Control method and system of cold source module in heat exchange test system and storage medium
By identifying temperature disturbances through discrete Kalman filtering and feature calculation, the optimal adjustment amount is generated to control the cold source module, which solves the problem of low temperature control accuracy of the cold source module and achieves fast response and high-precision temperature regulation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANGHAI EXXON CO LTD
- Filing Date
- 2026-04-14
- Publication Date
- 2026-07-14
AI Technical Summary
In existing technologies, the temperature control accuracy of cold source modules is low. In particular, when faced with small-amplitude, high-frequency and sudden disturbances, the PID algorithm cannot respond effectively, resulting in temperature control lag and insufficient accuracy.
Discrete Kalman filtering is used to filter real-time multidimensional signals to generate real-time liquid supply temperature and actuator feedback values. Temperature disturbance parameters and types are identified through feature calculation and classification. The temperature regulation response model is controlled to generate the fastest response parameter combination and the optimal adjustment amount, which is then allocated to the actuator for precise adjustment.
The temperature control accuracy of the cold source module has been improved, ensuring rapid response under small-amplitude, high-frequency and sudden disturbances, reducing the motion inertia of the actuator and the lag of the traditional compensation mechanism, and achieving higher temperature control accuracy.
Smart Images
Figure CN122018602B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of cold source module control, and in particular to control methods, systems and storage media for cold source modules in heat exchange testing systems. Background Technology
[0002] Precise temperature control of the cold source module is crucial for ensuring accurate reproduction of test results and real-world operating conditions in liquid cooling testing.
[0003] In related technologies, the liquid supply temperature control of the cold source module usually uses a PID algorithm as the control core. By detecting the liquid supply temperature, the difference between the liquid supply temperature and the set temperature is calculated to obtain a temperature deviation signal. The temperature deviation signal is then input into the PID algorithm, and the control quantity is obtained through proportional, integral and derivative operations. The control quantity is then output to the corresponding actuator, such as a variable frequency compressor or electronic expansion valve. In this way, the liquid supply temperature is changed through the control of the actuator, so that the liquid supply temperature converges to the set temperature.
[0004] Regarding the aforementioned technologies, using the PID algorithm as the control core to control the liquid supply temperature of the cold source module is essentially a post-compensation temperature control mechanism. When small-amplitude, high-frequency, and sudden disturbances occur in the liquid supply temperature, small-amplitude disturbances are easily filtered out. When high-frequency interference occurs, the inertia of the actuator causes the adjustment to lag. When sudden disturbances occur, the PID algorithm can only compensate when a temperature peak occurs, resulting in low temperature control accuracy of the cold source module, and there is still room for improvement. Summary of the Invention
[0005] To improve the temperature control accuracy of the cold source module, this application provides a control method, system, and storage medium for the cold source module in a heat exchange testing system.
[0006] Firstly, this application provides a control method for the cold source module in a heat exchange testing system, employing the following technical solution: The control method for the cold source module in the heat exchange testing system includes: Acquire real-time multidimensional signals from the cold source module; The real-time multidimensional signal is filtered based on a preset discrete Kalman filter to generate real-time liquid supply temperature and real-time actuator feedback value. Feature calculation and classification are performed based on real-time liquid supply temperature and real-time actuator feedback values to generate temperature disturbance parameters and temperature disturbance types; Based on the temperature disturbance parameters and temperature disturbance type, the preset temperature regulation response model is used to analyze the response speed of preset candidate regulation parameter combinations in order to generate the fastest response parameter combination and the optimal regulation amount. The preset actuator is controlled to perform the operation based on the optimal adjustment amount.
[0007] Optionally, the steps of performing feature calculation and classification based on real-time liquid supply temperature and real-time actuator feedback values to generate temperature disturbance parameters and temperature disturbance types include: The disturbance is extracted based on the real-time liquid supply temperature and the real-time actuator feedback value to generate the temperature disturbance. The time-domain characteristics of the temperature disturbance are calculated to generate the temperature disturbance amplitude and the rate of change of temperature disturbance; The temperature disturbance is analyzed in the frequency domain to generate the temperature disturbance frequency. The temperature disturbance amplitude, the rate of change of temperature disturbance, and the frequency of temperature disturbance are correlated to generate temperature disturbance parameters; The temperature disturbance type is determined by searching the preset disturbance classification relationship based on the temperature disturbance parameters.
[0008] Optionally, the step of extracting disturbance quantities based on real-time liquid supply temperature and real-time actuator feedback values to generate temperature disturbance quantities includes: The state transition matrix, control input matrix, observation matrix, and posterior state estimate are determined based on discrete Kalman filtering. Temperature prediction is performed based on the state transition matrix, posterior state estimate, control input matrix, and real-time actuator feedback value to generate a predicted temperature vector. The predicted temperature vector is mapped based on the observation matrix to generate the predicted liquid supply temperature; The difference between the predicted supply temperature and the real-time supply temperature is calculated to generate the temperature disturbance.
