Genetic algorithm optimization-based fuzzy PID control method, apparatus and device

Abstract

A genetic algorithm optimization-based fuzzy PID control method and apparatus, and a device. The method comprises: S1, acquiring a preset standard electrode plate quality, a measured electrode plate quality and a system default ray intensity, acquiring an ambient temperature parameter, retrieving a monitoring index, and monitoring the stability of a surface density measurement system according to the monitoring index; S2, on the basis of a preset system parameter of the surface density measurement system, setting a data sampling interval; S3, on the basis of the data sampling interval, continuously sampling a number N of pieces of data using a median average filtering algorithm, removing extreme values, calculating the arithmetic mean value of the N-2 pieces of data, and eliminating sampling value deviation of the arithmetic mean value, to obtain surface density sampling data; S4, performing fuzzification processing on the surface density sampling data, determining a fuzzy rule, and performing optimization in real time according to the fuzzy rule and by means of fuzzy reasoning, to obtain applicable PID parameters; S5, on the basis of the applicable PID parameters, using a genetic algorithm to control a servo motor of the surface density measurement system. Thus, the technical problems of parameter tuning being difficult and the robustness of closed-loop control being low are solved.

Description

Fuzzy PID control method, device and equipment based on genetic algorithm optimization Technical Field

[0001] The present invention relates to the field of data processing in the battery manufacturing industry, and in particular to a fuzzy PID control method, device and equipment based on genetic algorithm optimization. Background Art

[0002] The battery manufacturing industry, especially in the field of lithium-ion power batteries for new energy vehicles and energy storage, has increasingly higher requirements for battery consistency. The consistency of electrode coating is very critical. The lateral and longitudinal consistency of coating is mainly adjusted by adjusting the die gap and pump speed. In the past, the gap of the die was mainly adjusted manually by adjusting the micrometer, which not only affected the production cycle, but also caused huge waste of materials. With the rapid development of the lithium battery industry, the industry has introduced intelligent die heads to solve the problem of lateral consistency of coating. During coating, multiple servo motor-driven flow blocks are used to adjust the gap of the slurry outlet. The size of the outlet is adjusted in real time in a closed loop according to the measured value of the coating surface density to achieve the requirement of uniform coating surface density. At present, the closed-loop control of the intelligent die head is mainly through the traditional PID algorithm. For example, the existing invention patent application document with publication number CN116140161A is "An internal circulation flotation oven, coating equipment and PID control method thereof". The internal circulation flotation oven in this existing solution includes a box body, and the outer walls of both sides of the box body are respectively opened with openings, and an electrode drying channel is formed between the openings on both sides. The inside of the box body is provided with a first air supply blower and a second air supply blower on opposite sides of the electrode drying channel; the first air supply blower includes a first hot air circulation mechanism, a plurality of The first hot air circulation mechanism is connected to a first air nozzle and a first return air blower, the first return air blower is connected to the first hot air circulation mechanism, the second air supply blower includes a second hot air circulation mechanism, a plurality of second air nozzles connected to the second hot air circulation mechanism, and a second return air blower, the second return air blower is connected to the second hot air circulation mechanism, the first air nozzle and the second air nozzle are both facing the electrode drying channel to enable the transported electrode to be suspended, and air knife sealing devices for controlling the opening and closing of the opening are respectively provided on opposite sides of the opening, and the air knife sealing devices are connected to the air knife blower provided on the outer wall of the box. The advantage of the aforementioned traditional PID controller is that it is easy to implement and debug, but the coating system is a nonlinear, time-varying or multivariable complex system, and there are many factors that affect the coating surface density. Relying solely on the traditional PID algorithm is prone to false closed loops.

[0003] The coating system used in the prior art is a nonlinear, time-varying or multivariable complex system. For example, the existing invention patent application document with publication number CN105280957A, "A fully automatic production line for power type lithium batteries and its production process", in which a fully automatic production line for power type lithium batteries includes: a pole piece production unit, a battery assembly unit, a battery liquid injection unit and a factory processing unit arranged in sequence, each unit having a corresponding automatic control system; the pole piece production unit includes a mixer, a coating machine, a roller press, a continuous conveyor and a continuous conveyor. The battery assembly unit includes a stacking machine, a tab welding circulation line, an automatic aluminum-plastic film shell punching machine, and a packaging line arranged in sequence; the battery filling unit includes a filling machine, a tunnel furnace, a hot and cold press, a formation system, and a molding machine arranged in sequence. After the single lithium battery is filled with liquid, it is placed in a static tunnel furnace and left to bake online for more than 24 hours to allow the electrolyte to penetrate effectively. The PID temperature control method is used to ensure that the temperature control accuracy of the lithium battery is ±5°C during baking. When the baking is completed, the baked lithium battery is automatically loaded into the tray. In the aforementioned prior art, the factors affecting the coating surface density include the fluctuation of the incoming material and the influence of temperature on the measuring equipment, which will cause fluctuations in the surface density. Relying solely on the traditional PID algorithm is prone to false closed loops. In addition, in actual control systems, linear systems are a minority after all. Most systems are nonlinear systems with uncertain system models. In application scenarios with high control accuracy requirements, parameter tuning operations are difficult.

