Adaptive optimization control method and system for PID (Proportion Integration Differentiation) parameters of seasoning filling

Abstract

The invention belongs to the technical field of industrial automation control, and relates to a seasoning filling PID parameter adaptive optimization control method and system. The method comprises the steps that the real-time temperature of fluid in a filling pipeline, the static pressure of a valve inlet, the instantaneous flow and a valve opening instruction are collected, and moving average filtering preprocessing is carried out; constructing a self-adaptive forgetting factor calculation model based on the viscosity-temperature characteristics, and calculating a current optimal forgetting factor according to the fluid temperature change rate, the flow prediction error and the fluctuation condition of the flow data; a recursive algorithm is used for identifying the system process gain online, and the proportional gain of a PID controller is calculated in real time according to the identified process gain and the ratio of the hydrostatic pressure to the standard reference pressure; and the gain is input into a controller, and a final valve opening instruction is generated in combination with integral and differential terms. According to the method, the dynamic adjustment of the algorithm memory length can be realized, the viscosity change can be quickly adapted, excessive filling and slow response are eliminated, and high-precision filling under complex working conditions is ensured.

Description

Technical Field

[0001] This invention belongs to the field of industrial automation control technology, and in particular relates to a method and system for adaptive optimization control of PID parameters in condiment filling. Background Technology

[0002] In the food processing industry, the bottling production line for condiments such as oyster sauce, soy sauce, and concentrated fruit juice is a core component, as its bottling accuracy directly impacts product quality and production costs. Currently, conventional PID control algorithms are widely used in industrial settings to adjust the opening of bottling valves to achieve closed-loop control of fluid flow. However, the physical properties of these fluids are extremely sensitive to environmental changes, particularly their viscosity, which exhibits a significant nonlinear characteristic with temperature variations. This poses a serious challenge to traditional control strategies based on fixed parameters. In actual production, when fluctuations in ambient temperature cause changes in material viscosity, the original control parameters often fail to match the current fluid characteristics. Specifically, when temperature increases and fluid viscosity decreases, the flow rate increases significantly at the same valve opening, leading to overfilling or even overflow due to excessive control. Conversely, when temperature decreases and viscosity increases, the system response becomes sluggish, making it difficult to complete bottling within the specified time and reducing production efficiency.

[0003] To address the problem of fixed-parameter control failing to adapt to changing operating conditions, adaptive control strategies have been introduced in related technical fields. These methods typically utilize parameter identification algorithms such as recursive least squares to attempt to estimate the system's model parameters online and adjust the controller gain accordingly. The core mechanism involves introducing a forgetting factor to weight historical data. By gradually forgetting the influence of outdated data, the algorithm can track the dynamic characteristics of time-varying systems. In ideal steady-state or slowly changing processes, this adaptive algorithm based on a fixed forgetting factor can, to some extent, help the control system approximate the true physical model, thus achieving a balance between suppressing measurement noise and maintaining control stability.

[0004] However, in complex scenarios like condiment filling, characterized by frequent start-stop cycles and potentially sudden changes in operating conditions, traditional adaptive algorithms with fixed forgetting factors have insurmountable limitations. This is because the algorithm faces a fundamental contradiction between steady-state noise resistance and dynamic tracking: a larger forgetting factor is beneficial for suppressing noise when the system is stable, but it leads to a sluggish response from the model during sudden changes in operating conditions. Conversely, a smaller forgetting factor, while improving tracking speed, causes the system to become overly sensitive to noise in steady-state conditions, resulting in parameter oscillations. Existing technologies cannot automatically adjust this parameter based on the current operating conditions, causing model parameter updates to often lag significantly behind changes in actual fluid viscosity or pressure. This lag prevents the system from generating accurate control commands in a timely manner when faced with sudden filling volume deviations or supply pressure fluctuations, thus failing to meet the stringent requirements of high precision and consistency in modern production. Summary of the Invention

[0005] Therefore, the purpose of this invention is to propose an adaptive optimization control method and system for PID parameters in condiment filling, in order to solve the problems of fixed PID parameter failure caused by the nonlinear viscosity-temperature characteristics of condiments and the lag in updating traditional adaptive algorithms in existing filling production lines.