[0009] Optionally, the step of analyzing the response speed of a preset candidate control parameter combination based on the temperature disturbance parameters and the type of temperature disturbance to generate the fastest response parameter combination and the optimal control amount includes: The sequence of liquid supply temperature changes was collected based on candidate combinations of adjustment parameters; Feature extraction is performed based on the liquid supply temperature change sequence to generate inertial time feature parameters, lag time feature parameters, and static parameter gain; Based on the temperature control response model, the inertial time characteristic parameters and hysteresis time characteristic parameters are calculated to generate the coarse adjustment time and fine adjustment time of the liquid supply temperature. The preset adjustment decision rules are constrained according to the type of temperature disturbance to generate constrained decision rules; Based on the constraint decision rules, the coarse adjustment time of the liquid supply temperature, and the fine adjustment time of the liquid supply temperature, candidate adjustment parameter combinations and parameter static gains are screened to determine the fastest response parameter combination and the best static gain. Calculate the quotient of the temperature perturbation parameter and the optimal static gain to generate the optimal adjustment.
[0010] Optionally, the expression for the temperature control response model is: ; ; In the formula, For the coarse adjustment of the liquid supply temperature, The lag time characteristic parameter, For inertial time characteristic parameters, This is the preset natural logarithm constant.
[0011] Optionally, the steps for controlling the preset actuator to perform according to the optimal adjustment amount include: The optimal adjustment amount is allocated to the actuator according to the preset adjustment amount allocation rule to generate transient adjustment commands and basic adjustment commands; Dead-zone compensation is performed on transient adjustment commands to generate compensated transient commands; The system controls the corresponding actuators to perform operations based on the basic adjustment commands and the compensation transient commands, and collects the steady-state temperature deviation value. The transient control command is updated based on the steady-state deviation value, and the corresponding actuator is controlled to execute the updated transient control command.
[0012] Optionally, the step of performing dead-zone compensation on the transient control command to generate a compensated transient command includes: The transient control command is compensated according to the preset segmented compensation model of the control amount to generate the basic compensation command; Collect heating adjustment commands; The basic compensation command and heating adjustment command are analyzed according to the preset cold and heat interlock rules to determine the compensation transient command.
[0013] Optionally, the step of updating the transient control command based on the steady-state deviation value includes: The steady-state deviation value is analyzed according to the preset integral separation rule to generate integral separation coefficients; The preset steady-state control algorithm is adjusted based on the integral separation coefficient to generate an adjusted steady-state algorithm; The steady-state deviation value is calculated based on the steady-state adjustment algorithm to generate steady-state control commands, and the transient adjustment commands are updated based on the steady-state control commands.
[0014] Secondly, this application provides a control system for the cold source module in a heat exchange testing system, which adopts the following technical solution: The control system for the cold source module in the heat exchange testing system includes: The acquisition module is used to acquire real-time multidimensional signals; A memory for storing a program for controlling the cold source module in the heat exchange test system as described in any of the preceding claims; The processor and the program in the memory can be loaded and executed by the processor to implement the control method of the cold source module in the heat exchange test system as described in any of the above.
[0015] Thirdly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates improving the temperature control accuracy of the cold source module, and adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and execute the control method of the cold source module in any of the above-described heat exchange test systems.
[0016] In summary, this application includes at least one of the following beneficial technical effects: 1. Real-time multidimensional signals are filtered by discrete Kalman filtering to obtain real-time liquid supply temperature and real-time actuator feedback value. Compared with traditional filtering algorithms, this improves the accuracy of identifying small-amplitude disturbances. Then, based on the real-time liquid supply temperature and real-time actuator feedback value, temperature disturbance parameters and temperature disturbance types are determined. The temperature regulation response model is then controlled by the temperature disturbance parameters and temperature disturbance types to analyze the response speed of candidate adjustment parameter combinations, obtaining the fastest response parameter combination and the optimal adjustment amount. This compensates for the temperature control lag caused by the actuator's motion inertia and the ex-post compensation mechanism of traditional compensation algorithms, thereby improving the temperature control accuracy of the cold source module. 2. Temperature disturbance is obtained by extracting disturbance from real-time liquid supply temperature and real-time actuator feedback value. Then, temperature disturbance parameters including temperature disturbance amplitude, temperature disturbance change rate and temperature disturbance frequency are determined based on the temperature disturbance. The temperature disturbance type is found in the disturbance classification relationship based on the temperature disturbance parameters to ensure the accuracy and efficiency of temperature disturbance type determination. 3. By distributing the optimal adjustment amount to the actuators respectively, transient adjustment commands and basic adjustment commands are obtained. This allows for independent control of actuators with fast response speeds and those with slow response speeds. This avoids the lag of actuators with smaller inertia caused by actuators with larger motion inertia. Furthermore, dead-zone compensation is applied to the transient adjustment commands to overcome the mechanical dead zone of the actuators. This enables the control of the actuators to produce effective suppression of motion, thereby improving the accuracy of temperature control. Attached Figure Description
[0017] Figure 1 This is a flowchart of the control method for the cold source module in the heat exchange test system in the embodiments of this application.
[0018] Figure 2This is a flowchart of the steps in this application embodiment to perform feature calculation and classification based on real-time liquid supply temperature and real-time actuator feedback value to generate temperature disturbance parameters and temperature disturbance types.
[0019] Figure 3 This is a flowchart of the steps in this application embodiment to extract disturbance quantities based on real-time liquid supply temperature and real-time actuator feedback values to generate temperature disturbance quantities.
