Universe mk-level precision temperature control method based on thermocouple and Kalman filtering

Abstract

The invention relates to a thermocouple and Kalman filtering-based global mk-level precision temperature control method. The method comprises the following steps of S1, arranging a thermocouple temperature measurement network; s2, establishing a mathematical model of a temperature measurement and control system; s3, establishing a discrete temperature measurement and control system model containing process noise and a thermocouple observation model containing measurement noise; s4, acquiring a system temperature priori estimation value at the current moment, and calculating a priori state estimation error covariance matrix; s5, obtaining a system temperature posterior state estimation value, updating the posterior state error covariance matrix, and obtaining a temperature measurement and control system correction model; s6, obtaining an error vector of a temperature measurement and control system; s7, obtaining a full-domain control variable based on feedforward-feedback, and acting the full-domain control variable on the temperature control object through an execution component; and S8, repeating the steps S4-S7 to form closed-loop control so as to realize the global mk-level temperature control precision of the temperature measurement and control system. The method has the beneficial effects of good comprehensiveness and strong coordination.

Description

[Technical field]

[0001] The present invention relates to the field of satellite aerospace technology, and in particular to a global MK-level precision temperature control method based on thermocouples and Kalman filtering. [Background technology]

[0002] In spacecraft, precision temperature control technology is essential to ensure the normal operation of equipment and extend its service life. The active temperature control system adjusts the temperature in real time to ensure that each component operates within the optimal operating temperature range, thereby improving the overall performance and reliability of the spacecraft. The core of the system lies in its feedback control mechanism, which relies on high-precision temperature measurement and efficient control algorithms. Accurate temperature sensors and optimized control algorithms together determine the system's ability to achieve stable temperature control in complex environments.

[0003] At present, the temperature sensors commonly used in spacecraft mainly include three types: thermocouples, thermal resistors and thermistors, each with different characteristics: ① Thermocouples use the thermoelectric potential difference generated at the connection between two different metals to measure temperature, which has the advantages of good consistency, wide measurement range, fast response speed, simple structure and low cost, but its anti-interference ability is poor and its accuracy is not high; ② Thermal resistors measure temperature through the characteristics of material resistance changing with temperature. Commonly used materials are platinum (such as Pt100, Pt1000). They have high accuracy and good stability, but poor consistency, slow response speed and high price; ③ Thermistors are based on the characteristics of semiconductor material resistance changing with temperature. They have high sensitivity, but poor linearity and consistency, and high price. Due to the weak anti-interference ability of thermocouples, spacecraft currently use thermal resistors and thermistors as temperature sensors, but the consistency of the two is poor, and it is difficult to meet the needs of systematic precision temperature control; the response speed is slow, and it is difficult to accurately capture high-frequency temperature fluctuation signals; the price is high, and it is difficult to reduce the cost of spacecraft development.

[0004] The current temperature control algorithm mainly adopts model-free methods such as threshold control and PID control. Threshold control is a simple switch control method. The system sets an allowable temperature range (upper and lower limits). When the actual temperature exceeds the upper limit, the system turns off the heating device; when the actual temperature is lower than the lower limit, the system starts the heating device. This control method is concise and clear, without complex calculations, but the response characteristics are relatively rough, which can easily cause the temperature to fluctuate repeatedly around the target value and cannot achieve precise control. The PID controller consists of proportional (P), integral (I) and differential (D). These three parts coordinately adjust the system output to achieve the target value. P control responds to errors quickly, I control eliminates steady-state errors, and D control reduces overshoot and improves dynamic performance. However, PID control requires precise parameter adjustment (P, I, D optimization), which is difficult to adjust to the optimal one-time in a complex aerospace environment. It is sensitive to model changes and is prone to failure under complex working conditions.

