CN115108379A - Automatic tension adjusting system for cast film compounding process - Google Patents

Automatic tension adjusting system for cast film compounding process Download PDF

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CN115108379A
CN115108379A CN202210789063.4A CN202210789063A CN115108379A CN 115108379 A CN115108379 A CN 115108379A CN 202210789063 A CN202210789063 A CN 202210789063A CN 115108379 A CN115108379 A CN 115108379A
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module
motor
particle
particles
tension
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陈清
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Jiangsu Yingwei Medical Co ltd
Beijing Entropy Map Medical Technology Partnership LP
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Jiangsu Yingwei Medical Co ltd
Beijing Entropy Map Medical Technology Partnership LP
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65H—HANDLING THIN OR FILAMENTARY MATERIAL, e.g. SHEETS, WEBS, CABLES
    • B65H23/00—Registering, tensioning, smoothing or guiding webs
    • B65H23/04—Registering, tensioning, smoothing or guiding webs longitudinally
    • B65H23/18—Registering, tensioning, smoothing or guiding webs longitudinally by controlling or regulating the web-advancing mechanism, e.g. mechanism acting on the running web
    • B65H23/195—Registering, tensioning, smoothing or guiding webs longitudinally by controlling or regulating the web-advancing mechanism, e.g. mechanism acting on the running web in winding mechanisms or in connection with winding operations
    • B65H23/198—Registering, tensioning, smoothing or guiding webs longitudinally by controlling or regulating the web-advancing mechanism, e.g. mechanism acting on the running web in winding mechanisms or in connection with winding operations motor-controlled (Controlling electrical drive motors therefor)
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65H—HANDLING THIN OR FILAMENTARY MATERIAL, e.g. SHEETS, WEBS, CABLES
    • B65H26/00—Warning or safety devices, e.g. automatic fault detectors, stop-motions, for web-advancing mechanisms
    • B65H26/02—Warning or safety devices, e.g. automatic fault detectors, stop-motions, for web-advancing mechanisms responsive to presence of irregularities in running webs
    • B65H26/04—Warning or safety devices, e.g. automatic fault detectors, stop-motions, for web-advancing mechanisms responsive to presence of irregularities in running webs for variation in tension
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65H—HANDLING THIN OR FILAMENTARY MATERIAL, e.g. SHEETS, WEBS, CABLES
    • B65H26/00—Warning or safety devices, e.g. automatic fault detectors, stop-motions, for web-advancing mechanisms
    • B65H26/08—Warning or safety devices, e.g. automatic fault detectors, stop-motions, for web-advancing mechanisms responsive to a predetermined diameter
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65H—HANDLING THIN OR FILAMENTARY MATERIAL, e.g. SHEETS, WEBS, CABLES
    • B65H2511/00—Dimensions; Position; Numbers; Identification; Occurrences
    • B65H2511/10—Size; Dimensions
    • B65H2511/14—Diameter, e.g. of roll or package
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65H—HANDLING THIN OR FILAMENTARY MATERIAL, e.g. SHEETS, WEBS, CABLES
    • B65H2513/00—Dynamic entities; Timing aspects
    • B65H2513/10—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65H—HANDLING THIN OR FILAMENTARY MATERIAL, e.g. SHEETS, WEBS, CABLES
    • B65H2515/00—Physical entities not provided for in groups B65H2511/00 or B65H2513/00
    • B65H2515/30—Forces; Stresses
    • B65H2515/31—Tensile forces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65H—HANDLING THIN OR FILAMENTARY MATERIAL, e.g. SHEETS, WEBS, CABLES
    • B65H2551/00—Means for control to be used by operator; User interfaces
    • B65H2551/20—Display means; Information output means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65H—HANDLING THIN OR FILAMENTARY MATERIAL, e.g. SHEETS, WEBS, CABLES
    • B65H2557/00—Means for control not provided for in groups B65H2551/00 - B65H2555/00
    • B65H2557/20—Calculating means; Controlling methods
    • B65H2557/24—Calculating methods; Mathematic models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65H—HANDLING THIN OR FILAMENTARY MATERIAL, e.g. SHEETS, WEBS, CABLES
    • B65H2557/00—Means for control not provided for in groups B65H2551/00 - B65H2555/00
    • B65H2557/20—Calculating means; Controlling methods
    • B65H2557/264—Calculating means; Controlling methods with key characteristics based on closed loop control
    • B65H2557/2644—Calculating means; Controlling methods with key characteristics based on closed loop control characterised by PID control

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Abstract

The invention discloses an automatic tension adjusting system in a casting film compounding process, and relates to the technical field of programmable control. The adjusting system comprises an FPGA chip, a PID controller, a frequency converter, a motor model, a tension detection module, a processing module, an alarm module and a display module; PID controller composed of proportional unit, integral unit and differential unit p 、K i And K d Setting three parameters to control the frequency converter; the FPGA chip comprises a control module, a storage module, a multiplexer, an initialization module, a global optimal selection module, a local optimal selection module and a speed and position updating module. The invention relates to an automatic tension regulation system for a casting film compounding process, which sets PID controller parameters by using an improved particle swarm algorithm andand moreover, a hybrid operator is introduced, the particle swarm algorithm is improved, the diversity of particle swarm is ensured, and the algorithm is effectively prevented from falling into a local optimal solution.