[0004] In summary, the existing technology has technical problems such as difficulty in parameter tuning and low robustness of closed-loop control.

[0005] Summary of the Invention

[0006] The technical problem to be solved by the present invention is: how to solve the technical problems of difficult parameter setting and low robustness of closed-loop control in the prior art.

[0007] The present invention adopts the following technical solutions to solve the above technical problems:

[0008] In the first aspect, the fuzzy PID control method based on genetic algorithm optimization provided by an embodiment of the present invention includes: obtaining the preset standard pole piece mass, the measured pole piece mass and the system default radiation intensity, obtaining the ambient temperature parameters of the surface density measurement system according to the preset temperature correction mechanism, and obtaining the monitoring indicators according to the preset standard pole piece mass, the measured pole piece mass, the system default radiation intensity and the ambient temperature parameters to monitor the stability of the surface density measurement system; setting the data sampling interval according to the preset system parameters of the surface density measurement system; using the median average filtering algorithm, according to the data sampling interval, continuously sampling N data, removing extreme values, calculating the arithmetic mean of N-2 data, and eliminating the sampling value deviation of the arithmetic mean to obtain surface density sampling data; fuzzy processing is performed on the multi-surface density sampling data to determine the fuzzy rules, and then the applicable PID parameters are obtained by real-time optimization through fuzzy reasoning; according to the applicable PID parameters, the servo motor of the surface density measurement system is controlled by using a genetic algorithm.

[0009] In the second aspect, the fuzzy PID control device based on genetic algorithm optimization provided by the embodiment of the present invention includes: a system stability monitoring module, which is used to obtain the preset standard pole piece mass, the measured pole piece mass and the system default ray intensity, and obtain the ambient temperature parameters of the surface density measurement system according to the preset temperature repair mechanism, and obtain the monitoring index according to the preset standard pole piece mass, the measured pole piece mass, the system default ray intensity and the ambient temperature parameters, so as to monitor the stability of the surface density measurement system; a time interval setting module, which is used to set the data sampling interval according to the preset system parameters of the surface density measurement system; a sampling processing module, which is used to use the median average filtering algorithm according to Data sampling interval, continuously sampling N data, removing extreme values, calculating the arithmetic mean of N-2 data, and eliminating the sampling value deviation of the arithmetic mean to obtain surface density sampling data. The sampling processing module is connected to the time interval setting module; the PID parameter tuning module, fuzzy processing of multiple surface density sampling data, determining fuzzy rules, and based on fuzzy reasoning, real-time optimization to obtain applicable PID parameters. The PID parameter tuning module is connected to the sampling processing module; the servo motor control module is used to control the servo motor of the surface density measurement system using a genetic algorithm according to applicable PID parameters. The servo motor control module is connected to the PID parameter tuning module.

[0010] In a third aspect, an embodiment of the present invention provides a fuzzy PID control device based on genetic algorithm optimization, comprising: a memory, a processor, a communication interface, and a bus. The memory may include a large-capacity memory for data or instructions. By way of example, and not limitation, the memory may include an HDD, a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include removable or non-removable (or fixed) media. Where appropriate, the memory may be internal or external to the device for determining the battery operating voltage curve. In a specific embodiment, the memory is a non-volatile solid-state memory. In a specific embodiment, the memory includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0011] The present invention has the following advantages over the existing technology: By integrating the fuzzy PID control algorithm with production rules, the present invention controls the PID algorithm based on rules, improves the robustness of closed-loop control, reduces false closed loops, and improves production quality. By integrating the fuzzy PID control algorithm with production rules, the present invention integrates a large amount of prior knowledge of actual production with the control algorithm, and controls the PID algorithm based on rules, improves the robustness of closed-loop control, enhances the anti-interference ability of the closed-loop coating system, reduces false closed loops, and improves production quality.

[0012] This method uses a median average filtering algorithm to process multiple data sets. Area density measurements that trigger an alarm are directly discarded. This algorithm combines median filtering with arithmetic mean filtering. This filtering algorithm eliminates sampling deviations caused by occasional pulse interference.

[0013] The present invention establishes a specific sampling interval, and sets the sampling interval for the areal density measuring instrument at the oven outlet according to the count of the sampling interval and the scanning cycle of the areal density instrument, thereby ensuring the effectiveness of closed-loop control.

[0014] The present invention aims to solve the problem of difficulty in precision control of nonlinear systems and systems with uncertain models commonly used in existing control systems. It adopts fuzzy PID intelligent algorithm to quantify and accurately control the model when the control model is unknown.

