PLC control-based high-precision metering and weighing system

Abstract

A PLC control-based high-precision metering and weighing system. Product weight measurement and sorting are implemented by combining a pressure sensor, a weighing module, and a fuzzy PID control algorithm. The system can quickly convert weight information of a product to be tested into a voltage signal, which is amplified and subjected to analog-to-digital conversion to provide an accurate digital signal for a PLC. The PLC controller utilizes the fuzzy PID algorithm to perform error analysis and adaptive parameter adjustment, thereby improving control precision and response speed. In addition, by means of quantization processing and the use of a membership function, the system can effectively reduce errors and perform self-correction, thereby improving the accuracy and reliability of overall weighing and sorting. The weighing system can achieve efficient automatic control in industrial applications, and adapt to weighing requirements of different products.

Description

A high-precision metering and weighing system based on PLC control Technical Field

[0001] This invention relates to the field of metrology and weighing technology, and more specifically to a high-precision metrology and weighing system based on PLC control. Background Technology

[0002] Currently, traditional weighing systems face numerous challenges in the processing and metering of frozen aquatic products, primarily manifested in insufficient weighing accuracy, lack of real-time data processing capabilities, and the absence of combined optimization methods. Many existing devices employ a single-batch weighing method, which, influenced by environmental factors and equipment aging, leads to unstable weighing results; even minor errors can affect the final product quality. Simultaneously, traditional systems lack efficient data processing capabilities, failing to promptly identify effective weighing combinations, thus reducing production efficiency. Due to human intervention, the system often introduces additional errors due to human factors, resulting in instability in the weighing process. Furthermore, existing technologies fail to effectively handle unqualified weighing results, directly leading to resource waste, a lack of intelligence and adaptability, and slow response to the weighing needs of different types of materials.

[0003] Therefore, how to improve the accuracy and efficiency of weighing systems is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a high-precision metering and weighing system based on PLC control to solve the problems existing in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A high-precision weighing system based on PLC control includes: a pressure sensor, a weighing module, a PLC controller, and a set weight class sorting module; the pressure sensor, weighing module, PLC controller, and set weight class sorting module are connected in sequence.

[0007] The pressure sensor converts the weight of the product under test into a voltage signal through vertical displacement and sends it to the weighing module.

[0008] The weighing module amplifies and converts the received voltage signal into an analog-to-digital signal to obtain the digital signal corresponding to the pressure signal, and then transmits it to the PLC controller.

[0009] The PLC controller uses a fuzzy PID algorithm to analyze and process digital signals to obtain weight information, and then transmits it to the corresponding weight-level sorting module.

[0010] The weight-level sorting module is set up to receive weight information. When the displacement signal matches the corresponding sorting level unloading port, it controls the opening and closing of the corresponding solenoid valve to cause the rotary cylinder to move the product to be tested into the corresponding storage basket.

[0011] Optionally, the pressure sensor is a resistive sensor with a resistance strain gauge as the conversion element. The hardware structure consists of an elastic element, a resistance strain gauge, a conversion circuit, and a housing. The working principle is that when the elastic element is subjected to external pressure and deforms, the strain gauge attached to it deforms along with it, causing a change in the strain resistance, thereby measuring the current pressure value.

[0012] Optionally, the weighing module includes a preamplifier circuit, an AD conversion circuit, and a control chip circuit;

[0013] The preamplifier circuit amplifies the acquired voltage signal;

[0014] An AD conversion circuit converts an amplified voltage signal into a digital signal.

[0015] The control chip circuit receives digital signals from the AD conversion circuit, performs data preprocessing operations, and sends the processed digital signals to the PLC controller.