[0020] Figure 4 This is a flowchart illustrating the steps in this application embodiment of controlling a preset temperature regulation response model based on temperature disturbance parameters and temperature disturbance type to analyze the response speed of preset candidate regulation parameter combinations in order to generate the fastest response parameter combination and the optimal regulation amount.
[0021] Figure 5 This is a flowchart of the steps in this application embodiment to control the preset actuator to perform according to the optimal adjustment amount.
[0022] Figure 6 This is a flowchart of the steps in this application embodiment to perform dead-zone compensation on transient adjustment commands to generate compensated transient commands.
[0023] Figure 7 This is a flowchart of the steps for updating the transient adjustment command based on the steady-state deviation value in an embodiment of this application. Detailed Implementation
[0024] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 7 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.
[0025] Reference Figure 1 This application discloses a control method for the cold source module in a heat exchange testing system, including the following steps: Step S100: Acquire real-time multidimensional signals from the cold source module.
[0026] Among them, the real-time multidimensional signal refers to the multidimensional data related to temperature control in the cold source module, including the liquid supply detection temperature and the real-time actuator feedback value. It is detected by the high-precision temperature sensor installed at the liquid supply port of the cold source module and the feedback unit built into the actuator. The detection frequency is set to 100Hz to ensure that all characteristic signals of high-frequency temperature disturbances can be collected.
[0027] The liquid supply detection temperature refers to the real-time detection value of the liquid supply temperature of the cold source module. It is detected by a high-precision temperature sensor at the liquid supply port of the cold source module and sent to the processing terminal. The high-precision temperature sensor can be a Class A platinum resistance temperature sensor with a measurement accuracy of ±0.01℃, to ensure that small-amplitude and high-frequency temperature disturbance signals can be collected at the acquisition frequency.
[0028] Real-time actuator feedback values refer to the feedback values of different actuators, such as opening feedback values and power feedback values, which are sent to the processing terminal by the feedback unit built into the actuator.
[0029] By collecting real-time multidimensional signals from the cold source module, data support is provided for subsequent identification of the type of temperature disturbance in the cold source module and for temperature regulation of the cold source module.
[0030] Step S101: Filter the real-time multidimensional signal based on the preset discrete Kalman filter to generate the real-time liquid supply temperature and the real-time actuator feedback value.
[0031] Discrete Kalman filtering is an algorithm for reducing noise in real-time multidimensional signals of liquid supply temperature detection. The specific processing steps of discrete Kalman filtering are as follows: First, establish the state-space equations: Including the equation of state: .
[0032] In the formula, For the first The system state vector at each sampling moment reflects the true state and trend of the liquid supply temperature.
[0033] This is the state transition matrix, reflecting the transition relationship between system states at adjacent time points. In this embodiment, the sampling frequency is 100Hz, corresponding to a sampling period of 0.01s. Therefore... .
[0034] To control the input matrix, reflecting the impact of actuator adjustments on the system state, , The average static gain of the actuator adjustment parameters is extracted from the sequence of changes in the liquid supply temperature by applying a step excitation to the actuator.
[0035] For the first The total system control input at each sampling moment is obtained by weighted summation of the execution instructions of the actuator.
[0036] The system process noise follows a Gaussian distribution.
[0037] Including observation equations: .
[0038] For the first The observed value at each sampling time is the liquid supply detection temperature.
[0039] This is an observation matrix used to map the system state to observable actual temperature values. In the embodiments of this application, .
[0040] The noise measured by the sensor follows a Gaussian distribution.
[0041] Then, a discrete Kalman filter closed-loop iterative calculation is performed, once per sampling period, specifically including: The first step is to predict the prior state and obtain the estimated prior state value. The specific formula is as follows: .
[0042] In the formula, For the first The prior state estimate at each sampling time, i.e., based on The posterior state estimate at the sampling time n is related to the nth sampling time. The prediction results at each sampling time point.
[0043] for The posterior state estimate at each sampling time.
[0044] The second step is to predict the prior error covariance to obtain the prior error covariance matrix. The specific formula is as follows: .
[0045] In the formula, For the first The prior error covariance matrix at each sampling time reflects the magnitude of the error in the prior state estimate.
[0046] for The posterior error covariance matrix at each sampling time.
[0047] The system process noise covariance matrix is matched with the dimension of the system state vector.
[0048] The third step is to calculate the Kalman gain, using the following formula: .
[0049] In the formula, For the first The Kalman gain at each sampling time is used to balance the weights of prior predictions and current observations.
[0050] The noise covariance matrix of the sensor measurement is matched with the dimension of the system state vector.
[0051] The fourth step is to update the posterior state and obtain the estimated posterior state value. The specific formula is as follows: .
[0052] In the formula, For the first The posterior state estimate at each sampling time is the denoised liquid supply temperature.
[0053] The fifth step is to update the posterior covariance to obtain the posterior covariance matrix. The specific formula is as follows: .
[0054] In the formula, For the first The posterior covariance matrix at each sampling time provides input for the iterative calculation in the next cycle.
[0055] It is an identity matrix, matching the dimension of the state vector.