[0015] The present invention uses the time integral performance indicator of the absolute value of the error as the minimum objective function for parameter selection, achieving satisfactory dynamic characteristics of the transition process. The present invention also incorporates a squared term of the control input into the objective function to prevent excessive control energy. The present invention also employs a penalty function, using overshoot as an optimal indicator to avoid overshoot.

[0016] The present invention solves the technical problems of difficulty in parameter setting and low robustness of closed-loop control in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG1 is a schematic diagram of the basic steps of a fuzzy PID control method based on genetic algorithm optimization in one embodiment of the present invention;

[0018] FIG2 is a schematic diagram of the working principle of X-Ray surface density in one embodiment of the present invention;

[0019] FIG3 is a schematic diagram of setting a data sampling interval according to an embodiment of the present invention;

[0020] FIG4 is a schematic diagram of area division in the process of data analysis and processing in one embodiment of the present invention;

[0021] FIG5 is a flowchart of the overall framework of fuzzy PID closed-loop control in one embodiment of the present invention;

[0022] FIG6 is a schematic diagram of fuzzy PID control logic data flow processing in one embodiment of the present invention;

[0023] FIG7 is a specific flow chart of PID surface density control in one embodiment of the present invention;

[0024] FIG8 is a schematic diagram of a specific flow chart of genetic algorithm operation in one embodiment of the present invention;

[0025] FIG9 is a schematic structural diagram of a fuzzy PID control device based on genetic algorithm optimization in one embodiment of the present invention. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] Example 1

[0028] FIG1 is a schematic diagram of the basic steps of a fuzzy PID control method based on genetic algorithm optimization in one embodiment of the present invention. As shown in FIG1 , the fuzzy PID control method based on genetic algorithm optimization provided by the present invention includes the following basic steps:

[0029] S1. Monitor the stability of the measurement system;

[0030] In this embodiment, in order to ensure the accuracy of closed-loop control, the stability of the measuring device must be ensured first. Based on this closed-loop control system, measurement system stability monitoring is added.

[0031] FIG2 is a schematic diagram of the working principle of the X-Ray areal density in one embodiment of the present invention. As shown in FIG2, in this embodiment, due to the existence of a temperature correction mechanism, the calibration state of the areal density measuring instrument is consistent with the ecological environment, so the online measurement value is accurate. If the production state (temperature, humidity) changes, the measurement system has become unstable, resulting in inaccurate measurement values. If closed-loop control is performed based on this measurement value, there will be increased quality risks. In this embodiment, the associated information of the production state includes but is not limited to: temperature and humidity.

[0032] Among them, I0 represents the intensity of the ray before penetrating the material, I 样本 Indicates the forward width of the ray after penetrating the sample, I 极片 It indicates the intensity of the rays after penetrating the pole piece, msample refers to the mass of the standard piece, and mpole refers to the mass of the object being measured.

[0033] In this embodiment, it is obtained by solving:

[0034] In this embodiment, the current measurement system defaults to: I0=I 样本

[0035] However, in reality, the ambient temperature and humidity are constantly changing, and the radiation intensity and radiation sensor performance are slowly drifting. Therefore, stability monitoring for X-ray areal density instruments is established. The monitoring indicators are as follows:

[0036] In this embodiment, the measurement system stability monitoring is mainly used to measure the stability of the measurement system. The main application is when the measurement system is affected by external factors, and the value measured by the measurement system is directly discarded.

[0037] S2. Set the data sampling interval;

[0038] FIG3 is a schematic diagram of the data sampling interval setting in one embodiment of the present invention. As shown in FIG3 , in this embodiment, in order to ensure the effectiveness of closed-loop control, it is necessary to establish a suitable sampling interval. The areal density measuring instrument is located at the oven outlet, so a sampling interval needs to be set. The interval time is the oven length / coating speed. The sampling interval count is based on the scanning period of the areal density instrument. For example, if the oven length card is 50m, the coating speed can be 25m / min, and the scanning period of the areal density instrument is 10s, then the sampling interval can be 12 times, for example.

[0039] S3, data analysis and processing;

[0040] Figure 4 is a schematic diagram of the regional division process during data analysis and processing in one embodiment of the present invention. As shown in Figure 4, in this embodiment, the surface density detection data is output as the average of the regions where the film area is divided into several equal parts. Therefore, when customizing the die head, the film area and the T-block must be designed to have a one-to-one relationship. In addition, due to the fluctuations in the coating weight curve, the controller cannot close the loop using only one set of sampled data. Instead, multiple sets of data must be processed using a median average filtering algorithm. Surface density measurements that trigger an alarm in the measurement system are directly discarded.

[0041] The median average filtering algorithm used in this embodiment combines median filtering with arithmetic mean filtering. It continuously samples N data points, removes the maximum and minimum values, and then calculates the arithmetic mean of N-2 data points. This filtering algorithm can eliminate sampling deviations caused by occasional pulse interference. In this embodiment, the range of N values ​​includes, but is not limited to, [3, 14].

