A temperature control method and system based on partitioned thermal coupling modeling and collaborative model prediction
By using zoned thermal coupling modeling and collaborative model predictive control, the problems of difficult temperature measurement of the adhesive layer and inter-zone thermal mutual influence were solved, achieving precise temperature control and uniformity in the composite material repair process and improving repair quality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHANGZHOU UNIV
- Filing Date
- 2026-03-04
- Publication Date
- 2026-06-05
AI Technical Summary
In existing aerospace composite material repair technologies, the temperature of the adhesive layer is difficult to measure directly, and the mutual influence of heat between sections is not effectively utilized, resulting in insufficient temperature control accuracy and difficulty in ensuring temperature uniformity between sections.
A zoned thermal coupling modeling and collaborative model predictive control method is adopted. The surface temperature is measured by thermocouples and the joint state estimation is performed by Kalman filtering algorithm to establish a heat conduction state space model. The heating power distribution of each zone is calculated collaboratively to achieve multi-zone coordinated control.
It improves the control precision of adhesive layer temperature in the repair area and the uniformity of the overall temperature field, reduces the temperature gradient between sections, and enhances the stability of repair quality.
Smart Images
Figure CN122152010A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of aerospace composite material maintenance, and in particular to a temperature control method and system based on partitioned thermal coupling modeling and collaborative model predictive control. Background Technology
[0002] As aircraft age, composite material structures are prone to delamination, cracking, or localized damage in the skin and its bonding areas under long-term loads, environmental factors, and impact damage. To address this damage, patch repair has become a crucial technique in the maintenance of aerospace composite structures. During this repair process, the temperature evolution of the adhesive layer directly determines the quality of the adhesive curing and the mechanical properties of the repaired structure; therefore, precise temperature control in the repair area is of paramount importance.
[0003] In existing aerospace composite material repair processes, heating devices such as electric blankets are commonly used to heat the repair area, and closed-loop control is achieved through temperature feedback. However, since the adhesive layer is usually located inside the composite laminate structure, its temperature is difficult to measure directly. Existing technologies mostly use surface temperature as the control basis, resulting in a significant deviation between the actual controlled object and the measured object. To obtain the internal temperature information of the repair area, some technical solutions measure the adhesive layer temperature by embedding thermocouples, but this method requires destroying the integrity of the composite structure and is difficult to implement in complex curved surfaces or small-scale repair areas. Other technologies use non-contact temperature measurement methods such as infrared thermal imaging, but due to the thermal insulation characteristics of the composite laminate structure, it is difficult to accurately reflect the true temperature state of the deep adhesive area.
[0004] Regarding temperature control strategies, existing repair equipment mostly employs proportional-integral-derivative (PID) control or empirical rule-based control, adjusting heating power solely based on locally measured temperatures. This type of control method fails to adequately consider the time-delay and nonlinear characteristics of heat conduction in composite material structures, easily leading to overshoot or temperature fluctuations during heating, making it difficult to meet stringent curing process requirements.
[0005] As the complexity of repair structures increases, zoned heating is increasingly being applied in the field of aerospace composite material repair. This method divides the heating area into multiple zones and adjusts the heating power of each zone separately to improve the problem of uneven temperature distribution. However, existing zoned temperature control methods typically treat each heating zone as an independent control object and adjust its temperature separately, ignoring the mutual influence between different zones caused by heat conduction within the material.
[0006] In practice, in aerospace composite repair structures, the skin, patches, adhesive layers, and underlying support structures are tightly coupled in space. Especially in the presence of heat sink structures such as metal ribs and stringers, the heat dissipation capacity and heat flow paths of different zones vary significantly. The heating behavior of one zone often has a significant impact on the temperature changes of adjacent zones, making it difficult for traditional independent zone control methods to achieve coordinated control of the overall temperature field, and may even exacerbate temperature non-uniformity between zones.
[0007] Furthermore, with the development of control algorithms, advanced control methods such as model predictive control have been gradually applied to the field of thermal process control. However, existing technologies mostly focus on single-zone or independent multi-zone control, lacking systematic modeling and collaborative control strategies for the thermal interaction mechanism between zones, making it difficult to fully leverage their control advantages in complex aerospace composite material repair scenarios.
