Temperature control method and device and electronic equipment

Abstract

The invention belongs to the field of temperature control, and discloses a temperature control method and device and electronic equipment, and the method comprises the steps: obtaining a state space model of a temperature control system, determining a state estimation error and a state output slope of the state space model, and updating model parameters of the state space model according to the state estimation error and the state output slope, and performing temperature control by using the updated state space model. According to the method, the state estimation error and the state output slope can be accurately determined, and then model parameters are dynamically adjusted to reflect environment changes and system dynamics. The self-adaptive model updating mechanism remarkably improves the accuracy and the response speed of the control strategy, so that more stable and accurate temperature control is realized under variable operation conditions.

Description

[Technical field]

[0001] The present invention relates to the field of temperature control, and in particular to a temperature control method, device, electronic device and computer-readable storage medium. [Background technology]

[0002] In the field of high-precision temperature control, such as refrigeration of pharmaceutical products, precision chemical reaction processes, semiconductor processing, etc., accurate temperature control is crucial. Slight fluctuations in temperature may affect product quality, reaction efficiency, and even cause safety issues. Therefore, it is particularly important to develop a temperature control method that can adapt to environmental changes and provide high-precision control.

[0003] With the development of control theory, the Model Predictive Control (MPC) control strategy based on state-space model has attracted attention due to its application potential in multivariable, constrained and nonlinear systems.

[0004] MPC predicts future state changes by establishing a state space model of the system, and performs optimized control based on the prediction. However, in the process of implementing this application, the inventors found that there are at least the following technical problems in the prior art: the existing MPC control method may have limitations in some cases. In particular, when environmental conditions change, such as external temperature fluctuations, equipment load changes, or other operating conditions change, the originally established system model may no longer be accurate. These changes may cause model parameters, such as time constants and system gains, to no longer match the actual system behavior.

[0005] Therefore, how to improve the accuracy of fine temperature control is a technical problem that needs to be solved in this field. [Summary of the invention]

[0006] In order to effectively improve the problem of low accuracy of the temperature control method in the prior art, the present invention provides a temperature control method.

[0007] A temperature control method comprising:

[0008] Obtain a state space model of a temperature control system;

[0009] Determining a state estimation error and a state output slope of the state space model;

[0010] updating model parameters of the state space model according to the state estimation error and the state output slope;

[0011] Temperature control is performed using the updated state-space model.

[0012] Optionally, obtaining a state space model of the temperature control system includes:

[0013] The discrete state space model of the temperature control system is obtained as:

[0014] x(k+1)=Ax(k)+Bu(k)

[0015] Where x(k+1) is the system state vector at time k+1, A is the system matrix, x(k) is the system state vector at time k, B is the control matrix, and u(k) is the applied control input vector at time k;

[0016] The continuous state space model of the temperature control system is determined as:

[0017]

[0018] Among them, x(t) is the system output, u(t) is the control quantity of the system input, T is the time constant of the system response, K is the system gain, A c is the continuous form of the system matrix A, B c is the continuous form of the control matrix B.

[0019] Optionally, determining the state estimation error and the state output slope of the state space model includes:

[0020] The estimated value of the system state vector determined according to the discrete state space model is:

[0021]

[0022] in, is the estimated value of the system state vector at time k, x(k-1) is the system state vector at time k-1, and u(k-1) is the applied control input vector at time k-1;

[0023] The state estimation error is calculated based on the estimated value of the system state vector:

[0024]

[0025] Among them, h(k) is the state estimation error at time k;

[0026] The state output slope is determined according to the discrete state space model as:

[0027] p(k)=x(k)-x(k-1)

[0028] Among them, p(k) is the state output slope at time k.