[0042] S4, fuzzy PID closed loop control;

[0043] FIG5 is a flow chart of the overall framework of the fuzzy PID closed-loop control in one embodiment of the present invention. As shown in FIG5 , in the fuzzy PID closed-loop control process in this embodiment, fuzzy logic is used to optimize the PID parameters in real time according to specific fuzzy rules to overcome the shortcoming of the traditional PID algorithm that cannot adjust the PID parameters in real time.

[0044] In this embodiment, step S4 of the fuzzy PID closed-loop control further includes the following specific steps:

[0045] S41, fuzzy processing;

[0046] In this embodiment, the control system determines the deviation E of the current distance standard value and the difference Ec between the current deviation and the previous deviation through the surface density data collected by X-Ray;

[0047] In this embodiment, the X-ray areal density measuring instrument collects areal density data, and the areal density is processed to obtain two values: a deviation value E from the target value, and a change (difference) Ec between the current deviation and the previous deviation; the two values ​​are fuzzified;

[0048] In this embodiment, the fuzzy subsets of E and Ec are determined. In this embodiment, PID control uses linguistic variables. To express the fuzzy subsets, in this embodiment, the linguistic variables include but are not limited to: negative large [NB], negative medium [NM], negative small [NS], zero [ZO], positive small [PS], positive medium [PM], and positive large [PB]. Therefore, in this embodiment, the fuzzy subsets defining e and ec are all {NB, NM, NS, ZO, PS, PM, PB}. When the process control of the surface density is ±2, we divide the interval: (-2, 2) into 8 parts, defined as: {-2-1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2}.

[0049] In this embodiment, any physical quantity measurement signal has a measurement range, denoted as Vmax and Vmin, which is naturally the same as the expected range of the set value during PID regulation. Therefore, the range of the deviation e is within the range of Vmin-Vmax to Vmax-Vmin, and the incremental range of the deviation is twice that. In this embodiment, linear quantization is used, and the functional relationship includes:

[0050] Deviation range function relationship:

[0051] Deviation increment range function relationship:

[0052] In this embodiment, the aforementioned quantization function is used to quantize E and Ec, and a method such as rounding off can be used to obtain a specific fuzzy subset. However, considering that E and Ec change continuously, rounding off may affect control accuracy. Therefore, in this embodiment, membership is introduced to achieve this process.

[0053] In this embodiment, the membership of E and Ec on the fuzzy subsets is determined. The membership is a value between 0 and 1, which is used to describe the degree to which an input belongs to a fuzzy subset. In this embodiment, a triangular membership function is used;

[0054] S42, determine the fuzzy rules;

[0055] In this embodiment, fuzzy reasoning is performed according to given fuzzy rules;

[0056] In this embodiment, the functional relationship in the fuzzy combing operation is as follows:

[0057] In this embodiment, since fuzzy reasoning is the foundation of fuzzy control, in order to implement fuzzy reasoning, a rule base or knowledge base of fuzzy reasoning is first established, and then an inference engine is established to perform reasoning.

[0058] In this embodiment, a fuzzy rule base is established to adjust the three parameters Kp, Ki and Kd, so a fuzzy rule base of these three variables needs to be established.

[0059] Kp fuzzy rule table:

[0060] In this embodiment, the Kp value of the PID controller is determined by the system's response speed. Increasing Kp can improve response speed and reduce steady-state deviation; however, a too large Kp value can result in significant overshoot, potentially causing system instability. Reducing Kp can reduce overshoot and improve stability, but a too small Kp value can slow response and increase adjustment time. Therefore, a larger Kp value should be used in the initial adjustment phase to improve response speed. In the middle of the adjustment phase, a smaller Kp value should be used to minimize overshoot and maintain a certain response speed. Finally, the Kp value should be adjusted to a larger value in the later stages of the adjustment process to reduce static error and improve control accuracy.

[0061] In this embodiment, based on the above description, the fuzzy rule of Kp is defined as follows: Ki fuzzy rule design:

[0062] In system control, integral control is primarily used to eliminate the system's steady-state deviation. Due to certain reasons (such as saturation nonlinearity), the integral process may produce integral saturation in the early stages of the regulation process, resulting in a large overshoot of the regulation process. Therefore, in the early stages of the regulation process, to prevent integral saturation, the integral action should be weaker, or even zero; in the middle stages of the regulation process, to avoid affecting stability, the integral action should be more moderate; finally, in the later stages of the process, the integral action should be strengthened to reduce the regulation static error. Based on the above analysis, we formulate the Ki fuzzy rule as follows:

[0063] Kd fuzzy rule design:

