Temperature control method and system based on heating system characteristics

Abstract

The invention provides a temperature control method and system based on heating system characteristics. The method comprises the steps that a compensation function is set; establishing a prediction model; and forming temperature control according to the compensation function and the prediction model. According to the method, by introducing the compensation function and establishing the time domain prediction model, the rapidity, the real-time performance, the accuracy and the thermal load loading stability in the thermal test tracking control process are improved, the method can be applied to aerodynamic thermal simulation tests of multiple carrying and weapon models, and the test reliability is improved; a control strategy is established, the influence of long lag and large inertia of the controller is restrained, and the engineering problems of under-test, over-test and the like which seriously restrict the test completion quality under the condition that the safety margin of the structural strength is low are solved.

Description

Technical Field

[0001] This invention belongs to the field of thermal load loading technology for aerodynamic thermal simulation tests. Specifically, it relates to a temperature control method and system based on the characteristics of a heating system, and in particular, to a method for stable loading of thermal loads in aerodynamic thermal simulation tests. Background Technology

[0002] Aircraft are subjected to harsh aerodynamic and thermal environments during high-speed flight, so the reliability of the heat protection scheme must be verified in ground tests. However, during the thermal test control process, due to the long lag and large inertia of the controller, the tracking performance exhibits obvious hysteresis and overshoot, which is particularly serious under high heat flux test conditions.

[0003] This is a common phenomenon during thermal testing. When the structural strength safety margin is low, the resulting undertesting and overtesting can bring significant testing risks.

[0004] The patent document "Rapid Time-Varying Thermal Load Control Method in Thermal Testing for Strength Testing of Aerospace Aircraft" (CN114706295A) discloses a rapid time-varying thermal load control method. By acquiring thermal load control data, designing temperature zone division and calibration curves, and combining proportional feedforward, derivative feedforward and PID control parameters, a combination of self-tuning mode and PID control mode is achieved. The controller output voltage is adjusted to achieve precise control of the thermal load. However, it may still produce engineering problems such as undertesting and overtesting, which seriously restrict the quality of test completion, and its applicability is poor.

[0005] The patent document "A Model Predictive Compensation Control Method for High Heat Flux Tests" (CN119512258A) discloses a predictive model for controlling a high heat flux test system using a graphite heating finite element model. It expresses the radiative heating process of the graphite heating element on the test piece through the principle of virtual work, establishes the transfer function between the input electrical power and output radiant heat of the controlled object, and pre-adjusts and compensates the controller parameters through closed-loop control and a time-series predictive model. However, the graphite heater has very high thermal inertia and slow response speed, and its compensation logic is completely different from that of a quartz lamp, making it unsuitable for conversion and use.

[0006] A new temperature control method based on the characteristics of the heating system needs to be established, which can reduce the impact of large inertia and time delay in the characteristics of the heating system and effectively avoid over-testing or under-testing. Summary of the Invention

[0007] In view of the deficiencies in the prior art, the purpose of this invention is to provide a temperature control method and system based on the characteristics of a heating system.

[0008] A temperature control method based on heating system characteristics provided by the present invention includes: Step S1: Set the compensation function; Step S2: Establish a prediction model based on the compensation function and the predicted data; Step S3: Based on the compensation function and prediction model, form a temperature control.

[0009] Preferably, in step S1, the transfer function of the closed-loop system is obtained based on the relationship between the input and output of the heating system. for:

[0010] Constructing a compensation controller The new closed-loop control system obtained by replacing the original controller transfer function for:

[0011] Derivation of the Delay Element Compensation Controller The transfer function is:

[0012] Connect a compensation function in reverse parallel:

[0013] in, Represents the transfer function of the controller in the system; The transfer function of the controlled object in a closed-loop system; This represents the transfer function of the delayed portion of the controlled object in a closed-loop system.

[0014] Preferably, in step S2, based on the characteristics of the quartz lamp heating system, a time-domain prediction method is used to establish an ARMA model of the heating system, predict the output variables of the heating system, and determine the real-time predictive control strategy for the temperature of the heating system.