[0056] Real-time liquid supply temperature refers to the denoised liquid supply temperature, which is obtained by the processing terminal after denoising the liquid supply detection temperature in the real-time multidimensional signal through discrete Kalman filtering. Compared with traditional low-pass filtering, the 100Hz high-frequency filter combined with discrete Kalman filtering completely retains small temperature changes with amplitudes as low as ±0.05℃ while removing data noise, ensuring the accuracy of temperature change type identification and solving the blind spot of small temperature change identification.
[0057] The suppression of the real-time actuator feedback value compared to the real-time actuator feedback value disclosed in step S100 will not be elaborated here.
[0058] Step S102: Perform feature calculation and classification based on real-time liquid supply temperature and real-time actuator feedback value to generate temperature disturbance parameters and temperature disturbance type.
[0059] Among them, temperature disturbance parameters refer to characteristic parameters describing the temperature disturbance pattern, including temperature disturbance amplitude, temperature disturbance rate of change, and temperature disturbance frequency; temperature disturbance type refers to the specific type of temperature disturbance, including small-amplitude disturbance, high-frequency disturbance, and sudden disturbance, which is obtained by the processing terminal through feature calculation and classification based on real-time liquid supply temperature and real-time actuator feedback values. Specific methods are described in [reference needed]. Figure 2 The steps.
[0060] Step S103: Based on the temperature disturbance parameters and temperature disturbance type, the preset temperature regulation response model is used to analyze the response speed of the preset candidate regulation parameter combinations in order to generate the fastest response parameter combination and the optimal regulation amount.
[0061] Among them, the temperature control response model refers to the model that calculates the response time of candidate control parameter combinations to temperature control, and the specific formula is as follows: ; .
[0062] In the formula, For the coarse adjustment of the liquid supply temperature, The lag time characteristic parameter, For inertial time characteristic parameters, This is the preset natural logarithm constant.
[0063] Candidate adjustment parameter combinations refer to the parameter combinations of the actuator adjusted according to temperature disturbances. In the embodiments of this application, five combinations are described, including: the first is the opening adjustment amount of the mixed flow proportional valve; the second is the adjustment amount of the precision electric heating power; the third is the adjustment amount of the micro opening of the electronic expansion valve; the fourth is the coordinated adjustment of the mixed flow proportional valve and the electric heating; and the fifth is the coordinated adjustment of the mixed flow proportional valve and the electronic expansion valve.
[0064] The fastest response parameter combination refers to the candidate control parameter combination with the fastest temperature response. The optimal adjustment amount refers to the adjustment amount of different actuators in the fastest response parameter combination. It is obtained by the processing terminal after analyzing the response speed of the candidate control parameter combination based on the temperature disturbance parameters and the temperature disturbance type control temperature regulation response model, thereby ensuring that the temperature regulation response speed meets the requirements. For specific methods, refer to [link to relevant documentation]. Figure 4 The steps.
[0065] Step S104: Control the preset actuator to perform according to the optimal adjustment amount.
[0066] Among them, the actuator refers to the actuator related to temperature control in the cold source module, including two types: fast actuator and slow actuator. Fast actuators include mixed flow proportional valves, precision electric heaters, and miniature electronic expansion valves, while slow actuators include variable frequency compressors, main circulating water pumps, and main circuit electronic expansion valves. Fast actuators are used to eliminate transient temperature disturbances, while slow actuators are used to stabilize the basic cooling capacity.
[0067] After determining the optimal adjustment amount, the processing terminal controls the corresponding actuator to adjust according to the optimal adjustment amount, thereby eliminating disturbances in the liquid supply temperature and ensuring its stability. For specific methods, refer to... Figure 5 The steps.
[0068] Reference Figure 2 The steps for generating temperature disturbance parameters and types, based on real-time liquid supply temperature and real-time actuator feedback values, include feature calculation and classification. Step S200: Extract disturbance quantities based on real-time liquid supply temperature and real-time actuator feedback values to generate temperature disturbance quantities.
[0069] Among them, temperature disturbance refers to the sum of factors that cause the supply liquid temperature to deviate from the set temperature, including load thermal disturbance, external heat penetration disturbance, and system parameter drift disturbance, etc. It is extracted by the processing terminal based on the real-time supply liquid temperature and the real-time actuator feedback value. The specific method is as follows: Figure 3 The steps.
[0070] Step S201: Perform time-domain characteristic calculation on the temperature disturbance to generate the temperature disturbance amplitude and the temperature disturbance change rate.
[0071] Among them, the temperature disturbance amplitude refers to the amplitude of the temperature disturbance, which reflects the degree of deviation of the supply liquid temperature. It is used to distinguish whether the temperature disturbance is a small disturbance or a large disturbance, and is obtained by the processing terminal taking the absolute value of the temperature disturbance.
[0072] The temperature disturbance change rate refers to the speed at which the temperature disturbance changes. It is used to reflect the suddenness of the temperature disturbance. The larger the temperature disturbance change rate, the faster the temperature disturbance changes, and therefore the stronger the suddenness of the temperature disturbance. It is obtained by the processing terminal calculating the difference in temperature disturbance between adjacent sampling times and dividing it by the sampling period.
[0073] Step S202: Calculate the frequency domain characteristics of the temperature disturbance to generate the temperature disturbance frequency.