[0064] The differential action is primarily introduced for processes with high inertia. The differential action coefficient is designed to alter the system's dynamic characteristics. The differential action coefficient reflects the trend of signal changes and can introduce an effective early correction signal before the deviation signal changes significantly, thereby accelerating response, reducing adjustment time, and eliminating oscillations. Ultimately, this improves the system's dynamic performance. Therefore, the selection of the Kd value significantly impacts the dynamic characteristics of the control process. If the Kd value is too large, the braking of the control process will be premature, resulting in excessive adjustment time. If the Kd value is too small, the braking of the control process will lag, leading to increased overshoot. Based on practical experience, the differential action should be increased in the early stages of the control process to minimize or even eliminate overshoot. In the middle stages, since the control characteristics are sensitive to changes in the Kd value, the Kd value should be appropriately reduced and kept constant. Finally, in the later stages of the control process, the Kd value should be reduced to minimize the braking action of the controlled process, thereby compensating for the extended adjustment time caused by the large Kd value in the early stages of the control process. Based on the above analysis, the fuzzy rules for Kd are formulated as follows:

[0065] S43. Defuzzification.

[0066] FIG6 is a schematic diagram of fuzzy PID control logic data flow processing in an embodiment of the present invention. As shown in FIG6 , in this embodiment, the fuzzy parameters are analyzed and reasonable P, I, and D parameters are output to control the servo motor.

[0067] In this embodiment, the obtained target object is fuzzy processed to make it correspond to a specific physical quantity, and the desired Kp, Ki and Kd values ​​are obtained based on the result of fuzzy reasoning.

[0068] S5. Control the servo motor according to P, I, and D parameters;

[0069] FIG7 is a specific flow chart of PID surface density control in one embodiment of the present invention. As shown in FIG7 , in this embodiment, step S5 of servo motor control further includes the following specific steps of PID surface density control:

[0070] S51. Determine target surface density m t , actual surface density m c ;

[0071] S52, set PID parameters: k p 、k i 、k d ;

[0072] S53. Calculate e(k)=m t (k)-m c (k);

[0073] S54. Calculation: ΔU = k p (e k -e k-1 )+k i e k +k d [(e k -e k-1 )+(e k-1 -e k-2 )];

[0074] S55, judging whether ΔU is greater than 0;

[0075] S56: If yes, adjust the servo motor;

[0076] S57: If not, do not adjust the servo motor.

[0077] FIG8 is a schematic diagram of a specific operation flow of a genetic algorithm in an embodiment of the present invention. As shown in FIG8 , in this embodiment, the specific operation flow of the genetic algorithm used in step S5 of servo motor control further includes:

[0078] S51', set parameters and encode;

[0079] S52', initialize population GEN=1;

[0080] S53', calculate target fitness;

[0081] S54', judging whether the target fitness meets the requirements;

[0082] S55', if not, perform selection, crossover and mutation operations; if yes, jump to step S58';

[0083] S56', determine whether GEN is greater than the maximum number;

[0084] S57', if yes, then GEN=GEN+1;

[0085] S58': If not, perform decoding operation.

[0086] In this embodiment, the PID control signal:

[0087] In this embodiment, the transfer function:

[0088] Among them, k p is the proportionality coefficient, k i is the integration time constant, k d is the differential time constant, e(t) is the deviation, and u(t) is the control quantity;

[0089] In this embodiment, a triangular membership function is designed and the same quantization target, namely the domain {-2, 2}, is used. Therefore, at a certain moment, the membership of the fuzzy variables where the input and output are located is the same. Based on this, we use the centroid method to calculate the quantization value of each output quantity. The formula is as follows:

[0090] Among them, F is the fuzzy quantization value, M is the membership value;

[0091] In this embodiment, a genetic algorithm is used to initialize and assign PID parameters. To achieve satisfactory transient dynamic characteristics, the time integral performance indicator of the absolute error is used as the minimum objective function for parameter selection. To prevent excessive control energy, a squared term of the control input is added to the objective function. Furthermore, to prevent overshoot, a penalty function is implemented, with the overshoot amount used as the optimal indicator.

[0092] Where e(t) is the system error, u(t) is the output of the controller, and t u is the rise time of the step signal, w1, w2, w3 are e(t), u(t), t u The corresponding weight is ey(t)=y(t)-y(t-1), y(t) is the output of the control object, and w4 is the weight.

[0093] In the process of applying the genetic algorithm in this embodiment, binary coding is first used, and 10-bit binary codes are used to represent the three decision variables kP, ki, and kD respectively. The selection of the optimal index is the same as the PID tuning of the decimal coding genetic algorithm.

[0094] In this embodiment, the number of samples used in the genetic algorithm is Size=30;

[0095] In this embodiment, the crossover probability and mutation probability are: Pc=0.60, Mu=0.001-[1:1:Size]×0.001 / Size;

[0096] In this embodiment, the value range of parameter kP is [0, 20], the value range of ki and kD is [0, 1], and the values ​​of w1, w2, w3, and w4 are the same as the PID tuning of the decimal coded genetic algorithm.

