Intelligent temperature control method for low-temperature thermostat based on PID control

Abstract

The present application relates to the technical field of temperature control, in particular to a low-temperature thermostat intelligent temperature control method based on PID control, comprising: obtaining the deviation of the current temperature and the target temperature, and obtaining the initial control output through PID operation.Combining the real-time collected operating load and external environment disturbance data, the output value is compensated and corrected to generate a driving signal to control the heater and the refrigerator.The system monitors the temperature dynamic response process after the control action in real time, extracts the temperature change rate and overshoot data, and dynamically adjusts the proportional, integral and differential coefficients of the PID algorithm accordingly.The present application enhances the system's anti-interference ability and precision by introducing feedforward compensation for measurable disturbances;Through online self-tuning of parameters based on dynamic response, the controller can adapt to different working conditions, achieve better dynamic and steady-state control performance, and effectively solve the problem of insufficient adaptability of traditional PID control in complex application scenarios.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and in particular to an intelligent temperature control method for low-temperature thermostats based on PID control. Background Technology

[0002] In scientific research experiments, materials testing, and the operation of precision instruments, cryogenic thermostats require a highly stable and precise temperature environment. Currently, the temperature control of such devices commonly employs the classic PID control algorithm. This algorithm performs proportional, integral, and derivative calculations based on the deviation between the set target temperature and the actual measured temperature, and outputs a control signal to drive heating or cooling elements. This method is simple in structure and achieves good results in stable operating conditions.

[0003] Classical PID control has limitations when dealing with the complex operating conditions of cryogenic thermostats. Its control parameters are typically tuned offline and kept fixed for specific operating conditions. When the internal thermal load of the thermostat fluctuates drastically due to changes in experimental samples, device start-up and shutdown, or changes in external ambient temperature and cooling conditions, the fixed control parameters struggle to adapt to the changes in the dynamic characteristics of the controlled object. This easily leads to a decline in control quality, manifested as increased temperature overshoot, prolonged recovery time, and even continuous oscillations, failing to meet the requirements for high-precision temperature control. Control methods relying solely on temperature deviation feedback only begin to respond after a disturbance occurs, exhibiting a lag.

[0004] Conventional technologies lack the ability to perceive and autonomously optimize the dynamic performance of the control process online. Key transient characteristics such as overshoot and rate of change that occur during system response are not effectively utilized, and controller parameters cannot be automatically adjusted based on actual operating performance. Operators need to manually intervene in parameters based on experience, which is not only inefficient but also makes it difficult to maintain optimal control under changing operating conditions. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an intelligent temperature control method for low-temperature thermostats based on PID control.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a PID-based intelligent temperature control method for low-temperature thermostats, comprising:

[0007] Obtain the current internal temperature value of the low-temperature thermostat and the preset target temperature value, and calculate the current temperature deviation.

[0008] Based on the current temperature deviation, a preset PID control algorithm is invoked to perform calculations and generate an initial control output value;

[0009] Based on the initial control output value, combined with the real-time operating load data of the cryostat and the external environmental disturbance data, the output value is compensated and corrected to generate a first corrected control output value. Specifically, the following steps are taken: The current operating load data is acquired through the sensors built into the cryostat, including sample heat capacity data and internal heat flow disturbance data; the external environmental disturbance data is acquired through the external environment monitoring unit, including ambient temperature fluctuation data and cooling medium pressure fluctuation data; the real-time operating load data and the external environmental disturbance data are input into the feedforward compensation model to calculate the feedforward compensation value; the initial control output value and the feedforward compensation value are algebraically added, and the sum is used as the first corrected control output value.

[0010] The first corrected control output value is converted into a drive signal and applied to the heater and cooler of the low-temperature thermostat to adjust their working state.

[0011] Real-time monitoring of the dynamic temperature response process inside the low-temperature thermostat after the heater and cooler are adjusted, and data on temperature change rate and temperature overshoot are collected.

[0012] Based on the collected temperature change rate and temperature overshoot data, the proportional coefficient, integral coefficient, and derivative coefficient of the preset PID control algorithm are dynamically adjusted to generate an updated PID control algorithm.

[0013] As a further aspect of the present invention, based on the current temperature deviation, a preset PID control algorithm is invoked to perform calculations and generate an initial control output value, specifically as follows:

[0014] Read the value of the current temperature deviation and call the initial proportional coefficient, initial integral coefficient and initial derivative coefficient of the stored PID control algorithm;

[0015] Based on the initial proportional coefficient, the current temperature deviation is proportionally calculated to obtain the proportional control component;

[0016] The sequence of the current temperature deviation changing over time is integrated, and the integral control component is obtained based on the initial integral coefficient.

[0017] Calculate the rate of change of the current temperature deviation over time, and obtain the differential control component based on the initial differential coefficient;

[0018] The proportional control component, the integral control component, and the derivative control component are linearly superimposed to generate the initial control output value.

[0019] As a further aspect of the present invention, the real-time operating load data and the external environmental disturbance data are input into the feedforward compensation model to calculate the feedforward compensation value, specifically as follows:

[0020] Construct a feedforward compensation model that includes multiple input variables and one output variable. The input variables are the real-time operating load data and the external environmental disturbance data, and the output variable is the feedforward compensation value.

[0021] Using offline or online system identification methods, a transitive relationship between the input variables and the output variables is established, and the transitive relationship is represented as a weighted coefficient matrix;

[0022] In each control cycle, the real-time collected input variable values ​​are input to the transmission relationship, and the corresponding feedforward compensation value is calculated in real time through the operation of the weighting coefficient matrix.