[0008] In summary, existing temperature control technologies for repairing aerospace composite materials still have shortcomings, such as the inability to directly measure the critical temperature of the adhesive layer, the ineffective utilization of the thermal interaction between different zones, and the difficulty in ensuring the uniformity of the overall temperature field. There is an urgent need for a temperature control method and system that can consider the thermal coupling relationship between zones at the system level and achieve coordinated control of multiple zones, so as to improve the temperature control accuracy and repair quality stability during the repair process. Summary of the Invention
[0009] This invention further addresses the problem of significant thermal conduction coupling between adjacent zones during zone heating and the tendency of traditional independent zone control to cause local overheating or underheating. It proposes a collaborative temperature control method that considers the mutual influence between zones to achieve overall temperature field optimization control of the multi-zone heating system.
[0010] The purpose of this invention is to provide a temperature control method and system for the repair of aerospace composite materials based on partitioned thermal coupling modeling and collaborative model predictive control. Addressing the challenges of directly measuring the adhesive layer temperature, the strong nonlinearity of structural heat transfer, and the uneven temperature distribution across partitions caused by complex structural morphologies during the repair process, this invention establishes a thermal coupling model reflecting the mutual thermal influence between partitions. Combined with temperature state estimation and collaborative predictive control strategies, the heating power of each heating partition is coordinated and regulated. This achieves accurate control of the critical temperature of the patch-adhesive layer, reduces the temperature gradient between partitions, and improves the uniformity and control accuracy of the overall temperature field in the repair area. To achieve the above objectives, this invention adopts the following technical solution: A temperature control method based on partitioned thermal coupling modeling and cooperative model predictive control includes the following steps: Step 1: Collect surface temperature information of each heating zone by placing multiple thermocouples on the surface of the composite material to be repaired; Step 2: Based on the thermal properties of the composite material structure, adhesive layer and support structure, establish a thermal conduction state space model including inter-regional thermal conduction coupling terms. Use the Kalman filter algorithm to perform joint state estimation of the temperature at the junction of the adhesive layer under each patch. The state estimation results reflect the thermal interaction between the partitions. Step 3: Input the temperature state of the multi-zone adhesive layer obtained by joint estimation as the system state vector into the cooperative model predictive controller; Step 4: The collaborative model predictive controller is based on the partitioned thermal coupling model and comprehensively considers the following in the prediction time domain: the target process temperature curve of each partition; the thermal coupling constraints between partitions; the temperature gradient suppression constraints of each partition; and calculates the optimal power allocation scheme of the electric blankets in each partition by minimizing the cost function containing the partition temperature deviation term and the temperature gradient penalty term between partitions. Step 5: The power drive unit drives the electric blankets in each zone to work according to the optimal power allocation scheme, and updates the state estimation and control decision in real time during the control process.
[0011] As a preferred embodiment of the present invention, in step two, the heat conduction state-space model is as follows: ; in, It is the density of the material. It is specific heat capacity. It is the thermal conductivity in the vertical direction. It is the temperature at the adhesive joint. It is the surface temperature measured by a thermocouple. Here, t is the boundary temperature, t is time, and D is the distance from the surface measurement point to the estimated point. After simplification, we get: ; in For process noise, For the time step, let ;
[0012] The state equation is obtained as follows: ; The observation equation is: ; Where H is the observation matrix. These are observed values. It is observation noise.
[0013] As a preferred embodiment of the present invention, in step two: the specific steps for calculating the Kalman gain using the Kalman filter are as follows: Prior covariance update: ; Where Q is the process noise covariance matrix; Kalman gain calculation:
[0014] Where H is the transformation matrix from state variables to measurement variables, and R is the covariance matrix of measurement noise; Post-hoc state update: ; in Let be the posterior state estimate at time t. These are measured values; Posterior covariance update: ; The MPC prediction model is a lumped heat model, and its specific form is as follows: ; in For material density, It is specific heat capacity. It is the unit volume of the adhesive layer. It is the thermal conductivity in the z-direction. It is the thermal conductivity in the x-direction. is the thermal conductivity in the y-direction, A is the area on one side of the node, and L is the distance between the nodes. The heat input to the electric blanket, where k represents the k-th unit. Assuming there are n units in total, we can discretize them to obtain: ; in ; ; ; This is the estimated temperature at the cementation point obtained from the Kalman filter described above.