Description

Automatic tension adjusting system for cast film compounding process
Technical Field
The invention relates to the technical field of programmable control, in particular to an automatic tension adjusting system for a casting film compounding process.
Background
Cast film is a non-stretched, non-oriented cast film produced by melt casting quenching. Compared with the common inflation method, the method has the characteristics of high production speed, high yield, good film thickness uniformity, transparency, glossiness and the like. The production process comprises the steps of plasticizing and melting the raw materials by an extruder, extruding a melt through a T-shaped slit die head, cooling a casting sheet by a chill roll, and forming into a film. The method has fast production speed, the quenching roller is a steel roller with chrome-plated surface and high degree of finish, the transparency of the film is high, the thickness is uniform, the longitudinal and transverse performances of the film are consistent, and the single plastic film always has the inherent advantages brought by the resin and certain defects of the film, so that the method can not meet the requirements of more and more commodities on the wide and severe performances of the package. Therefore, film manufacturers develop the production of composite materials of films and films, paper, metal foils and the like to make up for each other's deficiencies and better meet the requirements of the packaging industry.
The production process of the cast film composite production molding comprises the following steps: mixing the dried resin raw material [ consisting of basic resin (LLDPE + LDPE, HDPE, EVA or PP) and inorganic filler (CaCO) 3 Content of 45-50%) mixed composition]After being melted and plasticized by an extruder, the mixture enters a casting T-shaped die head through a filter and a connecting pipe and is extruded through a slit section die lip. The film was directly applied to a chill roll, cooled and re-solidified by 2 chill rolls. And then, measuring the thickness of the film by a thickness gauge, longitudinally stretching the film by a preheating roller and a stretching roller, cutting the film to a thicker rim charge after shaping, cooling and corona, coiling the film into a film roll by a full-automatic coiling device, and cutting the film roll on line to obtain a cylindrical film product.
When the cast film is rolled in the market at present, the tension control of the film is particularly important, and the quality of the tension control of the film directly influences the manufacturing quality of the cast film; wherein often can adjust the tension of film through the rotational speed of manual regulation motor among the prior art, but the mode of manual regulation is very inaccurate, is unfavorable for processing high-quality curtain coating membrane.
Disclosure of Invention
The invention mainly aims to provide an automatic tension adjusting system in a casting film compounding process, which can effectively solve the problem that the tension of a film is adjusted by manually adjusting the rotating speed of a motor in the background technology, but the manual adjusting mode is very inaccurate and is not beneficial to processing a high-quality casting film.
In order to achieve the purpose, the invention adopts the technical scheme that: the automatic tension adjusting system for the cast film compounding process comprises an FPGA chip, a PID controller, a frequency converter, a motor model, a tension detecting module, a processing module, an alarm module and a display module;
the PID controller consists of a proportional unit, an integral unit and a differential unit and passes through K p 、 K i And K d Setting three parameters to control the frequency converter;
the frequency converter is used for receiving the signal of the PID controller, converting the signal and transmitting the signal to the motor model and controlling the operation of the motor model;
the tension detection module is used for detecting the analog quantity generated by the tension measurement mechanism and converting the output of the analog quantity into a digital signal which can be operated and processed by the processor;
the processing module is used for receiving signals of the alarm module and the tension detection module and making corresponding actions;
the alarm module is used for prompting and feeding back the abnormal phenomenon generated in the normal working process of the control system, so that the working personnel can conveniently process the abnormality or check the equipment condition in time;
the display module is used for receiving the signal of the processing module and displaying each parameter in the display screen;
the FPGA chip comprises a control module, a storage module, a multiplexer, an initialization module, a global optimal selection module, a local optimal selection module and a speed and position updating module;
the control module is used for coordinating and controlling the normal operation of the whole system;
the storage module is used for storing information related to particles, the storage module comprises a ram1 memory, a ram2 memory, a ram3 memory, a ram4 memory and a ram5 memory, and the ram1 memory is used for storing coded data of positions; the ram2 memory is used for storing the particle fitness value; the ram3 memory is used for storing the local optimal fitness value; the ram4 memory is used for storing local optimal values; the ram5 memory is used for storing the speed of the particles;
the multiplexer is used for selecting input and output data and recording the data as mux;