[0005] In addition, these algorithms are usually based on single-input single-output (SISO) system design, which is simple and easy to implement, but can only control one input and one output, and it is difficult to handle the complex relationship and cross-coupling problems between multiple inputs and outputs. In a multivariable system, it is necessary to decompose it into multiple independent SISO systems, which increases the complexity of the system and may reduce the control accuracy. Its performance is limited in complex engineering control problems and cannot meet the requirements of fast response, high-precision control or high stability. Therefore, when facing complex multivariable systems, it is necessary to consider using a multiple-input multiple-output (MIMO) system or other more complex control strategies.

[0006] Taking all the above factors into consideration, existing temperature sensors and control algorithms have deficiencies in measurement accuracy, anti-interference capability, response speed, and adaptability to complex environments, which greatly limits the development of mK-level precision temperature control technology for spacecraft. This requires new technological breakthroughs to improve the overall performance of spacecraft temperature control systems and meet future high-precision temperature control requirements.

[0007] The current spacecraft MK-level precision temperature control technology has the following deficiencies:

[0008] 1) Sensor performance limitations

[0009] Thermocouple technology has the problems of low accuracy and insufficient anti-interference ability in spacecraft precision temperature measurement. Although thermal resistors and thermistors have high measurement sensitivity, their accuracy and consistency are insufficient, which limits the reliability and accuracy of temperature measurement.

[0010] 2) Control response and stability challenges

[0011] The existing temperature control system has insufficient response speed and stability in dynamic environments, especially the slow response time of thermal resistors and thermistors and the limitations of control strategies, making it difficult to achieve fast and accurate temperature regulation.

[0012] 3) Lack of adaptability to complex environments

[0013] Spacecraft require a high degree of environmental adaptability under extreme conditions, but traditional SISO control and PID control expose their adaptability limitations in the face of multi-variable interactions and environmental changes, affecting temperature control accuracy and system robustness.

[0014] 4) Insufficient data processing and optimization technology

[0015] The lack of efficient sensor data processing technology, such as the application of Kalman filtering, limits the suppression of measurement noise and the improvement of temperature control accuracy, and is a technical bottleneck for achieving mK-level full-range precision temperature control.

[0016] 5) The balance between cost and consistency

[0017] The cost of high-performance sensors and the strict screening and calibration process required for each sensor make it difficult to ensure consistency between sensors and accuracy in long-term use while pursuing high sensitivity, affecting the reliability and economy of the system.

[0018] 6) Global perception and MIMO temperature control limitations

[0019] The high cost and poor consistency of thermal resistors and thermistors make it difficult to build a global temperature sensing network, limiting the ability to achieve high-precision temperature control in multiple-input multiple-output (MIMO) systems, and affecting the comprehensive, precise management and dynamic adjustment of the temperature field inside the spacecraft.

[0020] Kalman filtering is an algorithm that uses the linear system state equation to optimally estimate the system state through the system input and output observation data; since the observation data includes the influence of noise and interference in the system, the optimal estimate can also be regarded as a filtering process.

[0021] The present invention aims at the technical problems that existing temperature sensors and control algorithms have deficiencies in measurement accuracy, anti-interference ability, response speed and adaptability to complex environments, and makes technical improvements to the spacecraft temperature control method. [Summary of the invention]

[0022] The purpose of the present invention is to propose a method for comprehensive and coordinated temperature control of temperature-controlled objects on a controlled spacecraft with mK-level accuracy over the entire area.

[0023] To achieve the above object, the technical solution adopted by the present invention is a global mk-level precision temperature control method based on thermocouple and Kalman filter, comprising the following steps:

[0024] S1. Arrange a thermocouple temperature measurement network, and arrange all the thermocouple cold ends of the thermocouple temperature measurement network in a temperature source with mk-level accuracy;

[0025] S2. The temperature measurement and control system includes a temperature control object, a thermocouple temperature measurement network, a Kalman filter and a global controller based on feedforward-feedback. A mathematical model of the temperature measurement and control system is established, including system temperature, system control variables, system heat source and system ambient temperature.