[0023] As a further aspect of the present invention, the real-time monitoring of the dynamic temperature response process inside the low-temperature thermostat after the adjustment of the heater and cooler, and the collection of temperature change rate and temperature overshoot data, specifically includes:

[0024] After applying the first corrective control output value, the internal temperature value of the low-temperature thermostat is collected at a preset sampling frequency to form a real-time internal temperature change curve.

[0025] The internal temperature real-time change curve is differentiated to calculate the temperature change rate at each sampling time.

[0026] From the real-time internal temperature change curve, identify the maximum or minimum value during the temperature rise or fall process, compare it with the preset target temperature value, and calculate the absolute temperature overshoot.

[0027] Record the curve of the temperature change rate over time and the value of the absolute temperature overshoot.

[0028] As a further aspect of the present invention, the step of dynamically adjusting the proportional coefficient, integral coefficient, and derivative coefficient of the preset PID control algorithm based on the collected temperature change rate and temperature overshoot data specifically involves:

[0029] Establish a parameter adjustment mapping table that includes proportional coefficient, integral coefficient, and derivative coefficient. The inputs to the mapping table are the characteristic value of the rate of temperature change and the absolute temperature overshoot.

[0030] From the curve of the temperature change rate over time, feature values ​​are extracted, including the initial change rate, the maximum change rate before steady state, and the change rate corresponding to when the internal temperature value is continuously within the preset error allowable dead zone range centered on the preset target temperature value for a period of time, which is taken as the change rate when steady state is reached.

[0031] The extracted temperature change rate feature value and the absolute temperature overshoot are used as input to query the parameter adjustment mapping table;

[0032] Based on the query results, obtain the recommended adjustment amounts for the proportional coefficient, integral coefficient, and derivative coefficient for the dynamic response of the current control cycle;

[0033] The initial proportional coefficient, initial integral coefficient, and initial derivative coefficient in the preset PID control algorithm are respectively calculated with the corresponding recommended adjustment amount to generate updated proportional coefficient, updated integral coefficient, and updated derivative coefficient.

[0034] As a further aspect of the present invention, the initial proportional coefficient, initial integral coefficient, and initial derivative coefficient in the preset PID control algorithm are respectively calculated with the corresponding recommended adjustment amount to generate updated proportional coefficient, updated integral coefficient, and updated derivative coefficient, specifically as follows:

[0035] Boundary checks are performed on the recommended proportional coefficient adjustment, recommended integral coefficient adjustment, and recommended differential coefficient adjustment to ensure that the adjusted coefficient values ​​are within the preset coefficient boundary threshold.

[0036] If the recommended adjustment amount causes the adjusted coefficient to exceed the preset range, then the coefficient will be adjusted to the corresponding range boundary value.

[0037] For the recommended adjustment amount that is within the preset range, the initial proportional coefficient is added to the recommended proportional coefficient adjustment amount to obtain the updated proportional coefficient;

[0038] Add the recommended integral coefficient adjustment amount to the initial integral coefficient to obtain the updated integral coefficient;

[0039] The updated differential coefficients are obtained by adding the recommended differential coefficient adjustment amount to the initial differential coefficients;

[0040] The updated proportional coefficient, integral coefficient, and derivative coefficient are stored and used to replace the initial parameters in the preset PID control algorithm.

[0041] As a further aspect of the present invention, the calculation of the rate of change of the current temperature deviation over time, and the obtaining of the differential control component based on the initial differential coefficient, specifically involves:

[0042] The internal temperature values ​​of the low-temperature thermostat are continuously collected at multiple time points with a preset sampling period to form a temperature deviation time series.

[0043] The central difference algorithm is applied to the temperature deviation time series to calculate the rate of change of temperature deviation at each sampling time.

[0044] The calculated temperature deviation change rate is multiplied by the initial differential coefficient to obtain the corresponding differential control component value.

[0045] The values ​​of the differential control components from multiple consecutive sampling periods are subjected to moving average filtering to eliminate fluctuations caused by measurement noise.

[0046] The filtered differential control component value is output as the effective differential control component for the current control cycle.

[0047] As a further aspect of the present invention, the step of performing differential processing on the real-time internal temperature change curve to calculate the temperature change rate at each sampling moment specifically involves:

[0048] The real-time internal temperature change curve is processed using the five-point numerical differential method, and the temperature change rate is calculated using the temperature values ​​of the current sampling time and the two sampling points before and after it.

[0049] Kalman filtering is applied to the calculated raw temperature change rate data to suppress the effects of measurement noise and anomalous jumps.

[0050] Establish a confidence assessment mechanism for the rate of temperature change, conduct a reasonableness test on the filtered rate of temperature change data, and remove data points whose difference from adjacent sampling points exceeds the preset rate of change jump threshold;

[0051] The filtered and verified temperature change rate data are stored in chronological order to form a complete time series curve of temperature change rate.

[0052] As a further aspect of the present invention, the method further includes the step of constraining the state of the cryogenic thermostat actuator after generating the first corrected control output value:

[0053] Obtain the maximum allowable power of the heater and the maximum allowable power of the refrigerator for the cryostat;

[0054] The first corrected control output value is converted into the desired power demand for the heater or cooler;

[0055] The expected power demand is compared with the maximum allowable power. If the expected power demand exceeds the maximum allowable power, the output is limited to the maximum allowable power, and a second corrected control output value is generated.

[0056] If the expected power demand does not exceed the maximum allowable power, the first corrective control output value is directly used as the second corrective control output value.

[0057] The second corrected control output value is used to convert into a drive signal.