[0015] Preferably, step S2 includes: Step S2.1: Based on the experimental data of temperature changes in the heating system, analyze the output variables of the heating system. With input variables The variation pattern between these values ​​yields the transfer function, representing the characteristics of the heating system:

[0016] Step S2.2: Based on the predicted data, a data sequence is formed according to the changes over time. Then, based on the causal relationships between variables in the multiple linear regression analysis, the prediction model is obtained:

[0017] Where Y() represents the output variable of the closed-loop control system; X() represents the input variable of the closed-loop control system; K represents the system gain; T represents the system inertial time constant; and S represents the Laplace operator. , , , , These represent the preceding data sequences. Step value , , The coefficient; , , , , These represent different influencing factors. , , The coefficients; t represents the total ordinal number; i, m, and n represent different ordinal numbers; Z represents the total error.

[0018] Preferably, in step S3, the compensation function moves the delay element of the transfer function within the closed-loop system outside the closed-loop system.

[0019] A predictive model is introduced into the heating system to predict the future temperature trend of the heating system based on the past temperature and control parameters.

[0020] A temperature control system based on heating system characteristics provided by the present invention includes: Module M1, set the compensation function; Module M2: Establish a prediction model based on the compensation function and the predicted data; Module M3 forms temperature control based on the compensation function and prediction model.

[0021] Preferably, in module M1, the transfer function of the closed-loop system is obtained based on the relationship between the input and output of the heating system. for:

[0022] Constructing a compensation controller The new closed-loop control system obtained by replacing the original controller transfer function for:

[0023] Derivation of the Delay Element Compensation Controller The transfer function is:

[0024] Connect a compensation function in reverse parallel:

[0025] in, Represents the transfer function of the controller in the system; The transfer function of the controlled object in a closed-loop system; This represents the transfer function of the delayed portion of the controlled object in a closed-loop system.

[0026] Preferably, in module M2, based on the characteristics of the quartz lamp heating system, a time-domain prediction method is used to establish an ARMA model of the heating system, predict the output variables of the heating system, and determine the real-time predictive control strategy for the temperature of the heating system.

[0027] Preferably, the module M2 includes: Module M2.1: Based on the experimental data of temperature changes in the heating system, analyze the output variables of the heating system. With input variables The variation pattern between these values ​​yields the transfer function, representing the characteristics of the heating system:

[0028] Module M2.2: Based on the predicted data, a data sequence is formed according to the changes over time. Based on the causal relationships between variables in multiple linear regression analysis, a prediction model is obtained.

[0029] Where Y() represents the output variable of the closed-loop control system; X() represents the input variable of the closed-loop control system; K represents the system gain; T represents the system inertial time constant; and S represents the Laplace operator. , , , , These represent the preceding data sequences. Step value , , The coefficient; , , , , These represent different influencing factors. , , The coefficients; t represents the total ordinal number; i, m, and n represent different ordinal numbers; Z represents the total error.

[0030] Preferably, in module M3, the compensation function moves the delay element of the transfer function within the closed-loop system outside the closed-loop system.

[0031] A predictive model is introduced into the heating system to predict the future temperature trend of the heating system based on the past temperature and control parameters.

[0032] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention can be applied to aerodynamic and thermal simulation tests of multiple carrier and weapon models, improving the reliability of the tests and having high applicability.

[0033] 2. This invention improves the speed, real-time performance, accuracy, and stability of thermal load loading in the thermal test tracking control process by introducing a compensation function and establishing a time-domain prediction model.

[0034] 3. This invention establishes a control strategy for special controlled objects such as heating systems, which suppresses the effects of long controller lag and large inertia, and solves the engineering problems that seriously restrict the quality of test completion, such as undertesting and overtesting, when the structural strength safety margin is low. Attached Figure Description

[0035] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of a temperature control method based on the characteristics of a heating system. Figure 2 This is a schematic block diagram of the control system in the actual process of an embodiment of the present invention; Figure 3 This is a schematic diagram of a control system with a compensation controller added in an embodiment of the present invention; Figure 4 This is a schematic diagram of a control system incorporating a compensation function in an embodiment of the present invention; Figure 5 This is a schematic diagram of a control system incorporating a predictive model in an embodiment of the present invention; Figure 6 This is a schematic diagram of a control system structure based on heater characteristics. Detailed Implementation

[0036] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the scope of protection of the present invention.