the initialization module is used for generating an initial particle population;
the global optimal selection module is used for selecting and updating global optimal particles;
the local optimal selection module is used for selecting and updating local optimal particles;
the speed and position updating module is mainly used for updating the speed and the position of the particles;
the FPGA chip adopts an improved particle swarm algorithm, and the improved particle swarm algorithm is K p 、K i And K d The setting step is as follows:
s1, specifying the value ranges of three parameters of the PID controller and improving the maximum speed V of the particle swarm optimization algorithm max Thereafter, a group of locations x is initialized i (k +1) and velocity V i (k +1) particles both falling within this range; wherein x i (k +1) and V i The formula (k +1) is as follows:
V i (k+1)＝wV i (k)+C 1 r 1 [P i (k)-x i (t)]+C 2 r 2 [P g (k)-x i (t)]
x i (k+1)＝x i (k)+V i (k+1)
in the formula: v i (k) The velocity of the ith particle is calculated for the kth iteration; x is the number of i (k) The position of the ith particle in the k iterative computation is calculated; c 1 、C 2 Is a learning factor used for adjusting the degree of individual position cognition and group position cognition; r is 1 、r 2 Is an independent interval of [0,1]The random number of (2); p i Is the local optimal position of the population; p g The global optimal position of the particle swarm is defined, and w is the inertial weight;
s2, calculating the fitness value J of each particle by using the following formula 1 and formula 2 i ；
Equation 1 when ey (t) is ≧ 0,
Figure BDA0003733037870000031
equation 2 when (t)<0，
Figure BDA0003733037870000032
In the formula: the system error ey (t) y (t) -y (t-1), and y (t) is the output of the controlled object; u (t) is the controller output; t is t u Is the rise time; e (t) is an error; w is a 1 、w 2 、w 3 And w 4 As a weight value, w 1 ＝0.999、 w 2 ＝0.001、w 3 ＝2.0、w 4 ＝100；
S3, for each particle: if J i >J ibest Then J is ibest ＝J i ，P i ＝x i (ii) a If J i >J gbest Then J is gbest ＝J i ， P g ＝x i ；
In the above formula J i What is meant is the current adaptation value of the particle, J ibest The meaning represented is the best adaptation value, J, experienced by the individual particles gbest The meaning represented is the best adaptation value experienced globally;
s4, updating the velocity and position of the particles by the formula in step S1, and C 1 、C 2 Take [0, 2.5 ]]A random number within the range of the random number,
Figure BDA0003733037870000041
s5, introducing a hybridization operator, and improving a particle swarm optimization algorithm: replacing the particles with lower fitness value with the particles with higher fitness value, and performing hybridization operation on the particle swarm;
the introduction method of the hybridization operator comprises the following steps: initial season groupThe particles in the process carry a cross probability s, when iterative operation is carried out, a certain amount of particles are selected, then the particles are crossed pairwise according to the cross probability s to generate filial generations with the same number, and finally the parent particles are replaced by the obtained filial generation particles, and in the crossing process, the cross probability P is used for carrying out cross operation on the particles C Performing arithmetic weighting calculation on the particle position and the values in different dimensions between the particle positions, changing the current position of the particle under the condition of not being limited to the current optimal position, and performing re-search; mutation process according to the mutation probability P m And determining whether the particle needs to reinitialize the value of the position in a partial dimension, and assuming that m and n are 2 particles, the formula of the hybridization operation is as follows:
X m (t+1)＝s·X m (t)+(1-s)·X n (t)
X n (t+1)＝s·X n (t)+(1-s)·X m (t)
in the formula: 0< s < 1;
Figure BDA0003733037870000051
Figure BDA0003733037870000052
in the formula: f. of max Is the maximum fitness value in the population, f avg Is the population mean fitness value, f is the greater fitness value of the two individuals to be crossed; f' is the fitness value of the individual to be mutated; k is a radical of 1 、k 2 、k 3 And k 4 Is constant, and k 1 <k 2 ，k 3 <k 4 ；
And the inertia factor w can be further defined, and the calculation formula is as follows:
Figure BDA0003733037870000053
W max is the maximum inertia weight value, w min Is the minimum inertia weight value, t is the iteration number of the current particle, t max The maximum iteration times of the particle swarm algorithm are the maximum iteration times of the particle swarm algorithm;
s6, judging whether the operation times reach the maximum iteration times G k If not, returning to the step S2 to continue operation; if the algorithm is finished, obtaining K p 、K i And K d And is transmitted to a PID controller.
Preferably, the tension detection module comprises a tension sensor, a sampling module and an analog-digital processing module,
the tension sensor is used for detecting the tension of the system and conveying the tension to the sampling module;
the sampling module is used for receiving signals transmitted by the tension sensor and transmitting the signals to the analog-digital processing module;
the analog-digital processing module is used for converting the analog quantity signal collected by the sampling module into a digital quantity signal and transmitting the digital quantity signal to the processing module.
