A global mk order precision temperature control method based on thermocouple and kalman filter

Abstract

The present application relates to a kind of global mk level precision temperature control method based on thermocouple and Kalman filtering, comprising the following steps: S1, arrangement thermocouple temperature measurement network;S2, establish the mathematical model of temperature measurement and control system;S3, establish the discretization temperature measurement and control system model containing process noise and the thermocouple observation model containing measurement noise;S4, obtain the prior estimate of system temperature at current time, calculate prior state estimation error covariance matrix;S5, obtain the posterior state estimation value of system temperature, update posterior state error covariance matrix, obtain the correction model of temperature measurement and control system;S6, obtain temperature measurement and control system error vector;S7, obtain global control variable based on feedforward-feedback, by executing component acts on temperature control object;S8, repeat step S4-step S7, form closed loop control, realize the global mk level temperature control precision of temperature measurement and control system.The beneficial effect is good, strong coordination.

Description

TECHNICAL FIELD

[0001] The application relates to the field of satellite aerospace technology, and particularly relates to a global MK-level precision temperature control method based on a thermocouple and a Kalman filter. BACKGROUND

[0002] In spacecraft, precise temperature control technology is crucial for ensuring normal operation of equipment and prolonging service life. An active temperature control system adjusts the temperature in real time to ensure that each component operates within the optimal 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] Currently, the commonly used temperature measurement sensors in spacecraft mainly include three types: thermocouples, thermal resistors, and thermistors, each with different characteristics: ①Thermocouples measure temperature by using the thermoelectric potential difference generated at the junction of two different metals, which has the advantages of good consistency, wide measurement range, fast response speed, simple structure, and low cost, but its anti-interference performance is poor and its precision is not high; ②Thermal resistors measure temperature by using the property that the resistance of the material changes with temperature, and the commonly used materials are platinum (such as Pt100, Pt1000), which have high precision and good stability, but poor consistency, slow response speed, and high price; ③Thermistors are based on the property that the resistance of semiconductor materials changes with temperature, which has high sensitivity, but poor linearity and consistency, and high price. Due to the weak anti-interference performance of thermocouples, thermal resistors and thermistors are currently used as temperature measurement sensors in spacecraft, but their consistency is poor, making it difficult to meet the needs of systematic precise temperature control; their response speed is slow, making it difficult to accurately capture high-frequency temperature fluctuation signals; and their price is high, making it difficult to reduce the cost of spacecraft development.

[0004] Current temperature control algorithms mainly use threshold control and PID control, which are model-free methods. Threshold control is a simple on-off control method, in which the system sets an allowed 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 below the lower limit, the system starts the heating device. This control method is simple and clear, and does not require complex calculations, but its response characteristics are rough, which can easily lead to repeated fluctuations around the target value, making it impossible to achieve precise control. A PID controller consists of proportional (P), integral (I), and derivative (D) components, which work together to adjust the system output to achieve the target value. P control responds quickly to errors, 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 value at one time in complex space environments, and is sensitive to model changes, which can easily fail in complex working conditions.

[0005] Moreover, these algorithms are usually designed based on single-input single-output (SISO) systems, which are simple to implement but can only control one input and one output, making it difficult to handle complex relationships and cross-coupling problems between multiple inputs and outputs. In multivariable systems, it is necessary to decompose 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 faced with complex multivariable systems, it is necessary to consider using multi-input multi-output (MIMO) systems or other more complex control strategies.

[0006] In summary, the existing temperature sensors and control algorithms have deficiencies in measurement accuracy, anti-interference ability, response speed and adaptability to complex environments, which greatly limits the development of spacecraft to achieve mK-level precise temperature control technology. New technological breakthroughs are needed to improve the overall performance of spacecraft temperature control systems and meet future high-precision temperature control needs.

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

[0008] 1) Sensor performance limitations

[0009] Thermocouple technology has low precision and insufficient anti-interference ability in spacecraft precision temperature measurement. Although thermal resistance and thermistor have high measurement sensitivity, their accuracy and consistency are insufficient, limiting 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 resistance and thermistor and the limitations of control strategies, making it difficult to achieve fast and accurate temperature regulation.

[0012] 3) Insufficient adaptability to complex environments

[0013] Spacecrafts need high environmental adaptability under extreme conditions, but traditional SISO control and PID control have limitations in adaptability to multivariable interaction and environmental changes, affecting temperature control accuracy and system robustness.

[0014] 4) Lack of data processing and optimization technology

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

[0016] 5) Cost and consistency balance problem

[0017] The cost of high-performance sensors and the rigorous screening calibration process required for each sensor result in difficulty in ensuring consistency between sensors and accuracy over a long period of use while pursuing high sensitivity, affecting the reliability and economy of the system.