[0015] As a preferred embodiment of the present invention, in step three, the model prediction control cost function and related constraints of the collaborative model prediction controller are as follows:
[0016] ; in To predict the length of the time domain, To control the length of the time domain, This is the predicted value of the adhesive layer temperature at time t+i, based on time t. This is the set value for the process temperature curve at time t+i. Let be the rate of change of the control variable at time t, h be the output weight, r be the control weight, and u be the control input, which includes the power of each electric blanket. , , , These represent the minimum and maximum power and power change rate of each electric blanket.
[0017] Another object of the present invention is to provide a system for a temperature control method based on partitioned thermal coupling modeling and collaborative model prediction, including the temperature control method based on partitioned thermal coupling modeling and collaborative model prediction as described above; It also includes a main control board, a slave control board, an amplifier circuit board, and an AC power supply that are connected in sequence. It also includes the following steps: S1. Thermocouples measure the surface temperature of the heated material and transmit the signal to the main control board via an amplifier circuit board; S2. The main control board estimates the temperature at the junction using Kalman filtering and uses it as the state variable of the predictive controller in the cooperative model. It then uses the method of minimizing the cost function to calculate the optimal power. S3. The obtained optimal power is transmitted from the main control board to the slave control board via the serial port. The slave control board controls the on / off state of the solid-state relay based on the received signal. S4. Solid-state relays control the power supplied to the heating element by adjusting the PWM signal to achieve zoned heating.
[0018] As a preferred embodiment of the present invention, both the master control board and the slave control board are made of STM32.
[0019] As a preferred embodiment of the present invention, the amplifier circuit board consists of one AD595CQ chip and one MAXIM DG407 dual 8-channel CMOS analog multiplexer, uses a 120-volt, 15-amp Variac AC power supply and is equipped with four Omron G3NE-220TL solid-state relays.
[0020] The beneficial effects of this invention are: 1. Use a Kalman filter to estimate the temperature at the junction and use it as the state variable of the cooperative model predictive controller to calculate the optimal power. Use a zone heater for heating to improve the problems of insufficient control accuracy and uneven heating. Attached Figure Description
[0021] Figure 1 This is a flowchart of the control steps of the temperature control method and system based on partitioned thermal coupling modeling and collaborative model prediction of the present invention. Figure 2This is a structural diagram of the temperature control method and system based on partitioned thermal coupling modeling and collaborative model prediction of the present invention; Figure 3 These are three locations where the temperature was measured at the adhesive layer in the example. These three locations are in three different areas where the heating is uneven during the repair process. Figures 4 to 6 The temperature distribution estimated by Kalman filtering at different time points in the example; Figure 7 The real-time temperature of the above three locations under the condition of uniform heating according to the process temperature curve is shown in the example. Figure 8 As an example, the temperature change curves and electric blanket power curves were measured at the above three locations using a temperature control method based on partitioned thermal coupling modeling and collaborative model prediction. Detailed Implementation
[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0023] Example 1: like Figures 1 to 8 As shown, a temperature control method based on partitioned thermal coupling modeling and collaborative model prediction includes the following steps: Step 1: Collect surface temperature information of each heating zone by placing multiple thermocouples on the surface of the composite material to be repaired; Step 2: Based on the thermal properties of the composite material structure, adhesive layer and support structure, establish a thermal conduction state space model including inter-regional thermal conduction coupling terms. Use the Kalman filter algorithm to perform joint state estimation of the temperature at the junction of the adhesive layer under each patch. The state estimation results reflect the thermal interaction between the partitions. Step 3: Input the temperature state of the multi-zone adhesive layer obtained by joint estimation as the system state vector into the cooperative model predictive controller; Step 4: The collaborative model predictive controller is based on the partitioned thermal coupling model and comprehensively considers the following in the prediction time domain: the target process temperature curve of each partition; the thermal coupling constraints between partitions; the temperature gradient suppression constraints of each partition; and calculates the optimal power allocation scheme of the electric blankets in each partition by minimizing the cost function containing the partition temperature deviation term and the temperature gradient penalty term between partitions. Step 5: The power drive unit drives the electric blankets in each zone to work according to the optimal power allocation scheme, and updates the state estimation and control decision in real time during the control process.