[0058] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0059] After generating the initial PID control output value, real-time operating load data and external environmental disturbance data are simultaneously incorporated for joint calculation to compensate and correct the initial output. This approach essentially constitutes a composite control structure combining feedforward and feedback. The compensation mechanism can directly counteract measurable disturbance sources in advance, weakening the impact of sudden load changes and environmental fluctuations on the internal temperature of the thermostat. The anti-interference capability of the control system is enhanced, the temperature exhibits stronger robustness in the face of internal and external disturbances, the steady-state control accuracy is improved, and the amplitude and duration of temperature deviations caused by disturbances are reduced.

[0060] The system collects and analyzes data on the rate of change and overshoot during controlled temperature variations in real time, using these as dynamic indicators to evaluate control performance. Based on these online-acquired indicators, the proportional, integral, and derivative coefficients are automatically adjusted. This allows the controller parameters to adaptively change in response to variations in the system's dynamic characteristics and external operating conditions. At different operating points or when facing different disturbances, the system can autonomously maintain rapid, stable, and low-overshoot temperature regulation performance, avoiding problems such as slow response, excessive overshoot, or continuous fluctuations caused by fixed or mismatched parameters, achieving near-optimal control across the entire operating range. Attached Figure Description

[0061] Figure 1 The flowchart is a process for the intelligent temperature control method of a low-temperature thermostat based on PID control as described in this invention.

[0062] Figure 2 Flowchart for calculating feedforward compensation values;

[0063] Figure 3 A curve for dynamically adjusting PID parameters;

[0064] Figure 4 This is a graph showing the dynamic temperature response of a low-temperature thermostat.

[0065] Figure 5 A comparison chart showing the correction of constraints to control output values. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0067] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0068] See Figure 1 The system acquires the current internal temperature value of the cryogenic thermostat and the preset target temperature value, calculates the current temperature deviation, and calls a preset PID control algorithm to generate an initial control output value. Based on the initial control output value, and combined with the real-time operating load data of the cryogenic thermostat and external environmental disturbance data, the output value is compensated and corrected to generate a first corrected control output value. The first corrected control output value is converted into a drive signal and applied to the heater and cooler of the cryogenic thermostat to adjust their working state. The system monitors the dynamic response process of the internal temperature of the cryogenic thermostat after the heater and cooler are adjusted, and collects temperature change rate and temperature overshoot data. Based on the collected temperature change rate and temperature overshoot data, the system dynamically adjusts the proportional coefficient, integral coefficient, and derivative coefficient of the preset PID control algorithm to generate an updated PID control algorithm.

[0069] In one embodiment of the present invention, the temperature control system performs the step of generating an initial control output value. It reads the current temperature deviation value, which is the difference between a preset target temperature value and the real-time feedback value from the internal temperature sensor of the cryostat. It then calls the stored initial proportional coefficient, initial integral coefficient, and initial derivative coefficient of the PID control algorithm. These coefficients are pre-set fixed parameters stored in non-volatile memory or parameters optimized using historical data. Based on the initial proportional coefficient, a proportional operation is performed on the current temperature deviation to obtain the proportional control component. The proportional operation multiplies the current temperature deviation value by the initial proportional coefficient. An integral operation is performed on the sequence of the current temperature deviation over time, and an integral control component is obtained based on the initial integral coefficient. The integral operation is calculated by summing the historical temperature deviation values ​​within a control cycle. The rate of change of the current temperature deviation over time is calculated, and a derivative control component is obtained based on the initial derivative coefficient. The rate of change is calculated by comparing the temperature deviation differences between adjacent sampling cycles. The proportional control component, integral control component, and derivative control component are linearly superimposed to generate the initial control output value. This linear superposition is a simple algebraic addition process.

[0070] In some embodiments, the proportional control component is calculated directly using the product of the instantaneously acquired temperature deviation value of the current control cycle and the initial proportional coefficient. The integral control component is calculated by accessing the stored historical temperature deviation queue, summing all values ​​in the queue, and then multiplying the sum by the initial integral coefficient. The derivative control component is calculated by obtaining the current temperature deviation value and the temperature deviation value of the previous control cycle, calculating the difference between the two, dividing it by the sampling interval, and then multiplying the result by the initial derivative coefficient. The calculations of these three control components are performed in parallel or sequentially, and the final calculation result is sent to an adder unit.

[0071] Optionally, when calculating the rate of change of the current temperature deviation over time, the system records the temperature deviation values ​​of the most recent consecutive sampling times and forms a time series. The rate of change is calculated using the first-order backward difference method, i.e., the temperature deviation value at the current time is subtracted from the temperature deviation value at the previous time, and then divided by the time interval between the two sampling times. The calculated rate of change value is multiplied by the initial differential coefficients to obtain the differential control component value. For the integral control component, the system maintains an accumulator. Each control cycle, the current temperature deviation value is added to the accumulator, and the total value of the accumulator is multiplied by the initial integral coefficients.

[0072] It is understandable that the generation of the initial control output value strictly follows the classic structure of the PID algorithm, with the linear superposition relationship between the proportional control component, integral control component, and derivative control component being the core. This relationship can be expressed by a formula:

[0073]

[0074] in: Represents the initial control output value. Represents the initial scaling factor. This represents the current temperature deviation. Represents the initial integral coefficients. The integral approximation of the sequence representing the temperature deviation over time. Represents the initial differential coefficients. This represents the rate of change of the current temperature deviation over time. All variables in the formula are updated and recalculated in each control cycle.