[0037] During thermal testing, the controller exhibits significant hysteresis and overshoot due to long time lag and large inertia. This invention provides a temperature control method based on the characteristics of the heating system. By introducing a compensation function and establishing a time-domain prediction model, the method reduces the impact of large inertia and time lag in the heating system characteristics, improves the stability of thermal load application, and effectively avoids over-testing or under-testing. Figure 1 For example, including: Step S1: Introduce the compensation function.

[0038] Specifically, design the controller compensation function. Figure 2 For example, in the block diagram of a control system containing a pure time delay element in actual process, R(s) represents the input of the closed-loop control system, which is the given value of the system; Y(s) represents the output of the closed-loop control system, which is the feedback value of the system. This represents the transfer function of the controller in the system (the controller of a closed-loop system generally uses the traditional PID algorithm for control). This represents the transfer function of the controlled object in a closed-loop system. This represents the transfer function of the delayed portion of the controlled object in a closed-loop system.

[0039] Based on the relationship between the system's input and output, the transfer function of its closed-loop system is obtained as follows:

[0040] Among them, the transfer function The denominator contains a pure delay element. .

[0041] In this case, the delay element It is within a closed-loop control system. Such a structure will reduce the stability of the control system and affect the dynamic performance of the system.

[0042] For those with delays The control system, with Figure 3 For example, construct a compensation controller. To replace the previous controller in the closed-loop system, a closed-loop control system is obtained. The transfer function is:

[0043] Through the and The derivation and transformation of the expression yields the delay element compensation controller. The transfer function is:

[0044] The delay-element compensation controller is based on the original controller with a compensation element connected in reverse parallel. By using the relationship of the delay-element compensation controller and its control system structure, we can obtain... Figure 4 For example, in the controller of a practical closed-loop control system Based on this, a compensation function is connected in reverse parallel, which is the transfer function of the parallel compensation stage:

[0045] Step S2: Establish a system prediction model.

[0046] by Figure 5 For example, based on the characteristics of the quartz lamp heating system, a time-domain prediction method is used to establish an ARMA model of the heating system, predict the output variables of the quartz lamp heating system, and determine the real-time predictive control strategy for the heating system temperature.

[0047] The filament (usually a tungsten filament) of the quartz lamp heating system is extremely thin and encapsulated within a quartz tube in a vacuum or inert gas atmosphere. It has a small heat capacity and low mass. When energized, the filament reaches a high temperature almost instantaneously (primarily through radiative heat transfer), and cools down relatively quickly after power is cut off (although the quartz tube outer shell has some thermal inertia). Therefore, while the thermal inertia is relatively small, the temperature rises and falls extremely rapidly (milliseconds to seconds), resulting in a small internal temperature gradient and a fast response speed.

[0048] The compensation function focuses on: matching rapid response, suppressing overshoot, controlling heating / cooling rates, and preventing oscillation. The core is "precise control" and "preventing oscillation," which means that sensitive, fast, and accurate compensation is needed to manage its high-speed response capability. The key is to accurately track the set trajectory (especially the rate of change) and avoid loss of control (overshoot, oscillation) due to excessively fast response.

[0049] Therefore, the maximum instantaneous power of quartz lamp heating needs to be strictly controlled in the prediction model.

[0050] Specifically, including: Step S2.1: Based on the experimental data of temperature changes in the quartz lamp heating system, analyze the relationship between the system's temperature (output variable) and control voltage (input variable) to obtain the transfer function representing the heating system's characteristics:

[0051] Where X() represents the input variable, K represents the system gain, T represents the system inertial time constant, S represents the Laplace operator, and Y() represents the output variable.

[0052] Step S2.2: Discretize the transfer function of the heating system and obtain the Z-function form of the heating system through transformation equations:

[0053] Where k represents the number of sampling periods, n represents the number of delayed sampling periods, and Z -N This represents the corresponding delay operator in the Z-domain.

[0054] The predicted data forms a data sequence according to its changes over time. The sequence On the one hand, it will be affected by factors, that is, compared with the past Every Moment of Moment , , , , Related, can be expressed as:

[0055] in, Represents excitation error, , , , , Different influencing factors , , The coefficient.

[0056] On the other hand, data sequences It will also be affected by its own pattern of change, that is, its relationship with the past. Each value of the step , , The relevant pattern can be expressed by the following formula:

[0057] in, This represents the error in value selection. , , , , The data sequence before Step value , , The coefficient.