[0006] To address the above problems, the present invention proposes a technical solution for an adaptive optimization control method for PID parameters in condiment filling: An adaptive optimization control method for PID parameters in condiment filling includes the following steps: The system collects real-time temperature data of the fluid in the filling pipeline, static pressure data of the fluid at the valve inlet, instantaneous flow rate data, and current valve opening command data. The collected data is then preprocessed to obtain the effective sequence for calculation. An adaptive forgetting factor calculation model based on viscosity-temperature characteristics is constructed. The optimal forgetting factor at the current moment is calculated according to the fluid temperature change rate, flow prediction error and flow data fluctuation. Based on the optimal forgetting factor, the system process gain is identified online. According to the identified system process gain and the ratio of the fluid static pressure data to the standard reference pressure, the PID controller proportional gain at the current moment is calculated in real time. The calculated proportional gain of the PID controller is input into the PID controller, and the final valve opening control command is calculated by combining the integral and derivative terms to perform closed-loop control of the filling valve.

[0007] Furthermore, the acquisition of real-time temperature data of the fluid in the filling pipeline, static pressure data of the fluid at the valve inlet, instantaneous flow rate data, and current valve opening command data includes: Fluid temperature is collected using a resistance temperature detector (RTD) sensor. The static pressure of the fluid at the valve inlet is acquired by a pressure transmitter; Instantaneous flow rate is read using a mass flow meter; Read the current valve opening command from the PID controller.

[0008] Furthermore, the preprocessing of the collected data includes: setting a data collection period and performing a moving average filter of a preset length on all the collected raw data to filter out high-frequency noise.

[0009] Furthermore, the formula for calculating the optimal forgetting factor at the current moment is:

[0010] In the formula, For the current moment The optimal forgetting factor; This is the preset lower limit value for the forgetting factor; and These are the fluid temperatures at the current moment and the previous moment, respectively; This represents the error in flow prediction. The variance of the flow data within the current sliding window; This is the weighting coefficient for temperature change; These are the error weighting coefficients; It is a non-zero protection constant.

[0011] Furthermore, the formula for calculating the proportional gain of the PID controller at the current moment is:

[0012] In the formula, This represents the proportional gain of the PID controller at the current moment. The target loop gain constant; The process gain identified online at the current moment; The static pressure of the fluid at the valve inlet is the current value collected at that moment; Standard reference pressure; This is the pressure compensation coefficient.

[0013] Furthermore, the process gain A lower threshold is set. When the process gain identified online is less than the lower threshold, the process gain is assigned the value of the lower threshold.

[0014] Furthermore, the calculation formula for the final valve opening control command is as follows:

[0015] In the formula, This is the valve opening control command for the current moment; The difference between the set traffic volume and the actual traffic volume at the current moment; The integral coefficient; is the differential coefficient.

[0016] Furthermore, the hydrostatic pressure data is zero-point calibrated, and the hydrostatic pressure is always greater than or equal to a preset minimum positive pressure value.

[0017] Furthermore, the fluid temperature change rate is obtained by calculating the absolute value of the difference between the fluid temperature at the current moment and the fluid temperature at the previous moment; the flow prediction error is obtained by calculating the absolute value of the difference between the expected flow rate calculated at the previous moment and the instantaneous flow rate measured at the current moment.

[0018] The technical solution of the PID parameter adaptive optimization control system for condiment filling proposed in this invention is as follows: The condiment filling PID parameter adaptive optimization control system includes a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the condiment filling PID parameter adaptive optimization control method described in any of the above technical solutions is implemented.

[0019] The beneficial effects of this invention are as follows: By establishing a high-frequency data acquisition mechanism that includes temperature, pressure, flow rate, and valve opening, and combining it with moving average filtering preprocessing technology, this invention effectively filters out high-frequency noise interference caused by pump vibration or fluid turbulence. Furthermore, by establishing an adaptive mapping relationship between fluid viscosity-temperature characteristics and control parameters, this invention overcomes the shortcomings of traditional fixed-parameter PID control, which cannot adapt to nonlinear changes in fluid viscosity. When the material temperature increases, leading to a decrease in viscosity, the system can automatically reduce the controller gain to prevent flow overshoot; conversely, when the viscosity increases, the system automatically increases the gain to eliminate response lag, thereby ensuring that materials stored at different ambient temperatures can achieve highly consistent filling results.