[0026] S3. Establish a discretized temperature measurement and control system model containing process noise and a thermocouple observation model containing measurement noise, and clarify the process noise covariance matrix and the measurement noise covariance matrix;

[0027] S4, through the Kalman filter, the system temperature posterior estimation value and control variables at the previous moment are input into the prediction model of the temperature measurement and control system, the system temperature prior estimation value at the current moment is obtained, and the prior state estimation error covariance matrix is ​​calculated;

[0028] S5. Using the Kalman filter, the Kalman gain matrix is ​​calculated using the prior state estimation error covariance matrix and the process noise covariance matrix, and the system temperature prior estimation value and the actual thermocouple sampling value are integrated to obtain the system temperature posterior state estimation value. Using the prior state error covariance matrix and the Kalman gain matrix, the posterior state error covariance matrix is ​​updated to obtain the temperature measurement and control system correction model.

[0029] S6, setting the global temperature control target vector of the temperature measurement and control system, and obtaining the temperature measurement and control system error vector after comparing it with the system temperature posterior estimation value obtained by Kalman filtering;

[0030] S7, taking the error vector of the temperature measurement and control system, the measured value of the heat source itself and the measured value of the ambient temperature as the input of the global controller based on feedforward-feedback, obtaining the global control variable based on feedforward-feedback, and acting on the temperature control object through the execution component;

[0031] S8. Repeat steps S4 to S7 to form a closed-loop control to achieve mk-level temperature control accuracy for the entire temperature measurement and control system.

[0032] Preferably, step S2: the state expression of the temperature field of the temperature-controlled object in, The vector is the system temperature, The vector is the system control variable, The vector is the system self-heat source variable, The vector is the system ambient temperature variable, the A matrix is ​​the system thermal characteristic parameter matrix, the B1 matrix is ​​the control variable distribution matrix, the B2 matrix is ​​the system heat source parameter distribution matrix, and the B3 matrix is ​​the system ambient temperature parameter distribution matrix.

[0033] Preferably, step S3:

[0034] Discrete model expression of temperature measurement and control system with process noise Among them, k represents the discrete moment, represents the process error vector at time k-1;

[0035] Setting the process noise vector of the heat transfer model of the temperature measurement and control system Follow the normal distribution Where, Γ is the process noise covariance matrix;

[0036] Temperature Observation Equation for Thermocouple Networks in, is the temperature measurement value of the thermocouple network at time k, C is the observation matrix, is the measurement noise vector of the measurement system at time k;

[0037] Set the measurement noise vector for the thermocouple observation model Sorta distribution Where Ψ is the measurement noise covariance matrix.

[0038] Preferably, step S4:

[0039] A priori estimate of system temperature in, is the prior estimate of the system temperature at time k, is the prior estimate of the system temperature at time k-1;

[0040] The prior state estimation error covariance matrix is ​​P - [k]=AP[k-1]A T +Γ, where P - [k] is the covariance matrix of the prior state estimation error at time k, and P[k-1] is the covariance matrix of the posterior state estimation error at time k-1.

[0041] Preferably, step S5:

[0042] Kalman gain matrix

[0043] A posteriori state estimate of system temperature Among them, k k is the Kalman gain matrix;

[0044] The posterior state error covariance matrix P[k] = (Ik k C)P - [k], where I is the identity matrix.

[0045] Preferably, step S6:

[0046] Global control variables based on feedforward-feedback in, is the global control variable based on feedforward-feedback at time k, To control the target variable, the deviation vector

[0047] Feedback amount of global control variables based on feedforward-feedback Among them, k p The feedback amount Adjustable parameter matrix;

[0048] Feedforward amount of global control variables based on feedforward-feedback in, The vector is the known target value. is the system's own heat source disturbance value, is the system ambient temperature vector.