[0075] See Figure 2In one embodiment of the present invention, an output value compensation correction is performed based on the initial control output value to generate a first corrected control output value. Current operating load data, including sample heat capacity data and internal heat flow disturbance data, is acquired through a sensor built into the cryogenic thermostat. External environmental disturbance data, including ambient temperature fluctuation data and cooling medium pressure fluctuation data, is acquired through an external environmental monitoring unit. The real-time operating load data and external environmental disturbance data are input into the feedforward compensation model to calculate the feedforward compensation value. The initial control output value and the feedforward compensation value are algebraically added, and the sum is used as the first corrected control output value. In a specific implementation, the construction of the feedforward compensation model involves a structure containing multiple input variables and one output variable. The input variables are the real-time operating load data and the external environmental disturbance data, and the output variable is the feedforward compensation value. An offline or online system identification method is used to establish the transitivity between the input and output variables. The transitivity is represented as a weighted coefficient matrix. In each control cycle, the real-time collected input variable values ​​are input into the transitivity, and the corresponding feedforward compensation value is calculated in real time through the operation of the weighted coefficient matrix. In practical industrial applications, operational load data specifically corresponds to, for example, changes in heat capacity caused by the insertion, removal, or phase change of the sample under test as required by the experiment, as well as internal heat flow disturbances generated when heating elements such as the magnet coil and signal amplifier inside the thermostat are started and stopped. External environmental disturbance data specifically corresponds to, for example, fluctuations in ambient temperature in the laboratory caused by the operation of the air conditioning system, personnel activity, or day-night cycle, as well as pressure fluctuations in the circulating cooling water system that provides cooling capacity to the thermostat or vibration interference from the compressor refrigeration system. These are all typical, quantitatively measurable, or estimable disturbance sources in industrial scenarios, making the feedforward compensation model have clear physical inputs and engineering feasibility.

[0076] In some embodiments, the offline system identification method applies a series of known load changes and environmental disturbances to the cryostat under laboratory conditions beforehand, while recording the system's temperature response and control output requirements. It then uses the least squares method or a neural network training algorithm to fit the mathematical relationship between the input and output variables, and stores this relationship as a weighted coefficient matrix in the controller's non-volatile memory. The online system identification method, on the other hand, continuously collects input variables and corresponding ideal compensation output data during cryostat operation. It updates the weighted coefficient matrix in real time using a recursive least squares algorithm, enabling the feedforward compensation model to adapt to time drift of system parameters or long-term changes in environmental conditions. The dimension of the weighted coefficient matrix depends on the number of input variables. For example, when the operating load data includes both sample heat capacity data and internal heat flow disturbance data, and the external environmental disturbance data includes both ambient temperature fluctuation data and cooling medium pressure fluctuation data, the input variable vector is four-dimensional, and the weighted coefficient matrix is ​​a four-row, one-column matrix. The weighted coefficient matrix represents the input variable vector. The output value is composed of real-time operating load data and external environmental disturbance data, along with feedforward compensation. The linear transitive relationship between them, i.e.

[0077]

[0078] in, The weighted coefficient matrix or vector to be identified. This represents its transpose. The least squares identification method involves collecting multiple sets of data. Find the solution that minimizes the sum of squared errors. .

[0079] Optionally, in each control cycle, the real-time acquired input variable values ​​are organized into a column vector. This column vector is then multiplied by a stored weighting coefficient matrix. The result of the matrix multiplication is a scalar value, which is the feedforward compensation value. The calculation of the feedforward compensation value and the generation of the initial control output value are performed synchronously, ensuring time consistency during algebraic addition. The algebraic addition operation is completed in a dedicated adder hardware circuit or software addition module. The initial control output value is the output of the PID control algorithm, and the feedforward compensation value is the output of the feedforward compensation model. The result of the addition operation is directly used as the first corrected control output value and sent to subsequent processing stages.

[0080] In one embodiment of the present invention, the dynamic response process of the internal temperature of the low-temperature thermostat after the heater and cooler are adjusted is monitored in real time and data is collected. After applying the first correction control output value, the internal temperature value of the low-temperature thermostat is collected at a preset sampling frequency to form an internal temperature real-time change curve. The internal temperature real-time change curve is differentiated to calculate the temperature change rate at each sampling moment. The maximum or minimum value during the temperature rise or fall process is identified from the internal temperature real-time change curve and compared with the preset target temperature value to calculate the absolute temperature overshoot. The curve of the temperature change rate changing with time and the value of the absolute temperature overshoot are recorded. Based on the collected temperature change rate and temperature overshoot data, the parameters of the preset PID control algorithm are dynamically adjusted. A parameter adjustment mapping table containing proportional coefficient, integral coefficient, and derivative coefficient is established. The inputs to the mapping table are the characteristic value of the temperature change rate and the absolute temperature overshoot. Characteristic values ​​are extracted from the curve of temperature change rate over time, including the initial change rate, the maximum change rate before steady state, and the change rate corresponding to the period when the internal temperature value is continuously within the preset error allowable dead zone centered on the preset target temperature value. The extracted temperature change rate characteristic value and absolute temperature overshoot are used as input combinations to query the parameter adjustment mapping table. Based on the query results, the recommended proportional coefficient adjustment amount, recommended integral coefficient adjustment amount, and recommended derivative coefficient adjustment amount for the dynamic response of the current control cycle are obtained. The initial proportional coefficient, initial integral coefficient, and initial derivative coefficient in the preset PID control algorithm are calculated with the corresponding recommended adjustment amounts to generate updated proportional coefficient, updated integral coefficient, and updated derivative coefficient. The preset error allowable dead zone range is usually set according to the temperature control accuracy requirements, such as within ±0.1°C of the target temperature value. The maximum allowable power of the heater and the maximum allowable power of the cooler are determined by the hardware specifications of the thermostat itself. The initial values ​​and adjustment boundaries of the proportional coefficient, integral coefficient, and derivative coefficient need to be obtained through preliminary testing based on the thermal inertia, volume, and actuator response characteristics of the specific thermostat.