Preferably, the alarm module adopts a double-color lamp and a red lamp in a circuit; when the equipment is in a standby state, the two-color lamp lights a yellow lamp; when the lamp works normally, the two-color lamp is turned on; and if the equipment stops or faults such as disconnection and the like occur, displaying a red light.
Preferably, the display module passes through I 2 And the communication mode is connected with the processing module, and the display module adopts an LCD display screen.
Preferably, the motor model comprises a cooling motor and a winding motor, and the expression of the motor model is as follows:
Figure BDA0003733037870000061
Figure BDA0003733037870000062
in the formula: defining the cooling motor and the take-up motor separatelyThe motors 1 and 2 are respectively corresponding to the subscripts 1 and 2 in the above formulas; wherein, omega is the synchronous angular velocity of the motor, omega r Is the stator angular velocity of each motor, # is the rotor flux linkage of each motor, J is the moment of inertia of each motor, T L Is the load torque, T, of each motor r Is the time constant of each motor, L r 、L m Respectively the inductance and mutual inductance of each motor, n p Is the number of pairs of poles of the motor, F 12 Is the tension between the motor 1 and the motor 2.
Preferably, the frequency converter is electrically connected with the motor model, and a photoelectric encoder is arranged between the motor model and the frequency converter for feedback connection.
Preferably, the tension detection module further comprises a roll diameter detection module, and the calculation method of the roll diameter detection module is as follows:
Figure BDA0003733037870000063
in the formula: d (t) is the winding diameter at the time t, delta is the thickness of the wound film, and V is the linear speed of the winding drum.
Preferably, the processing module is provided with a roll diameter judging module, and the roll diameter judging module is used for judging the relationship between the roll diameter preset in the processing module and the existing roll diameter, and thereby controlling whether the system performs a roll change action.
Preferably, the roll changing action needs to be stopped for a short time, and the winding shaft needs to be stopped by k after rewinding b The speed is doubled and the running time is t b The calculation formula is as follows:
Figure BDA0003733037870000071
in the formula: t is t 0 For down time, k b Is a constant.
Compared with the prior art, the invention has the following beneficial effects:
in the invention, the whole process is finished by proposing an improved particle swarm algorithmThe PID controller parameters are determined, the hybrid operator is introduced, the particle swarm algorithm is improved, the diversity of particle swarm is ensured, the algorithm is effectively prevented from falling into the local optimal solution, in addition, the cross process is realized according to the cross probability P C Performing arithmetic weighting calculation on the particle positions and values in different dimensions between the particle positions, changing the current position of the particle under the condition of not being limited to the current optimal position, and performing re-search; mutation process according to the mutation probability P m And whether the particle needs to reinitialize the value of the position of the particle in a part of the dimensions is determined, and the improved PID controller enters a steady state at a higher speed. In addition, in the process of optimization by the standard particle swarm optimization, the maximum overshoot of 3.2% is obviously greater than the maximum overshoot of 2.6% optimized by the improved particle swarm optimization, so that the PID controller using the improved particle swarm optimization has better performance.
Drawings
FIG. 1 is a schematic flow diagram of an automatic tension adjustment system for a cast film lamination process according to the present invention;
FIG. 2 is a schematic flow diagram of a PID controller of a particle swarm algorithm for an improved automatic tension adjustment system for a cast film compounding process according to the present invention;
FIG. 3 is a schematic view of a motor model structure of an automatic tension adjusting system for a casting film compounding process according to the present invention;
FIG. 4 is a schematic view of a control structure of an automatic tension adjusting system for a casting film compounding process according to the present invention;
FIG. 5 is a schematic view of a tension detecting module of an automatic tension adjusting system for a casting film compounding process according to the present invention;
FIG. 6 is a schematic circuit diagram of a tension detection module of an automatic tension adjustment system for a cast film lamination process according to the present invention;
FIG. 7 is a signal lamp and alarm circuit diagram of the automatic tension adjustment system for the cast film compounding process of the present invention;
fig. 8 is a simulation graph of an example and a comparative example provided by the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained by combining the specific embodiments.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be configured in a specific orientation, and operate, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "connected," and the like are to be construed broadly, such as "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be connected internally or indirectly through two or more elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Referring to fig. 1-7, the invention relates to an automatic tension adjusting system for a casting film compounding process, which comprises an FPGA chip, a PID controller, a frequency converter, a motor model, a tension detecting module, a processing module, an alarm module and a display module;
PID controller composed of proportional unit, integral unit and differential unit p 、K i And K d Three parameters are set to control the frequency converter;
the frequency converter is used for receiving the signal of the PID controller, converting the signal and transmitting the signal to the motor model and controlling the operation of the motor model;
the tension detection module is used for detecting the analog quantity generated by the tension measurement mechanism and converting the output of the analog quantity into a digital signal which can be operated and processed by the processor;