[0097] Figure 9 is a structural diagram of a fuzzy PID control device based on genetic algorithm optimization in one embodiment of the present invention. As shown in Figure 9, the device 300 for determining the battery operating voltage curve includes a memory 301, a processor 302, a communication interface 303 and a bus 304.

[0098] Memory 301 may include a large-capacity memory for data or instructions. By way of example, and not limitation, memory 301 may include an HDD, a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 301 may include removable or non-removable (or fixed) media. Where appropriate, memory 301 may be internal or external to device 300 for determining the battery operating voltage curve. In certain embodiments, memory 301 is a non-volatile solid-state memory. In certain embodiments, memory 301 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0099] In one example, the processor 302 may include a central processing unit (CPU) or an application specific integrated circuit (ASIC).

[0100] Alternatively, it may be configured as one or more integrated circuits implementing the embodiments of the present invention.

[0101] The communication interface 303 is mainly used to implement communication between modules and units in the embodiment of the present invention.

[0102] The memory 301 , the processor 302 and the communication interface 303 are connected via the bus 304 and communicate with each other.

[0103] That is, the memory 301 is used to store program code. The processor 302 reads the executable program code stored in the memory 301 and runs the program corresponding to the executable program code to execute the fuzzy PID control method based on genetic algorithm optimization. The fuzzy PID control method based on genetic algorithm optimization includes but is not limited to steps S1 to S8 in the above embodiment.

[0104] It should be understood that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For the device embodiments, the relevant parts can be referred to the method embodiment description part. The present invention is not limited to the specific steps and structures described above and shown in the figures. Those skilled in the art can make various changes, modifications and additions, or change the order of the steps after understanding the spirit of the present invention. In addition, for the sake of brevity, a detailed description of known method technologies is omitted here.

[0105] The functional modules and functional units shown in the above-described structured block diagram can be implemented as hardware, software, firmware or their combination. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), suitable firmware, a plug-in unit, a function card or the like. When implemented in software, elements of the present invention are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by the data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information.

Claims

1. The fuzzy PID control method based on genetic algorithm optimization is characterized by: The method comprises: S1, obtaining the preset standard pole piece mass, the pole piece mass to be measured and the system default ray intensity, obtaining the ambient temperature parameters of the surface density measurement system according to the preset temperature repair mechanism, and obtaining the monitoring index according to the preset standard pole piece mass, the pole piece mass to be measured, the system default ray intensity and the ambient temperature parameters, so as to monitor the stability of the surface density measurement system; S2. Setting a data sampling interval according to preset system parameters of the surface density measurement system; S3, using a median average filtering algorithm, continuously sampling N data according to the data sampling interval, removing extreme values, calculating the arithmetic mean of N-2 data, and eliminating the sampling value deviation of the arithmetic mean to obtain surface density sampling data; S4, performing fuzzy processing on the surface density sampling data, determining fuzzy rules, and optimizing and obtaining applicable PID parameters in real time through fuzzy reasoning; S5. Control the servo motor of the surface density measurement system using a genetic algorithm according to the applicable PID parameters.

2. The fuzzy PID control method based on genetic algorithm optimization according to claim 1 is characterized in that: The step S1 comprises: S11, using the following logic to obtain the preset standard pole piece mass: S12, using the following logic to obtain the mass of the electrode to be measured: In the formula, I0 represents the intensity of the ray before penetrating the material, I 样本 Represents the intensity of the ray after penetrating the sample, I 极片 Indicates the intensity of the ray after penetrating the pole piece, m 样本 Refers to the mass of the standard film, m 极片 is the mass of the electrode to be measured, λ represents the absorption coefficient per unit area of ​​the membrane; S13, according to the intensity of the ray after penetrating the sample and the intensity of the ray before penetrating the substance, obtain the monitoring index:

3. The fuzzy PID control method based on genetic algorithm optimization according to claim 2 is characterized in that: In step S12, the relationship between the preset standard pole piece mass, the pole piece mass to be measured, the intensity of the ray after penetrating the sample, and the intensity of the ray before penetrating the substance is expressed according to the following logic:

4. The fuzzy PID control method based on genetic algorithm optimization according to claim 1 is characterized in that: The step S2 further includes: S21, calculating the interval time according to the oven length and coating speed of the surface density measurement system; S22, determining a sampling interval count according to a scanning period of the surface density measurement system; S23. Determine the data sampling interval according to the interval time and the sampling interval count.