[0074] Among them, the temperature disturbance frequency refers to the fluctuation frequency of the temperature disturbance, which reflects the speed of the temperature disturbance fluctuation and is used to distinguish whether the temperature disturbance is a high-frequency disturbance or a low-frequency disturbance. By performing frequency domain analysis on the temperature disturbance amount of 200 consecutive sampling periods through fast Fourier transform, the spectral amplitude sequence is obtained, and the frequency point with the largest spectral amplitude is selected as the temperature disturbance frequency.
[0075] Step S203: Correlate the temperature disturbance amplitude, temperature disturbance rate of change, and temperature disturbance frequency to generate temperature disturbance parameters.
[0076] In this step, the temperature disturbance parameters are the same as those in step S102. The processing terminal organizes the temperature disturbance amplitude, temperature disturbance change rate, and temperature disturbance frequency into a data vector according to the set data order, so as to facilitate accurate retrieval in subsequent use.
[0077] Step S204: Find the temperature disturbance type in the preset disturbance classification relationship based on the temperature disturbance parameters.
[0078] The disturbance classification relationship refers to the correspondence between different temperature disturbance parameters and disturbance types, including: when the temperature disturbance amplitude is less than 0.1 degrees Celsius, the temperature disturbance change rate is less than 0.5 degrees Celsius per second, and the temperature disturbance frequency is less than 1 Hz, it is a small-amplitude disturbance; when the temperature disturbance frequency is greater than 1 Hz, it is a high-frequency disturbance; and when the temperature disturbance change rate is greater than 0.5 degrees Celsius per second, it is a sudden disturbance. High-frequency and sudden disturbances are given priority.
[0079] The temperature disturbance type in this step is the same as the temperature disturbance type in step S102, and is determined by the processing terminal after comparing the temperature disturbance parameters in the mapping table corresponding to the disturbance classification relationship.
[0080] Reference Figure 3 The steps for extracting disturbance quantities based on real-time liquid supply temperature and real-time actuator feedback values to generate temperature disturbance quantities include: Step S300: Determine the state transition matrix, control input matrix, observation matrix, and posterior state estimate based on discrete Kalman filtering.
[0081] In this step, the state transition matrix, control input matrix, observation matrix, and posterior state estimate are consistent with those disclosed in step S101, and are obtained directly by the processing terminal. They will not be elaborated here.
[0082] Step S301: Based on the state transition matrix, posterior state estimate, control input matrix and real-time actuator feedback value, perform temperature prediction to generate a predicted temperature vector.
[0083] The predicted temperature vector refers to the temperature vector predicted based on the state vector of the previous moment and the execution action of the current moment. The liquid supply temperature and temperature change rate of the previous moment are calculated by the processing terminal based on the state transition matrix, the posterior state estimate, the control input matrix and the feedback value of the real-time actuator, combined with the prior state prediction process in step S101.
[0084] Step S302: Map the predicted temperature vector based on the observation matrix to generate the predicted liquid supply temperature.
[0085] The predicted liquid supply temperature refers to the temperature predicted based on the state vector of the previous moment and the execution action of the current moment. It is calculated by the processing terminal based on the observation matrix and the predicted temperature vector, combined with the observation equation in step S101.
[0086] Step S303: Calculate the difference between the predicted liquid supply temperature and the real-time liquid supply temperature to generate the temperature disturbance.
[0087] The temperature disturbance in this step is the same as that in step S200, and is obtained by the processing terminal by calculating the difference between the predicted liquid supply temperature and the real-time liquid supply temperature.
[0088] Reference Figure 4 The steps for analyzing the response speed of preset candidate adjustment parameter combinations based on temperature disturbance parameters and temperature disturbance type to generate the fastest response parameter combination and optimal adjustment amount include: Step S400: Collect the liquid supply temperature change sequence based on the candidate adjustment parameter combination.
[0089] Among them, the liquid supply temperature change sequence refers to the temperature change sequence when temperature is regulated by candidate adjustment parameter combinations. In each 10 sampling periods, a step excitation with a duration of 5 inertial time constants is applied to each candidate adjustment parameter combination, and the excitation amplitude does not exceed 0.2. The change value of liquid supply temperature is collected synchronously to form the liquid supply temperature change sequence of candidate adjustment parameter combinations.
[0090] Step S401: Perform feature extraction based on the liquid supply temperature change sequence to generate inertial time feature parameters, hysteresis time feature parameters, and static gain of parameters.
[0091] Among them, the inertial time characteristic parameter refers to the time required for the liquid supply temperature to adjust from the initial value to a steady-state value of 63.2%, the hysteresis characteristic time refers to the time from the execution of the actuator to the appearance of a detectable change in the liquid supply temperature, and the parameter static gain refers to the steady-state change of the liquid supply temperature under step excitation. It is obtained by the processing terminal using a two-point method to extract features from the liquid supply temperature change sequence. The specific method is as follows: at the steady-state value of the liquid supply temperature change sequence, two time points are determined where the characteristic value reaches the steady-state value of 39.3% and 63.2%. The difference between the two time points is calculated and multiplied by 1.5 to obtain the inertial time characteristic parameter. The difference between the product of the time points corresponding to the steady-state value of 1.5 and 39.3% and the product of the time points corresponding to the steady-state value of 0.5 and 63.2% is calculated to obtain the hysteresis characteristic parameter. The quotient of the steady-state value and the amplitude of the step excitation is calculated to obtain the parameter static gain.