[0016] Optionally, based on the structural characteristics of the CS5532 chip, an AD conversion circuit is designed; the analog power supply VA+ = +5V, VA- = 0V, the digital power supply VD+ = +3.3V, and the reference voltage VREF is a stable external 3.3V power supply; the C1 and C2 pins are connected to a 22nF gain amplifier connection capacitor to suppress amplifier potential rise and noise; the A0 and A1 pins are the analog logic output terminals of the chip; the OSC1 and OSC2 pins are connected to the two ends of a 4.9125M external crystal oscillator circuit to provide an accurate clock for the system; the serial output pins CS, SDI, SDO, and SCLK are connected to the NSS, MOSI, MISO, and SCK pins of the AD control chip's SPI bus to realize communication with peripherals. Among them, CS is the chip select pin, which controls the serial port enable and is active low; SDI is the data input terminal; SDO is the data output terminal; and SCLK is the clock input terminal, which controls the shift of A / D serial port data.

[0017] Optionally, the specific principle of the PLC controller using the fuzzy PID algorithm to analyze and process digital signals is as follows:

[0018] S1. Obtain the initial preset weight value of the weighing module and the actual weight value after the product to be tested is placed down, and obtain the error e and the error change rate ec between the preset weight value and the actual weight value.

[0019] S2. Based on the error e and the error change rate ec, the proportional gain Kp, integral gain ki and derivative gain kd in the PID controller are obtained by adopting the control rules of the fuzzy adaptive algorithm.

[0020] S3. Set the actual domain of error e and error change rate ec, and obtain the quantization factor to convert the error e and error change rate ec of the actual weighing value into the quantized error E and error change rate Ec.

[0021] S4. Then, based on the quantized error E, the error change rate Ec, and the universe of discourse of the proportional gain Kp, integral gain ki, and differential gain kd, set the corresponding fuzzy subsets.

[0022] S5. Determine the membership function, and based on the membership function, determine the fuzzy control, calculate the fuzzy relation R, and determine the fuzzy output signal;

[0023] S6. The fuzzy output signal is clarified using the maximum membership method, transforming it into a clear output signal U. Optionally, this also includes a PID self-tuning correction process: kp=kp′+Δkp ki=kp′+Δki kd=kd+Δkd

[0024] In the formula, kp′, kp′, and kd′ are the proportional gain, reference gain, and differential gain before correction; Δkp, Δki, and Δkd are the correction coefficients.

[0025] The correction coefficient, determined based on the error e of the actual weighing value and the rate of change of error ec, is substituted into the formula to complete the correction.

[0026] Optionally, the quantization factor is:

[0027] In the formula, e L e represents the minimum value of the error. H This represents the maximum value of the error; This represents the minimum rate of change of error. This represents the maximum value of the rate of change of error; m and n represent the widths of the universes of discourse for the error and the rate of change of error, respectively.

[0028] Optionally, the membership function is chosen to be in trigonometric form, and its mathematical expression is as follows:

[0029] Where a represents the x-coordinate of the left endpoint of the triangle membership function, b represents the x-coordinate of the right endpoint of the triangle membership function, and c is the x-coordinate of the vertex of the triangle membership function.

[0030] Optionally, the scaling factor can be expressed in the following form:

[0031] In the formula, uH u L Let be the maximum and minimum values ​​of the proportional gain Kp, integral gain ki, and differential gain kd; and let y be the universe of discourse of the proportional gain Kp, integral gain ki, and differential gain kd.

[0032] As can be seen from the above technical solution, compared with the prior art, this invention discloses a high-precision weighing system based on PLC control. By combining a pressure sensor, a weighing module, and a fuzzy PID control algorithm, it achieves accurate product weight measurement and sorting. The system can quickly convert the weight information of the product to be measured into a voltage signal, which, after amplification and analog-to-digital conversion, provides an accurate digital signal to the PLC. The PLC uses the fuzzy PID algorithm for error analysis and adaptive parameter adjustment, improving control accuracy and response speed. In addition, through quantization processing and the use of membership functions, the system can effectively reduce errors and self-correct, thereby improving the overall accuracy and reliability of weighing and sorting. This design enables the weighing system to achieve efficient automated control in industrial applications, adapting to the weighing needs of different products. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0034] Figure 1 is a schematic diagram of the system structure provided by the present invention;