[0029] Optionally, updating the model parameters of the state space model according to the state estimation error and the state output slope includes:

[0030] The differential equation that determines the continuous state space model is:

[0031]

[0032] The solution of the differential equation that determines the continuous state-space model is:

[0033] Δx(t)=K·Δu(t)·(1-e -t / T )

[0034] The time step is obtained, and the corresponding relationship between the state estimation error and the time constant of the system response and the system gain is determined according to the solution of the differential equation of the continuous state space model:

[0035]

[0036] Among them, t s is the time step;

[0037] Determine the time constant of the system response and the system gain according to the state estimation error and the state output slope:

[0038]

[0039] The parameters A and B of the discrete state space model are updated according to the time constant of the system response and the system gain, and the parameter A of the continuous state space model is updated. c and B c to update.

[0040] Optionally, after updating the model parameters of the state space model according to the state estimation error and the state output slope, the method further includes:

[0041] Determine the current system steady-state error according to the updated state space model;

[0042] Determining whether the system steady-state error is within a set value;

[0043] If not, returning to the step of determining the state estimation error and the state output slope of the state space model;

[0044] If so, the step of using the updated state-space model to perform temperature control is performed.

[0045] Optionally, before determining whether the system steady-state error is within a set value, the method further includes:

[0046] Execute the input modification command to modify the set value.

[0047] Optionally, after updating the model parameters of the state space model according to the state estimation error and the state output slope, the method further includes:

[0048] Obtaining the update number of the state space model;

[0049] Determining whether the number of updates reaches a preset threshold;

[0050] If not, returning to the step of determining the state estimation error and the state output slope of the state space model;

[0051] If so, the step of using the updated state-space model to perform temperature control is performed.

[0052] A temperature control device, comprising:

[0053] A first acquisition module is used to acquire a state space model of a temperature control system;

[0054] A first determination module, used to determine the state estimation error and the state output slope of the state space model;

[0055] A model updating module, used for updating the model parameters of the state space model according to the state estimation error and the state output slope;

[0056] A temperature control module is used to perform temperature control using the updated state-space model.

[0057] An electronic device, comprising:

[0058] A processor and a memory, wherein the memory is used to store at least one instruction, and when the instruction is loaded and executed by the processor, the temperature control method as described in any one of the above items is implemented.

[0059] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements any one of the temperature control methods described above.

[0060] The temperature control method provided by the embodiment of the present invention has at least the following beneficial effects:

[0061] The present invention first obtains the state space model of the temperature control system, then determines the state estimation error and state output slope of the state space model, then updates the model parameters of the state space model according to the state estimation error and state output slope, and finally uses the updated state space model to perform temperature control. The present invention can accurately determine the state estimation error and state output slope, and then dynamically adjust the model parameters to reflect environmental changes and system dynamics. This adaptive model update mechanism significantly improves the accuracy and response speed of the control strategy, thereby achieving more stable and accurate temperature control under variable operating conditions.

Brief Description of the Drawings

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0063] Figure 1 It is a structural schematic diagram of a model predictive controller in the prior art;

[0064] Figure 2 A flow chart of a temperature control method provided by an embodiment of the present invention;

[0065] Figure 3 A flow chart of another temperature control method provided by an embodiment of the present invention;

[0066] Figure 4 A schematic structural diagram of a temperature control device provided by an embodiment of the present invention. [Specific implementation method]

[0067] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0068] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0070] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0071] MPC uses the existing model, the current state of the system and the future control quantity to predict the future output of the system, and then compares it with the expected output to obtain the optimization objective function. Through the optimization method, the future control quantity is optimized to minimize the optimization objective function. The optimized control quantity is the algorithm output.

[0072] Please refer to Figure 1 , is a schematic diagram of the structure of a model predictive controller in the prior art. Figure 1 As shown in the figure, r(h) is the system input value at time h, u(h) is the control input vector applied at time h, d(h) is the environmental variable at time h, y(h) is the system output value at time h, y(h|h) is the system predicted value at time h, and y(h+j|h) is the system predicted value at time h+j.

