Adaptive environment test box control method based on multi-parameter coupling model
By establishing a dynamic coupling model and a feedforward decoupling control strategy, the problem of multi-parameter coordinated control of environmental testing equipment under complex working conditions was solved, achieving high-precision, fast-response, and energy-saving control effects.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SUZHOU SUSHI TESTING INSTR CO LTD
- Filing Date
- 2026-02-09
- Publication Date
- 2026-05-19
AI Technical Summary
Existing environmental testing equipment struggles to achieve multi-parameter coordinated control under complex operating conditions, resulting in low control accuracy, high energy consumption, and problems such as system oscillation and excessively long settling time.
An adaptive control method based on a multi-parameter coupling model is adopted. By establishing a dynamic coupling model to quantify the influence between parameters, a feedforward decoupling control strategy and a load-adaptive PID algorithm are used to achieve active suppression of coupling disturbances and timely adjustment of parameters.
It improves control precision and response speed, significantly suppresses system dynamic oscillations, shortens adjustment and stabilization time, reduces energy consumption, and enhances the control performance and operating efficiency of equipment under complex working conditions.
Smart Images
Figure CN122063871A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of environmental testing equipment control technology, specifically to an adaptive environmental test chamber control method based on a multi-parameter coupling model. Background Technology
[0002] Currently, for multi-parameter environmental testing equipment such as temperature and humidity vibration test chambers and high and low temperature low pressure test chambers, the mainstream control scheme generally adopts an independent PID controller to perform closed-loop regulation and control of environmental parameters such as temperature, humidity, and air pressure. This control method has a simple structure and low implementation cost, and has been widely used in simple test scenarios with single parameters and stable operating conditions, thus meeting basic parameter control requirements.
[0003] However, under actual complex experimental conditions, the aforementioned independent PID control scheme has significant technical defects and is difficult to adapt to the requirements of multi-parameter collaborative control. On the one hand, there are strong coupling interference relationships between various environmental physical quantities. For example, rapid cooling will cause a sharp increase in the relative humidity of the air inside the chamber, which will trigger the humidity controller to perform a violent dehumidification operation. The dehumidification process is usually accompanied by a cooling action, which will cause secondary interference to the temperature control system, ultimately leading to problems such as continuous oscillation and excessively long adjustment and stabilization time in the entire system, seriously affecting the control accuracy. On the other hand, the existence of coupling interference causes frequent ineffective coordination or even mutual resistance between actuators (such as compressors, heaters, and humidifiers). For example, the system may perform heating and cooling operations simultaneously, resulting in a large amount of energy being wasted, significantly increasing the energy consumption of the equipment, which is inconsistent with the industry development trend of energy conservation and consumption reduction.
[0004] Therefore, how to overcome the shortcomings of the existing technology is the subject of this invention. Summary of the Invention
[0005] The purpose of this invention is to provide an adaptive environmental test chamber control method based on a multi-parameter coupling model.
[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0007] An adaptive environmental test chamber control method based on a multi-parameter coupling model includes the following steps:
[0008] Step 1: Establish a dynamic coupling model for at least two environmental parameters. This dynamic coupling model can quantify the impact of changes in any environmental parameter on other environmental parameters.
[0009] Step 2: Send adjustment control commands to any environmental parameter change actuator through the main controller, and at least calculate the estimated impact of the adjustment control commands on other environmental parameters and obtain the coupling disturbance amount through the dynamic coupling model;
[0010] Step 3: The main controller sends a feedforward compensation command to the corresponding environmental parameter change actuator based on the coupling interference amount;
[0011] Step 4: Run the environmental parameter change actuator that receives either the regulation control command or the feedforward compensation command to adjust the corresponding environmental parameters.
[0012] In this application, "multiple" refers to at least two.
[0013] This application uses at least two of the environmental parameters, including temperature, humidity and air pressure, as examples, but does not impose any limitations.
[0014] The control method described in this application is not limited to any particular type of test chamber. It is known that existing test chambers adjust their internal environmental parameters through an actuator that changes environmental parameters in order to achieve the test objective.
[0015] In step one, a database can be established to collect test data, such as data on the effects of different temperature changes on humidity and air pressure. Based on this data, a dynamic coupling model is trained, which can then predict the impact of a change in one environmental parameter on other environmental parameters. This application does not establish three independent control loops for temperature, humidity, and air pressure. Rather, the control method described above is based on a closely linked coupled system. Through offline system identification or online learning, a dynamic mathematical model that accurately describes the interactions between parameters is established. This dynamic mathematical model can be obtained by fitting experimental data. For example, this dynamic mathematical model can quantify the following coupling relationships: the effect of temperature changes on relative humidity, the air pressure changes caused by temperature changes, the effect of humidification / dehumidification processes on temperature, and the effect of pressure changes on dew point temperature.
[0016] In step two, for example, the main controller (or controller) sends an adjustment control command to the temperature environment parameter change actuator. The purpose is to raise the current actual temperature to the target parameter temperature. The dynamic coupling model predicts the amount of coupling interference that will be generated on the other two parameters (humidity and air pressure) after the execution of the command.
[0017] In step three, the coupling interference is used as a feedforward compensation signal and superimposed on the output of the actuator corresponding to the changes in environmental parameters of humidity and air pressure, thereby achieving active suppression of coupling interference.
[0018] In step four, the temperature environment parameter change actuator, humidity environment parameter change actuator, and air pressure environment parameter change actuator are operated to achieve the purpose of temperature regulation while solving or reducing the impact of the temperature regulation process on humidity and air pressure changes.