the processing module is used for receiving signals of the alarm module and the tension detection module and making corresponding actions;
the alarm module is used for prompting and feeding back the abnormal phenomenon generated in the normal working process of the control system, so that a worker can conveniently process the abnormality or check the equipment condition in time;
the display module is used for receiving the signal of the processing module and displaying each parameter in the display screen;
the FPGA chip comprises a control module, a storage module, a multiplexer, an initialization module, a global optimal selection module, a local optimal selection module and a speed and position updating module;
the control module is used for coordinating and controlling the normal operation of the whole system;
the storage module is used for storing the related information of the particles, and comprises an ram1 memory, a ram2 memory, a ram3 memory, a ram4 memory and a ram5 memory, wherein the ram1 memory is used for storing the coded data of the positions; ram2 memory for storage of particle fitness values; the ram3 memory is used for storing the local optimal fitness value; ram4 memory for storing local optimum values; ram5 memory for storing the velocity of the particles;
the multiplexer is used for selecting input and output data and recording the data as mux;
an initialization module for generating an initial population of particles;
the global optimal selection module is used for selecting and updating global optimal particles;
the local optimal selection module is used for selecting and updating local optimal particles;
the speed and position updating module is mainly used for updating the speed and the position of the particles;
the FPGA chip adopts an improved particle swarm algorithm and an improved particle swarm algorithm pair K p 、K i And K d The setting steps are as follows:
S1specifying the value ranges of three parameters of the PID controller and improving the maximum speed V of the particle swarm optimization algorithm max Thereafter, a group of locations x is initialized i (k +1) and velocity V i (k +1) particles both falling within this range; wherein x i (k +1) and V i The formula for the calculation (k +1) is as follows:
V i (k+1)＝wV i (k)+C 1 r 1 [P i (k)-x i (t)]+C 2 r 2 [P g (k)-x i (t)]
x i (k+1)＝x i (k)+V i (k+1)
in the formula: v i (k) The velocity of the ith particle is calculated for the kth iteration; x is the number of i (k) The position of the ith particle in the k iterative computation is calculated; c 1 、C 2 Is a learning factor used for adjusting the degree of individual position cognition and group position cognition; r is 1 、r 2 Is an independent interval of [0,1]The random number of (2); p i Is the local optimal position of the population; p g The global optimal position of the particle swarm is defined, and w is the inertial weight;
s2, calculating the fitness value J of each particle by using the following formula 1 and formula 2 i ；
Equation 1 when ey (t) is ≧ 0,
Figure BDA0003733037870000101
equation 2 when (t)<0，
Figure BDA0003733037870000102
In the formula: the system error ey (t) y (t) -y (t-1), and y (t) is the output of the controlled object; u (t) is the controller output; t is t u Is the rise time; e (t) is an error; w is a 1 、w 2 、w 3 And w 4 As a weight value, w 1 ＝0.999、 w 2 ＝0.001、w 3 ＝2.0、w 4 ＝100；
S3, for each particle: if J i >J ibest Then J is ibest ＝J i ，P i ＝x i (ii) a If J i >J gbest Then J is gbest ＝J i ， P g ＝x i ；
In the above formula J i What is meant is the current adaptation value of the particle, J ibest The meaning represented is the best adaptation value, J, experienced by the individual particles gbest The meaning represented is the best adaptation value experienced globally;
s4, updating the speed and position of the particle by the formula in step S1, and C 1 、C 2 Take [0, 2.5 ]]A random number within the range of the random number,
Figure BDA0003733037870000103
s5, introducing a hybridization operator, and improving a particle swarm optimization algorithm: replacing the particles with lower fitness value with the particles with higher fitness value, and performing hybridization operation on the particle swarm;
the introduction method of the hybridization operator is as follows: carrying the cross probability s by the particles in the initial time group, selecting a certain amount of particles during the iterative operation, then enabling the particles to be crossed pairwise according to the cross probability s to generate the same number of filial generations, finally replacing the parent particles with the obtained filial generation particles, and carrying out the crossing process according to the cross probability P C Performing arithmetic weighting calculation on values in different dimensions between the particle positions, changing the current position of the particle under the condition of not being limited to the current optimal position, and performing re-search; mutation process according to the mutation probability P m And determining whether the particle is to reinitialize its position in a partial dimension, assuming that m and n are 2 particles, the hybridization operation is formulated as follows:
X m (t+1)＝s·X m (t)+(1-s)·X n (t)
X n (t+1)＝s·X n (t)+(1-s)·X m (t)
in the formula: 0< s < 1;
Figure BDA0003733037870000111
Figure BDA0003733037870000112
in the formula: f. of max Is the maximum fitness value in the population, f avg Is the population mean fitness value, f is the greater fitness value of the two individuals to be crossed; f' is the fitness value of the individual to be mutated; k is a radical of 1 、k 2 、k 3 And k 4 Is constant, and k 1 <k 2 ，k 3 <k 4 ；
And the inertia factor w can be further defined, and the calculation formula is as follows:
Figure BDA0003733037870000113
W max is the maximum inertia weight value, w min Is the minimum inertia weight value, t is the iteration number of the current particle, t max The maximum iteration times of the particle swarm algorithm are the maximum iteration times of the particle swarm algorithm;
s6, judging that the operation times reach the maximum iteration times G k If not, returning to the step S2 to continue the operation; if the algorithm is finished, obtaining K p 、K i And K d And is transmitted to a PID controller.