[0020] This invention introduces a dynamic forgetting factor calculation model based on temperature change rate and flow prediction error, enabling the system to intelligently identify the current operating condition. When the operating condition is stable, the algorithm uses a larger forgetting factor to enhance the filtering ability of measurement noise; while during sudden temperature changes or model mismatches, the algorithm rapidly reduces the forgetting factor to accelerate the elimination of old data, thereby achieving millisecond-level rapid tracking of the system process gain and eliminating control deviations caused by parameter update lag.

[0021] This invention utilizes the inverse relationship between the process gain identified online and the proportional gain of the controller, combined with a logarithmic pressure feedforward compensation mechanism, to enable the system to counteract the impact of feed pump pressure pulsations on flow control in real time. Even under significant fluctuations in feed pressure, the control algorithm maintains a constant loop gain, ensuring the accuracy of valve opening adjustment under different pressure conditions and avoiding filling volume fluctuations caused by sudden pressure changes. Attached Figure Description

[0022] Figure 1 This is a flowchart of the steps of the PID parameter adaptive optimization control method for seasoning filling of the present invention; Figure 2 This is a comparative analysis chart of filling accuracy under complex working conditions according to an embodiment of the present invention; Figure 3 This is a diagram illustrating the adaptive adjustment process of control parameters based on temperature sensing according to an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0024] Specific embodiments of the adaptive optimization control method for PID parameters in condiment filling proposed in this invention: like Figure 1 As shown, the adaptive optimization control method for PID parameters in condiment filling specifically includes the following steps: S1. Collect real-time temperature data of the fluid in the filling pipeline, static pressure data of the fluid at the valve inlet, instantaneous flow rate data, and current valve opening command data, and preprocess the collected data to obtain the effective sequence for calculation.

[0025] In this step, high-precision sensor arrays are installed at the main pipeline inlet of the filling machine and at the front end of each filling head. Specifically, PT1000 thin-film resistance thermometers with a response time of less than 0.5 seconds are used to collect fluid temperature data in real time. The unit is degrees Celsius. A diffused silicon pressure transmitter with a range of 0 to 1 MPa is used to collect the static pressure of the fluid at the valve inlet. The unit is MPa, and the pressure data is zero-calibrated to ensure that its value is always greater than the preset minimum positive pressure value, thus ensuring... The instantaneous flow rate of the fluid is read using a Coriolis mass flow meter. The unit is mL / s. Simultaneously, the current valve opening command is read from the PLC controller. This command is normalized to a value between 0 and 1, corresponding to the valve's state from fully closed to fully open.

[0026] The system is set to collect data over a period of time. To eliminate measurement noise caused by pump vibration, the system performs a moving average filter of length 5 on all the raw data to calculate the effective sequence for subsequent calculations. For example, if five consecutively collected flow rates are 100 mL / s, 102 mL / s, 98 mL / s, 101 mL / s, and 99 mL / s, the effective flow rate after filtering is the arithmetic mean of these five values, which is 100 mL / s. By frequently collecting and filtering the dataset, more accurate and smooth process data can be obtained, providing a reliable input basis for subsequent model identification and reducing noise interference with the control algorithm.

[0027] S2. Construct an adaptive forgetting factor calculation model based on viscosity-temperature characteristics. Calculate the optimal forgetting factor at the current moment based on the fluid temperature change rate, flow prediction error, and fluctuations in flow data.

[0028] In this step, the memory length of the algorithm, also known as the forgetting factor, is dynamically adjusted using fluid temperature changes and model prediction errors. The forgetting factor determines the algorithm's reliance on historical data. When the system is in a stable state, the forgetting factor is increased to a value close to 1 to utilize more historical data to suppress noise; when temperature drift causes viscosity changes, or when a sudden increase in prediction error indicates model mismatch, the forgetting factor is decreased to quickly forget old data and capture new characteristics of the system.

[0029] Specifically, the formula for calculating the forgetting factor is as follows:

[0030] in, For the current moment The optimal forgetting factor, which is a dimensionless physical quantity, has a value range of [value missing]. . This is a preset lower limit for the forgetting factor, such as 0.85, used to prevent the algorithm from diverging due to excessively rapid forgetting.