[0019] The adaptive environmental test chamber control method in this application implements feedforward decoupling control based on a dynamic coupling model. Specifically, when the main controller issues an adjustment control command to any environmental parameter change actuator, it will (preferably simultaneously) calculate the estimated disturbance amount that the command will generate on other environmental parameters through the dynamic coupling model. Subsequently, the main controller will add the estimated disturbance amount as a feedforward quantity to the output of the relevant environmental parameter change actuator, thereby compensating for the coupling disturbance, avoiding large changes in non-target adjustment parameters (such as humidity mentioned above) that would affect the test operation of the test chamber, and realizing timely adjustment of non-target adjustment parameters to avoid prolonging the environmental parameter adjustment stage in the test chamber.
[0020] It is understandable that the temperature is an active adjustment parameter (target adjustment parameter), while humidity is a passive adjustment parameter (non-target adjustment parameter). If humidity is adjusted back after the temperature adjustment process is completed, it will prolong the time required for the adjustment phase and may also affect the temperature.
[0021] In summary, the adaptive environmental test chamber control method in this application can achieve the goal of improving control accuracy and response speed. Specifically, the control method adopts a feedforward decoupling control strategy to eliminate the coupling interference between various control parameters from the control mechanism level, significantly suppress the dynamic oscillation of the system, shorten the adjustment and stabilization time, and achieve high-precision and fast tracking control of complex environmental curves.
[0022] In a further technical solution, in step two, at most one of the environmental parameter change actuators receives and executes the adjustment control command within the same time period, until the adjustment control command is completed or execution is stopped.
[0023] For example: If a regulation control command is issued with the goal of raising the temperature by ten degrees, another regulation control command (such as adjusting the air pressure from 100 kPa to 101 kPa) can only be issued if this goal is achieved or abandoned (i.e., the regulation control command is terminated). Alternatively, even if another regulation control command is issued, the actuator will not execute the corresponding environmental parameter change. If two regulation control commands are issued simultaneously, the regulation control command will not be executed or one regulation control command will be executed randomly.
[0024] If multiple parameters need to be actively adjusted within the same time period, such as adjusting air pressure after adjusting temperature but before the adjustment is complete, the air pressure adjustment process will significantly affect the temperature adjustment process. This can easily lead to a deviation between the final temperature value and the target value, or prolong the temperature adjustment process (e.g., adjusting the temperature again based on the dynamic coupling model), which also increases the computational load based on the dynamic coupling model, thus increasing the corresponding cost. The settings in this section effectively solve this problem.
[0025] A further technical solution is that if a new adjustment control command (hereinafter referred to as the subsequent adjustment control command) is issued before the previous adjustment control command (hereinafter referred to as the preceding adjustment control command) has been executed, the execution of the preceding adjustment control command is suspended and the subsequent adjustment control command is executed instead. In this case, there is no need to add an operation to suspend the preceding adjustment control command, thus simplifying the command execution transition process.
[0026] In a further technical solution, step two involves calculating the estimated impact of the adjustment control command on other environmental parameters and obtaining the coupling interference amount using only a dynamic coupling model.
[0027] For example, consider the following: A control command is issued with the goal of raising the temperature by ten degrees. Temperature adjustment will affect air pressure, so air pressure also needs to be adjusted. However, this air pressure adjustment process will affect the temperature, so the temperature needs to be adjusted again, thus creating a cycle of repeated adjustments that affects the efficiency of environmental parameter regulation. This problem can be effectively solved by calculating the estimated impact of the control command on other environmental parameters using a dynamic coupling model, while ignoring the subsequent repeated impacts.
[0028] It should be noted that, for example, when a control command is issued to adjust the temperature, the temperature affects the air pressure. If the air pressure is adjusted, it will affect the temperature. However, the adjusted value (air pressure) is generally smaller than the adjustment value based on the control command, so its impact on the temperature is limited.
[0029] A further technical solution involves setting a change threshold and using a coupling interference amount exceeding this threshold as a prerequisite for issuing the corresponding feedforward compensation command. In this case, the estimated impact of the feedforward compensation command on other environmental parameters can be calculated based on a dynamic coupling model to obtain the coupling interference amount, and then a new feedforward compensation command can be obtained. That is, this part does not directly ignore the aforementioned iterative influence process. The change threshold is determined according to requirements, and the change thresholds for different environmental parameters may be inconsistent.
[0030] For example, if a control command is issued with the goal of raising the temperature by ten degrees, and it is predicted that the air pressure will change from 100 kPa to 101 kPa, which is greater than the air pressure change threshold (e.g., 0.5 kPa), then step three can be executed, issuing a feedforward compensation command to adjust the air pressure. If it is predicted that this air pressure adjustment process will cause a temperature change of 0.1 degrees, which is less than the temperature change threshold (e.g., 0.2 degrees), then no new feedforward compensation command will be generated.
[0031] In a further technical solution, step four, the actuator that receives either the regulation control command or the feedforward compensation command simultaneously adjusts the corresponding environmental parameters.
[0032] For example, by using a dynamic coupling model to predict the impact of cooling operations on humidity in advance, and activating the humidity change actuator (such as a dehumidifier) at the same time as the temperature change actuator, it is possible to avoid a surge in humidity due to temperature drop, thus preventing secondary temperature interference caused by the subsequent drastic operation of the humidity change actuator. If the temperature change actuator is activated first, and the humidity decreases due to temperature drop after a period of time (the decrease is described as large), then activating the humidity change actuator at this point would require the humidity change actuator to operate at high power to quickly restore the humidity. This drastic humidity restoration process would affect the temperature regulation process.
[0033] To illustrate, the temperature change actuator receives an adjustment control command, and then generates a first feedforward compensation command based on the adjustment control command. The humidity change actuator receives the first feedforward compensation command, and then generates a second feedforward compensation command based on the first feedforward compensation command. The temperature change actuator receives the second feedforward compensation command. How the temperature change actuator adjusts is guided by the adjustment control command and the second feedforward compensation command. The temperature change actuator and the humidity change actuator start together.