5. The fuzzy PID control method based on genetic algorithm optimization according to claim 1 is characterized in that: The step S4 comprises: S41, processing the surface density sample data to obtain a deviation value and a deviation change value, thereby obtaining a deviation range function and a deviation increment range function, and then performing fuzzification processing to obtain a deviation fuzzy subset and a deviation change fuzzy subset, thereby determining the membership degree of the deviation on the fuzzy subset and the membership degree of the deviation change on the fuzzy subset; S42, establishing a fuzzy reasoning rule base, based on which the fuzzy rules of the PID parameters are designed, and based on which fuzzy reasoning results are obtained; S43. Perform a defuzzification operation according to the fuzzy inference result to obtain applicable PID parameters.

6. The fuzzy PID control method based on genetic algorithm optimization according to claim 5 is characterized in that: The step S41 comprises: S411. Use the following logic to express the deviation range function: S412. Use the following logic to express the deviation increment range function: S413. Design a triangular membership function using the following logic: Where F is the fuzzy quantization value and M is the membership value.

7. The fuzzy PID control method based on genetic algorithm optimization according to claim 1 is characterized in that: The step S5 comprises: S51, obtaining and calculating the relationship between the surface density and the PID parameters according to the surface target density, the actual surface density, and the applicable PID parameters: e(k)=m t (k)-m c (k) In the formula, m t represents the target surface density, m c represents the actual surface density, e k 、e k-1 、e k-2 Represent the difference between the actual surface density and the target surface density at time k-1, k-2 and k-3 respectively; S52, according to the relationship between the surface density and the PID parameter, obtain the potential parameter: ΔU=k p (and k -and k-1 )+k i and k +k d [(and k -and k-1 )+(and k-1 -and k-2 )]; S53. Control the adjustment state of the servo motor according to the potential parameter ΔU.

8. The fuzzy PID control method based on genetic algorithm optimization according to claim 1 is characterized in that: In step S5, the genetic algorithm is used to process the PID control signal according to the applicable PID parameters: In the genetic algorithm, the transfer function is determined using the following logic: In the formula, k p is the proportionality coefficient, k i is the integration time constant, k d is the differential time constant, e(t) is the deviation, and u(t) is the control quantity.

9. The fuzzy PID control method based on genetic algorithm optimization according to claim 1 is characterized in that: In step S5, the applicable PID parameters are initialized and assigned using a genetic algorithm, and the integral performance index of the absolute value of the error over time is obtained to serve as a parameter selection minimum objective function, wherein the parameter selection minimum objective function is determined using the following logic: Where e(t) is the system error, u(t) is the output of the controller, and t u is the rise time of the step signal, w1, w2, w3 are e(t), u(t), t u The corresponding weight, ey(t)=y(t)-y(t-1), y(t) is the output of the controlled object, and w4 is the weight.

10. A fuzzy PID control device based on genetic algorithm optimization, characterized in that: The device comprises: A system stability monitoring module is used to obtain the preset standard pole piece mass, the pole piece mass to be measured and the system default ray intensity, and obtain the ambient temperature parameters of the surface density measurement system according to the preset temperature repair mechanism, and obtain the monitoring index according to the preset standard pole piece mass, the pole piece mass to be measured, the system default ray intensity and the ambient temperature parameters, so as to monitor the stability of the surface density measurement system; A time interval setting module, used to set a data sampling interval according to preset system parameters of the surface density measurement system; A sampling processing module, used for continuously sampling N data according to the data sampling interval using a median average filtering algorithm, removing extreme values, calculating the arithmetic mean of N-2 data, and eliminating the sampling value deviation of the arithmetic mean to obtain surface density sampling data, the sampling processing module being connected to the time interval setting module; A PID parameter tuning module performs fuzzy processing on the surface density sampling data to determine fuzzy rules, and optimizes and obtains applicable PID parameters in real time through fuzzy reasoning. The PID parameter tuning module is connected to the sampling processing module; A servo motor control module is used to control the servo motor of the surface density measurement system by using a genetic algorithm according to the applicable PID parameters. The servo motor control module is connected to the PID parameter tuning module.

11. The fuzzy PID control device based on genetic algorithm optimization according to claim 10, characterized in that: The system stability monitoring module includes: The standard pole piece quality processing unit is used to obtain the preset standard pole piece quality using the following logic: The measured pole piece quality processing unit is used to obtain the measured pole piece quality by using the following logic: In the formula, I0 represents the intensity of the ray before penetrating the material, I 样本 Represents the intensity of the ray after penetrating the sample, I 极片 Indicates the intensity of the ray after penetrating the pole piece, m 样本 Refers to the mass of the standard film, m 极片 is the mass of the electrode to be measured, λ represents the absorption coefficient per unit area of ​​the membrane; The monitoring index processing unit is used to obtain the monitoring index according to the intensity of the ray after penetrating the sample and the intensity of the ray before penetrating the substance:

12. The fuzzy PID control device based on genetic algorithm optimization according to claim 11, characterized in that: In the measured pole piece mass processing unit, the relationship between the preset standard pole piece mass, the measured pole piece mass, the ray intensity after penetrating the sample, and the ray intensity before penetrating the substance is expressed according to the following logic:

13. The fuzzy PID control device based on genetic algorithm optimization according to claim 10, characterized in that: The time interval setting module comprises: A time interval obtaining unit, used for obtaining the interval time according to the oven length and coating speed of the surface density measurement system; An interval count processing unit, for determining a sampling interval count according to a scanning period of the surface density measurement system; The sampling interval processing unit is used to determine the data sampling interval according to the interval time and the sampling interval count.