Examples
Pair K of improved particle swarm optimization algorithm utilizing the invention p 、K i And K d The three parameters are set, and are transmitted to a PID controller after the setting is finished, so that the PID controller controls a frequency converter, and then the frequency converter controls a motor model, namely the rotating speeds of a winding motor and a cooling motor are controlled, and the expression of the motor model is as follows:
Figure BDA0003733037870000121
Figure BDA0003733037870000122
in the formula: respectively defining the cooling motor and the winding motor as motors 1 and 2 which respectively correspond to subscripts 1 and 2 in the above formulas; wherein, omega is the synchronous angular velocity of the motor, omega r Is the stator angular velocity of each motor, # is the rotor flux linkage of each motor, J is the moment of inertia of each motor, T L Is the load torque, T, of each motor r Is the time constant of each motor, L r 、L m Respectively the inductance and mutual inductance of each motor, n p Is the number of pole pairs, F, of the motor 12 Is the tension between the motor 1 and the motor 2; it can be seen that even though the rotor flux linkage of the motor is regarded as constant, the motor speed and the system tension are strongly coupled, when the load of the system is changed, the load torque, the rotational inertia and the system tension of the motor are changed accordingly, so that the motor speed can be controlled to control the tension change, and the motor model is also connected with the frequency converter in a feedback manner by the photoelectric encoder, so that the control on the motor speed can be further improved, in the winding process, the tension detection module further comprises a winding diameter detection module, and the calculation method of the winding diameter detection module comprises the following steps:
Figure BDA0003733037870000131
in the formula: d (t) is the roll diameter at the time t, delta is the thickness of the rolled film, V is the linear velocity of the rolled roll, the rolled roll diameter is detected, and the signal is transmitted to a processing module, finally, a roll diameter judging module in the processing module is used for judging the relationship between the roll diameter preset in the processing module and the existing roll diameter, and whether the system performs roll changing action is controlled, when the roll changing action is needed, the machine needs to be stopped temporarily, and after the roll is rolled again, the rolling shaft needs to be stopped by k b The speed is multiplied by the running time t b The calculation formula is as follows:
Figure BDA0003733037870000132
in the formula: t is t 0 For down time, k b Is constant, so that the automatic roll change is realized.
Comparative example
And a standard particle swarm algorithm is adopted to control the PID controller.
Simulation experiment verification
The examples were subjected to simulation experiments using MATLAB. The experiment has a transfer function of
Figure BDA0003733037870000133
Parameter K p 、K i And K d Are respectively in the range of [0,20 ]]、[0,1]And [0,1 ]]. The population size was 20, the maximum number of iterations was 50, and the controller output ranged from-10, 10]. MATLAB simulation is carried out, and a simulation curve of the method is shown in the following figure, wherein a dotted line represents an embodiment;
the proportion was simulated using MATLAB. The experiment has a transfer function of
Figure RE-GDA0003806349720000161
Parameter K p 、K i And K d Are respectively in the range of [0,20 ]]、[0,1]And [0,1 ]]. The population size is 20, the maximum number of iterations is 50, and the controller output range is [ -10,10]. The MATLAB simulation results show that the simulation curve is shown in FIG. 8, wherein the solid line represents the proportion.
As can be seen from fig. 8: the real part is the simulation result of the PID controller of the standard particle swarm algorithm, and the dotted part is the simulation result of the PID controller of the improved particle swarm algorithm. The performance of the two implementations is compared as shown in the following table. Under the condition of the same parameters, the overall performance of the improved PID controller of the particle swarm algorithm is superior to that of a standard PID controller of the particle swarm algorithm. From the response time of the PID controller, the improved particle swarm algorithm meets the system requirement in the vicinity of 0.20s, the standard particle swarm algorithm meets the requirement in the vicinity of 0.23s, and the improved PID controller is high in speed of entering a stable state. In addition, in the process of optimization by the standard particle swarm optimization, the maximum overshoot of 3.2% is obviously greater than the maximum overshoot of 2.6% optimized by the improved particle swarm optimization, so that the improved particle swarm optimization PID controller has better performance.