[0034] In some implementations, the power of the actuator receiving the feedforward compensation command is adjusted according to a predetermined variation pattern, which includes any one or a combination of gradual reduction at specific points in time and gradual reduction over a period of time. For example, if the actuator receiving the feedforward compensation command is a dehumidifier, during operation, the dehumidifier first runs at high intensity (high power) for 1 minute, then reduces its power and continues running for 1 minute, then reduces its power again and continues running for 1 minute. This section primarily emphasizes that the power of the actuator receiving the feedforward compensation command gradually decreases to minimize the possibility of significant changes in the corresponding environmental parameters.
[0035] A further technical solution involves establishing a dynamic coupling model for the three environmental parameters of temperature, humidity, and air pressure in step one. This dynamic coupling model quantifies the impact of any one of the temperature, humidity, and air pressure changes on the other two.
[0036] In step two, while the main controller sends a regulation control command to any one of the temperature change actuator, humidity change actuator, and air pressure change actuator to adjust the corresponding environmental parameters, at least the estimated impact of the regulation control command on other environmental parameters is calculated through the dynamic coupling model, and the coupling interference is obtained.
[0037] This section uses temperature, humidity, and air pressure as environmental parameters, and temperature change actuators, humidity change actuators, and air pressure change actuators as environmental parameter change actuators, making the adaptive environmental test chamber control method in this application particularly suitable for integrated test chambers involving temperature control, humidity control, and air pressure control.
[0038] For example, a temperature change actuator may include a refrigeration compressor, a humidity change actuator may include a dehumidifying evaporator, and a pressure change actuator may include an intake pump; the specific construction is not limited.
[0039] In some implementations, a dynamic coupling model can be established based on only any two of the three environmental parameters: temperature, humidity, and air pressure.
[0040] In a further technical solution, in step four, the main controller, based on a load-adaptive PID algorithm, controls the actuator receiving the adjustment control command to adjust the corresponding environmental parameter from its current value to the target value. The load-adaptive PID algorithm adaptively adjusts the proportional coefficient K based on the actual difference between the current value and the target value. p Integration time T t With differential time T d .
[0041] In traditional PID algorithms, the three core parameters (proportional coefficient, integral time, and derivative time) are fixed. This application introduces the concept of "system load" (hereinafter referred to as load), where load refers to the degree of difference between the current state (i.e., current value) and the target state (i.e., target value). The load-adaptive PID algorithm dynamically adjusts (i.e., adaptively adjusts) the proportional coefficient K based on real-time load. p Integration time T t With differential time T d This adaptive PID algorithm ensures optimal control performance across the entire operating range, avoiding the problem of parameters tuned under one operating condition becoming unstable under another. For an explanation of stability, please refer to the following.
[0042] Further technical solutions include setting load thresholds;
[0043] In step four, the difference between the current value and the target value is calculated in real time to obtain the real-time load value, and the real-time load value is compared with the load threshold.
[0044] When the real-time load value is greater than the load threshold, the first proportional coefficient K is used. p First integration time T t With the first differential time T d ;
[0045] When the real-time load value is less than or equal to the load threshold, the second proportional coefficient K is used. p Second integration time T t With the second differential time T d ;
[0046] First proportionality coefficient K p Greater than the second proportionality coefficient K pFirst integration time T t Less than the second integration time T t The first differential time T d Less than the second differential time T d .
[0047] Based on the difference between the current value and the target value, the parameters of the PID algorithm are adaptively adjusted. When the load is high (meaning the real-time load value is greater than the load threshold), an aggressive strategy is adopted to improve the response speed and achieve the goal of getting closer to the target value as soon as possible. When the load is low (meaning the real-time load value is less than or equal to the load threshold), a conservative strategy is adopted to ensure stability, suppress overshoot, and improve stability accuracy.
[0048] In summary, by combining the adaptive PID algorithm, on the one hand, the adaptive environmental test chamber control method of this application can optimize control parameters in real time according to changes in operating conditions, ensuring that the system maintains at least relatively good control performance across the entire operating range. This effectively avoids problems such as control instability and accuracy degradation under different operating conditions with fixed parameters, thereby improving the reliability and consistency of system operation. On the other hand, the adaptive environmental test chamber control method of this application eliminates energy loss between actuators through decoupling control, while relying on adaptive control to suppress control overshoot, reducing ineffective energy loss and achieving efficient collaborative operation of the actuators, significantly reducing the overall energy consumption of the test chamber. Furthermore, the adaptive environmental test chamber control method of this application has the ability to adapt to operating conditions and self-optimize parameters, enabling the test chamber to autonomously cope with complex and ever-changing operating conditions without requiring operators to repeatedly tune control parameters based on experience, reducing reliance on operational experience and improving the convenience and intelligence of equipment use.
[0049] The terms "first," "second," etc., used in this article do not specifically refer to order or sequence, nor are they intended to limit this case; they are merely used to distinguish components or operations described using the same technical terms.
[0050] The terms “include,” “including,” and “have” used in this article are all open-ended, meaning they include but are not limited to.
[0051] Unless otherwise specified, the terms used herein generally have their ordinary meaning in the context of the art, the subject matter, and the specific context. Certain terms used to describe this case will be discussed below or elsewhere in this specification to provide additional guidance to those skilled in the art in describing this case.
[0052] The working principle and advantages of this invention are as follows:
[0053] The adaptive environment test chamber control method includes the following steps:
[0054] Step 1: Establish a dynamic coupling model for at least two environmental parameters. This dynamic coupling model can quantify the impact of changes in any environmental parameter on other environmental parameters.