[0073] Conventional MPC control algorithms based on space state models identify the parameters of the model based on the structure of the state space model in an ideal environment, thereby determining the system model parameters. The essence of "feedback correction" in model predictive control is to correct the initial model according to actual conditions. Standard MPC control based on state space models includes:

[0074] The state space expression of the controlled object in the first-order model is:

[0075] x(k+1)=Ax(k)+Bu(k)

[0076] The prediction equation is expressed as:

[0077]

[0078] Among them, x(k+1) is the system state vector at time k+1, A is the system matrix, x(k) is the system state vector at time k, B is the control matrix, u(k) is the control input vector applied at time k, and x(k+1|k) is the predicted value of the state at time k+1.

[0079] Its matrix form is as follows:

[0080] X k =Mx k +CU k

[0081]

[0082] The definition of the optimization objective function is as follows:

[0083]

[0084] The above formula can be rewritten into the augmented form of the matrix:

[0085]

[0086] Among them, X s is the desired target state vector, Q is a real symmetric semi-positive definite error weight matrix, R is a real symmetric positive definite control weight matrix, and f is the terminal weight value. The above semi-positive definite and positive definite matrices guarantee the semi-positive definiteness and lower bound of the performance index J, and it is mathematically meaningful to minimize it.

[0087] In order to obtain the optimal control variable U k * , then it needs to satisfy So the optimal control quantity can be obtained as:

[0088] U k * =H (-1) (P T X s -x k E T )

[0089] P=QC,E=M T QC,H=C T QC+R

[0090] According to the concept of rolling optimization, for the optimal control quantity U k * Sequence, only the first control quantity in the control sequence is sent, u k * Then it is the optimal control quantity that needs to be issued:

[0091] u k * =g T U k *

[0092]

[0093] However, MPC predicts future state changes by building a state space model of the system and performs optimal control based on the prediction. However, the existing MPC control method based on the state space model has some limitations:

[0094] 1) Model accuracy problem: In practical applications, the state space model of the system may become inaccurate due to environmental changes or unmodeled dynamic behaviors.

[0095] 2) Adaptability to environmental changes: When environmental conditions change, such as increases or decreases in heat load, external temperature fluctuations, etc., the existing model may not be able to reflect these changes in a timely manner, resulting in a decrease in control effect.

[0096] 3) Prediction accuracy: If the model fails to accurately reflect the current state of the system, the prediction based on this model will lose accuracy and affect the control decision.

[0097] Therefore, the present invention provides a temperature control method to solve the above problems.

[0098] Please refer to Figure 2 , is a flow chart of a temperature control method provided by an embodiment of the present invention, comprising the following steps:

[0099] Step S01, obtaining a state space model of a temperature control system.

[0100] In this embodiment, obtaining the state space model of the temperature control system can specifically be to establish a mathematical model to describe the evolution of the system state over time during the temperature control process, which is usually expressed as a set of first-order linear differential equations. For example, in a temperature control system of a precision manufacturing laboratory, the state variables may include the temperature in the machine room, the energy output of the air conditioner, etc. The reason for doing this is to be able to accurately predict how the system state (i.e., the laboratory temperature) will change over time under different control inputs (such as heating or cooling power).

[0101] In this embodiment, the reason for obtaining the state space model of the temperature control system is that the state space model provides a systematic framework for analyzing and designing control strategies, so that control engineers can predict the response of the system to specific inputs and design algorithms that can achieve precise control goals. For example, in the high-precision semiconductor production process, precise temperature control is crucial to ensure product quality. By obtaining the state space model of the temperature control system, engineers can predict the temperature changes of the production environment under different control strategies and optimize the control parameters to ensure that the temperature is maintained within a strict range, thereby improving production efficiency and product quality.

[0102] In some embodiments, the state-space model may be an existing one of the current temperature control system, or it may be established by the system itself based on historical temperature data. For example, the dynamic characteristics of the system may be identified through experiments or historical data to establish differential equations, and then discretize them; define state variables and control inputs, and perform parameter estimation; and finally verify the accuracy of the model by comparing the model predictions with the actual system responses.