[0035] Figure 2 is a schematic diagram of the system structure AD conversion circuit design provided by the present invention;

[0036] Figure 3 is a schematic diagram of the fuzzy PID weighing principle provided by the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] This invention discloses a high-precision weighing system based on PLC control, as shown in Figure 1, including: a pressure sensor, a weighing module, a PLC controller, and a set weight class sorting module; the pressure sensor, weighing module, PLC controller, and set weight class sorting module are connected in sequence.

[0039] The pressure sensor converts the weight of the product under test into a voltage signal through vertical displacement and sends it to the weighing module.

[0040] The weighing module amplifies and converts the received voltage signal into an analog-to-digital signal to obtain the digital signal corresponding to the pressure signal, and then transmits it to the PLC controller.

[0041] The PLC controller uses a fuzzy PID algorithm to analyze and process digital signals to obtain weight information, and then transmits it to the corresponding weight-level sorting module.

[0042] The weight-level sorting module is set up to receive weight information. When the displacement signal matches the corresponding sorting level unloading port, it controls the opening and closing of the corresponding solenoid valve to cause the rotary cylinder to move the product to be tested into the corresponding storage basket.

[0043] In one specific embodiment, the pressure sensor is a resistive sensor using a resistance strain gauge as the conversion element. The hardware structure consists of an elastic element, a resistance strain gauge, a conversion circuit, and a housing. Its working principle is that when the elastic element deforms under external pressure, the strain gauge attached to it deforms along with it, causing a change in strain resistance, thereby measuring the current pressure value. The HBM PW4M load cell can convert the pressure signal into an electrical signal and output a voltage signal of 0–20 mV. Subsequent data acquisition requires conditioning of the signal amplitude, driving capability, linearity, and anti-interference ability, achieved by constructing amplification circuits with various characteristics. The accuracy of the load cell is shown in Table 1.

[0044] Table 1 Accuracy of Weighing Sensors

[0045] In one specific embodiment, the weighing module includes a preamplifier circuit, an AD conversion circuit, and a control chip circuit;

[0046] The preamplifier circuit amplifies the acquired voltage signal;

[0047] An AD conversion circuit converts an amplified voltage signal into a digital signal.

[0048] The control chip circuit receives digital signals from the AD conversion circuit, performs data preprocessing operations, and sends the processed digital signals to the PLC controller.

[0049] Considering the low output signal level and high internal resistance of the pressure sensor, the amplifier requirements include: 1. The resistance of the signal source must be lower than the input impedance; otherwise, a load effect will occur, leading to output voltage deviation. 2. It must effectively suppress operational amplifier noise, minimizing noise and input offset when obtaining the signal-to-noise ratio, ensuring absolute gain accuracy within a specific range, and guaranteeing the final output performance. Based on the amplifier design requirements, this invention designs a dual operational amplifier preamplifier circuit. The circuit uses a differential input module composed of two stages of operational amplifiers. The first stage uses the OPA2277 operational amplifier, which has low noise, effectively improves signal resolution, limits the signal output to a smaller range, and greatly improves the linearity of the system. Ferrite beads FB7 and FB8, and capacitor C16 are used to filter out noise generated in the preamplifier stage. The second stage uses the TLC2272 chip, a dual operational amplifier with full-power amplitude output. This chip features high input impedance, low noise, high common-mode rejection ratio, and good linear output characteristics for millivolt-level input signals. With an input excitation voltage of 5V, this system can accurately amplify a signal from 0 to 20mV to 2.5V to 5V, meeting the needs of subsequent AD conversion circuits.