[0092] Step S402: Calculate the inertial time characteristic parameters and hysteresis time characteristic parameters based on the temperature control response model to generate the coarse adjustment time and fine adjustment time of the liquid supply temperature.
[0093] Among them, the coarse adjustment time of the liquid supply temperature refers to the time required to adjust the liquid supply temperature to 90%, reflecting the speed of temperature response under the combined control of parameters; the fine adjustment time of the liquid supply temperature refers to the time required for the temperature error to be within ±2% after the liquid supply temperature is adjusted, reflecting the speed of temperature convergence under the combined control of parameters; it is calculated by the processing terminal by inputting the inertial time characteristic parameters and lag time characteristic parameters into the temperature control response model.
[0094] Step S403: Constrain the preset adjustment decision rules according to the temperature disturbance type to generate constrained decision rules.
[0095] Among them, the adjustment decision rule refers to the basic rule for selecting the actual use parameter combination, which includes two priorities. The first is to sort the coarse adjustment time of the liquid supply temperature of all candidate adjustment parameter combinations and select the candidate adjustment parameter combination with the minimum coarse adjustment time of the liquid supply temperature as the actual use parameter combination. The second is that if the difference between the minimum coarse adjustment time of the liquid supply temperature of multiple candidate adjustment parameter combinations is less than 0.05, then the candidate adjustment parameter combination with the minimum fine adjustment time of the liquid supply temperature is selected as the actual use parameter combination.
[0096] Constraint decision rules refer to parameter decision rules modified according to the type of disturbance. Based on the adjustment decision rules, if the temperature disturbance type is a small disturbance, a single-dimensional adjustment parameter is preferred to reduce the coupling effect caused by multi-parameter adjustment; if the temperature disturbance type is a high-frequency and sudden disturbance, a combination of multi-dimensional adjustment parameters is preferred to ensure the efficiency of adjustment.
[0097] Step S404: Based on the constraint decision rules, the coarse adjustment time of the liquid supply temperature and the fine adjustment time of the liquid supply temperature, the candidate adjustment parameter combinations and the static gain of the parameters are screened to determine the fastest response parameter combination and the optimal static gain.
[0098] Among them, the fastest response parameter combination refers to the candidate adjustment parameter combination actually used, and the best static gain refers to the parameter static gain corresponding to the candidate adjustment parameter combination actually used. It is obtained by the processing terminal through screening the candidate adjustment parameter combination and parameter static gain according to the constraint decision rules, the coarse adjustment time of the liquid supply temperature, and the fine adjustment time of the liquid supply temperature. It will not be elaborated here.
[0099] Step S405: Calculate the quotient of the temperature disturbance parameter and the optimal static gain to generate the optimal adjustment amount.
[0100] In this step, the optimal adjustment amount is consistent with the optimal adjustment amount in step S103. It is obtained by the processing terminal calculating the opposite of the quotient of the temperature disturbance parameter and the optimal static gain, ensuring that the adjustment direction of the optimal adjustment amount is opposite to the disturbance direction.
[0101] Reference Figure 5 The steps for controlling the preset actuator according to the optimal adjustment amount include: Step S500: Allocate the optimal adjustment amount to the actuator according to the preset adjustment amount allocation rule to generate transient adjustment command and basic adjustment command.
[0102] Among them, the adjustment allocation rule refers to the rule for allocating the adjustment amount to different actuators, including: allocating the optimal adjustment amount to the fast actuator to form a transient adjustment command, while the basic adjustment command of the slow actuator is obtained by collecting the temperature deviation at the current time as the input of the PI algorithm, and then performing proportional and integral calculations respectively.
[0103] Transient adjustment commands are control commands for fast actuators, used to eliminate the effects of transient temperature disturbances. Basic adjustment commands are control commands for slow actuators, ensuring that the temperature tends to the set temperature. The optimal adjustment amount is allocated by the processing terminal according to the adjustment amount allocation rules.
[0104] Step S501: Perform dead-zone compensation on the transient adjustment command to generate a compensated transient command.
[0105] Among them, the compensated transient command refers to the control command obtained after compensating for the mechanical dead zone of the fast-acting mechanism. The specific method is described in [reference needed]. Figure 6 The steps.
[0106] Step S502: Control the corresponding actuator to execute according to the basic adjustment command and the compensation transient command, and collect the steady-state temperature deviation value.
[0107] Specifically, after determining the basic adjustment command and the compensation transient command, the basic adjustment command is sent to the slow actuator, which causes the slow actuator to adjust the temperature towards the set temperature. The compensation transient command is sent to the fast actuator, which causes the fast actuator to eliminate the temperature disturbance that occurs during the temperature adjustment process of the slow actuator, ensuring temperature stability. The fast actuator also detects the steady-state temperature deviation value, providing data support for the subsequent update of the transient command.
[0108] The steady-state temperature deviation value refers to the deviation between the actuator's adjusted liquid supply temperature and the set temperature. It is obtained by detecting the temperature with a high-precision temperature sensor and subtracting it from the set value.
[0109] Step S503: Update the transient adjustment command based on the steady-state deviation value, and control the corresponding actuator to execute the updated transient adjustment command.