[0018] 6) Global temperature sensing and MIMO temperature control

[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 regulation in a multiple-input multiple-output (MIMO) system, affecting the comprehensive and accurate management and dynamic adjustment of the internal temperature field of the spacecraft.

[0020] Kalman filtering is an algorithm that uses linear system state equations to optimally estimate the system state based on system input and output observation data; since the observation data includes the effects of noise and interference in the system, the optimal estimation can also be considered as a filtering process.

[0021] The present application aims to improve the spacecraft temperature control method in view of the technical problems of existing temperature sensors and control algorithms in terms of measurement accuracy, anti-interference ability, response speed and adaptability to complex environments. SUMMARY

[0022] The purpose of the present application is to provide a method for global mK-level precision temperature regulation of temperature control objects on a controlled spacecraft with good comprehensiveness and strong coordination.

[0023] To achieve the above-mentioned purpose, the technical solution adopted by the present application is a global mk-level precision temperature control method based on thermocouples and Kalman filtering, comprising the following steps:

[0024] S1, arranging a thermocouple temperature measurement network, and arranging all cold ends of the thermocouples in the thermocouple temperature measurement network in a temperature source with mk-level precision;

[0025] S2, the temperature measurement and control system includes temperature control objects, 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 environment temperature;

[0026] S3, a discretized temperature measurement and control system model containing process noise and a thermocouple observation model containing measurement noise are established, and the process noise covariance matrix and the measurement noise covariance matrix are determined;

[0027] S4, the system temperature posteriori estimation value of the previous time, the control variable is input into the measurement and control temperature system prediction model through the Kalman filter, the system temperature priori estimation value of the current time is obtained, and the priori state estimation error covariance matrix is calculated;

[0028] S5, the Kalman gain matrix is calculated by using the priori state estimation error covariance matrix and the process noise covariance matrix through the Kalman filter, the system temperature priori estimation value and the actual thermocouple sampling value are fused to obtain the system temperature posteriori state estimation value, the posteriori state error covariance matrix is updated by using the priori state error covariance matrix and the Kalman gain matrix, and the measurement and control temperature system correction model is obtained;

[0029] S6, the global temperature control target vector of the measurement and control temperature system is set, and the measurement and control temperature system error vector is obtained by comparing the system temperature posteriori estimation value obtained by the Kalman filter;

[0030] S7, the measurement and control temperature system error vector, the actual measured value of the self heat source and the actual measured value of the environment temperature are taken as the input of the global controller based on the feedforward-feedback, the global control variable based on the feedforward-feedback is obtained, and the global control variable based on the feedforward-feedback is obtained. The controller acts on the temperature control object through the execution component;

[0031] S8, steps S4-S7 are repeated to form a closed loop control, and the global mk level temperature control precision of the measurement and control temperature system is realized.

[0032] Preferably, step S2: the state expression of the temperature field of the temperature control object Wherein, 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 environment 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 environment temperature parameter distribution matrix.

[0033] Preferably, step S3:

[0034] The discrete measurement and control temperature system model expression containing process noise Wherein, k represents the time after discretization, The process error vector of k-1 time is represented;

[0035] The process noise vector of the measurement and control temperature system heat transfer model is set It obeys the normal distribution Wherein, Γ is the process noise covariance matrix;

[0036] The temperature observation equation of the thermocouple network Wherein, is the temperature measurement value of the thermocouple network at time k, C is an observation matrix, is the measurement noise vector of the measurement system at time k;

[0037] The measurement noise vector of the thermocouple observation model is set obeys a normal distribution wherein Ψ is a measurement noise covariance matrix.

[0038] Preferably, step S4:

[0039] System temperature prior estimate wherein, is the system temperature prior estimate at time k, is the system temperature prior estimate at time k-1;

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

[0041] Preferably, step S5:

[0042] Kalman gain matrix

[0043] System temperature posterior state estimate wherein k k is the Kalman gain matrix;

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

[0045] Preferably, step S6:

[0046] Feedforward-feedback-based global control variable wherein, is the feedforward-feedback-based global control variable at time k, is the control target variable, and the bias vector

[0047] Feedback amount of the feedforward-feedback-based global control variable wherein k p is the feedback amount Adjustable parameter matrix;

[0048] Feedforward amount of the feedforward-feedback-based global control variable wherein vector is a known set target value, is a system itself heat source disturbance value, is a system ambient temperature vector.