[0031] and These are the fluid temperatures at the current moment and the previous moment, respectively, and the absolute value of the difference between them. It characterizes the degree of drastic temperature change, that is, the rate of change of fluid temperature.

[0032] The flow prediction error is calculated by subtracting the current actual measured flow from the predicted flow calculated from the model parameters identified at the previous time step. The absolute value obtained afterwards.

[0033] The variance of the flow data within the current sliding window, which can contain the most recent 50 sampling points, is used as the denominator to assess the degree of turbulence in the flow.

[0034] This is the weighting coefficient for temperature change, in units of... .

[0035] These are error weighting coefficients, in units of... .

[0036] This is a non-zero protection constant, such as setting it to 1. This is designed to prevent variance caused by fluid stasis. A division by zero error occurs when the value is 0.

[0037] To more intuitively illustrate the calculation logic of this formula, the current system preset parameters are as follows: lower limit of forgetting factor. The weighting factor for temperature change is 0.85. The error weighting coefficient is 1. The value is 0.5, a non-zero protection constant. The value is 1. Under this parameter setting, two typical scenarios, namely stable operating conditions and sudden changes in operating conditions, are explained respectively.

[0038] First, consider a scenario with stable operating conditions. In this case, the fluid temperature shows no significant change, and the absolute value of the temperature difference is 0; the flow prediction error is extremely small, taking a value of 0.2; and the flow variance is 5. Substituting these values ​​into the exponent term, the numerator is... The denominator is The score of the exponential term is obtained as follows: Further calculate the negative exponent of this value. The final calculated optimal forgetting factor is approximately 0.983. The result is very close to 1, indicating that the system mainly retains historical data to maintain control stability.

[0039] Secondly, consider scenarios where operating conditions change abruptly, such as during material switching. In this case, the temperature changes significantly, and the absolute value of the difference rises to 2; accompanied by an increase in prediction error to 10, the flow variance becomes 8. Substituting these values ​​into the formula again, the numerator of the exponential term becomes... The denominator is The calculated score for the exponential term is: The corresponding negative exponent value. It decreased significantly to 0.459. At this point, the optimal forgetting factor was calculated. The significant decrease in this result indicates that the system begins to quickly forget old data, thus adapting sensitively to new operating conditions.

[0040] Thus, by constructing a dynamic forgetting factor that includes temperature and error terms, the operating conditions can be intelligently identified, ensuring noise resistance when stable and tracking speed when changing, effectively solving the lag problem of traditional fixed parameter algorithms.

[0041] S3. Based on the optimal forgetting factor, the system process gain is identified online. Based on the identified system process gain and the ratio of the fluid static pressure data to the standard reference pressure, the proportional gain of the PID controller at the current moment is calculated in real time.

[0042] In this step, the optimal forgetting factor obtained in step S2 is first used. Substituting this into the recursive least squares algorithm, the current system process gain can be identified in real time. The process gain This represents the change in flow rate caused by a unit change in valve opening, i.e., the sensitivity of the physical system. To maintain the stability of the closed-loop control system, the proportional gain of the controller... It should be related to the process gain of the physical system. Inversely proportional. At the same time, a pressure compensation term is introduced to counteract fluctuations in the material supply pressure.

[0043] Specifically, the proportional gain of the PID controller The calculation formula is as follows:

[0044] in, The target loop gain constant for the system design is determined by the engineer based on the system's expected response speed and is a fixed value.

[0045] To identify the process gain online, a lower limit threshold greater than zero needs to be set for it. If the identified value is less than this lower limit threshold, it should be forcibly assigned to this lower limit threshold to prevent the denominator from approaching 0.

[0046] The static pressure of the fluid at the valve inlet at the current moment must be greater than or equal to the preset minimum pressure value.

[0047] This is the standard reference pressure. This is the pressure compensation coefficient, and its value is usually between 0.2 and 0.5.

[0048] To further explain the calculation logic of this formula, the system is set with the following parameters: target loop gain constant. The standard reference pressure is 2. The pressure compensation coefficient is 0.4 MPa. It is 0.3.