[0103] In some embodiments, the state space model of the temperature control system mentioned in step S01 may be obtained by:

[0104] The discrete state space model of the temperature control system is obtained as:

[0105] x(k+1)=Ax(k)+Bu(k)

[0106] Where x(k+1) is the system state vector at time k+1, A is the system matrix, x(k) is the system state vector at time k, B is the control matrix, and u(k) is the applied control input vector at time k;

[0107] The continuous state space model of the temperature control system is determined as:

[0108]

[0109] Among them, x(t) is the system output, u(t) is the control quantity of the system input, T is the time constant of the system response, K is the system gain, A c is the continuous form of the system matrix A, B c is the continuous form of the control matrix B.

[0110] Step S02, determining the state estimation error and the state output slope of the state space model.

[0111] In this embodiment, the state estimation error refers to the difference between the state predicted by the system model and the state actually observed, and the state output slope describes the rate at which the system state changes over time. For example, the state estimation error can be calculated by comparing the actually measured temperature with the temperature predicted by the state space model, and the state output slope can be calculated as the derivative of the state change at two consecutive time points.

[0112] The reason why the state estimation error and state output slope of the state space model are determined in this embodiment is that the prediction accuracy and response speed of the current model to the system dynamics are evaluated by analyzing the state estimation error and the state output slope. If the state estimation error is large or the slope is not as expected, it may indicate that the model needs further correction or that environmental conditions have changed. For example, in the precision manufacturing process, even small fluctuations in temperature may affect product quality, so it is necessary to adjust the control strategy through accurate state estimation and slope analysis to ensure the stability and accuracy of temperature control. In this way, the model parameters can be updated in real time, and the adaptability of the control algorithm can be improved, thereby maintaining the high-performance operation of the system in the face of environmental changes or system aging.

[0113] Based on the above embodiments, in some embodiments, determining the state estimation error and the state output slope of the state space model includes:

[0114] The estimated value of the system state vector determined according to the discrete state space model is:

[0115]

[0116] in, is the estimated value of the system state vector at time k, x(k-1) is the system state vector at time k-1, and u(k-1) is the applied control input vector at time k-1;

[0117] The state estimation error is calculated based on the estimated value of the system state vector:

[0118]

[0119] Among them, h(k) is the state estimation error at time k;

[0120] The state output slope is determined according to the discrete state space model as:

[0121] p(k)=x(k)-x(k-1)

[0122] Among them, p(k) is the state output slope at time k.

[0123] In this embodiment, the system state vector at time k can be predicted by combining the discrete state space model with the system state vector and the control input vector at time k-1, that is, the estimated value of the system state vector at time k. At this time, the state estimation error between the actually observed system state vector and the estimated value of the system state vector predicted by the system model can be calculated based on the obtained system state vector at time k. The state output slope is determined by the difference between the actually observed system state vector at the previous moment and the actually observed system state vector at the current moment.

[0124] Step S03: updating the model parameters of the state space model according to the state estimation error and the state output slope.

[0125] The existing MPC control based on the state space model does not consider that if the environmental state changes during the control process, the previously established system model may also change at the same time. At this time, if the prediction is made on the original model, the accuracy and precision of the prediction will be greatly reduced. This embodiment can dynamically adjust the model parameters by real-time monitoring of the state estimation error and slope to more accurately reflect the dynamic characteristics of the current system. For example, if it is found that the state estimation error is systematically too large or too small, the system gain can be increased or decreased accordingly; if the slope analysis shows that the system response speed is faster or slower than expected, the time constant can be adjusted to optimize the model. Such updates ensure that the control system can adapt to these changes and maintain high-precision and high-stability temperature control, thereby ensuring the quality and safety of the process in applications that require strict temperature control.