[0049] The global mK-level precision temperature control method based on thermocouples and Kalman filtering has the following beneficial effects: the thermocouple temperature measurement technology and the digital Kalman filtering technology are integrated, on the basis of realizing mK-level precision sensing, a global temperature field regulation method with feedforward-feedback as the core is adopted. 1. Integrated innovative technology to enhance precision, by combining the direct temperature measurement capability of the thermocouple and the data optimization function of the Kalman filtering algorithm, the problems of noise interference and signal drift in traditional thermocouple technology in high-precision measurement are effectively overcome, the dynamic adaptability of Kalman filtering not only realizes real-time data processing, but also greatly improves the purity and accuracy of the measurement data through adaptive adjustment, laying a foundation for mK-level temperature sensing; 2. Precise sensing of global mK-level temperature field, breaking through the limitation of traditional local measurement, using MIMO architecture to integrate multi-point thermocouple data, through Kalman filtering processing, high-resolution temperature sensing of the whole spacecraft is realized, and this kind of global view temperature monitoring provides detailed and reliable data support for accurate temperature control strategy; 3. MIMO system promotes multi-dimensional temperature regulation, aiming at the complexity of spacecraft thermal management, the MIMO system designed in the application can process multiple inputs (sensor data) and outputs (temperature control instructions) at the same time, realizing efficient cooperative control of multi-region temperature, which 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 in the application has high environmental adaptability, can quickly adjust the control strategy according to the dynamic changes of the external environment, ensures the timeliness and accuracy of temperature control, and provides a stable and reliable temperature control environment for spacecraft to perform complex tasks; 5. Optimization of cost and integration, strengthening practical value, aiming at the cost and integration challenges of high-precision temperature control system, the application fully considers the optimization of cost effectiveness and system integration in the design, through technology integration and design simplification, the implementation cost is successfully reduced, while the maintainability and long-term reliability of the system are improved, greatly enhancing the practicality and popularization potential of the technology. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 A global mK-level precision temperature control method based on thermocouples and Kalman filtering is a thermocouple temperature measurement network schematic diagram.

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

[0052] Figure 3A flowchart of a global mk-level precision temperature control method based on thermocouple and Kalman filtering. DETAILED DESCRIPTION

[0053] The application will be further described below with reference to the embodiments and the accompanying drawings.

[0054] EMBODIMENT

[0055] The embodiment realizes a global mk-level precision temperature control method based on thermocouple and Kalman filtering.

[0056] The embodiment aims to provide a temperature control method for space exploration instruments to meet their global mK-level temperature control requirements. The embodiment method uses thermocouple temperature measurement principle and Kalman filtering technology to achieve accurate temperature perception at the global mK level at low cost; based on this, combined with the multi-input multi-output temperature control algorithm, the adaptability of the temperature control strategy to complex thermal environment is improved, and finally the global mk-level temperature control precision is realized.

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

[0058] The embodiment method has the following specific features:

[0059] 1) Integration of thermocouple and Kalman filtering technology

[0060] The traditional thermocouple temperature measurement technology is combined with the advanced digital Kalman filtering algorithm, the high consistency of thermocouple and the application experience in the aerospace field are utilized, and the data processing is optimized in real time through Kalman filtering technology to improve the temperature measurement accuracy to the mK level.

[0061] 2) Global mK-level accurate temperature perception

[0062] Global high-resolution perception of the internal temperature of the spacecraft is realized, which can monitor the temperature changes of the key areas in real time and accurately, providing a solid data foundation for accurate temperature control.

[0063] 3) Multi-input multi-output (MIMO) system design

[0064] A MIMO architecture is designed for complex spacecraft thermal management systems, which can process the input information of multiple temperature sensors and dynamically adjust the output of multiple temperature control devices, ensuring the comprehensiveness and coordination of system temperature control, and improving the overall control efficiency and stability of the system.

[0065] 4) Self-adapting to the environment and dynamic adjustment capability

[0066] The method of this embodiment can automatically adjust the control strategy to cope with different external environment 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 Kalman filtering algorithm not only realizes mK-level precision under the premise of cost control, but also simplifies and optimizes system integration, reduces cost and improves system reliability and maintainability.

[0069] This embodiment method uses thermocouples as temperature measurement sensors, reduces cost while fully utilizing their temperature measurement principle to improve the consistency between temperature measurement points. To address the problem that thermocouple temperature measurement is easily disturbed, digital Kalman filtering technology is introduced, and a thermocouple temperature measurement network is constructed to achieve global mK-level precision temperature perception. This embodiment proposes a multi-input multi-output (MIMO) temperature control method based on feedforward-feedback, which dynamically adjusts the output of multiple temperature control devices by processing the input information of multiple temperature sensors to ensure the comprehensiveness and coordination of system temperature control, and realizes global mK-level precision temperature regulation 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). A thermocouple is composed of two conductors (usually metals or alloys) of different materials, and the two conductors are welded together at one end to form a measurement end, which is placed in the 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, which describes the phenomenon that when the contact point of two different metals is at different temperatures, a potential difference is generated, which is proportional to the temperature difference.