[0049] The present invention provides a global mK-level precision temperature control method based on thermocouple and Kalman filtering, which has the following beneficial effects: it integrates thermocouple temperature measurement technology and digital Kalman filtering technology, and adopts a global temperature field control method with feedforward-feedback as the core on the basis of realizing mK-level precision perception. 1. Integrate innovative technologies to enhance accuracy. By combining the direct temperature measurement capability of thermocouples with the data optimization function of the Kalman filter algorithm, the noise interference and signal drift problems faced by traditional thermocouple technology in high-precision measurement are effectively overcome. The dynamic adaptability of the Kalman filter not only realizes real-time data processing, but also greatly improves the purity and accuracy of the measurement data through adaptive adjustment, laying the foundation for mK-level temperature perception; 2. Precise perception of the global mK-level temperature field breaks through the limitations of traditional local measurements. The MIMO architecture is used to integrate multi-point thermocouple data. Through Kalman filter processing, high-resolution temperature perception of the entire spacecraft is achieved. This global temperature monitoring provides detailed and reliable data support for precise temperature control strategies; 3. The MIMO system promotes multi-dimensional temperature control. In view of the complexity of spacecraft thermal management, the MIMO system designed by the present invention can process multiple Input (sensor data) and output (temperature control instructions) realize efficient coordinated control of multi-zone temperatures. This design improves the system response speed, overall efficiency and stability, and ensures effective thermal management under complex working conditions; 4. Adaptive environmental response and dynamic adjustment strategy. The feedforward-feedback control mechanism adopted by the present invention has a high degree of environmental adaptability and can quickly adjust the control strategy according to the dynamic changes of the external environment to ensure the timeliness and accuracy of temperature control, providing a stable and reliable temperature control environment for spacecraft to perform complex tasks; 5. Optimize cost and integration, and enhance practical value. In view of the cost and integration challenges of high-precision temperature control systems, the present invention fully considers the optimization of cost-effectiveness and system integration in the design. Through technology integration and design simplification, it successfully reduces the implementation cost, while improving the maintainability and long-term operation reliability of the system, greatly enhancing the practicality and promotion potential of the technology.

Brief Description of the Drawings

[0050] Figure 1 It is a global mk-level precision temperature control method based on thermocouple and Kalman filter. Schematic diagram of thermocouple temperature measurement network.

[0051] Figure 2 It is a schematic diagram of the temperature measurement and control system of a global MK-level precision temperature control method based on thermocouples and Kalman filtering.

[0052] Figure 3It is a flow chart of a global mk-level precision temperature control method based on thermocouples and Kalman filtering. [Specific implementation method]

[0053] The present invention will be further described below in conjunction with embodiments and with reference to the accompanying drawings.

[0054] Example

[0055] This embodiment implements a global mk-level precision temperature control method based on thermocouples and Kalman filtering.

[0056] This embodiment aims to provide a temperature control method for space exploration instruments to meet their full-domain mK-level temperature control requirements. This embodiment method uses the thermocouple temperature measurement principle and Kalman filtering technology to achieve full-domain mK-level accurate temperature perception at low cost; based on this, combined with a multi-input and multi-output temperature control algorithm, the adaptability of the temperature control strategy to complex thermal environments is improved, and ultimately the full-domain mK-level temperature control accuracy is achieved.

[0057] The method of this embodiment integrates thermocouple temperature measurement technology and digital Kalman filtering technology. It not only combines the high consistency of thermocouple sensors and their extensive application experience in the aerospace field, but also optimizes temperature measurement data in real time through the Kalman filtering algorithm, effectively suppresses noise, and significantly improves the accuracy of temperature measurement. This embodiment designs a global multi-input multi-output (MIMO) system for high-precision temperature field control to achieve mK-level temperature control for the entire cabin.

[0058] The specific features of the method in this embodiment are as follows:

[0059] 1) Integrated thermocouple and Kalman filter technology

[0060] Combining traditional thermocouple temperature measurement technology with advanced digital Kalman filter algorithm, taking advantage of the high consistency of thermocouples and application experience in the aerospace field, and optimizing data processing in real time through Kalman filter technology, the temperature measurement accuracy is improved to mK level.