[0126] In some embodiments, the updating of the model parameters of the state space model according to the state estimation error and the state output slope mentioned in step S03 may specifically be:

[0127] The differential equation that determines the continuous state space model is:

[0128]

[0129] The solution of the differential equation that determines the continuous state-space model is:

[0130] Δx(t)=K·Δu(t)·(1-e -t / T )

[0131] The time step is obtained, and the corresponding relationship between the state estimation error and the time constant of the system response and the system gain is determined according to the solution of the differential equation of the continuous state space model:

[0132]

[0133] Among them, t s is the time step;

[0134] Determine the time constant of the system response and the system gain according to the state estimation error and the state output slope:

[0135]

[0136] The parameters A and B of the discrete state space model are updated according to the time constant of the system response and the system gain, and the parameter A of the continuous state space model is updated. c and B c to update.

[0137] In this embodiment, by determining the differential equation of the continuous state space model and solving it, we can obtain x(t)=K·u(t)·(1-e -t / T ), which can be changed into the incremental form Δx(t) = K·Δu(t)·(1-e -t / T ), the solution of the differential equation of the continuous state space model is rewritten into a discrete form, and the state output slope p(k) = x(k)-x(k-1) at time k is substituted into it, and the corresponding relationship between the state estimation error and the time constant of the system response and the system gain can be obtained. The value of p(k) can be obtained by solving the system state vector x(k) at time k and the system state vector x(k-1) at time k-1.

[0138] According to the known h(k) and p(k), the time constant T of the system response and the system gain K can be obtained according to the above formula, and then A, B, A can be calculated according to the above formula. c and B c The value of makes the corrected model realize the prediction of the state at the next moment.

[0139] This embodiment ensures that the temperature control system maintains a high degree of prediction accuracy and control precision even when environmental conditions change or system characteristics drift by identifying and adjusting the system's time constants and gains in real time. This approach enables the system to adaptively respond to internal and external changes, thereby improving the robustness of the control strategy, enhancing system stability, reducing steady-state errors, and optimizing energy efficiency. Ultimately, these improvements ensure more reliable and economical operation in applications that require strict temperature control, such as precision manufacturing, medical refrigeration, or high-end laboratory equipment.

[0140] Step S04: Use the updated state space model to perform temperature control.

[0141] In this embodiment, the updated state space model can better reflect the current system state and dynamic characteristics, thereby improving the prediction accuracy and response speed of the control algorithm and ensuring that the temperature is maintained within a strict set range, which is essential for ensuring the repeatability, reliability and accuracy of the experimental results. In this way, more accurate and energy-saving temperature control can be achieved, production efficiency and product quality can be improved, while reducing energy costs and improving system stability and reliability.

[0142] Based on the above embodiments, in some embodiments, after executing step S03 to update the model parameters of the state space model according to the state estimation error and the state output slope, the accuracy of temperature control can be further improved by executing the following steps:

[0143] Obtaining the update number of the state space model;

[0144] Determining whether the number of updates reaches a preset threshold;

[0145] If not, return to step S02 to determine the state estimation error and state output slope of the state space model;

[0146] If yes, step S04 is executed to perform temperature control using the updated state space model.

[0147] This embodiment can achieve efficient management of the control system's adaptability by acquiring the number of updates of the state-space model in real time, determining whether the update frequency reaches a preset threshold, and deciding whether to continue adjusting the model or switch to the temperature control execution step accordingly. It ensures that the model can be updated in a timely manner when the environment changes or the system response drifts, avoiding the waste of computing resources and possible control overshoot caused by excessively frequent updates. At the same time, by limiting the number of updates to no more than the necessary threshold, the system can remain in a fully verified stable state, thereby improving the reliability and efficiency of the control process. When the number of updates accumulates to a certain level, it indicates that the model has been sufficiently verified. At this time, directly using the updated state-space model for temperature control can ensure the accuracy and real-time performance of the control strategy.

[0148] Based on the above technical solution, the present invention first obtains the state space model of the temperature control system, then determines the state estimation error and state output slope of the state space model, then updates the model parameters of the state space model according to the state estimation error and state output slope, and finally uses the updated state space model to perform temperature control. The present invention can accurately determine the state estimation error and state output slope, and then dynamically adjust the model parameters to reflect environmental changes and system dynamics. This adaptive model update mechanism significantly improves the accuracy and response speed of the control strategy, thereby achieving more stable and accurate temperature control under variable operating conditions.