[0055] Step 2: Send adjustment control commands to any environmental parameter change actuator through the main controller, and at least calculate the estimated impact of the adjustment control commands on other environmental parameters and obtain the coupling disturbance amount through the dynamic coupling model;
[0056] Step 3: The main controller sends a feedforward compensation command to the corresponding environmental parameter change actuator based on the coupling interference amount;
[0057] Step 4: Run the environmental parameter change actuator that receives either the regulation control command or the feedforward compensation command to adjust the corresponding environmental parameters.
[0058] In step one, for example, it is not about setting up three independent control loops based on the three parameters of temperature, humidity, and air pressure. Instead, it can be understood as being based on a closely related coupled system, establishing a dynamic mathematical model that can accurately describe the mutual influence between parameters.
[0059] In step two, for example, the main controller sends an adjustment control command to the temperature environment parameter change actuator. The purpose is to raise the current actual temperature to the target parameter temperature. The dynamic coupling model predicts the amount of coupling interference that will be generated on the other two parameters (humidity and air pressure) after the execution of this command.
[0060] In step three, the coupling interference is used as a feedforward compensation signal and superimposed on the output of the actuator corresponding to the changes in environmental parameters of humidity and air pressure, thereby achieving active suppression of coupling interference.
[0061] In step four, the temperature environment parameter change actuator, humidity environment parameter change actuator, and air pressure environment parameter change actuator are operated to achieve the purpose of temperature regulation while solving or reducing the impact of the temperature regulation process on humidity and air pressure changes.
[0062] The adaptive environmental test chamber control method in this application implements feedforward decoupling control based on a dynamic coupling model. Specifically, when the main controller issues an adjustment control command to any environmental parameter change actuator, it calculates the estimated disturbance amount that the command will generate on other environmental parameters through the dynamic coupling model. Subsequently, the main controller adds the estimated disturbance amount as a feedforward quantity to the output of the relevant environmental parameter change actuator, thereby compensating for the coupling disturbance and avoiding large changes in non-target adjustment parameters (such as humidity mentioned above) that would affect the test operation of the test chamber. It also enables timely adjustment of non-target adjustment parameters, avoiding the extension of the environmental parameter adjustment stage in the test chamber.
[0063] In summary, the adaptive environmental test chamber control method in this application can achieve the goal of improving control accuracy and response speed. Specifically, the control method adopts a feedforward decoupling control strategy to eliminate the coupling interference between various control parameters from the control mechanism level, significantly suppress the dynamic oscillation of the system, shorten the adjustment and stabilization time, and achieve high-precision and fast tracking control of complex environmental curves. Attached Figure Description
[0064] Figure 1 This is a flowchart of the adaptive environment test chamber control method in an embodiment of the present invention;
[0065] Figure 2 This is a flowchart of a specific implementation of the adaptive environment test chamber control method in this invention. Detailed Implementation
[0066] The present invention will be further described below with reference to the accompanying drawings and embodiments:
[0067] Example: The present invention will be clearly described below with illustrations and detailed description. Any person skilled in the art who understands the examples of the present invention can make changes and modifications based on the technology taught in the present invention without departing from the spirit and scope of the present invention.
[0068] The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the scope of this work. Singular forms such as “a,” “this,” “this,” “the,” and “the” as used herein also include plural forms.
[0069] See Figure 1 An adaptive environmental test chamber control method based on a multi-parameter coupling model includes the following steps:
[0070] Step 1: Establish a dynamic coupling model for at least two environmental parameters. This dynamic coupling model can quantify the impact of changes in any environmental parameter on other environmental parameters.
[0071] Step 2: Send adjustment control commands to any environmental parameter change actuator through the main controller, and at least calculate the estimated impact of the adjustment control commands on other environmental parameters and obtain the coupling disturbance amount through the dynamic coupling model;
[0072] Step 3: The main controller sends a feedforward compensation command to the corresponding environmental parameter change actuator based on the coupling interference amount;
[0073] Step 4: Run the environmental parameter change actuator that receives either the regulation control command or the feedforward compensation command to adjust the corresponding environmental parameters.
[0074] In this embodiment, there are at least two fingers.
[0075] This embodiment uses at least two of the environmental parameters, including temperature, humidity and air pressure, as examples, but does not impose any limitations.
[0076] The control method in this embodiment is not limited to any type of test chamber. It is known that existing test chambers adjust their internal environmental parameters through an environmental parameter change actuator to achieve the test objective.
[0077] In step one, a database can be established to collect test data, such as data on the effects of different temperature changes on humidity and air pressure. Based on this data, a dynamic coupling model is trained, which can then predict the impact of a change in one environmental parameter on other environmental parameters. This embodiment does not set up three independent control loops for temperature, humidity, and air pressure. Rather, the control method described above is based on a closely linked coupled system. Through offline system identification or online learning, a dynamic mathematical model that accurately describes the interactions between parameters is established. This dynamic mathematical model can be obtained by fitting experimental data. For example, this dynamic mathematical model can quantify the following coupling relationships: the effect of temperature changes on relative humidity, the air pressure changes caused by temperature changes, the effect of humidification / dehumidification processes on temperature, and the effect of pressure changes on dew point temperature.
[0078] In step two, for example, the main controller (or controller) sends an adjustment control command to the temperature environment parameter change actuator. The purpose is to raise the current actual temperature to the target parameter temperature. The dynamic coupling model predicts the amount of coupling interference that will be generated on the other two parameters (humidity and air pressure) after the execution of the command.
[0079] In step three, the coupling interference is used as a feedforward compensation signal and superimposed on the output of the actuator corresponding to the changes in environmental parameters of humidity and air pressure, thereby achieving active suppression of coupling interference.