[0024] In step two, the state-space model for heat conduction is as follows:
[0025] in, It is the density of the material. It is specific heat capacity. It is the thermal conductivity in the vertical direction. It is the temperature at the adhesive joint. It is the surface temperature measured by a thermocouple. Here, t is the boundary temperature, t is time, and D is the distance from the surface measurement point to the estimated point. After simplification, we get: ; in For process noise, For the time step, let ; The state equation is obtained as follows: ; The observation equation is: ; Where H is the observation matrix. These are observed values. It is observation noise.
[0027] In step two, the specific steps for calculating the Kalman gain using the Kalman filter are as follows: Prior covariance update: ; Where Q is the process noise covariance matrix; Kalman gain calculation: ; Where H is the transformation matrix from state variables to measurement variables, and R is the covariance matrix of measurement noise; Post-hoc state update: ; in Let be the posterior state estimate at time t. These are measured values; Posterior covariance update: ; The MPC prediction model is a lumped heat model, and its specific form is as follows: ; in For material density, It is specific heat capacity. It is the unit volume of the adhesive layer. It is the thermal conductivity in the z-direction. It is the thermal conductivity in the x-direction. is the thermal conductivity in the y-direction, A is the area on one side of the node, and L is the distance between the nodes. The heat input to the electric blanket, where k represents the k-th unit. Assuming there are n units in total, we can discretize them to obtain: ; in ; ; ; This is the estimated temperature at the cementation point obtained from the Kalman filter described above.
[0028] In step three, the model predictive control cost function and related constraints of the collaborative model predictive controller are as follows:
[0029]
[0030] in To predict the length of the time domain, To control the length of the time domain, This is the predicted value of the adhesive layer temperature at time t+i, based on time t. This is the set value for the process temperature curve at time t+i. Let be the rate of change of the control variable at time t, h be the output weight, r be the control weight, and u be the control input, which includes the power of each electric blanket. , , , These represent the minimum and maximum power and power change rate of each electric blanket.
[0031] like Figures 4 to 6 As shown, the temperature distribution at three different locations was estimated using Kalman filtering in this embodiment. It can be seen that the temperature distribution is not uniform due to the different composite material structures at different locations.
[0032] like Figure 7 The figure shows a temperature curve, which shows that the temperature varies at different locations under the same heating conditions due to the different structures of the composite materials.
[0033] like Figure 8 As shown, the temperature changes at the three locations are basically the same. By applying different power to the electric blanket, the purpose of achieving uniform temperature of the heated material can be achieved.
[0034] In summary: using a Kalman filter to estimate the temperature at the junction and using it as the state variable of the cooperative model predictive controller to calculate the optimal power, and using a zone heater for heating, can improve the problems of insufficient control accuracy and uneven heating.
[0035] All the devices selected in this application are general standard parts or components known to those skilled in the art. Their structures and principles can be learned by those skilled in the art through technical manuals or conventional experimental methods.
[0036] In the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.
[0037] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0038] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.
Claims
1. A temperature control method based on partitioned thermal coupling modeling and collaborative model prediction, characterized in that, Includes the following steps: Step 1: Collect surface temperature information of each heating zone by placing multiple thermocouples on the surface of the composite material to be repaired; Step 2: Based on the thermal properties of the composite material structure, adhesive layer and support structure, establish a thermal conduction state space model including inter-regional thermal conduction coupling terms. Use the Kalman filter algorithm to perform joint state estimation of the temperature at the junction of the adhesive layer under each patch. The state estimation results reflect the heat transfer mutual influence between the partitions. Step 3: Input the temperature state of the partitioned adhesive layer obtained by joint estimation as the system state vector into the cooperative model predictive controller; Step 4: The collaborative model predictive controller is based on the partitioned thermal coupling model and comprehensively considers the following in the prediction time domain: the target process temperature curve of each partition; the thermal coupling constraints between partitions; the temperature gradient suppression constraints of each partition; and calculates the optimal power allocation scheme of the electric blankets in each partition by minimizing the cost function containing the partition temperature deviation term and the temperature gradient penalty term between partitions. Step 5: The power drive unit drives the electric blankets in each zone to work according to the optimal power allocation scheme, and updates the state estimation and control decision in real time during the control process.