[0061] 2) mK-level accurate temperature sensing over the entire area

[0062] It can achieve full-area high-resolution perception of the temperature inside the spacecraft, and can accurately monitor temperature changes in key areas in real time, providing a solid data foundation for precise temperature control.

[0063] 3) Multiple-Input Multiple-Output (MIMO) System Design

[0064] A MIMO architecture is designed for complex spacecraft thermal management systems that can process input information from multiple temperature sensors and dynamically adjust the output of multiple temperature control devices to ensure the comprehensiveness and coordination of the system's temperature control and improve the system's overall control efficiency and stability.

[0065] 4) Adaptive environment and dynamic adjustment capabilities

[0066] The method of this embodiment can automatically adjust the control strategy to cope with different external environmental changes, quickly respond to temperature changes, and ensure the continuity and accuracy of the internal and external environment of the spacecraft.

[0067] 5) Cost-effectiveness and system integration optimization

[0068] Optimizing the use of thermocouples and integrating the Kalman filter algorithm not only achieves mK-level accuracy under the premise of cost control, but also simplifies and optimizes system integration, reduces costs and improves system reliability and maintainability.

[0069] The method of this embodiment uses thermocouples as temperature measurement sensors, which reduces costs while making full use of its temperature measurement principle to improve the consistency between temperature measurement points. In view of the problem that thermocouple temperature measurement is easily interfered with, digital Kalman filtering technology is introduced, and temperature perception with full mK level accuracy is achieved by constructing a thermocouple temperature measurement network. This embodiment proposes a multi-input multi-output (MIMO) temperature control method based on feedforward-feedback. By processing the input information of multiple temperature sensors, the output of multiple temperature control devices is dynamically adjusted to ensure the comprehensiveness and coordination of the system temperature control, and to achieve full-domain mK level accuracy temperature control of the controlled object.

[0070] The specific steps are as follows:

[0071] 1. Thermocouple

[0072] The principle of thermocouple temperature measurement is based on the thermoelectric effect in physics, which can convert temperature differences into potential differences (voltage). Thermocouples are composed of two conductors of different materials (usually metals or alloys). One end of the two conductors is welded together to form a measuring end, which is placed in an environment where the temperature needs to be measured; the other end is the reference end, which is usually in a known or constant temperature environment. The specific principle is as follows:

[0073] 1) The Seebeck effect is the basis of the working principle of thermocouples. It describes the phenomenon that when the contact points of two different metals are at different temperatures, an electric potential difference will be generated, which is proportional to the temperature difference.

[0074] 2) The magnitude of the thermoelectric potential depends on the temperature difference and the materials that make up the thermocouple. Each metal combination has specific thermoelectric properties, which can be found on a thermocouple graduation table.

[0075] 3) In order to ensure the accuracy of the measurement, cold-end compensation is required to compensate for the effect of changes in cold-end temperature on the thermoelectric potential.

[0076] 4) The temperature-EMF relationship of a thermocouple is usually nonlinear, so the International Electrotechnical Commission and other organizations define standard thermocouple types, each with a specific temperature-EMF characteristic curve.

[0077] 5) By measuring the electromotive force generated by the thermocouple and comparing it with a scale, or using electronic instruments for automatic conversion, accurate temperature measurement values ​​can be obtained. Thermocouples have important applications in industry, scientific research, aerospace and other fields due to their wide range of application scenarios and superior characteristics.

[0078] Thermocouples have the advantages of high consistency between individuals, a wide range, fast response time, low cost, excellent durability, automatic power supply (no excitation signal required) and no self-heating effect. However, their temperature measurement accuracy is easily affected by external interference.