[0080] In step four, the temperature environment parameter change actuator, humidity environment parameter change actuator, and air pressure environment parameter change actuator are operated to achieve the purpose of temperature regulation while solving or reducing the impact of the temperature regulation process on humidity and air pressure changes.
[0081] The adaptive environmental test chamber control method in this embodiment realizes feedforward decoupling control based on a dynamic coupling model. Specifically, when the main controller issues an adjustment control command to any environmental parameter change actuator, it will (preferably simultaneously) calculate the estimated disturbance amount that the command will generate on other environmental parameters through the dynamic coupling model. Subsequently, the main controller will add the estimated disturbance amount as a feedforward amount to the output of the relevant environmental parameter change actuator, thereby compensating for the coupling disturbance, avoiding large changes in non-target adjustment parameters (such as humidity mentioned above) that would affect the test operation of the test chamber, and realizing timely adjustment of non-target adjustment parameters, thus avoiding the extension of the environmental parameter adjustment stage in the test chamber.
[0082] It is understandable that the temperature is an active adjustment parameter (target adjustment parameter), while humidity is a passive adjustment parameter (non-target adjustment parameter). If humidity is adjusted back after the temperature adjustment process is completed, it will prolong the time required for the adjustment phase and may also affect the temperature.
[0083] In summary, the adaptive environmental test chamber control method in this embodiment can achieve the goal of improving control accuracy and response speed. Specifically, the control method adopts a feedforward decoupling control strategy to eliminate the coupling interference between various control parameters from the control mechanism level, significantly suppress the dynamic oscillation of the system, shorten the adjustment and stabilization time, and achieve high-precision and fast tracking control of complex environmental curves.
[0084] In one embodiment of this application, in step two, within the same time period, at most one of the environmental parameter change actuators receives and executes the adjustment control command until the adjustment control command is completed or execution is stopped.
[0085] For example: If a regulation control command is issued with the goal of raising the temperature by ten degrees, another regulation control command (such as adjusting the air pressure from 100 kPa to 101 kPa) can only be issued if this goal is achieved or abandoned (i.e., the regulation control command is terminated). Alternatively, even if another regulation control command is issued, the actuator will not execute the corresponding environmental parameter change. If two regulation control commands are issued simultaneously, the regulation control command will not be executed or one regulation control command will be executed randomly.
[0086] If multiple parameters need to be actively adjusted within the same time period, such as adjusting air pressure after adjusting temperature but before the adjustment is complete, the air pressure adjustment process will significantly affect the temperature adjustment process. This can easily lead to a deviation between the final temperature value and the target value, or prolong the temperature adjustment process (e.g., adjusting the temperature again based on the dynamic coupling model), which also increases the computational load based on the dynamic coupling model, thus increasing the corresponding cost. The settings in this section effectively solve this problem.
[0087] In one embodiment of this application, if a new adjustment control instruction (hereinafter referred to as a subsequent adjustment control instruction) is issued before the previous adjustment control instruction (hereinafter referred to as the preceding adjustment control instruction) has been completed, the execution of the preceding adjustment control instruction is suspended and the subsequent adjustment control instruction is executed instead. In this case, there is no need to add an operation to suspend the preceding adjustment control instruction, thus simplifying the instruction execution transition process.
[0088] In one embodiment of this application, in step two, the estimated impact of the adjustment control command on other environmental parameters is calculated only through a dynamic coupling model to obtain the coupling interference amount.
[0089] For example, consider the following: A control command is issued with the goal of raising the temperature by ten degrees. Temperature adjustment will affect air pressure, so air pressure also needs to be adjusted. However, this air pressure adjustment process will affect the temperature, so the temperature needs to be adjusted again, thus creating a cycle of repeated adjustments that affects the efficiency of environmental parameter regulation. This problem can be effectively solved by calculating the estimated impact of the control command on other environmental parameters using a dynamic coupling model, while ignoring the subsequent repeated impacts.
[0090] It should be noted that, for example, when a control command is issued to adjust the temperature, the temperature affects the air pressure. If the air pressure is adjusted, it will affect the temperature. However, the adjusted value (air pressure) is generally smaller than the adjustment value based on the control command, so its impact on the temperature is limited.
[0091] In one embodiment of this application, a change threshold is set, and the coupling interference exceeding this threshold is set as a prerequisite for issuing the corresponding feedforward compensation command. In this case, the estimated impact of the feedforward compensation command on other environmental parameters can be calculated based on the dynamic coupling model to obtain the coupling interference, and then a new feedforward compensation command can be obtained. That is, this part does not directly ignore the aforementioned iterative influence process. The change threshold is determined according to requirements, and the change thresholds corresponding to different environmental parameters may be inconsistent.
[0092] For example, if a control command is issued with the goal of raising the temperature by ten degrees, and it is predicted that the air pressure will change from 100 kPa to 101 kPa, which is greater than the air pressure change threshold (e.g., 0.5 kPa), then step three can be executed, issuing a feedforward compensation command to adjust the air pressure. If it is predicted that this air pressure adjustment process will cause a temperature change of 0.1 degrees, which is less than the temperature change threshold (e.g., 0.2 degrees), then no new feedforward compensation command will be generated.
[0093] In one embodiment of this application, in step four, the environmental parameter change actuator, which receives either the adjustment control command or the feedforward compensation command, simultaneously adjusts the corresponding environmental parameter.
[0094] For example, by using a dynamic coupling model to predict the impact of cooling operations on humidity in advance, and activating the humidity change actuator (such as a dehumidifier) at the same time as the temperature change actuator, it is possible to avoid a surge in humidity due to temperature drop, thus preventing secondary temperature interference caused by the subsequent drastic operation of the humidity change actuator. If the temperature change actuator is activated first, and the humidity decreases due to temperature drop after a period of time (the decrease is described as large), then activating the humidity change actuator at this point would require the humidity change actuator to operate at high power to quickly restore the humidity. This drastic humidity restoration process would affect the temperature regulation process.