[0081] In some embodiments, the formation of the real-time internal temperature change curve relies on a high-precision temperature sensor installed at a key location inside the thermostat. The sensor samples at a frequency much higher than that of the main control loop, and the sampled data points are connected in chronological order to form the curve. The real-time internal temperature change curve is differentiated using a numerical differentiation method. The temperature difference between each sampling point and the adjacent previous sampling point is calculated and then divided by the sampling time interval to obtain the instantaneous temperature change rate at that point. Identifying the maximum or minimum value during the temperature rise or fall process is accomplished by scanning the data sequence of the real-time internal temperature change curve. After a step increase from the temperature setpoint, the system finds the highest temperature value during the temperature rise process; after a step decrease from the temperature setpoint, the system finds the lowest temperature value during the temperature fall process. The difference between this extreme value and the preset target temperature value is taken as the absolute value to obtain the absolute temperature overshoot.

[0082] Optionally, the parameter adjustment mapping table is a predefined multidimensional lookup table stored in the controller's memory. The row and column indices of the mapping table correspond to the discretized temperature change rate characteristic value and the absolute temperature overshoot range, respectively. The data stored in the table cells are the recommended proportional coefficient adjustment, recommended integral coefficient adjustment, and recommended derivative coefficient adjustment. The initial rate of change characteristic value is calculated in the first sampling period after applying the control output. The maximum rate of change characteristic value before steady state is recorded when the temperature change rate curve reaches its peak. The rate of change characteristic value at steady state is calculated as the average of the temperature change rate over a period of time when the internal temperature fluctuates within a small dead zone near the preset target temperature value. When querying the parameter adjustment mapping table, the continuous extracted characteristic values ​​and absolute temperature overshoot values ​​are mapped to the nearest discrete index value in the mapping table using rounding or floor selection, thus locating the corresponding table cell.

[0083] It is understandable that the parameter adjustment mapping table is based on prior knowledge of the characteristics of the cryogenic thermostat or a large amount of simulation experimental data. Its core logic is to establish an empirical relationship between the system's dynamic response characteristics and the PID parameter adjustment amounts. This relationship can be represented by a function:

[0084]

[0085] in: This represents an adjustment amount that includes the recommended ratio coefficient. Recommended integral coefficient adjustment amount Recommended adjustment amount of differential coefficients The adjustment vector, This represents a mapping function implemented by adjusting a parameter mapping table. This represents the extracted initial rate of change feature value. This represents the extracted feature value of the maximum rate of change before steady state. This represents the extracted rate of change feature value when the system reaches a steady state. This represents the calculated absolute temperature overshoot. (Function) The specific form is defined by the data within the mapping table. In practice, after the recommended adjustment amount is retrieved, the update process involves algebraic operations, changing the initial proportional coefficient... Plus Get new , the initial integration coefficients Plus Get new , initial differential coefficients Plus Get new In some embodiments, if the recommended adjustment value found results in an updated coefficient value of zero or negative, the system will replace it with a preset, extremely small lower limit for positive coefficients to ensure that the basic function of the PID control algorithm is not compromised. Mapping table This is a set of discrete rules based on experience. Its establishment principle is based on the absolute temperature overshoot. When the initial rate of change is large, the proportional coefficient and integral coefficient should be reduced; when the initial rate of change is large... If the value is too small, the proportional coefficient should be increased to speed up the response; when the rate of change reaches a steady state... If the integral coefficient remains non-zero, it should be adjusted to eliminate steady-state error. Recommended adjustment amount. The sign and magnitude are determined according to the above principles and the specific values ​​of the characteristic values. Optionally, the parameter adjustment mapping table can be fine-tuned online based on empirical data from long-term system operation. For example, the actual control effect after each parameter adjustment can be recorded. If the effect is better than expected, the current mapping relationship is strengthened; otherwise, it is weakened.

[0086] See Figure 3 This is a dynamic adjustment curve of PID parameters, showing the dynamic changes of the three core parameters in the PID control algorithm of a cryogenic thermostat over time. The increase of Kp and Kd reflects the system's improvement in control accuracy and stability by enhancing proportional and derivative actions in response to temperature changes. The stability of Ki ensures that the system eliminates steady-state error without excessive overshoot. This graph visually illustrates the core idea of ​​intelligent PID control: the parameters are not static but adaptively adjusted based on the system's real-time dynamic response. In this way, the cryogenic thermostat can maintain optimal control performance under different operating conditions and disturbances. The curve's changing pattern provides a reference for PID parameter tuning in practical engineering. Engineers can optimize their control algorithms based on similar trends to improve the temperature control accuracy and response speed of the cryogenic thermostat.