[0050] In a specific embodiment, an AD conversion circuit is designed based on the structural characteristics of the CS5532 chip. As shown in Figure 2, the analog power supply VA+ = +5V, VA- = 0V, the digital power supply VD+ = +3.3V, and the reference voltage VREF is a stable external 3.3V power supply. The C1 and C2 pins are connected to the 22nF gain amplifier connection capacitor to suppress amplifier potential rise and noise. The A0 and A1 pins are the analog logic output terminals of the chip. The OSC1 and OSC2 pins are connected to the two ends of the 4.9125M external crystal oscillator circuit to provide an accurate clock for the system. The serial output pins CS, SDI, SDO, and SCLK are connected to the NSS, MOSI, MISO, and SCK pins of the SPI bus of the AD control chip to realize communication with peripherals. Among them, CS is the chip select pin, which controls the serial port enable and is active low. SDI is the data input terminal, SDO is the data output terminal, and SCLK is the clock input terminal, which controls the shift of A / D serial port data.

[0051] In a specific embodiment, as shown in Figure 3, the specific principle of the PLC controller using the fuzzy PID algorithm to analyze and process digital signals is as follows:

[0052] S1. Obtain the initial preset weight value of the weighing module and the actual weight value after the product to be tested is placed down, and obtain the error e and the error change rate ec between the preset weight value and the actual weight value.

[0053] S2. Based on the error e and the rate of change of error ec, the proportional gain Kp, integral gain ki and derivative gain kd in the PID controller are obtained by adopting the control rules of the fuzzy adaptive algorithm.

[0054] S3. Set the actual domain of error e and error change rate ec, and obtain the quantization factor to convert the error e and error change rate ec of the actual weighing value into the quantized error E and error change rate Ec.

[0055] S4. Then, based on the quantized error E, the error change rate Ec, and the universe of discourse of the proportional gain Kp, integral gain ki, and differential gain kd, set the corresponding fuzzy subsets.

[0056] S5. Determine the membership function, and based on the membership function, determine the fuzzy control, calculate the fuzzy relation R, and determine the fuzzy output signal;

[0057] S6. The fuzzy output signal is clarified by the maximum membership method and transformed into a clear output signal U, where U includes correction coefficients Δkp, Δki, and Δkd.

[0058] The so-called maximum membership method assigns the element with the highest membership degree in the universe of discourse corresponding to the fuzzy output signal to the sharp output signal U as the result of sharpening.

[0059] The following is a detailed explanation of the fuzzy PID algorithm:

[0060] 1. Model parameters: Select two inputs and three outputs.

[0061] Input variables:

[0062] The error e between the actual weighing value and the set weighing value

[0063] rate of change of weighing error (ec)

[0064] Output variables:

[0065] Kp, Ki, Kd;

[0066] 2. Define the actual domain of e: e∈[-2g, 2g];

[0067] Let the actual domain of ec be: ec∈[-1,1];

[0068] Quantize e and ec to the fuzzy domain, with a quantization factor of .

[0069] In the formula, e L e represents the minimum value of the error. H This represents the maximum value of the error; This represents the minimum rate of change of error. This represents the maximum value of the rate of change of error; m and n represent the widths of the universes of discourse for the error and the rate of change of error, respectively. e = [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]

[0070] Where -6 = -2g, 6 = 2g, and so on. ec = [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]

[0071] Among them, -6 = -1, 6 = 1, and so on.

[0072] Therefore, the corresponding fuzzy subset is K. e = [Negative large, negative medium, negative small, zero, positive small, positive medium, positive large]

[0073] That is: NB, NM, NS, ZO, PS, PM, PB, K ec = [Negative large, negative medium, negative small, zero, positive small, positive medium, positive large]

[0074] That is: NB, NM, NS, ZO, PS, PM, PB

[0075] Therefore: Quantification factor

[0076] Obtaining the quantization factor K e and K ec Then, the error e and the error change rate ec are converted into the quantified error E and the error change rate Ec. The conversion formulas are shown in equations (3) and (4).

[0077] Here, INT[] represents the integer operation.