[0049] First, consider the scenario where viscosity decreases and pressure remains normal. When the fluid temperature increases, causing viscosity to decrease and the fluid to become thinner, the identified process gain will increase. Let's assume that the process gain is as follows. The pressure reached 5, while the valve inlet pressure The pressure is maintained at a normal level of 0.4 MPa. At this point, the pressure ratio is 1, and its natural logarithm is 0. Substituting these values ​​into the formula, the PID proportional gain is obtained. The gain is 0.4. This low controller gain effectively prevents system overshoot caused by excessive fluid sensitivity.

[0050] Next, consider the scenario where viscosity increases and pressure rises. When the fluid temperature decreases, causing viscosity to increase and the fluid to thicken, the process gain will decrease accordingly. Let's assume that the process gain is lower in this case. The pressure drops to 2; simultaneously, the feed pump pressure fluctuation increases to 0.6 MPa. At this point, the pressure ratio is 1.5, its natural logarithm is approximately 0.405, and the calculated pressure compensation term is 1.1215. The final calculated PID proportional gain... The value is 1.1215. This result shows that the controller gain is significantly improved. On the one hand, the inverse relationship compensates for the sluggish response caused by the increase in viscosity, and on the other hand, the pressure compensation term offsets the back pressure resistance caused by high pressure, thereby ensuring the stability and response speed of the control system.

[0051] In this way, by inverse mapping of process gain and pressure feedforward compensation, the PID parameters can be matched with the current fluid physical characteristics in real time, ensuring that the system loop gain is constant, thereby guaranteeing the stability and consistency of control.

[0052] S4. Input the calculated proportional gain of the PID controller into the PID controller, and combine the integral and derivative terms to calculate the final valve opening control command to perform closed-loop control of the filling valve.

[0053] The formula for calculating the valve opening control command is as follows:

[0054] in, This is the valve opening control command for the current moment; The difference between the set traffic volume and the actual traffic volume at the current moment; The integral coefficient; is the differential coefficient.

[0055] Before outputting instructions to the actuator, it is necessary to... Amplitude limiting is applied to restrict the value to a range of 0% to 100%, thereby completing the final control action.

[0056] To illustrate the control command generation process, let's assume the proportional gain at the current moment... The error is 0.4. The flow rate is 10 mL / s. The cumulative value of the integral term is set to 0.5, and the value of the differential term is set to 0. Under these conditions, the calculated output of the proportion term is... The total output is obtained by adding up all the individual items. This corresponds to a 4.5% valve opening adjustment.

[0057] In summary, using this adaptively updated PID parameter for closed-loop control can effectively eliminate filling overflow and overshoot caused by the decrease in fluid viscosity. Furthermore, this method can avoid filling volume fluctuations due to sudden changes in feed pressure, thus achieving high-precision filling control under various complex operating conditions.

[0058] The following combination Figure 2 and Figure 3 The effects of the present invention will be further explained as follows: like Figure 2 As shown in the figure, this graph illustrates a comparative analysis of filling accuracy under complex working conditions. The dashed line represents the existing technical solution; within the ambient temperature drift range, this curve exhibits violent up-and-down oscillations, indicating that fixed parameters cannot adapt to viscosity changes, resulting in significant filling volume deviations. The solid line represents the technical solution of this invention; within the same range, this curve consistently maintains stable fluctuations within a very small range near the zero mark. This result directly demonstrates that the present invention can effectively suppress deviations under varying temperature and viscosity conditions.

[0059] like Figure 3 As shown in the figure, this diagram illustrates the adaptive adjustment process of control parameters based on temperature sensing. The curve corresponding to the left vertical axis shows the fluid temperature gradually increasing from 25 degrees Celsius to 40 degrees Celsius, while the curve corresponding to the right vertical axis shows the change of the controller's proportional gain parameter over time. It can be seen that as the temperature increases, which implies a decrease in viscosity and an increase in system process gain, the controller's proportional gain exhibits a clear downward trend. This phenomenon visually demonstrates the core control logic that the system automatically adjusts the gain to offset the oversensitivity risk caused by the thinning of the fluid, thereby effectively preventing overshoot.