[0087] In one embodiment of the present invention, the initial proportional coefficient, initial integral coefficient, and initial derivative coefficient in the preset PID control algorithm are respectively calculated with the corresponding recommended adjustment amount to generate updated coefficients. The recommended proportional coefficient adjustment amount, recommended integral coefficient adjustment amount, and recommended derivative coefficient adjustment amount are subjected to boundary verification. The boundary verification process ensures that the adjusted coefficient values ​​are within the preset coefficient boundary threshold. If the recommended adjustment amount causes the adjusted coefficients to exceed the preset range, the coefficients are adjusted to the corresponding range boundary values. For recommended adjustment amounts within the preset range, the initial proportional coefficient is added to the recommended proportional coefficient adjustment amount to obtain the updated proportional coefficient, the initial integral coefficient is added to the recommended integral coefficient adjustment amount to obtain the updated integral coefficient, and the initial derivative coefficient is added to the recommended integral coefficient adjustment amount to obtain the updated derivative coefficient. The updated proportional coefficient, integral coefficient, and derivative coefficient are stored and used to replace the initial parameters in the preset PID control algorithm. In practice, the rate of change of the current temperature deviation over time is calculated, and the differential control component is obtained based on the initial differential coefficient. The internal temperature values ​​of the low-temperature thermostat are continuously collected at multiple time points with a preset sampling period, and the temperature deviation is calculated to form a temperature deviation time series. The central difference algorithm is applied to the temperature deviation time series. The central difference algorithm calculates the temperature deviation rate of change value at each sampling time. The calculated temperature deviation rate of change value is multiplied by the initial differential coefficient to obtain the corresponding differential control component value. The differential control component values ​​of multiple consecutive sampling periods are subjected to moving average filtering. The moving average filtering process eliminates fluctuations caused by measurement noise. The filtered differential control component value is used as the effective differential control component output for the current control period.

[0088] In some embodiments, the preset boundary thresholds for boundary checks are a set of constants predefined and stored in the controller's non-volatile memory, with each coefficient independently having a lower and upper limit value. When the system obtains the recommended proportional coefficient adjustment, recommended integral coefficient adjustment, and recommended derivative coefficient adjustment, it calculates temporary update values ​​for each: the temporary proportional coefficient equals the initial proportional coefficient plus the recommended proportional coefficient adjustment; the temporary integral coefficient equals the initial integral coefficient plus the recommended integral coefficient adjustment; and the temporary derivative coefficient equals the initial derivative coefficient plus the recommended derivative coefficient adjustment. The system then compares each temporary coefficient with its corresponding preset range. If the temporary proportional coefficient is less than the lower limit, the updated proportional coefficient takes the lower limit value; if the temporary proportional coefficient is greater than the upper limit, the updated proportional coefficient takes the upper limit value; and if the temporary proportional coefficient is between the lower and upper limits, the updated proportional coefficient equals the temporary proportional coefficient. The same comparison and assignment logic is performed on the integral and derivative coefficients.

[0089] See Figure 4This is a dynamic temperature response curve of a cryogenic thermostat. The temperature reaches its peak at approximately 80 seconds, slightly above 25°C, then falls back and stabilizes near the target value. The overshoot is controlled within a very small range, demonstrating the effectiveness of the control strategy. The temperature eventually stabilizes near the target value with almost no steady-state error, thanks to the integral term. This graph provides the most direct verification of the PID control algorithm's effectiveness. The smooth response curve, minimal overshoot, and rapid steady-state convergence all prove the effectiveness of this intelligent temperature control method. The smooth transition of the temperature curve in the graph is achieved through real-time adjustment of the PID parameters, allowing the system to effectively suppress overshoot while maintaining a fast response speed. This provides an intuitive reference standard for the design and parameter tuning of similar temperature control systems, allowing engineers to optimize their control algorithms by comparing the response curves.

[0090] In one embodiment of the present invention, the real-time internal temperature change curve is differentiated to calculate the temperature change rate at each sampling moment. A five-point numerical differentiation method is used to process the real-time internal temperature change curve. This method calculates the temperature change rate using the temperature values ​​at the current sampling moment and the temperature values ​​at two sampling points before and after it. Specifically, the current sampling moment is set as follows: The temperature is The temperatures at the two sampling points before and after it were respectively The sampling interval is Then at time Temperature change rate The calculation formula is The calculated raw temperature change rate data is then subjected to Kalman filtering. The Kalman filtering process uses the temperature change rate calculated by the aforementioned numerical differentiation as the observed value. The true estimate of the rate of temperature change This refers to a state variable. Its core recursive process includes state prediction and updating, where the prediction is... The predicted covariance is updated as follows Kalman gain is calculated as Status updated to Covariance updated to ,in, and Let be the prior predicted value and the prior predicted covariance of the state at time k, respectively. and These are the posterior estimate and the posterior covariance, respectively. This represents the optimal state estimate at time k-1. This represents the posterior estimated covariance at time k-1. This represents the Kalman gain at time k, between 0 and 1. This represents the observation value at time k. For process noise variance, To observe the noise variance, it is necessary to set the parameters according to the actual system noise characteristics. Kalman filtering is used to suppress the effects of measurement noise and anomalous jumps. A confidence assessment mechanism for the temperature change rate is established to verify the rationality of the filtered temperature change rate data. The confidence assessment mechanism removes data points whose difference from adjacent sampling points exceeds a preset change rate jump threshold. The filtered and verified temperature change rate data are stored in chronological order to form a complete time series curve of the temperature change rate.

[0091] The method further includes a step of constraining the state of the cryogenic thermostat actuator after generating the first corrected control output value, obtaining the maximum allowable power of the heater and the maximum allowable power of the cooler of the cryogenic thermostat, converting the first corrected control output value into the desired power demand of the heater or cooler, comparing the desired power demand with the maximum allowable power, if the desired power demand exceeds the maximum allowable power, then limiting the output to the maximum allowable power, generating a second corrected control output value, if the desired power demand does not exceed the maximum allowable power, then the first corrected control output value is directly used as the second corrected control output value, and the second corrected control output value is used to convert into a drive signal.