[0149] Please refer to Figure 3 , which is a flow chart of another temperature control method provided by an embodiment of the present invention. Based on the above embodiments, in some embodiments, in order to further improve the accuracy of temperature control, the following may also be performed: Figure 3 Steps shown:

[0150] Step S01, obtaining a state space model of a temperature control system.

[0151] Step S02, determining the state estimation error and the state output slope of the state space model.

[0152] Step S03: updating the model parameters of the state space model according to the state estimation error and the state output slope.

[0153] Step S11, determining the current system steady-state error according to the updated state space model.

[0154] In this embodiment, the system steady-state error is the difference between the output of the calculated system after a long period of operation and the expected set point, after the model parameters have been updated in real time to reflect the latest dynamics of the system. This process involves using the updated model to simulate the system response and analyzing the error size when the system reaches a steady state. The purpose of this is to evaluate whether the control performance meets the predetermined standards and ensure that the system can maintain the required accuracy and stability throughout the entire operating cycle. By monitoring the steady-state error, it can be determined whether the control strategy or model parameters need to be further adjusted to achieve better control results, especially in applications with extremely high temperature control accuracy requirements, such as semiconductor processing or storage of biological products. Accurate steady-state error assessment is crucial to ensuring product quality and process reliability.

[0155] In some embodiments, the system simulation can be performed using the updated state space model parameters to predict the long-term behavior of the system under a given control input. When the simulation runs for a long enough time, the system will reach a steady state, at which time the output value of the system is recorded. The difference between the steady-state output value and the expected set point is calculated to obtain the steady-state error. By analyzing the size of this error, the accuracy and stability of the system control are evaluated.

[0156] Step S12, determining whether the system steady-state error is within a set value.

[0157] If not, return to step S02; if so, execute step S04.

[0158] In this embodiment, the system will evaluate whether the steady-state error after the model parameter update meets the preset control accuracy requirements. If the steady-state error exceeds the set threshold, it indicates that the current control performance fails to achieve the expected goal. At this time, the system needs to re-execute step S02, that is, to re-determine the state estimation error and the state output slope, so as to further adjust and optimize the model parameters. The purpose of this approach is to continuously refine the model parameters through iteration until the control output can be stabilized within the expected range. On the contrary, if the steady-state error is within the set value, it means that the control performance meets the requirements. The system can enter step S04 and use the current updated state space model to perform actual temperature control to ensure that the temperature is maintained near the required set point. This decision-making and iterative process is the core of the closed-loop control system, which ensures the adaptability and robustness of the control strategy, so that high-precision and high-stability temperature control can be maintained in the face of system dynamic changes or external disturbances.

[0159] In some embodiments, in order to adjust the temperature set point according to real-time monitoring or specific needs, thereby improving operational flexibility and enabling the control system to respond to changes in external conditions or specific instructions from the operator, before executing step S12 to determine whether the system steady-state error is within the set value, the input modification command can also be executed to modify the set value.

[0160] In this embodiment, the reason for modifying the set value is that new operating requirements or environmental changes may occur in actual applications, which requires the control system to quickly adapt to these changes to ensure continuous optimization of the control process and optimization of system performance. By allowing dynamic adjustment of the set value, the method not only improves the adaptability and user interactivity of the control system, but also enhances the control accuracy, ensuring that the system can still achieve accurate temperature control under variable operating conditions.

[0161] Step S04: Use the updated state space model to perform temperature control.

[0162] Based on the above technical solution, this embodiment can quantify the control performance and evaluate whether it meets the expected accuracy requirements by determining the steady-state error of the system after the model is updated; if the steady-state error exceeds the set value, the system will automatically return to recalculate the state estimation error and the state output slope, which ensures the continuous optimization of the model parameters and the timely adjustment of the control strategy, and strengthens the adaptive ability of the system; if the steady-state error is within the set range, the updated model is used for temperature control to ensure the efficiency and stability of the control process. This intelligent feedback loop not only improves the accuracy and reliability of temperature control, but also reduces manual intervention and optimizes resource allocation. It is particularly suitable for high-precision manufacturing and research environments that require strict temperature control standards.