[0095] To illustrate, the temperature change actuator receives an adjustment control command, and then generates a first feedforward compensation command based on the adjustment control command. The humidity change actuator receives the first feedforward compensation command, and then generates a second feedforward compensation command based on the first feedforward compensation command. The temperature change actuator receives the second feedforward compensation command. How the temperature change actuator adjusts is guided by the adjustment control command and the second feedforward compensation command. The temperature change actuator and the humidity change actuator start together.
[0096] In some embodiments, the power of the actuator receiving the feedforward compensation command is adjusted according to a predetermined variation pattern, which includes any one or a combination of gradual reduction at specific points in time and gradual reduction over a period of time. For example, if the actuator receiving the feedforward compensation command is a dehumidifier, during operation, the dehumidifier first runs at high intensity (high power) for 1 minute, then reduces its power and continues running for 1 minute, then reduces its power again and continues running for 1 minute. This section primarily emphasizes that the power of the actuator receiving the feedforward compensation command gradually decreases to minimize the possibility of significant changes in the corresponding environmental parameters.
[0097] See Figure 2 In one embodiment of this application, in step one, a dynamic coupling model is established for the three environmental parameters of temperature, humidity and air pressure. The dynamic coupling model is used to quantify the influence of any one of the temperature change, humidity change and air pressure change on the other two.
[0098] In step two, while the main controller sends a regulation control command to any one of the temperature change actuator, humidity change actuator, and air pressure change actuator to adjust the corresponding environmental parameters, at least the estimated impact of the regulation control command on other environmental parameters is calculated through the dynamic coupling model, and the coupling interference is obtained.
[0099] This section uses temperature, humidity, and air pressure as environmental parameters, and temperature change actuators, humidity change actuators, and air pressure change actuators as environmental parameter change actuators. This makes the adaptive environmental test chamber control method in this embodiment particularly suitable for comprehensive test chambers involving temperature control, humidity control, and air pressure control.
[0100] For example, a temperature change actuator may include a refrigeration compressor, a humidity change actuator may include a dehumidifying evaporator, and a pressure change actuator may include an intake pump; the specific construction is not limited.
[0101] In some embodiments, a dynamic coupling model can be established based on only any two of the three environmental parameters: temperature, humidity, and air pressure.
[0102] The following example illustrates the multi-parameter feedforward decoupling control process after the refrigeration compressor starts up, to aid understanding:
[0103] Initial conditions of the test chamber: Current temperature is 25℃; current humidity is 50%RH; current air pressure is 101.3kPa.
[0104] Test chamber target conditions: target temperature 10℃; target humidity 50%RH; target air pressure 101.3kPa;
[0105] Control action: Start the refrigeration compressor and run it for 5 minutes to lower the temperature from 25°C to 10°C;
[0106] (1) Predicting coupling interference
[0107] Based on the dynamic coupling model, the main controller predicts the following effects on humidity and air pressure after 5 minutes of cooling:
[0108] Effect on humidity (ΔH): Humidity increases from 50%RH to approximately 80%RH;
[0109] Effect on air pressure (ΔP): Air pressure drops from 101.3 kPa to approximately 100.8 kPa;
[0110] (2) Generate feedforward compensation instructions
[0111] When starting the refrigeration compressor, the main controller sends the predicted disturbance quantities (ΔH and ΔP) and generates the following compensation instructions:
[0112] Compensation for humidity control:
[0113] Compensation equipment: Dehumidifier evaporator;
[0114] Compensation timing: Start-up synchronized with the refrigeration compressor;
[0115] Compensation logic: To prevent humidity from soaring due to temperature drop, the dehumidifier evaporator starts in advance, and the operating intensity is fed forward based on the predicted ΔH;
[0116] Running time: The dehumidifier should be run at high intensity (no specific power limit) for 3 minutes first, and then fine-tuned according to the actual humidity feedback;
[0117] Compensation for air pressure control:
[0118] Compensation equipment: intake pump;
[0119] Compensation timing: Start-up synchronized with the refrigeration compressor;
[0120] Compensation logic: To compensate for the decrease in air pressure caused by the drop in temperature, the main controller instructs the air pump to replenish a small amount of air to maintain stable air pressure;
[0121] Running time: The intake pump runs briefly for about 1 minute to offset the predicted 0.5 kPa pressure drop;
[0122] The actual control performance comparison (differences from traditional PID control) is as follows:
[0123] Control method Temperature change Humidity changes air pressure change System response characteristics Traditional independent PID control Slow descent may indicate overshoot. The price spiked initially, then fluctuated violently, with the dehumidifier starting up delayed. A slight decrease may go unnoticed. Significant oscillations, long stabilization time, and high energy consumption. Feedforward decoupling control Smoothly decline to the target value There was basically no fluctuation, and it remained around 50% RH. There was basically no fluctuation, and it remained at 101.3 kPa. Fast, stable, and energy-efficient
[0124] Analysis of the above comparison results shows that this application predicts the interference of refrigeration operation on humidity and air pressure in advance through a dynamic coupling model, and starts the dehumidifying evaporator and intake pump at the same time as the refrigeration compressor starts to perform feedforward compensation, achieving the following effects:
[0125] 1. Humidity will not spike due to temperature drop, avoiding secondary temperature interference caused by the subsequent vigorous operation of the dehumidifier;
[0126] 2. The air pressure remains stable, and no major correction is required;
[0127] 3. The overall response is faster, more stable, and more energy-efficient, significantly better than the traditional independent PID control method with lag response.