[0058] Based on the causal relationships between variables in multiple linear regression analysis, the most commonly used expression for the ARMA model is shown in the following formula:

[0059] After further processing, the following results were obtained:

[0060] By utilizing the hysteresis property of the Z-transform to transform each part of the system's Z-function, the difference equation of the heating system is obtained:

[0061] Therefore, a time series prediction model for the heating system is established, and then based on the input variables of the system at past times... and output variables Predicting system temperature in the near future The changing state of the temperature is recorded, and the predicted value of the heating temperature at the corresponding time is obtained.

[0062] For special controlled objects such as heating systems, a control strategy was established to suppress the effects of long controller lag and large inertia, and to solve engineering problems such as undertesting and overtesting that seriously restrict the quality of test completion when the structural strength safety margin is low.

[0063] Step S3: Develop a temperature control method.

[0064] Specifically, with Figure 6 For example, by introducing a compensation function and establishing a time-domain prediction model, a temperature control method is formed, which improves the speed, real-time performance, and accuracy of the thermal test tracking control process.

[0065] The controller compensation function moves the delay element of the transfer function within the closed-loop system outside the closed-loop system to reduce the impact of the delay element on the control process and control performance of the closed-loop system.

[0066] The key to solving the problem of large inertia in heating systems lies in accelerating system regulation so that the controlled variable can reach a given value in a short time. Inspired by the principle of prediction, a predictive model is introduced into the heating system. This model can predict the future temperature trend of the heating system based on the past temperature and control variables, thus feeding the predicted temperature value back into the system in advance and accelerating the controller's regulation.

[0067] The present invention also provides a temperature control system based on the characteristics of a heating system. The temperature control system based on the characteristics of a heating system can be implemented by executing the process steps of the temperature control method based on the characteristics of a heating system. That is, those skilled in the art can understand the temperature control method based on the characteristics of a heating system as a preferred embodiment of the temperature control system based on the characteristics of a heating system.

[0068] A temperature control system based on heating system characteristics provided by the present invention includes: Module M1, set the compensation function; Module M2: Establish a prediction model based on the compensation function and the predicted data; Module M3 forms temperature control based on the compensation function and prediction model.

[0069] In more preferred embodiments, the transfer function of the closed-loop system is obtained in module M1 based on the relationship between the input and output of the heating system. for:

[0070] Constructing a compensation controller The new closed-loop control system obtained by replacing the original controller transfer function for:

[0071] Derivation of the Delay Element Compensation Controller The transfer function is:

[0072] Connect a compensation function in reverse parallel:

[0073] in, Represents the transfer function of the controller in the system; The transfer function of the controlled object in a closed-loop system; This represents the transfer function of the delayed portion of the controlled object in a closed-loop system.

[0074] In more preferred embodiments, in module M2, based on the characteristics of the quartz lamp heating system, a time-domain prediction method is used to establish an ARMA model of the heating system, predict the output variables of the heating system, and determine the real-time predictive control strategy for the temperature of the heating system.

[0075] In more preferred embodiments, module M2 includes: Module M2.1: Based on the experimental data of temperature changes in the heating system, analyze the output variables of the heating system. With input variables The variation pattern between these values ​​yields the transfer function, representing the characteristics of the heating system:

[0076] Module M2.2: Based on the predicted data, a data sequence is formed according to the changes over time. Based on the causal relationships between variables in multiple linear regression analysis, a prediction model is obtained.

[0077] Where Y() represents the output variable of the closed-loop control system; X() represents the input variable of the closed-loop control system; K represents the system gain; T represents the system inertial time constant; and S represents the Laplace operator. , , , , These represent the preceding data sequences. Step value , , The coefficient; , , , , These represent different influencing factors. , , The coefficients; t represents the total ordinal number; i, m, and n represent different ordinal numbers; Z represents the total error.

[0078] In more preferred embodiments, in module M3, the compensation function moves the delay element of the transfer function within the closed-loop system outside the closed-loop system.

[0079] A predictive model is introduced into the heating system to predict the future temperature trend of the heating system based on the past temperature and control parameters.

[0080] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function as logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0081] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A temperature control method based on the characteristics of a heating system, characterized in that, include: Step S1: Set the compensation function; Step S2: Establish a prediction model based on the compensation function and the predicted data; Step S3: Based on the compensation function and prediction model, form a temperature control.