[0092] In some embodiments, the five-point numerical differentiation method requires maintaining a sliding window containing at least five consecutive timestamped temperature readings, with the center point of the window corresponding to the moment when the rate of change needs to be calculated. The five-point numerical differentiation method estimates the temperature change rate at the center point by weighted summation of the five temperature values ​​within the window using a fixed coefficient template and then dividing by the sampling time interval. The calculated raw temperature change rate data is fed into a discrete-time Kalman filter. Based on the process model and sensor observation model of the temperature change rate, the Kalman filter performs optimal estimation of the raw data, effectively filtering out random noise and smoothing abnormal jumps caused by interference. A temperature change rate confidence assessment mechanism checks the filtered data. For example, when the thermostat is in a stable heat preservation phase, the absolute value of the temperature change rate should be close to zero. If the calculated value remains large at this time, it is determined that the difference with adjacent sampling points exceeds a preset rate of change jump threshold. Another check is the continuity of the rate of change; the rate of change between adjacent sampling points should not undergo abrupt changes exceeding the limits of the physical system.

[0093] Optionally, the constraint processing of the actuator occurs after the generation of the first corrected control output value and before the drive signal conversion. The first corrected control output value is a dimensionless digital quantity representing the direction and intensity of the control action. This digital quantity needs to be mapped to the desired power demand of the heater or cooler through a linear or nonlinear transformation relationship. For example, an output value of 0 to 1.0 may correspond to a heater power demand of 0 to 100 watts, while an output value of -1.0 to 0 may correspond to a cooler power demand of 0 to 80 watts. The system reads the pre-set maximum allowable power of the heater and the maximum allowable power of the cooler from the storage unit. These two values ​​are hardware limit parameters that ensure the safe operation of the equipment. The comparison logic is determined separately: if the desired power demand is positive (request for heating), it is compared with the maximum allowable power of the heater; if the desired power demand is negative (request for cooling), its absolute value is compared with the maximum allowable power of the cooler.

[0094] See Figure 5 This is a comparison chart of control output value constraint correction, visually demonstrating the change in control output value of a cryogenic thermostat before and after actuator constraint processing. This chart illustrates the limiting effect of power constraint on the control output signal. Without this step, the high output value generated by the PID algorithm would directly drive the heater or cooler to operate at power far exceeding its limits, which could not only damage the equipment but also cause safety accidents. While constraint processing ensures safety, it also limits control performance to some extent. The chart shows that the system's response speed is affected when constraints are in effect, reflecting the fundamental principle of "safety first" in engineering design. This provides an important reference for the design of similar temperature control systems. Engineers can analyze the frequency and timing of constraint activation to optimize PID parameters or upgrade hardware to reduce the impact of constraints on control performance.

[0095] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for intelligent temperature control of a low-temperature thermostat based on PID control, characterized in that, Includes the following steps: Obtain the current internal temperature value of the low-temperature thermostat and the preset target temperature value, and calculate the current temperature deviation. Based on the current temperature deviation, a preset PID control algorithm is invoked to perform calculations and generate an initial control output value; Based on the initial control output value, combined with the real-time operating load data of the cryostat and the external environmental disturbance data, the output value is compensated and corrected to generate a first corrected control output value. Specifically, the current operating load data is obtained through the sensors built into the cryostat, including sample heat capacity data and internal heat flow disturbance data; the external environmental disturbance data is obtained through the external environment monitoring unit, including ambient temperature fluctuation data and cooling medium pressure fluctuation data. The real-time operating load data and the external environmental disturbance data are input into the feedforward compensation model to calculate the feedforward compensation value. The initial control output value and the feedforward compensation value are algebraically added together, and the sum is used as the first corrected control output value. The first corrected control output value is converted into a drive signal and applied to the heater and cooler of the low-temperature thermostat to adjust their working state. Real-time monitoring of the dynamic temperature response process inside the low-temperature thermostat after the heater and cooler are adjusted, and data on temperature change rate and temperature overshoot are collected. Based on the collected temperature change rate and temperature overshoot data, the proportional coefficient, integral coefficient, and derivative coefficient of the preset PID control algorithm are dynamically adjusted to generate an updated PID control algorithm.

2. The intelligent temperature control method for a low-temperature thermostat based on PID control according to claim 1, characterized in that, Based on the current temperature deviation, a preset PID control algorithm is invoked to generate an initial control output value, specifically: Read the value of the current temperature deviation and call the initial proportional coefficient, initial integral coefficient and initial derivative coefficient of the stored PID control algorithm; Based on the initial proportional coefficient, the current temperature deviation is proportionally calculated to obtain the proportional control component; The sequence of the current temperature deviation changing over time is integrated, and the integral control component is obtained based on the initial integral coefficient. Calculate the rate of change of the current temperature deviation over time, and obtain the differential control component based on the initial differential coefficient; The proportional control component, the integral control component, and the derivative control component are linearly superimposed to generate the initial control output value.

3. The intelligent temperature control method for a low-temperature thermostat based on PID control according to claim 2, characterized in that, The real-time operating load data and the external environmental disturbance data are input into the feedforward compensation model to calculate the feedforward compensation value, specifically: Construct a feedforward compensation model that includes multiple input variables and one output variable. The input variables are the real-time operating load data and the external environmental disturbance data, and the output variable is the feedforward compensation value. Using offline or online system identification methods, a transitive relationship between the input variables and the output variables is established, and the transitive relationship is represented as a weighted coefficient matrix; In each control cycle, the real-time collected input variable values ​​are input to the transmission relationship, and the corresponding feedforward compensation value is calculated in real time through the operation of the weighting coefficient matrix.