Comparison of the Performance of the examples with that of the comparative examples
Figure BDA0003733037870000142
Figure BDA0003733037870000151
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are given by way of illustration of the principles of the present invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (9)

1. The utility model provides a curtain coating membrane composite process automatically regulated tension governing system which characterized in that: the tension detection device comprises an FPGA chip, a PID controller, a frequency converter, a motor model, a tension detection module, a processing module, an alarm module and a display module;
the PID controller consists of a proportional unit, an integral unit and a differential unit and passes through K p 、K i And K d Setting three parameters to control the frequency converter;
the frequency converter is used for receiving the signal of the PID controller, converting the signal and transmitting the signal to the motor model and controlling the operation of the motor model;
the tension detection module is used for detecting the analog quantity generated by the tension measurement mechanism and converting the output of the analog quantity into a digital signal which can be operated and processed by the processor;
the processing module is used for receiving signals of the alarm module and the tension detection module and making corresponding actions;
the alarm module is used for prompting and feeding back an abnormal phenomenon generated in the normal working process of the control system, so that a worker can conveniently process the abnormality or check the equipment condition in time;
the display module is used for receiving the signal of the processing module and displaying each parameter in the display screen;
the FPGA chip comprises a control module, a storage module, a multiplexer, an initialization module, a global optimal selection module, a local optimal selection module and a speed and position updating module;
the control module is used for coordinating and controlling the normal operation of the whole system;
the storage module is used for storing information related to the particles, the storage module comprises a ram1 memory, a ram2 memory, a ram3 memory, a ram4 memory and a ram5 memory, and the ram1 memory is used for storing coded data of the positions; the ram2 memory is used for storing particle fitness values; the ram3 memory is used for storing the local optimal fitness value; the ram4 memory is used for storing local optimal values; the ram5 memory is used for storing the speed of the particles;
the multiplexer is used for selecting input and output data and recording the data as mux;
the initialization module is used for generating an initial particle population;
the global optimal selection module is used for selecting and updating global optimal particles;
the local optimal selection module is used for selecting and updating local optimal particles;
the speed and position updating module is mainly used for updating the speed and the position of the particles;
the FPGA chip adopts an improved particle swarm algorithm, and the improved particle swarm algorithm is K p 、K i And K d The setting step is as follows:
s1, specifying the value range of three parameters of PID controllerMaximum speed V of particle swarm optimization algorithm max Thereafter, a group of locations x is initialized i (k +1) and velocity V i (k +1) particles all falling within this range; wherein x is i (k +1) and V i The formula for the calculation (k +1) is as follows:
V i (k+1)＝wV i (k)+C 1 r 1 [P i (k)-x i (t)]+C 2 r 2 [P g (k)-x i (t)]
x i (k+1)＝x i (k)+V i (k+1)
in the formula: v i (k) The velocity of the ith particle is calculated for the kth iteration; x is the number of i (k) The position of the ith particle in the k iterative computation is calculated; c 1 、C 2 Is a learning factor used for adjusting the degree of individual position cognition and group position cognition; r is 1 、r 2 Is an independent interval of [0,1]The random number of (2); p i Is the local optimal position of the group; p g The global optimal position of the particle swarm is defined, and w is the inertial weight;
s2, calculating the fitness value J of each particle by using the following formula 1 and formula 2 i ；
Equation 1 when ey (t) is ≧ 0,
Figure FDA0003733037860000031
equation 2 when ey (t) <0,
Figure FDA0003733037860000032
in the formula: the system error ey (t) y (t) -y (t-1), and y (t) is the output of the controlled object; u (t) is the controller output; t is t u Is the rise time; e (t) is an error; w is a 1 、w 2 、w 3 And w 4 As a weight value, w 1 ＝0.999、w 2 ＝0.001、w 3 ＝2.0、w 4 ＝100；
S3, for each particle: if J i >J ibest Then J is ibest ＝J i ，P i ＝x i (ii) a If J i >J gbest Then J is gbest ＝J i ，P g ＝x i ；
In the above formula J i What is meant is the current adaptation value of the particle, J ibest The meaning represented is the best adaptation value, J, experienced by the individual particles gbest The meaning represented is the best adaptation value experienced globally;
s4, updating the speed and position of the particle by the formula in step S1, and C 1 、C 2 Take [0, 2.5 ]]A random number within the range of the random number,
Figure FDA0003733037860000033
s5, introducing a hybridization operator, and improving a particle swarm optimization algorithm: replacing the particles with lower fitness value with the particles with higher fitness value, and performing hybridization operation on the particle swarm;
the introduction method of the hybridization operator comprises the following steps: carrying the cross probability s by the particles in the initial time group, selecting a certain amount of particles during the iterative operation, enabling the particles to be crossed pairwise according to the cross probability s to generate the same number of filial generations, replacing the parent particles with the obtained filial generation particles, and carrying out a crossing process according to the cross probability P C Performing arithmetic weighting calculation on the particle positions and the values in different dimensions between the particle positions, changing the current position of the particle under the condition of not being limited to the current optimal position, and performing re-search; mutation process according to the mutation probability P m And determining whether the particle is to reinitialize its position in a partial dimension, assuming that m and n are 2 particles, the formula of the hybridization operation is as follows:
X m (t+1)＝s·X m (t)+(1-s)·X n (t)
X n (t+1)＝s·X n (t)+(1-s)·X m (t)
in the formula: 0< s < 1;
Figure FDA0003733037860000041
Figure FDA0003733037860000042
in the formula: f. of max Is the maximum fitness value in the population, f avg Is the population mean fitness value, f is the greater fitness value of the two individuals to be crossed; f' is the fitness value of the individual to be mutated; k is a radical of 1 、k 2 、k 3 And k 4 Is constant, and k 1 <k 2 ，k 3 <k 4 ；
And the inertia factor w can be further defined, and the calculation formula is as follows:
Figure FDA0003733037860000043
W max is the maximum inertia weight value, w min Is the minimum inertia weight value, t is the iteration number of the current particle, t max The maximum iteration times of the particle swarm algorithm are the maximum iteration times of the particle swarm algorithm;
s6, judging that the operation times reach the maximum iteration times G k If not, returning to the step S2 to continue the operation; if the algorithm is finished, obtaining K p 、K i And K d And is transmitted to a PID controller.