[0060] Specific embodiments of the PID parameter adaptive optimization control system for condiment filling proposed in this invention are as follows: The condiment filling PID parameter adaptive optimization control system includes a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the condiment filling PID parameter adaptive optimization control method in the above embodiments is implemented.

[0061] The PID parameter adaptive optimization control system for condiment filling also includes other components well known to those skilled in the art, such as communication buses and communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0062] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.

Claims

1. A method for adaptive optimization control of PID parameters in condiment filling, characterized in that, Includes the following steps: The system collects real-time temperature data of the fluid in the filling pipeline, static pressure data of the fluid at the valve inlet, instantaneous flow rate data, and current valve opening command data. The collected data is then preprocessed to obtain the effective sequence for calculation. An adaptive forgetting factor calculation model based on viscosity-temperature characteristics is constructed. The optimal forgetting factor at the current moment is calculated according to the fluid temperature change rate, flow prediction error and flow data fluctuation. Based on the optimal forgetting factor, the system process gain is identified online. According to the identified system process gain and the ratio of the fluid static pressure data to the standard reference pressure, the PID controller proportional gain at the current moment is calculated in real time. The calculated proportional gain of the PID controller is input into the PID controller, and the final valve opening control command is calculated by combining the integral and derivative terms to perform closed-loop control of the filling valve.

2. The adaptive optimization control method for PID parameters in condiment filling according to claim 1, characterized in that, The data collected includes real-time temperature data of the fluid in the filling pipeline, static pressure data of the fluid at the valve inlet, instantaneous flow rate data, and current valve opening command data, including: Fluid temperature is collected using a resistance temperature detector (RTD) sensor. The static pressure of the fluid at the valve inlet is acquired by a pressure transmitter; Instantaneous flow rate is read using a mass flow meter; Read the current valve opening command from the PID controller.

3. The adaptive optimization control method for PID parameters in condiment filling according to claim 2, characterized in that, The preprocessing of the collected data includes: setting a data collection period and performing a moving average filter of a preset length on all the collected raw data to filter out high-frequency noise.

4. The adaptive optimization control method for PID parameters in condiment filling according to claim 1, characterized in that, The formula for calculating the optimal forgetting factor at the current moment is: In the formula, For the current moment The optimal forgetting factor; This is the preset lower limit value for the forgetting factor; and These are the fluid temperatures at the current moment and the previous moment, respectively; This represents the error in flow prediction. The variance of the flow data within the current sliding window; This is the weighting coefficient for temperature change; These are the error weighting coefficients; It is a non-zero protection constant.

5. The adaptive optimization control method for PID parameters in condiment filling according to claim 1, characterized in that, The formula for calculating the proportional gain of the PID controller at the current moment is: In the formula, This represents the proportional gain of the PID controller at the current moment. The target loop gain constant; The process gain identified online at the current moment; The static pressure of the fluid at the valve inlet is the current value collected at that moment; Standard reference pressure; This is the pressure compensation coefficient.

6. The adaptive optimization control method for PID parameters in condiment filling according to claim 5, characterized in that, The process gain A lower threshold is set. When the process gain identified online is less than the lower threshold, the process gain is assigned the value of the lower threshold.

7. The adaptive optimization control method for PID parameters in condiment filling according to claim 5, characterized in that, The formula for calculating the final valve opening control command is as follows: In the formula, This is the valve opening control command for the current moment; The difference between the set traffic volume and the actual traffic volume at the current moment; The integral coefficient; is the differential coefficient.

8. The adaptive optimization control method for PID parameters in condiment filling according to claim 5, characterized in that, The hydrostatic pressure data is zero-point calibrated, and the hydrostatic pressure is always greater than or equal to the preset minimum positive pressure value.

9. The adaptive optimization control method for PID parameters in condiment filling according to claim 1, characterized in that, The fluid temperature change rate is obtained by calculating the absolute value of the difference between the fluid temperature at the current moment and the fluid temperature at the previous moment; the flow prediction error is obtained by subtracting the absolute value of the difference between the expected flow rate calculated at the previous moment and the instantaneous flow rate measured at the current moment.

10. A PID parameter adaptive optimization control system for condiment filling, characterized in that, It includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement the adaptive optimization control method for PID parameters in condiment filling as described in any one of claims 1-9.

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