[0163] Please refer to Figure 4 , is a schematic diagram of the structure of a temperature control device provided by an embodiment of the present invention, the temperature control device comprising:

[0164] A first acquisition module 100 is used to acquire a state space model of a temperature control system;

[0165] A first determination module 200, used to determine the state estimation error and the state output slope of the state space model;

[0166] A model updating module 300, configured to update model parameters of the state space model according to the state estimation error and the state output slope;

[0167] The temperature control module 400 is used to perform temperature control using the updated state-space model.

[0168] Based on the above embodiment, in a specific embodiment, the first acquisition module 100 can be specifically used for:

[0169] The discrete state space model of the temperature control system is obtained as:

[0170] x(k+1)=Ax(k)+Bu(k)

[0171] Where x(k+1) is the system state vector at time k+1, A is the system matrix, x(k) is the system state vector at time k, B is the control matrix, and u(k) is the applied control input vector at time k;

[0172] The continuous state space model of the temperature control system is determined as:

[0173]

[0174] Among them, x(t) is the system output, u(t) is the control quantity of the system input, T is the time constant of the system response, K is the system gain, A c is the continuous form of the system matrix A, B c is the continuous form of the control matrix B.

[0175] Based on the above embodiment, in a specific embodiment, the first determining module 200 can be specifically used for:

[0176] The estimated value of the system state vector determined according to the discrete state space model is:

[0177]

[0178] in, is the estimated value of the system state vector at time k, x(k-1) is the system state vector at time k-1, and u(k-1) is the applied control input vector at time k-1;

[0179] The state estimation error is calculated based on the estimated value of the system state vector:

[0180]

[0181] Among them, h(k) is the state estimation error at time k;

[0182] The state output slope is determined according to the discrete state space model as:

[0183] p(k)=x(k)-x(k-1)

[0184] Among them, p(k) is the state output slope at time k.

[0185] Based on the above embodiment, in a specific embodiment, the model updating module 300 can be specifically used for:

[0186] The differential equation that determines the continuous state space model is:

[0187]

[0188] The solution of the differential equation that determines the continuous state-space model is:

[0189] Δx(t)=K·Δu(t)·(1-e -t / T )

[0190] The time step is obtained, and the corresponding relationship between the state estimation error and the time constant of the system response and the system gain is determined according to the solution of the differential equation of the continuous state space model:

[0191]

[0192] Among them, t s is the time step;

[0193] Determine the time constant of the system response and the system gain according to the state estimation error and the state output slope:

[0194]

[0195] The parameters A and B of the discrete state space model are updated according to the time constant of the system response and the system gain, and the parameter A of the continuous state space model is updated. c and B c to update.

[0196] Based on the above embodiment, in a specific embodiment, the temperature control device may further include:

[0197] A second determination module is used to determine the current system steady-state error according to the updated state space model;

[0198] The first judgment module is used to judge whether the system steady-state error is within the set value; if not, return to execute the step of determining the state estimation error and state output slope of the state space model; if so, execute the step of using the updated state space model to perform temperature control.

[0199] Based on the above embodiment, in a specific embodiment, the temperature control device may further include:

[0200] The modification module is used to execute the input modification command to modify the set value.

[0201] Based on the above embodiment, in a specific embodiment, the temperature control device may further include:

[0202] A second acquisition module is used to obtain the update times of the state space model;

[0203] The second judgment module is used to judge whether the number of updates reaches a preset threshold; if not, return to execute the step of determining the state estimation error and state output slope of the state space model; if so, execute the step of using the updated state space model to perform temperature control.