[0074] 2) The size of the thermoelectric potential depends on the temperature difference and the material of the thermocouple, and each metal combination has specific thermoelectric characteristics, which can be found in the thermocouple scale.

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

[0076] 4) The temperature-potential relationship of thermocouples is generally non-linear, so the International Electrotechnical Commission and other organizations have defined standard thermocouple types, each with a specific temperature-potential characteristic curve.

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

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

[0079] Figure 1 A global mk-level precision temperature control method based on thermocouples and Kalman filtering is provided. As shown in Figure 1 To perceive the temperature field distribution of the measured system in all directions, a thermocouple temperature measurement network is arranged; 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 precision.

[0080] 2. Temperature measurement and control system

[0081] Figure 2 A global mk-level precision temperature control method based on thermocouples and Kalman filtering is provided. As shown in Figure 2 The temperature measurement and control system is composed of a controlled object, a thermocouple network, and a control system, and 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-heating source variable, the vector is the system ambient temperature variable, the matrix A is the system thermal characteristic parameter matrix, the matrix B1 is the control variable distribution matrix, the matrix B2 is the system heat source parameter distribution matrix, and the matrix B3 is the system ambient temperature parameter distribution matrix.

[0084] 3. Kalman filter

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

[0086] 1) Discrete system

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

[0088]

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

[0090] The process noise vector of the heat transfer model is set as obeys a normal distribution, that is:

[0091]

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

[0093] The temperature observation equation of 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] The measurement noise vector of the observation model is set as obeys a normal distribution, that is:

[0097]

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

[0099] 2) State prediction

[0100] The prior state estimate is:

[0101]

[0102] In the above formula, is the prior state estimate at time k, is the posterior state estimate 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 prior state estimation error covariance matrix at time k, and P[k-1] is the posterior state estimation error covariance matrix at time k-1.

[0106] 3) Update correction

[0107] The Kalman gain is as follows:

[0108]

[0109] The posteriori estimation is as follows:

[0110]

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

[0112] The posteriori 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, is the control target variable, and the bias vector is calculated as follows:

[0119]

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

[0121]

[0122] The feedforward amount 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 actual conditions; the vector in the feedforward control vector is the known set target value; is the self heat source disturbance value, which can be obtained by real-time measurement of the power value; is the environmental temperature vector, which can be read in real time by a temperature sensor.

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

[0126] 1) According to the actual demand, arrange the thermocouple temperature measurement network, and arrange all the cold ends of the thermocouple temperature measurement network in the temperature source with mk-level precision;

[0127] 2) According to the actual temperature control object and the measurement and control temperature system, a mathematical model is established, including system temperature, control variable, self heat source and environment temperature;

[0128] 3) Establish a discrete system model containing process noise and a thermocouple observation model containing measurement noise, and determine the process noise covariance matrix and the measurement noise covariance matrix;

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

[0130] 5) Using the priori state estimation error covariance matrix and the process noise covariance matrix, the Kalman gain matrix is calculated, the priori estimation value of the system temperature and the actual thermocouple sampling value are fused to obtain the posteriori state estimation value of the system temperature. Using the priori state error covariance matrix and the Kalman gain matrix, the posteriori state error covariance matrix is updated.

[0131] 6) Set the global temperature control target vector, and obtain the system error vector by comparing the Kalman filtering posteriori estimation temperature.

[0132] 7) Taking the system error vector, the measured value of the self heat source and the measured value of the environment temperature as the input of the controller, the global control variable based on feedforward-feedback is obtained, and the controller is applied to the temperature control object through the execution component.

[0133] 8) Repeat steps 4-7 to form a closed loop control and realize global mk-level temperature control precision.

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

[0135] 1) Global mK-level temperature precision sensing: by integrating thermocouple temperature measurement and Kalman filtering technology, the embodiment realizes global mK-level temperature precision sensing capability. This technology breaks through the spatial limitation of traditional technology and can comprehensively and meticulously monitor the temperature distribution in the spacecraft, providing accurate and 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) High adaptability MIMO system design: This system not only can handle and coordinate multiple input-output channels simultaneously, achieving precise control of multi-zone temperature, but also shows good adaptability in complex dynamic environments. By dynamically adjusting the control strategy, the system can quickly respond to environmental changes, ensuring the stability of the spacecraft's internal temperature, greatly enhancing the reliability of the system and the success rate of task execution.