2. The cast film compounding process auto-adjusting tension tuning system of claim 1, wherein: the tension detection module comprises a tension sensor, a sampling module and an analog-digital processing module,
the tension sensor is used for detecting the tension of the system and conveying the tension to the sampling module;
the sampling module is used for receiving signals transmitted by the tension sensor and transmitting the signals to the analog-digital processing module;
the analog-digital processing module is used for converting the analog quantity signal collected by the sampling module into a digital quantity signal and transmitting the digital quantity signal to the processing module.
3. The cast film compounding process auto-adjusting tension tuning system of claim 2, wherein: the alarm module adopts a double-color lamp and a red lamp in a circuit; when the equipment is in a standby state, the yellow lamp is turned on by the double-color lamp; when the lamp works normally, the two-color lamp is turned on; if the equipment stops or faults such as disconnection occur, a red light is displayed.
4. The cast film compounding process auto-adjusting tension tuning system of claim 3, wherein: the display module passes through I 2 And the communication mode is connected with the processing module, and the display module adopts an LCD display screen.
5. The cast film compounding process auto-adjusting tension tuning system of claim 4, wherein: the motor model comprises a cooling motor and a winding motor, and the expression of the motor model is as follows:
Figure FDA0003733037860000061
Figure FDA0003733037860000062
in the formula: respectively defining the cooling motor and the winding motor as motors 1 and 2 which respectively correspond to subscripts 1 and 2 in the above formulas; wherein, omega is the synchronous angular velocity of the motor, omega r Is the stator angular velocity of each motor, # is the rotor flux linkage of each motor, J is the moment of inertia of each motor, T L Is the load torque, T, of each motor r Is the time constant of each motor, L r 、L m Respectively the inductance and mutual inductance of each motor, n p Is the number of pole pairs, F, of the motor 12 As an electric motor1 and motor 2.
6. The cast film compounding process auto-adjusting tension tuning system of claim 5, wherein: the frequency converter is electrically connected with the motor model, and a photoelectric encoder is arranged between the motor model and the frequency converter for feedback connection.
7. The cast film compounding process auto-adjusting tension tuning system of claim 6, wherein: the tension detection module further comprises a roll diameter detection module, and the calculation method of the roll diameter detection module is as follows:
Figure FDA0003733037860000063
in the formula: d (t) is the winding diameter at the time t, delta is the thickness of the wound film, and V is the linear speed of the winding drum.
8. The cast film compounding process auto-adjusting tension tuning system of claim 7, wherein: the processing module is internally provided with a roll diameter judging module which is used for judging the relationship between the size of the roll diameter preset in the processing module and the size of the existing roll diameter and controlling whether the system performs roll changing action or not.
9. The cast film compounding process auto-adjusting tension tuning system of claim 8, wherein: the roll changing action needs to be stopped for a short time, and a rolling shaft needs to be stopped by k after being rewound b The speed is multiplied by the running time t b The calculation formula is as follows:
Figure FDA0003733037860000071
in the formula: t is t 0 For down time, k b Is a constant.
CN202210789063.4A 2022-07-06 2022-07-06 Automatic tension adjusting system for cast film compounding process Pending CN115108379A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116873640A (en) * 2023-09-07 2023-10-13 宁德时代新能源科技股份有限公司 Coiled material winding control method and winding equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116873640A (en) * 2023-09-07 2023-10-13 宁德时代新能源科技股份有限公司 Coiled material winding control method and winding equipment
CN116873640B (en) * 2023-09-07 2024-02-20 宁德时代新能源科技股份有限公司 Coiled material winding control method and winding equipment

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