[0078] Then, based on the fuzzy universes of the quantized error E, error change rate Ec, and Kp, Ki, and Kd, the corresponding fuzzy subsets are set.

[0079] The fuzzy universe of discourse for Kp, Ki, and Kd is [-1, 1], therefore the corresponding fuzzy subsets

[0080] Kp' = {Negative Large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Large (PB)}

[0081] Ki' = {Negative Large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Large (PB)}

[0082] Kd' = {Negative Large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Large (PB)}.

[0083] Among them, negative large corresponds to -1 in the fuzzy domain, negative medium represents -2 / 3, negative small represents -1 / 3, zero represents 0, positive small represents 1 / 3, positive medium represents 2 / 3, and positive large represents 1.

[0084] The mathematical expression for determining the trigonometric form of the membership function is as follows:

[0085] Where a represents the x-coordinate of the left endpoint of the triangle membership function, b represents the x-coordinate of the right endpoint of the triangle membership function, and c is the x-coordinate of the vertex of the triangle membership function.

[0086] 4. Establish fuzzy control rules

[0087] Fuzzy conditional propositions take the form "If A and B then C, D, and E". Using the IF-then statement, 49 rules are listed in 7 sections. The format is as follows:

[0088] If E is NB and EC is NB then X is PB and Y is NB and Z is PS,

[0089] If E is NB and EC is NM then X is PB and Y is NB and Z is NS etc.

[0090] The fuzzy rule control tables are shown in Tables 1, 2 and 3.

[0091] Table 2 ΔKp Fuzzy Rule Control Table

[0092] Table 3 ΔKi Fuzzy Rule Control Table

[0093] Table 4. Fuzzy Rule Control Table for ΔKd

[0094] The proportionality factor is expressed as follows:

[0095] In the formula, u H u L Let be the maximum and minimum values ​​of the proportional gain Kp, integral gain ki, and differential gain kd; and let y be the universe of discourse of the proportional gain Kp, integral gain ki, and differential gain kd.

[0096] This leads to the expression for the output signal U:

[0097] This yields the correction coefficients corresponding to the proportional gain, reference gain, and differential gain, respectively.

[0098] It also includes the PID self-tuning correction process: kp=kp′+Δkp ki=kp′+Δki kd=kd′+Δkd

[0099] In the formula, kp′, kp′, and kd′ are the proportional gain, reference gain, and differential gain before correction; Δkp, Δki, and Δkd are the correction coefficients.

[0100] The correction coefficient, determined based on the error e of the actual weighing value and the rate of change of error ec, is substituted into the formula to complete the correction.

[0101] The overall process of this invention is as follows: system start → sampling and filtering → sampling time expires → whether e and ec are out of range → calculate e and ec, and fuzzify them according to the above design route → obtain the increments such as Δkp by looking up the table based on the simulation data → calculate kp, etc. → fuzzy PID tuning → end → control setting heavyweight sorting module operation.