[0079] Figure 1 This is a global mk-level precision temperature control method based on thermocouples and Kalman filtering. Schematic diagram of thermocouple temperature measurement network. Figure 1 As shown, in order to fully perceive the temperature field distribution of the measured system, a thermocouple temperature measurement network is arranged; in order to improve the consistency and accuracy of the system, all cold ends of the thermocouple temperature measurement network are arranged in a temperature source with mk-level accuracy.

[0080] 2. Temperature measurement and control system

[0081] Figure 2 This is a temperature control system diagram of a global mk-level precision temperature control method based on thermocouples and Kalman filtering. Figure 2 As shown in the figure, the temperature measurement and control system consists of three parts: the controlled object, the thermocouple network and the control system. The state expression of the temperature field is as follows:

[0082]

[0083] In the above formula, The vector is the system temperature, The vector is the system control variable, The vector is the system self-heat source variable, The vector is the system ambient temperature variable, the A matrix is ​​the system thermal characteristic parameter matrix, the B1 matrix is ​​the control variable distribution matrix, the B2 matrix is ​​the system heat source parameter distribution matrix, and the B3 matrix is ​​the system ambient temperature parameter distribution matrix.

[0084] 3. Kalman filter

[0085] The steps of constructing a Kalman filter include discretizing the system model (including noise), predicting the state, and updating the correction.

[0086] 1) Discrete systems

[0087] The discrete expression of the system containing process noise is as follows:

[0088]

[0089] In the above formula, k represents the discrete time. represents the process error vector at time k-1.

[0090] Setting the process noise vector for the heat transfer model It obeys the normal distribution, that is:

[0091]

[0092] In the above formula, Γ is the covariance matrix of process noise.

[0093] The temperature observation equation for the thermocouple network is as follows:

[0094]

[0095] In the above formula, is the temperature measurement value of the thermocouple network at time k, C is the observation matrix, is the measurement noise vector of the measurement system at time k.

[0096] Set the measurement noise vector of the observation model It obeys the normal distribution, that is:

[0097]

[0098] In the above formula, Ψ is the covariance matrix of the measurement noise.

[0099] 2) Status prediction

[0100] The prior state estimate is:

[0101]

[0102] In the above formula, is the prior state estimate at time k, is the estimated value of the posterior state at time k-1.

[0103] The prior state estimation error covariance matrix is:

[0104] P - [k] = AP[k-1]A T +Γ (7)

[0105] In the above formula, P - [k] is the covariance matrix of the prior state estimation error at time k, and P[k-1] is the covariance matrix of the posterior state estimation error at time k-1.

[0106] 3) Update and Correction

[0107] The Kalman gain is as follows:

[0108]

[0109] The posterior estimate is as follows:

[0110]

[0111] In the above formula, k k is the Kalman gain matrix.

[0112] The posterior state estimation error covariance matrix is ​​as follows:

[0113]

[0114] In the above formula, I is the unit matrix.

[0115] 4. Global temperature control method

[0116] The feedforward-feedback control variables of the discrete system designed in this embodiment are as follows:

[0117]

[0118] In the above formula is the control variable at time k, To control the target variable, the deviation vector The calculation formula is as follows:

[0119]

[0120] The feedback quantity in formula (11) is:

[0121]

[0122] The feedforward in formula (11) is:

[0123]

[0124] In the above formula, k p is the feedback control vector The adjustable parameter matrix can be adjusted according to the actual situation; the feedforward control vector The vector is the known set target value; It is the disturbance value of its own heat source, which can be obtained by measuring the power value in real time; is the ambient temperature vector, which can be read in real time by the temperature sensor.