14. The fuzzy PID control device based on genetic algorithm optimization according to claim 10, characterized in that: The PID parameter tuning module includes: A fuzzification processing and membership acquisition unit, used for processing the surface density sample data to obtain a deviation value and a deviation change value, thereby obtaining a deviation range function and a deviation increment range function, thereby performing fuzzification processing to obtain a deviation fuzzy subset and a deviation change fuzzy subset, thereby determining the membership of the deviation on the fuzzy subset and the membership of the deviation change on the fuzzy subset; A fuzzy rule design unit, used for establishing a fuzzy reasoning rule base, for designing the fuzzy rules of the PID parameters, and for obtaining fuzzy reasoning results; The defuzzification unit is used to perform a defuzzification operation according to the fuzzy inference result to obtain the applicable PID parameters.

15. The fuzzy PID control method based on genetic algorithm optimization according to claim 14, characterized in that: The fuzzy processing and membership degree acquisition unit comprises: The deviation range component is used to express the deviation range function using the following logic: The incremental range component is used to express the deviation incremental range function using the following logic: The triangular membership processing component is used to design the triangular membership function using the following logic: Where F is the fuzzy quantization value and M is the membership value.

16. The fuzzy PID control device based on genetic algorithm optimization according to claim 10, characterized in that: The servo motor control module comprises: The surface density and PID parameter relationship processing unit is used to obtain and calculate the surface density and PID parameter relationship according to the surface target density, the actual surface density, and the applicable PID parameters: e(k)=m t (k)-m c (k) In the formula, m t represents the target surface density, m c represents the actual surface density, e k 、e k-1 、e k-2 Represent the difference between the actual surface density and the target surface density at time k-1, k-2 and k-3 respectively; The potential parameter processing unit is used to obtain the potential parameter according to the relationship between the surface density and the PID parameter: ΔU=k p (and k -and k-1 )+k i and k +k d [(and k -and k-1 )+(and k-1 -and k-2 )]; The servo motor adjustment unit is used to control the adjustment state of the servo motor according to the potential parameter ΔU.

17. The fuzzy PID control device based on genetic algorithm optimization according to claim 10, characterized in that: In the servo motor control module, the genetic algorithm is used to process the PID control signal according to the applicable PID parameters: In the genetic algorithm, the transfer function is determined using the following logic: In the formula, k p is the proportionality coefficient, k i is the integration time constant, k d is the differential time constant, e(t) is the deviation, and u(t) is the control quantity.

18. The fuzzy PID control device based on genetic algorithm optimization according to claim 10, characterized in that: In the servo motor control module, a genetic algorithm is used to initialize and assign values ​​to the applicable PID parameters, and an integral performance index of an absolute value of an error over time is obtained to serve as a parameter selection minimum objective function, wherein the parameter selection minimum objective function is determined using the following logic: Where e(t) is the system error, u(t) is the output of the controller, and t u is the rise time of the step signal, w1, w2, w3 are e(t), u(t), t u The corresponding weight, ey(t)=y(t)-y(t-1), y(t) is the output of the controlled object, and w4 is the weight.

19. A fuzzy PID control device based on genetic algorithm optimization, characterized in that: include: Memory, processor, communication interface and bus; The memory, the processor and the communication interface are connected via the bus and communicate with each other; The memory is used to store program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the fuzzy PID control method based on genetic algorithm optimization, wherein the fuzzy PID control method based on genetic algorithm optimization includes: Obtain the preset standard electrode quality, the measured electrode quality and the system default radiation intensity, and according to the preset temperature repair mechanism, Acquire the ambient temperature parameters of the surface density measurement system, and obtain monitoring indicators according to the preset standard pole piece mass, the pole piece mass to be measured, the system default ray intensity and the ambient temperature parameters, so as to monitor the stability of the surface density measurement system; Setting a data sampling interval according to preset system parameters of the areal density measurement system; Using the median average filtering algorithm, according to the data sampling interval, continuously sample N data, remove extreme values, calculate the arithmetic mean of N-2 data, and eliminate the sampling value deviation of the arithmetic mean to obtain surface density sampling data; The surface density sampling data are subjected to fuzzy processing to determine fuzzy rules, and then suitable PID parameters are obtained through real-time optimization through fuzzy reasoning; According to the applicable PID parameters, a genetic algorithm is used to control the servo motor of the surface density measurement system.

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