[0128] In summary, this application is particularly suitable for environmental test chambers with rapidly changing operating conditions, and can effectively solve industry pain points such as coupled oscillation, overshoot, and high energy consumption.
[0129] In one embodiment of this application, in step four, the main controller, based on a load-adaptive PID algorithm, controls the environmental parameter change actuator receiving the adjustment control command to adjust the corresponding environmental parameter from its current value to the target value. The load-adaptive PID algorithm adaptively adjusts the proportional coefficient K based on the actual difference between the current value and the target value. p Integration time T t With differential time T d .
[0130] In traditional PID algorithms, the three core parameters (proportional coefficient, integral time, and derivative time) are fixed. This application introduces the concept of "system load" (hereinafter referred to as load), where load refers to the degree of difference between the current state (i.e., current value) and the target state (i.e., target value). The load-adaptive PID algorithm dynamically adjusts (i.e., adaptively adjusts) the proportional coefficient K based on real-time load. p Integration time T t With differential time T d This adaptive PID algorithm ensures optimal control performance across the entire operating range, avoiding the problem of parameters tuned under one operating condition becoming unstable under another. For an explanation of stability, please refer to the following.
[0131] In one embodiment of this application, a load threshold is set;
[0132] In step four, the difference between the current value and the target value is calculated in real time to obtain the real-time load value, and the real-time load value is compared with the load threshold.
[0133] When the real-time load value is greater than the load threshold, the first proportional coefficient K is used. p First integration time T t With the first differential time T d ;
[0134] When the real-time load value is less than or equal to the load threshold, the second proportional coefficient K is used. p Second integration time T t With the second differential time T d ;
[0135] First proportionality coefficient K p Greater than the second proportionality coefficient K p First integration time T t Less than the second integration time T t The first differential time T d Less than the second differential time T d .
[0136] Based on the difference between the current value and the target value, the parameters of the PID algorithm are adaptively adjusted. When the load is high (meaning the real-time load value is greater than the load threshold), an aggressive strategy is adopted to improve the response speed and achieve the goal of getting closer to the target value as soon as possible. When the load is low (meaning the real-time load value is less than or equal to the load threshold), a conservative strategy is adopted to ensure stability, suppress overshoot, and improve stability accuracy.
[0137] In summary, by combining the adaptive PID algorithm, on the one hand, the adaptive environmental test chamber control method in this embodiment can optimize control parameters in real time according to changes in operating conditions, ensuring that the system maintains at least relatively good control performance across the entire operating range. This effectively avoids problems such as control instability and accuracy degradation under different operating conditions with fixed parameters, thus improving the reliability and consistency of system operation. On the other hand, the adaptive environmental test chamber control method in this embodiment eliminates energy loss between actuators through decoupling control, while relying on adaptive control to suppress control overshoot, reducing ineffective energy loss and achieving efficient collaborative operation of the actuators, significantly reducing the overall energy consumption of the test chamber. Furthermore, the adaptive environmental test chamber control method in this embodiment has the ability to adapt to operating conditions and self-optimize parameters, enabling the test chamber to autonomously cope with complex and ever-changing operating conditions without requiring operators to repeatedly tune control parameters based on experience, reducing reliance on operational experience and improving the convenience and intelligence of equipment use.
[0138] Here's an example illustrating the specific application of the load-adaptive PID algorithm in temperature control to aid understanding:
[0139] Control objective: To rapidly reduce the temperature inside the test chamber from 25℃ to -40℃, with minimal overshoot and short settling time; Controlled equipment: refrigeration compressor;
[0140] The preset load threshold is 20℃. High load stage refers to a temperature difference > 20℃, and low load stage refers to a temperature difference ≤ 20℃.
[0141] Control process decomposition:
[0142] Phase 1: High Load Phase
[0143] Scenario Analysis: The current temperature is 25℃, the target is -40℃, the temperature difference is as high as 65℃, which is far greater than the load threshold of 20℃. The system is determined to be in a "high load" state. The core task is to quickly reduce the temperature difference, that is, to provide full cooling. Slight overshoot can be tolerated for the time being.
[0144] Adaptive PID parameter tuning:
[0145] proportionality coefficient K p A larger value (for example, the specific value is not limited and can be set to be larger relative to the initial value) makes the control system more sensitive to temperature differences, enabling it to output stronger cooling power and drive the temperature to drop rapidly.
[0146] Integration time T t The value is relatively small (for example, the specific value is not limited, and it can be set to decrease relative to the initial value), which strengthens the integration effect and can accumulate errors more quickly, aiming to accelerate the elimination of huge static errors.
[0147] Differential time T d: Smaller (using this as an example, the specific value is not limited, it can be set to be smaller relative to the initial value). When the load changes drastically during the high load stage, the derivative action is prone to introducing noise and oscillation, so it should be appropriately weakened to ensure speed first.
[0148] Actuator performance: The refrigeration compressor starts at high or full power, and the temperature curve shows a rapidly decreasing slope;
[0149] Phase Two: Low-Load Phase
[0150] Scenario Analysis: The temperature has dropped to -25℃, and the real-time load value is 15, which is less than the load threshold of 20. At this time, the primary task of the system changes from "rapid pursuit" to "precise aiming and stabilization". It is necessary to avoid the temperature from exceeding the target point (i.e., overshoot) and causing oscillation.
[0151] Adaptive PID parameter tuning:
[0152] proportionality coefficient K p Significantly reduced (compared to the above-mentioned high load stage), which is considered to reduce the system's response sensitivity, to prevent the temperature from exceeding the target point due to inertia or excessive control when approaching the target temperature value, such as dropping to -42℃;
[0153] Integration time T t The increase (compared to the above high load stage) weakens the integral action, preventing the integral term from oversaturating within a small error range, thereby effectively suppressing overshoot;
[0154] Differential time T d Increase the differential action (compared to the high load stage mentioned above). At this time, the system tends to stabilize. Increasing the differential action can accurately predict the trend of temperature change (such as deceleration too fast or the start of rebound) and apply reverse braking in advance to further enhance stability.