[0125] Figure 3 This is a flow chart of a global mk-level precision temperature control method based on thermocouples and Kalman filtering. Figure 3As shown, the steps of the global mk-level precision temperature control method based on thermocouple and Kalman filtering provided in this embodiment are as follows:

[0126] 1) Arrange the thermocouple temperature measurement network according to actual needs, and arrange all cold ends of the thermocouple temperature measurement network in a temperature source with mk-level accuracy;

[0127] 2) Establish a mathematical model based on the actual temperature control object and the temperature measurement and control system, including system temperature, control variables, self-heat source and ambient temperature;

[0128] 3) Establish a discretized system model with process noise and a thermocouple observation model with measurement noise, and clarify the process noise covariance matrix and measurement noise covariance matrix;

[0129] 4) Input the a priori estimated value of the system temperature at the previous moment, the control variables and other parameters into the prediction model, obtain the a priori estimated temperature of the system at the current moment, and calculate the a priori state estimation error covariance matrix;

[0130] 5) Using the prior state estimation error covariance matrix and the process noise covariance matrix, calculate the Kalman gain matrix, fuse the prior estimate of the system temperature and the actual thermocouple sampling value, and obtain the posterior state estimate of the system temperature. Using the prior state error covariance matrix and the Kalman gain matrix, update the posterior state error covariance matrix.

[0131] 6) Set the global temperature control target vector and obtain the system error vector after comparing it with the Kalman filter posterior estimated temperature.

[0132] 7) The system error vector, the measured value of the heat source itself and the measured value of the ambient temperature are used as the input of the controller to obtain the global control variable based on feedforward-feedback, and act on the temperature control object through the execution component.

[0133] 8) Repeat steps 4 to 7 to form a closed-loop control and achieve full-range mk-level temperature control accuracy.

[0134] The positive effects of the method of this embodiment include but are not limited to the following aspects:

[0135] 1) Global mK-level temperature precision perception: By integrating thermocouple temperature measurement and Kalman filtering technology, this embodiment achieves global mK-level temperature precision perception capability. This technology breaks through the spatial limitations of traditional technologies, can comprehensively and meticulously monitor the temperature distribution within the spacecraft, and provides accurate real-time data for temperature management in complex environments, which is the key to ensuring the success of high-precision scientific experiments and space missions.

[0136] 2) Highly adaptable MIMO system design: The system can not only process and coordinate multiple input and output channels simultaneously, achieving precise control of multi-zone temperature, but also exhibits good adaptability in complex dynamic environments. By dynamically adjusting the control strategy, the system can quickly respond to environmental changes and ensure the stability of the internal temperature of the spacecraft, greatly enhancing the reliability of the system and the success rate of mission execution.

[0137] 3) Balance between low cost and high efficiency: Through the well-designed thermocouple usage strategy and the efficient integration of the Kalman filter algorithm, the overall cost of the high-precision temperature sensing system is effectively reduced, while the system's operating efficiency and ease of maintenance are improved.

[0138] 4) Promoting effect on related fields: The high precision, high adaptability and low cost of this embodiment have a positive impact on aerospace, deep space exploration, satellite technology, ground-based high-precision laboratories and other fields. It not only improves the success rate of tasks with strict requirements for temperature control in these fields, but also opens up new paths for other high-tech research and application that require precision temperature management, and promotes the development and progress of related technologies.

[0139] A person of ordinary skill in the art can understand that all or part of the steps to implement the above embodiments can be completed by hardware, or can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, wherein the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0140] The above is only a preferred embodiment of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and supplements without departing from the principle of the present invention. These improvements and supplements should also be regarded as the scope of protection of the present invention.