[0110] After determining the steady-state deviation value, the processing terminal updates the transient adjustment command based on the steady-state deviation value. The specific method is described in [reference needed]. Figure 7 The steps are implemented, and the corresponding actuators are controlled by the updated transient adjustment commands to ensure that the liquid supply temperature can quickly converge to the set temperature after the temperature disturbance is eliminated, thereby eliminating steady-state error.
[0111] Reference Figure 6 The steps for performing dead-zone compensation on transient control commands to generate compensated transient commands include: Step S600: Compensate the transient control command according to the preset segmented compensation model of the adjustment amount to generate the basic compensation command.
[0112] The segmented adjustment model refers to a model that compensates for the adjustment amount of transient adjustment commands based on the mechanical dead zone of the actuator. When the adjustment amount of the transient adjustment command is 0, the dead zone compensation amount is 0. When the adjustment amount of the transient adjustment command is greater than 0, the sum of the actuator dead zone compensation amount and the adjustment amount is calculated to obtain the basic compensation command. When the adjustment amount of the transient adjustment command is less than 0, the difference between the actuator dead zone compensation amount and the adjustment amount is calculated to obtain the basic compensation command. The actuator dead zone compensation amount is obtained by applying a positive step signal from small to large to the actuator, while simultaneously collecting the liquid supply temperature. When the temperature changes continuously, the amplitude of the current step signal is recorded and averaged.
[0113] Step S601: Collect heating adjustment command.
[0114] Among them, the heating adjustment command refers to the control command for precision electric heating. A positive value increases the heating power, a negative value decreases the heating power, and 0 indicates standby. It is obtained by the processing terminal from the optimal adjustment value.
[0115] Step S602: Analyze the basic compensation command and heating adjustment command according to the preset cold and heat interlock rules to determine the compensation transient command.
[0116] Among them, the cold and heat interlock rule refers to the rule to avoid conflict between cold and heat regulation. Specifically, the product of the regulation amount corresponding to the basic compensation command and the regulation amount corresponding to the heating regulation command cannot be greater than 0, otherwise it will lead to heating on one side and cooling on the other. If it is greater than 0, the command with the largest absolute value of the two is taken as the compensation transient command. If it is less than 0, the basic compensation command is retained as the compensation transient command.
[0117] The compensation transient command in this step is the same as the compensation transient command in step S501. It is obtained by the processing terminal after analyzing the basic compensation command and the heating adjustment command according to the cold and heat interlock rules, and will not be described in detail here.
[0118] Reference Figure 7 The steps for updating the transient control command based on the steady-state deviation value include: Step S700: Analyze the steady-state deviation value according to the preset integral separation rule to generate integral separation coefficients.
[0119] The integral separation rule refers to the rule of determining the integral separation coefficient based on the magnitude of the steady-state deviation value. When the absolute value of the steady-state deviation value is greater than 0.1 degrees Celsius, the integral separation coefficient is determined to be 1. At this time, the integral element participates in the update of the transient control command, thereby eliminating the steady-state error. When the absolute value of the steady-state deviation value is less than 0.1 degrees Celsius, the integral separation coefficient is determined to be 0. At this time, the integral element does not participate in the update of the transient control command, thereby avoiding overshoot and oscillation caused by integral saturation.
[0120] The integral separation coefficient refers to the coefficient used to control the integral term of the steady-state control algorithm. It includes 0 and 1 and is obtained by the processing terminal through analysis of the steady-state deviation value according to the integral separation rule. It will not be elaborated here.
[0121] Step S701: Adjust the preset steady-state control algorithm according to the integral separation coefficient to generate an adjusted steady-state algorithm.
[0122] Among them, the steady-state control algorithm refers to the model used to eliminate steady-state error. It adopts the PID algorithm, which includes proportional, integral and derivative terms, and takes the steady-state deviation value as input.
[0123] The steady-state adjustment algorithm refers to the steady-state control algorithm after adjusting the integral term. It is obtained by the processing terminal calculating the integral separation coefficient while keeping the integral, proportional and differential terms in the steady-state control algorithm unchanged.
[0124] Step S702: Calculate the steady-state deviation value based on the steady-state adjustment algorithm to generate a steady-state control command, and update the transient adjustment command with the steady-state control command.
[0125] Among them, the steady-state control command refers to the additional adjustment amount of the fast actuator when eliminating steady-state error. It is calculated by the processing terminal by inputting the steady-state deviation value into the steady-state adjustment algorithm.
[0126] After determining the steady-state control command, the adjustment amount corresponding to the steady-state control command is added to the adjustment amount corresponding to the transient adjustment command, thereby eliminating the steady-state error after the actuator is executed, and making the liquid supply temperature quickly converge to the set temperature.
[0127] Based on the same inventive concept, embodiments of this application provide a control system for the cold source module in a heat exchange testing system, including: The acquisition module is used to acquire real-time multidimensional signals, liquid supply temperature change sequences, steady-state temperature deviation values, and heating adjustment commands; The memory is used to store the program for controlling the cold source module in the heat exchange test system; The processor is a method for controlling the cold source module in a heat exchange test system by loading and executing programs in the memory.
[0128] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0129] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed to control a cold source module in a heat exchange test system.
[0130] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.
[0131] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the control method of the cold source module in the heat exchange test system.
[0132] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0133] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.