[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0103] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A high-precision weighing system based on PLC control, characterized in that, include: Pressure sensor, weighing module, PLC controller, and weight-level sorting module are connected in sequence. The pressure sensor converts the weight of the product under test into a voltage signal through vertical displacement and sends it to the weighing module. The weighing module amplifies and converts the received voltage signal into an analog-to-digital signal to obtain the digital signal corresponding to the pressure signal, and then transmits it to the PLC controller. The PLC controller uses a fuzzy PID algorithm to analyze and process digital signals to obtain weight information, and then transmits it to the corresponding weight-level sorting module. The weight-level sorting module is set up to receive weight information. When the displacement signal matches the corresponding sorting level unloading port, it controls the opening and closing of the corresponding solenoid valve to cause the rotary cylinder to move the product to be tested into the corresponding storage basket. The specific principle behind the PLC controller's use of the fuzzy PID algorithm to analyze and process digital signals is as follows: S1. Obtain the initial preset weight value of the weighing module and the actual weight value after the product to be tested is placed down, and obtain the error e and the error change rate ec between the preset weight value and the actual weight value. S2. Based on the error e and the error change rate ec, the proportional gain Kp, integral gain ki and derivative gain kd in the PID controller are obtained by adopting the control rules of the fuzzy adaptive algorithm. S3. Set the actual domain of error e and error change rate ec, and obtain the quantization factor to convert the error e and error change rate ec of the actual weighing value into the quantized error E and error change rate Ec. S4. Then, based on the quantized error E, the error change rate Ec, and the universe of discourse of the proportional gain Kp, integral gain ki, and differential gain kd, set the corresponding fuzzy subsets. S5. Determine the membership function, and based on the membership function, determine the fuzzy control, calculate the fuzzy relation R, and determine the fuzzy output signal; S6. The fuzzy output signal is clarified by using the maximum membership method and transformed into a clear output signal U; It also includes the PID self-tuning and correction process: kp=kp′+Δkp ki=kp′+Δki kd=kd′+Δkd In the formula, kp′, kp′, and kd′ are the proportional gain, reference gain, and differential gain before correction; Δkp, Δki, and Δkd are the correction coefficients. The correction coefficient, determined based on the error e of the actual weighing value and the rate of change of error ec, is substituted into the formula to complete the correction. The quantification factor is: In the formula, e L e represents the minimum value of the error. H This represents the maximum value of the error; This represents the minimum rate of change of error. This represents the maximum value of the rate of change of error; m and n represent the widths of the universes of discourse for the error and the rate of change of error, respectively. If the membership function is chosen to be in trigonometric form, its mathematical expression is as follows: Where a represents the x-coordinate of the left endpoint of the triangle membership function, b represents the x-coordinate of the right endpoint of the triangle membership function, and c is the x-coordinate of the vertex of the triangle membership function. The scaling factor is expressed in the following form: In the formula, u H u L Let Kp be the maximum and minimum values ​​of the proportional gain, ki be the integral gain, and kd be the differential gain. y is the universe of discourse for the proportional gain Kp, integral gain ki, and differential gain kd.

2. The high-precision weighing system based on PLC control according to claim 1, characterized in that, The pressure sensor is a resistive sensor with a resistance strain gauge as the conversion element. The hardware structure consists of an elastic element, a resistance strain gauge, a conversion circuit and a housing. The working principle is that when the elastic element is subjected to external pressure and deforms, the strain gauge attached to it deforms along with it, causing a change in the strain resistance, thereby measuring the current pressure value.

3. The high-precision weighing system based on PLC control according to claim 1, characterized in that, The weighing module includes a preamplifier circuit, an AD conversion circuit, and a control chip circuit. The preamplifier circuit amplifies the acquired voltage signal; An AD conversion circuit converts an amplified voltage signal into a digital signal. The control chip circuit receives digital signals from the AD conversion circuit, performs data preprocessing operations, and sends the processed digital signals to the PLC controller.

4. The high-precision weighing system based on PLC control according to claim 1, characterized in that, Based on the structural characteristics of the CS5532 chip, an AD conversion circuit is designed. The analog power supply VA+ = +5V, VA- = 0V, the digital power supply VD+ = +3.3V, and the reference voltage VREF is a stable external 3.3V power supply. The C1 and C2 pins are connected to a 22nF gain amplifier connection capacitor to suppress amplifier potential rise and noise. The A0 and A1 pins are the analog logic output terminals of the chip. The OSC1 and OSC2 pins are connected to the two ends of a 4.9125MHz external crystal oscillator circuit to provide an accurate clock for the system. The serial output pins CS, SDI, SDO, and SCLK are connected to the NSS, MOSI, MISO, and SCK pins of the AD control chip's SPI bus to realize communication with peripherals. Among them, CS is the chip select pin, which controls the serial port enable and is active low. SDI is the data input terminal, SDO is the data output terminal, and SCLK is the clock input terminal, which controls the shift of A / D serial port data.

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