Claims

1. A global mk-level precision temperature control method based on thermocouple and Kalman filter, characterized in that The following steps are involved: S1. Arrange a thermocouple temperature measurement network, and arrange all the thermocouple cold ends of the thermocouple temperature measurement network in a temperature source with mk-level accuracy; S2. The temperature measurement and control system includes a temperature control object, a thermocouple temperature measurement network, a Kalman filter and a global controller based on feedforward-feedback. A mathematical model of the temperature measurement and control system is established, including system temperature, system control variables, system heat source and system ambient temperature. S3. Establish a discretized temperature measurement and control system model containing process noise and a thermocouple observation model containing measurement noise, and clarify the process noise covariance matrix and the measurement noise covariance matrix; S4, through the Kalman filter, the system temperature posterior estimation value and control variables at the previous moment are input into the prediction model of the temperature measurement and control system, the system temperature prior estimation value at the current moment is obtained, and the prior state estimation error covariance matrix is ​​calculated; S5. Using the Kalman filter, the Kalman gain matrix is ​​calculated using the prior state estimation error covariance matrix and the process noise covariance matrix, and the system temperature prior estimation value and the actual thermocouple sampling value are integrated to obtain the system temperature posterior state estimation value. Using the prior state error covariance matrix and the Kalman gain matrix, the posterior state error covariance matrix is ​​updated to obtain the temperature measurement and control system correction model. S6, setting the global temperature control target vector of the temperature measurement and control system, and obtaining the temperature measurement and control system error vector after comparing it with the system temperature posterior estimation value obtained by Kalman filtering; S7, taking the error vector of the temperature measurement and control system, the measured value of the heat source itself and the measured value of the ambient temperature as the input of the global controller based on feedforward-feedback, obtaining the global control variable based on feedforward-feedback, and acting on the temperature control object through the execution component; S8. Repeat steps S4 to S7 to form a closed-loop control to achieve mk-level temperature control accuracy for the entire temperature measurement and control system.

2. A global mk-level precision temperature control method based on thermocouple and Kalman filtering according to claim 1, characterized in that Step S2: State expression of the temperature field of the temperature-controlled object in, The vector is the system temperature, The vector is the system control variable, The vector is the system self-heat source variable, The vector is the system ambient temperature variable, the A matrix is ​​the system thermal characteristic parameter matrix, the B1 matrix is ​​the control variable distribution matrix, the B2 matrix is ​​the system heat source parameter distribution matrix, and the B3 matrix is ​​the system ambient temperature parameter distribution matrix.

3. A global mk-level precision temperature control method based on thermocouple and Kalman filtering according to claim 2, characterized in that Step S3: Discrete model expression of temperature measurement and control system with process noise Among them, k represents the discrete moment, represents the process error vector at time k-1; Setting the process noise vector of the heat transfer model of the temperature measurement and control system Sorta distribution Where, Γ is the process noise covariance matrix; Temperature Observation Equation for Thermocouple Networks in, is the temperature measurement value of the thermocouple network at time k, C is the observation matrix, is the measurement noise vector of the measurement system at time k; Set the measurement noise vector for the thermocouple observation model Sorta distribution Where Ψ is the measurement noise covariance matrix.

4. A global mk-level precision temperature control method based on thermocouple and Kalman filtering according to claim 3, characterized in that Step S4: A priori estimate of system temperature in, is the prior estimate of the system temperature at time k, is the prior estimate of the system temperature at time k-1; The prior state estimation error covariance matrix is ​​P - [k] = AP[k-1]A T +Γ, where P - [k] is the covariance matrix of the prior state estimation error at time k, and P[k-1] is the covariance matrix of the posterior state estimation error at time k-1.

5. A global mk-level precision temperature control method based on thermocouple and Kalman filtering according to claim 4, characterized in that Step S5: Kalman gain matrix A posteriori state estimate of system temperature Among them, k k is the Kalman gain matrix; The posterior state error covariance matrix P[k] = (Ik k C)P - [k], where I is the identity matrix.

6. A global mk-level precision temperature control method based on thermocouple and Kalman filtering according to claim 5, characterized in that Step S6: Global control variables based on feedforward-feedback in, is the global control variable based on feedforward-feedback at time k, To control the target variable, the deviation vector Feedback amount of global control variables based on feedforward-feedback Among them, k p The feedback amount Adjustable parameter matrix; Feedforward amount of global control variables based on feedforward-feedback in, The vector is the known target value. is the system's own heat source disturbance value, is the system ambient temperature vector.

Images