[0137] 3) Balance between low cost and high performance: Through the careful design of thermocouple usage strategy and the efficient integration of Kalman filtering algorithm, the overall cost of high-precision temperature sensing system is effectively reduced, while the running efficiency and maintenance simplicity of the system are improved.

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

[0139] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiments can be completed by hardware, or by program to instruct related hardware to complete, and the program can be stored in a computer readable storage medium, wherein the storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc.

[0140] The above is only the preferred embodiment of the present application. It should be noted that those skilled in the art can make several improvements and supplements without departing from the principles of the present application, and these improvements and supplements should also be considered within the protection scope of the present application.

Claims

1. A global mk-order accuracy temperature control method based on thermocouple and Kalman filter, characterized in that The method comprises the following steps: S1, arranging a thermocouple temperature measurement network, and arranging all cold ends of the thermocouples in the thermocouple temperature measurement network in a temperature source with mk level accuracy; S2, the temperature measurement and control system comprises a temperature control object, a thermocouple temperature measurement network, a Kalman filter and a global controller based on feedforward-feedback, a prediction model of the temperature measurement and control system is established, and the prediction model comprises system temperature, system control variables, system heat source and system environment temperature; S3, a discrete prediction model of the temperature measurement and control system containing process noise and a thermocouple observation model containing measurement noise are established, and a process noise covariance matrix and a measurement noise covariance matrix are determined; S4, the Kalman filter is used to input the system temperature posterior state estimation value at the previous moment and the system control variables into the prediction model of the temperature measurement and control system, to obtain a system temperature prior estimation value at the current moment, and to calculate a prior state estimation error covariance matrix; S5, the Kalman filter is used to calculate a Kalman gain matrix by using the prior state estimation error covariance matrix and the process noise covariance matrix, to obtain a system temperature posterior state estimation value by fusing the system temperature prior estimation value and an actual thermocouple sampling value, and to update the posterior state error covariance matrix by using the prior state estimation error covariance matrix and the Kalman gain matrix, so as to obtain a correction model of the temperature measurement and control system; S6, a global temperature control target vector of the temperature measurement and control system is set, and a temperature measurement and control system error vector is obtained by comparing the global temperature control target vector with the system temperature posterior state estimation value obtained by the Kalman filter; S7, the temperature measurement and control system error vector, a measured value of the system heat source and a measured value of the environment temperature are taken as inputs of the global controller based on feedforward-feedback, to obtain a global control variable based on feedforward-feedback, and the global control variable based on feedforward-feedback is applied to the temperature control object through an execution component; S8, steps S4 to S7 are repeated to form a closed loop control, and global mk level temperature control accuracy of the temperature measurement and control system is realized.

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

3. The global precision temperature control method based on thermocouple and Kalman filter of claim 2, wherein Step S3: Expression of discretization measurement and control temperature system prediction model containing process noise wherein k represents the time after discretization, represents the process error vector at k-1 time Setting process noise vector for a predictive model of a temperature control system Subjection to normal distribution where Γ is a process noise covariance matrix; Thermocouple observation model wherein, is the thermocouple network temperature measurement at time k, C is the observation matrix, is the measurement noise vector of the measurement system at time k; Setting a measurement noise vector for a thermocouple observation model Subjection to normal distribution where Ψ is a measurement noise covariance matrix.

4. The global precision temperature control method based on thermocouple and Kalman filter of claim 3, wherein Step S4: System temperature prior estimate wherein, is the system temperature prior estimate at time k, is the system temperature prior estimate at time k-1; The prior state estimation error covariance matrix is P - [k] = AP[k-1]A T + Γ, wherein P - [k] is the prior state estimation error covariance matrix at time k, and P[k-1] is the posterior state estimation error covariance matrix at time k-1.

5. The global precision temperature control method based on thermocouple and Kalman filter of claim 4, wherein Step S5: kalman gain matrix System temperature posterior state estimate where k k is the Kalman gain matrix; Posterior state error covariance matrix P[k] = (I - k k C)P - [k], where I is the identity matrix.

6. The global precision temperature control method based on thermocouple and Kalman filter of claim 5, wherein Step S6: feedforward-feedback based global control variable wherein, feedforward-feedback based global control variable at time k, control target variable, bias vector Feedback quantity of global control variable based on feedforward-feedback where k p is the feedback quantity Adjustable parameter matrix feedforward amount of a global control variable based on feedforward-feedback wherein, a set target value is known, is a system self heat source disturbance value, is a system ambient temperature vector.

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