4. The intelligent temperature control method for a low-temperature thermostat based on PID control according to claim 3, characterized in that, The real-time monitoring of the dynamic temperature response process inside the low-temperature thermostat after the adjustment of the heater and cooler, and the collection of temperature change rate and temperature overshoot data, specifically: After applying the first corrective control output value, the internal temperature value of the low-temperature thermostat is collected at a preset sampling frequency to form a real-time internal temperature change curve. The internal temperature real-time change curve is differentiated to calculate the temperature change rate at each sampling time. From the real-time internal temperature change curve, identify the maximum or minimum value during the temperature rise or fall process, compare it with the preset target temperature value, and calculate the absolute temperature overshoot. Record the curve of the temperature change rate over time and the value of the absolute temperature overshoot.

5. The intelligent temperature control method for a low-temperature thermostat based on PID control according to claim 4, characterized in that, The proportional coefficient, integral coefficient, and derivative coefficient of the preset PID control algorithm are dynamically adjusted based on the collected temperature change rate and temperature overshoot data, specifically as follows: Establish a parameter adjustment mapping table that includes proportional coefficient, integral coefficient, and derivative coefficient. The inputs to the mapping table are the characteristic value of the rate of temperature change and the absolute temperature overshoot. From the curve of the temperature change rate over time, feature values ​​are extracted, including the initial change rate, the maximum change rate before steady state, and the change rate corresponding to when the internal temperature value is continuously within the preset error allowable dead zone range centered on the preset target temperature value for a period of time, which is taken as the change rate when steady state is reached. The extracted temperature change rate feature value and the absolute temperature overshoot are used as input to query the parameter adjustment mapping table; Based on the query results, obtain the recommended adjustment amounts for the proportional coefficient, integral coefficient, and derivative coefficient for the dynamic response of the current control cycle; The initial proportional coefficient, initial integral coefficient, and initial derivative coefficient in the preset PID control algorithm are respectively calculated with the corresponding recommended adjustment amount to generate updated proportional coefficient, updated integral coefficient, and updated derivative coefficient.

6. The intelligent temperature control method for a low-temperature thermostat based on PID control according to claim 5, characterized in that, The initial proportional coefficient, initial integral coefficient, and initial derivative coefficient in the preset PID control algorithm are respectively calculated with the corresponding recommended adjustment amount to generate updated proportional coefficient, updated integral coefficient, and updated derivative coefficient, specifically: Boundary checks are performed on the recommended proportional coefficient adjustment, recommended integral coefficient adjustment, and recommended differential coefficient adjustment to ensure that the adjusted coefficient values ​​are within the preset coefficient boundary threshold range. If the recommended adjustment amount causes the adjusted coefficient to exceed the preset range, then the coefficient will be adjusted to the corresponding range boundary value. For the recommended adjustment amount that is within the preset range, the initial proportional coefficient is added to the recommended proportional coefficient adjustment amount to obtain the updated proportional coefficient; Add the recommended integral coefficient adjustment amount to the initial integral coefficient to obtain the updated integral coefficient; The updated differential coefficients are obtained by adding the recommended differential coefficient adjustment amount to the initial differential coefficients; The updated proportional coefficient, integral coefficient, and derivative coefficient are stored and used to replace the initial parameters in the preset PID control algorithm.

7. The intelligent temperature control method for a low-temperature thermostat based on PID control according to claim 2, characterized in that, The calculation of the rate of change of the current temperature deviation over time, and the obtaining of the differential control component based on the initial differential coefficients, specifically involves: The internal temperature values ​​of the low-temperature thermostat are continuously collected at multiple time points with a preset sampling period to form a temperature deviation time series. The central difference algorithm is applied to the temperature deviation time series to calculate the rate of change of temperature deviation at each sampling time. The calculated temperature deviation change rate is multiplied by the initial differential coefficient to obtain the corresponding differential control component value. The values ​​of the differential control components from multiple consecutive sampling periods are subjected to moving average filtering to eliminate fluctuations caused by measurement noise. The filtered differential control component value is output as the effective differential control component for the current control cycle.

8. The intelligent temperature control method for a low-temperature thermostat based on PID control according to claim 4, characterized in that, The process of differentiating the real-time internal temperature change curve to calculate the temperature change rate at each sampling moment is as follows: The real-time internal temperature change curve is processed using the five-point numerical differential method, and the temperature change rate is calculated using the temperature values ​​of the current sampling time and the two sampling points before and after it. Kalman filtering is applied to the calculated raw temperature change rate data to suppress the effects of measurement noise and anomalous jumps. Establish a confidence assessment mechanism for the rate of temperature change, conduct a reasonableness test on the filtered rate of temperature change data, and remove data points whose difference from adjacent sampling points exceeds the preset rate of change jump threshold; The filtered and verified temperature change rate data are stored in chronological order to form a complete time series curve of temperature change rate.

9. The intelligent temperature control method for a low-temperature thermostat based on PID control according to claim 1, characterized in that, The method further includes a step of constraining the state of the cryogenic thermostat actuator after generating the first corrected control output value: Obtain the maximum allowable power of the heater and the maximum allowable power of the refrigerator for the cryostat; The first corrected control output value is converted into the desired power demand for the heater or cooler; The expected power demand is compared with the maximum allowable power. If the expected power demand exceeds the maximum allowable power, the output is limited to the maximum allowable power, and a second corrected control output value is generated. If the expected power demand does not exceed the maximum allowable power, the first corrective control output value is directly used as the second corrective control output value. The second corrected control output value is used to convert into a drive signal.

Images