[0155] Actuator performance: The power of the refrigeration compressor is smoothly and precisely regulated, which may manifest as pulse operation or low-power operation. The temperature curve gradually becomes flat and eventually stabilizes smoothly and without overshoot at the target value of -40℃.
[0156] The actual control effects at different load stages are compared as follows:
[0157] Control phase System load (temperature difference) PID parameter strategy Control Target High load phase >20℃ (e.g., 65℃) <![CDATA[K p ↑ (indicates a large value), T t ↓,T d ↓(Aggressive strategy)]]> Fast response, reduced error Low load phase ≤20℃ (e.g., 15℃) <![CDATA[K p ↓ (indicates a smaller value), T t ↑,T d ↑(Conservative strategy)]]> Suppress overshoot and stabilize accuracy
[0158] Advantages compared to traditional fixed PID control algorithms:
[0159] Traditional fixed PID algorithm:
[0160] If the PID parameters are kept at the same level as those during the high-load phase, the system will overshoot severely when approaching the target temperature, causing the system to oscillate repeatedly around -40℃, resulting in a long stabilization time, frequent compressor start-stop, and high energy consumption.
[0161] Maintaining the PID parameters as described above during the low-load phase, it will take a relatively long time to cool down from 25℃ to -40℃.
[0162] In summary, traditional fixed PID algorithms cannot balance speed and stability.
[0163] The adaptive PID algorithm in this application:
[0164] It features a dynamic adjustment control strategy, which shortens the cooling time by rapidly cooling down initially and then slows down the cooling rate to achieve zero or minimal overshoot, thus avoiding ineffective actuator actions, significantly saving energy, and achieving a shorter overall stabilization time.
[0165] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.
Claims
1. A control method for an adaptive environmental test chamber based on a multi-parameter coupling model, characterized in that: Includes the following steps: Step 1: Establish a dynamic coupling model for at least two environmental parameters. This dynamic coupling model can quantify the impact of changes in any environmental parameter on other environmental parameters. Step 2: Send adjustment control commands to any environmental parameter change actuator through the main controller, and at least calculate the estimated impact of the adjustment control commands on other environmental parameters and obtain the coupling disturbance amount through the dynamic coupling model; Step 3: The main controller sends a feedforward compensation command to the corresponding environmental parameter change actuator based on the coupling interference amount; Step 4: Run the environmental parameter change actuator that receives either the regulation control command or the feedforward compensation command to adjust the corresponding environmental parameters.
2. The adaptive environmental test chamber control method based on a multi-parameter coupling model according to claim 1, characterized in that: In step two, within the same time period, at most one of the environmental parameter change actuators will receive and execute the adjustment control command until the adjustment control command is completed or execution is stopped.
3. The adaptive environmental test chamber control method based on a multi-parameter coupling model according to claim 2, characterized in that: If a new adjustment control command is issued before the previous adjustment control command has been completed, the previous adjustment control command will be suspended and the subsequent adjustment control command will be executed instead.
4. The adaptive environmental test chamber control method based on a multi-parameter coupling model according to claim 1, characterized in that: In step two, the estimated impact of the adjustment control command on other environmental parameters is calculated using only the dynamic coupling model, and the coupling disturbance is obtained.
5. The adaptive environmental test chamber control method based on a multi-parameter coupling model according to claim 1, characterized in that: Set a change threshold, and set the coupling interference amount to be greater than the change threshold as a prerequisite for issuing the corresponding feedforward compensation command.
6. A control method for an adaptive environmental test chamber based on a multi-parameter coupling model according to any one of claims 1-5, characterized in that: In step four, the actuator that receives either the regulation control command or the feedforward compensation command simultaneously adjusts the corresponding environmental parameters.
7. A control method for an adaptive environmental test chamber based on a multi-parameter coupling model according to any one of claims 1-5, characterized in that: In step one, a dynamic coupling model is established for the three environmental parameters of temperature, humidity and air pressure. This dynamic coupling model is used to quantify the impact of any one of the temperature, humidity and air pressure changes on the other two. In step two, while the main controller sends a regulation control command to any one of the temperature change actuator, humidity change actuator, and air pressure change actuator to adjust the corresponding environmental parameters, at least the estimated impact of the regulation control command on other environmental parameters is calculated through the dynamic coupling model, and the coupling interference is obtained.
8. A control method for an adaptive environmental test chamber based on a multi-parameter coupling model according to any one of claims 1-5, characterized in that: In step four, the main controller, based on the load-adaptive PID algorithm, controls the actuator that receives the adjustment control command to adjust the corresponding environmental parameter from its current value to the target value. The load-adaptive PID algorithm adaptively adjusts the proportional coefficient K based on the actual difference between the current value and the target value. p Integration time T t With differential time T d .
9. The adaptive environmental test chamber control method based on a multi-parameter coupling model according to claim 8, characterized in that: Set load threshold; In step four, the difference between the current value and the target value is calculated in real time to obtain the real-time load value, and the real-time load value is compared with the load threshold. When the real-time load value is greater than the load threshold, the first proportional coefficient K is used. p First integration time T t With the first differential time T d ; When the real-time load value is less than or equal to the load threshold, the second proportional coefficient K is used. p Second integration time T t With the second differential time T d ; First proportionality coefficient K p Greater than the second proportionality coefficient K p First integration time T t Less than the second integration time T t The first differential time T d Less than the second derivative time T d .