Claims
1. A control method for the cold source module in a heat exchange testing system, characterized in that, include: Acquire real-time multidimensional signals from the cold source module; The real-time multidimensional signal is filtered based on a preset discrete Kalman filter to generate real-time liquid supply temperature and real-time actuator feedback value. Feature calculation and classification are performed based on real-time liquid supply temperature and real-time actuator feedback values to generate temperature disturbance parameters and temperature disturbance types; Based on the temperature disturbance parameters and temperature disturbance type, the preset temperature regulation response model is used to analyze the response speed of the preset candidate regulation parameter combinations in order to generate the fastest response parameter combination and the optimal regulation amount. The preset actuator is controlled to perform the operation based on the optimal adjustment amount; The steps for generating temperature disturbance parameters and types by performing feature calculations and classification based on real-time liquid supply temperature and real-time actuator feedback values include: The disturbance is extracted based on the real-time liquid supply temperature and the real-time actuator feedback value to generate the temperature disturbance. The time-domain characteristics of the temperature disturbance are calculated to generate the temperature disturbance amplitude and the rate of change of temperature disturbance; The temperature disturbance is analyzed in the frequency domain to generate the temperature disturbance frequency. The temperature disturbance amplitude, the rate of change of temperature disturbance, and the frequency of temperature disturbance are correlated to generate temperature disturbance parameters; The temperature disturbance type is determined by searching the preset disturbance classification relationship based on the temperature disturbance parameters. The steps for analyzing the response speed of preset candidate control parameter combinations based on temperature disturbance parameters and temperature disturbance type to generate the fastest response parameter combination and optimal control amount include: The sequence of liquid supply temperature changes was collected based on candidate combinations of adjustment parameters; Feature extraction is performed based on the liquid supply temperature change sequence to generate inertial time feature parameters, lag time feature parameters, and static parameter gain; Based on the temperature control response model, the inertial time characteristic parameters and hysteresis time characteristic parameters are calculated to generate the coarse adjustment time and fine adjustment time of the liquid supply temperature. The preset adjustment decision rules are constrained according to the type of temperature disturbance to generate constrained decision rules; Based on the constraint decision rules, the coarse adjustment time of the liquid supply temperature, and the fine adjustment time of the liquid supply temperature, candidate adjustment parameter combinations and static gain of parameters are screened to determine the fastest response parameter combination and the best static gain. Calculate the quotient of the temperature disturbance parameter and the optimal static gain to generate the optimal adjustment amount; The steps for extracting disturbance quantities based on real-time liquid supply temperature and real-time actuator feedback values to generate temperature disturbance quantities include: The state transition matrix, control input matrix, observation matrix, and posterior state estimate are determined based on discrete Kalman filtering. Temperature prediction is performed based on the state transition matrix, posterior state estimate, control input matrix, and real-time actuator feedback value to generate a predicted temperature vector. The predicted temperature vector is mapped based on the observation matrix to generate the predicted liquid supply temperature; Calculate the difference between the predicted supply temperature and the real-time supply temperature to generate the temperature disturbance. The steps for controlling the preset actuator according to the optimal adjustment amount include: The optimal adjustment amount is allocated to the actuator according to the preset adjustment amount allocation rule to generate transient adjustment commands and basic adjustment commands; Dead-zone compensation is performed on transient adjustment commands to generate compensated transient commands; The system controls the corresponding actuators to perform operations based on the basic adjustment commands and the compensation transient commands, and collects the steady-state temperature deviation value. The transient control command is updated based on the steady-state deviation value, and the corresponding actuator is controlled to execute the updated transient control command.
2. The control method for the cold source module in the heat exchange testing system according to claim 1, characterized in that, The expression for the temperature control response model is: ； ； In the formula, For the coarse adjustment of the liquid supply temperature, The lag time characteristic parameter, For inertial time characteristic parameters, This is the preset natural logarithm constant.
3. The control method for the cold source module in the heat exchange testing system according to claim 1, characterized in that, The steps for performing dead-zone compensation on transient control commands to generate compensated transient commands include: The transient control command is compensated according to the preset segmented compensation model of the control amount to generate the basic compensation command; Collect heating adjustment commands; The basic compensation command and heating adjustment command are analyzed according to the preset cold and heat interlock rules to determine the compensation transient command.
4. The control method for the cold source module in the heat exchange testing system according to claim 1, characterized in that, The steps for updating transient control commands based on steady-state deviation values include: The steady-state deviation value is analyzed according to the preset integral separation rule to generate integral separation coefficients; The preset steady-state control algorithm is adjusted based on the integral separation coefficient to generate an adjusted steady-state algorithm; The steady-state deviation value is calculated based on the steady-state adjustment algorithm to generate steady-state control commands, and the transient adjustment commands are updated based on the steady-state control commands.
5. The control system for the cold source module in the heat exchange testing system, characterized in that, include: The acquisition module is used to acquire real-time multidimensional signals; A memory for storing a program for controlling the cold source module in the heat exchange test system as described in any one of claims 1 to 4; The processor and the program in the memory can be loaded and executed by the processor to implement the control method of the cold source module in the heat exchange test system as described in any one of claims 1 to 4.
6. A computer-readable storage medium, characterized in that, The system contains a computer program that can be loaded by a processor and execute the control method of the cold source module in the heat exchange test system as described in any one of claims 1 to 4.