2. The temperature control method based on partitioned thermal coupling modeling and collaborative model prediction as described in claim 1, characterized in that, In step two, the state-space model for heat conduction is as follows: in, It is the density of the material. It is specific heat capacity. It is the thermal conductivity in the vertical direction. It is the temperature at the adhesive joint. It is the surface temperature measured by a thermocouple. Here, t is the boundary temperature, t is time, and D is the distance from the surface measurement point to the estimated point. After simplification, we get: ； in For process noise, For the time step, let ； ； The state equation is obtained as follows: ； The observation equation is: ； Where H is the observation matrix. These are observed values. It is observation noise.
3. The temperature control method based on partitioned thermal coupling modeling and collaborative model prediction as described in claim 2, characterized in that, In step two: the specific steps for calculating the Kalman gain using the Kalman filter are as follows: Prior covariance update: ； Where Q is the process noise covariance matrix; Kalman gain calculation: ； Where H is the transformation matrix from state variables to measurement variables, and R is the covariance matrix of measurement noise; Post-hoc state update: ； in Let be the posterior state estimate at time t. These are measured values; Posterior covariance update: ； The MPC prediction model is a lumped heat model, and its specific form is as follows: ； in For material density, It is specific heat capacity. It is the unit volume of the adhesive layer. It is the thermal conductivity in the z-direction. It is the thermal conductivity in the x-direction. is the thermal conductivity in the y-direction, A is the area on one side of the node, and L is the distance between the nodes. The heat input to the electric blanket, where k represents the k-th unit. Assuming there are n units in total, we can discretize them to obtain: ； ； in ； ； ； This is the estimated temperature at the cementation point obtained from the Kalman filter described above.
4. The temperature control method based on partitioned thermal coupling modeling and collaborative model prediction as described in claim 3, characterized in that, In step three, the model predictive control cost function and related constraints of the collaborative model predictive controller are as follows: ； ； in To predict the length of the time domain, To control the length of the time domain, This is the predicted value of the adhesive layer temperature at time t+i, based on time t. This is the set value for the process temperature curve at time t+i. Let be the rate of change of the control variable at time t, h be the output weight, r be the control weight, and u be the control input, which includes the power of each electric blanket. , , , These represent the minimum and maximum power and power change rate of each electric blanket.
5. A system for temperature control based on partitioned thermal coupling modeling and collaborative model prediction, characterized in that, Including the temperature control method based on partitioned thermal coupling modeling and collaborative model prediction as described in any one of claims 1-4; It also includes a main control board, a slave control board, an amplifier circuit board, and an AC power supply that are connected in sequence. It also includes the following steps: S1. Thermocouples measure the surface temperature of the heated material and transmit the signal to the main control board via an amplifier circuit board; S2. The main control board estimates the temperature at the junction using Kalman filtering and uses it as the state variable of the predictive controller in the cooperative model. It then uses the method of minimizing the cost function to calculate the optimal power. S3. The obtained optimal power is transmitted from the main control board to the slave control board via the serial port. The slave control board controls the on / off state of the solid-state relay based on the received signal. S4. Solid-state relays control the power supplied to the heating element by adjusting the PWM signal to achieve zoned heating.
6. The system of the temperature control method based on partitioned thermal coupling modeling and collaborative model prediction as described in claim 5, characterized in that, Both the master control board and the slave control board are made of STM32.
7. The system of the temperature control method based on partitioned thermal coupling modeling and collaborative model prediction as described in claim 6, characterized in that, The amplifier circuit board consists of one AD595CQ chip and one MAXIM DG407 dual 8-channel CMOS analog multiplexer, uses a 120-volt, 15-amp Variac AC power supply, and is equipped with four Omron G3NE-220TL solid-state relays.