[0204] This embodiment provides an electronic device, including a processor and a memory, the memory is used to store at least one instruction, and when the instruction is loaded and executed by the processor, the above-mentioned temperature control method is implemented. Its execution method and beneficial effects are similar and will not be repeated here.

[0205] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the temperature control method described above is implemented. The execution method and beneficial effects thereof are similar and will not be described in detail here.

[0206] It should be noted that although the above describes the various steps in a specific order, it does not mean that the various steps must be executed in the above specific order. In fact, some of these steps can be executed concurrently or even in a different order as long as the required functions can be achieved.

[0207] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A temperature control method, characterized in that: include: Obtain a state space model of a temperature control system; Determining a state estimation error and a state output slope of the state space model; updating model parameters of the state space model according to the state estimation error and the state output slope; Temperature control is performed using the updated state-space model.

2. The method according to claim 1, characterized in that The step of obtaining a state space model of the temperature control system comprises: The discrete state space model of the temperature control system is obtained as: x(k+1)=Ax(k)+Bu(k) Where x(k+1) is the system state vector at time k+1, A is the system matrix, x(k) is the system state vector at time k, B is the control matrix, and u(k) is the applied control input vector at time k; The continuous state space model of the temperature control system is determined as: Among them, x(t) is the system output, u(t) is the control quantity of the system input, T is the time constant of the system response, K is the system gain, A c is the continuous form of the system matrix A, B c is the continuous form of the control matrix B.

3. The method according to claim 2, characterized in that Determining the state estimation error and the state output slope of the state space model includes: The estimated value of the system state vector determined according to the discrete state space model is: in, is the estimated value of the system state vector at time k, x(k-1) is the system state vector at time k-1, and u(k-1) is the applied control input vector at time k-1; The state estimation error is calculated based on the estimated value of the system state vector: Among them, h(k) is the state estimation error at time k; The state output slope is determined according to the discrete state space model as: p(k)=x(k)-x(k-1) Among them, p(k) is the state output slope at time k.

4. The method according to claim 3, characterized in that The updating of the model parameters of the state space model according to the state estimation error and the state output slope includes: The differential equation that determines the continuous state space model is: The solution of the differential equation that determines the continuous state-space model is: Δx(t)=K·Δu(t)·(1-e -t / T ) The time step is obtained, and the corresponding relationship between the state estimation error and the time constant of the system response and the system gain is determined according to the solution of the differential equation of the continuous state space model: Among them, t s is the time step; Determine the time constant of the system response and the system gain according to the state estimation error and the state output slope: The parameters A and B of the discrete state space model are updated according to the time constant of the system response and the system gain, and the parameter A of the continuous state space model is updated. c and B c to update.

5. The method according to any one of claims 1 to 4, characterized in that: After updating the model parameters of the state space model according to the state estimation error and the state output slope, the method further includes: Determine the current system steady-state error according to the updated state space model; Determining whether the system steady-state error is within a set value; If not, returning to the step of determining the state estimation error and the state output slope of the state space model; If so, the step of using the updated state-space model to perform temperature control is performed.

6. The method according to claim 5, characterized in that Before determining whether the system steady-state error is within a set value, the method further includes: Execute the input modification command to modify the set value.

7. The method according to claim 1, characterized in that After updating the model parameters of the state space model according to the state estimation error and the state output slope, the method further includes: Obtaining the update number of the state space model; Determining whether the number of updates reaches a preset threshold; If not, returning to the step of determining the state estimation error and the state output slope of the state space model; If so, the step of using the updated state-space model to perform temperature control is performed.

8. A temperature control device, characterized in that: include: A first acquisition module is used to acquire a state space model of a temperature control system; A first determination module, used to determine the state estimation error and the state output slope of the state space model; A model updating module, used for updating the model parameters of the state space model according to the state estimation error and the state output slope; A temperature control module is used to perform temperature control using the updated state-space model.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store at least one instruction, and when the instruction is loaded and executed by the processor, the temperature control method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the temperature control method according to any one of claims 1 to 7 is implemented.

Images