a performance evaluation index; the optimization objective is to minimize the improved integral time absolute error value.

10. The fuzzy control method for a cigarette rolling machine according to claim 9, characterized in that, It also includes a step of adjusting and updating the weighting coefficients in the improved integral time absolute error formula, including: Updated formula: in, The adaptive gain matrix is ​​set to diag[5,5,5,5]. The output value vector of the self-adjusting membership function of the running parameters; The attenuation coefficient is set to 0.

002. A four-dimensional vector composed of the deviations of the operating parameters; This is the rule weight matrix.

11. The fuzzy control method for a cigarette rolling machine according to claim 1, characterized in that, It also includes a stability evaluation step for the control scheme, including: If the control scheme meets the preset requirements, then the control scheme is stable and the adjustment ends; If the control scheme does not meet the preset requirements, the step of obtaining control values ​​is repeated until the control scheme meets the preset requirements.

Images