2. The temperature control method based on the characteristics of the heating system according to claim 1, characterized in that, In step S1, the transfer function of the closed-loop system is obtained based on the relationship between the input and output of the heating system. for: Constructing a compensation controller The new closed-loop control system obtained by replacing the original controller transfer function for: Derivation of the Delay Element Compensation Controller The transfer function is: Connect a compensation function in reverse parallel: in, Represents the transfer function of the controller in the system; The transfer function of the controlled object in a closed-loop system; This represents the transfer function of the delayed portion of the controlled object in a closed-loop system.

3. The temperature control method based on the characteristics of the heating system according to claim 1, characterized in that, In step S2, based on the characteristics of the quartz lamp heating system, a time-domain prediction method is used to establish an ARMA model of the heating system, predict the output variables of the heating system, and determine the real-time predictive control strategy for the temperature of the heating system.

4. The temperature control method based on the characteristics of the heating system according to claim 3, characterized in that, Step S2 includes: Step S2.1: Based on the experimental data of temperature changes in the heating system, analyze the output variables of the heating system. With input variables The variation pattern between these values ​​yields the transfer function, representing the characteristics of the heating system: Step S2.2: Based on the predicted data, a data sequence is formed according to the changes over time. Then, based on the causal relationships between variables in the multiple linear regression analysis, the prediction model is obtained: Where Y() represents the output variable of the closed-loop control system; X() represents the input variable of the closed-loop control system; K represents the system gain; T represents the system's inertial time constant; S represents the Laplace operator; , , , , These represent the preceding data sequences. Step value , , The coefficient; , , , , These represent different influencing factors. , , The coefficient; t represents the total ordinal number; i, m, and n represent different ordinal numbers; Z represents the total error.

5. The temperature control method based on the characteristics of the heating system according to claim 1, characterized in that, In step S3, the compensation function moves the delay element of the transfer function within the closed-loop system to outside the closed-loop system. A predictive model is introduced into the heating system to predict the future temperature trend of the heating system based on the past temperature and control parameters.

6. A temperature control system based on the characteristics of a heating system, characterized in that, include: Module M1, set the compensation function; Module M2: Establish a prediction model based on the compensation function and the predicted data; Module M3 forms temperature control based on the compensation function and prediction model.

7. The temperature control system based on the characteristics of the heating system according to claim 6, characterized in that, In module M1, the transfer function of the closed-loop system is obtained based on the relationship between the input and output of the heating system. for: Constructing a compensation controller The new closed-loop control system obtained by replacing the original controller transfer function for: Derivation of the Delay Element Compensation Controller The transfer function is: Connect a compensation function in reverse parallel: in, Represents the transfer function of the controller in the system; The transfer function of the controlled object in a closed-loop system; This represents the transfer function of the delayed portion of the controlled object in a closed-loop system.

8. The temperature control system based on the characteristics of the heating system according to claim 6, characterized in that, In module M2, based on the characteristics of the quartz lamp heating system, a time-domain prediction method is used to establish an ARMA model of the heating system, predict the output variables of the heating system, and determine the real-time predictive control strategy for the temperature of the heating system.

9. The temperature control system based on the characteristics of the heating system according to claim 8, characterized in that, The module M2 includes: Module M2.1: Based on the experimental data of temperature changes in the heating system, analyze the output variables of the heating system. With input variables The variation pattern between these values ​​yields the transfer function, representing the characteristics of the heating system: Module M2.2: Based on the predicted data, a data sequence is formed according to the changes over time. Based on the causal relationships between variables in multiple linear regression analysis, a prediction model is obtained. Where Y() represents the output variable of the closed-loop control system; X() represents the input variable of the closed-loop control system; K represents the system gain; T represents the system's inertial time constant; S represents the Laplace operator; , , , , These represent the preceding data sequences. Step value , , The coefficient; , , , , These represent different influencing factors. , , The coefficient; t represents the total ordinal number; i, m, and n represent different ordinal numbers; Z represents the total error.

10. The temperature control system based on the characteristics of the heating system according to claim 6, characterized in that, In module M3, the compensation function moves the delay element of the transfer function within the closed-loop system to outside the closed-loop system; A predictive model is introduced into the heating system to predict the future temperature trend of the heating system based on the past temperature and control parameters.

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