PLC-based ALC plate and thermal insulation integrated aluminum plate outer wall assembly control system

Abstract

The application provides an ALC plate and heat preservation integrated aluminum plate outer wall assembly control system based on PLC, and relates to the field of industrial automatic control systems.The system comprises a multi-source sensor group, a PLC controller and a servo pendant execution mechanism;the multi-source sensor group comprises a temperature and humidity sensor, a strain sensor and a laser range finder, and collects environmental temperature, ALC plate shrinkage strain, plate joint width and position deviation.The PLC controller integrates a prediction model unit, a deviation comparison unit, an MPC control unit and an online model correction unit, calculates target plate joint width and optimal displacement compensation, and drives the servo pendant execution mechanism to adjust the plate joint;the laser range finder feeds back the adjusted plate joint, the online model correction unit updates model parameters, and the application realizes high-precision self-adaptive closed-loop control of the plate joint.

Description

Technical Field

[0001] This invention relates to the field of industrial automatic control systems, specifically to a PLC-based assembly control system for ALC panels and integrated thermal insulation aluminum panels for exterior walls. Background Technology

[0002] In the assembly and construction of prefabricated exterior wall panels, especially in the installation of composite exterior walls involving ALC panels and integrated aluminum insulated panels, the width of the joint between adjacent panels is a key parameter affecting the wall's waterproofing, airtightness, and thermal insulation performance. Currently, conventional practices mainly rely on an open-loop control method combining manual measurement and mechanical positioning. However, the ambient temperature and humidity at actual construction sites are not constant. In particular, drastic changes in temperature and humidity can cause significant shrinkage or expansion deformation of ALC panels due to dryness or moisture, while integrated aluminum insulated panels will also undergo nonlinear thermal expansion and contraction due to temperature changes.

[0003] In existing technologies, some solutions attempt to monitor changes in the plate seam by deploying temperature and humidity sensors and displacement sensors, and combine them with adjustable hangers for manual or semi-automatic compensation. However, these solutions lack the ability to predict the dynamic changes in the plate seam in real time. Especially when the ambient temperature changes rapidly, existing sensor sampling strategies cannot synchronously respond to the transient components of material deformation, resulting in compensation actions lagging behind actual deformation, and the plate seam deviation often exceeding the allowable range. Because the drying shrinkage strain of ALC plates has a humidity-dependent time-cumulative effect, and the thermal expansion strain of aluminum plates exhibits a nonlinear relationship with temperature, the deformation rates of different materials differ significantly. The inability to distinguish the contributions of these two different sources of deformation online makes it difficult to correct model mismatches caused by material batch differences or environmental drift during long-term operation, resulting in repeated deviations in the plate seam. Summary of the Invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solution: a PLC-based ALC board and thermal insulation integrated aluminum panel exterior wall assembly control system, including a multi-source sensor group, a PLC controller and a servo mounting actuator; The multi-source sensor group includes a temperature and humidity sensor, a strain sensor, and a laser rangefinder. The temperature and humidity sensor is configured to collect the ambient temperature in real time, the strain sensor is configured to collect the shrinkage strain of the ALC board in real time, and the laser rangefinder is configured to collect the current board gap width in real time and obtain the position deviation parameters of the main building structure relative to the reference baseline. The PLC controller is electrically connected to the multi-source sensor group. The PLC controller integrates a prediction model unit, a deviation comparison unit, and an MPC control unit that are connected in sequence by signals. The prediction model unit is configured to receive the ambient temperature, shrinkage strain, and position deviation parameters, and calculate the target plate gap width under the current working conditions based on the preset ALC plate shrinkage deformation model and the thermal expansion deformation model of the integrated thermal insulation aluminum plate. The deviation comparison unit is configured to receive the target gap width and the current gap width transmitted back by the laser rangefinder, compare the two and calculate the gap deviation value. The MPC control unit is configured to receive the plate gap deviation value, execute the model predictive control algorithm, use the hanging point displacement compensation amount as the control variable, use the minimization of the plate gap tracking error as the control objective, and combine the displacement stroke constraint of the servo hanger actuator to calculate the optimal displacement compensation amount corresponding to each hanging point and output the drive command. The servo mounting actuator is electrically connected to the MPC control unit and is configured to drive the corresponding mounting point to generate displacement compensation according to the drive command, so as to adjust the width of the gap between adjacent plates. The laser rangefinder is also configured to collect the adjusted gap width in real time and feed it back to the deviation comparison unit to form adaptive dynamic compensation control.

[0005] As a further technical solution, the temperature and humidity sensor in the multi-source sensor group is configured to continuously collect ambient temperature with a temperature resolution of 0.2 degrees Celsius and a sampling period of 1 second, and calculate the temperature change rate in real time. The calculation method for the temperature change rate is as follows: using the ambient temperature values ​​of the three consecutive sampling points before the current moment, the slope is obtained through least-squares linear fitting. This slope is the temperature change rate at the current moment, expressed in degrees Celsius per minute. Specifically: let the three sampling points before the current moment be T(k-2), T(k-1), and T(k). After centering, the slope a = [T(k) - T(k-2)] / 2 is obtained through least-squares fitting, and then converted to the temperature change rate per minute according to the sampling period. When the absolute value of the temperature change rate is less than 0.5℃ / min, it is determined to be a steady-state temperature condition, and the temperature and humidity sensor maintains a sampling period of 1 second, while the strain sensor operates at the default sampling frequency of 1 Hz. When the absolute value of the temperature change rate is greater than or equal to 0.5℃ / min, it is determined to be... In transient temperature conditions, the sampling period of the temperature and humidity sensor immediately switches to 0.2 seconds, and simultaneously triggers the strain sensor to synchronously increase its sampling frequency to 10 Hz. Under transient conditions, the system continuously monitors the rate of temperature change. Only when the absolute value of the rate of temperature change calculated in each of the three consecutive sampling periods falls below 0.5℃ / min is the transient condition considered to have been exited. At this point, the sampling period of the temperature and humidity sensor returns to 1 second, and the sampling frequency of the strain sensor returns to 1 Hz. Through the above-mentioned adaptive switching strategy with hysteresis judgment logic, frequent oscillations in the sampling frequency caused by temperature noise are avoided, ensuring that the system can capture temperature and humidity dynamics with higher time resolution when the ambient temperature changes drastically, providing accurate input parameters for subsequent prediction models. At the same time, the increased sampling frequency of the strain sensor under transient conditions can accurately capture the transient component of shrinkage strain generated by the ALC plate during rapid temperature changes, thereby effectively improving the calculation accuracy of the target plate gap width.

[0006] As a further technical solution, the prediction model unit integrates an ALC plate shrinkage deformation model and an integrated aluminum insulation plate thermal expansion deformation model; specifically, the ALC plate shrinkage deformation model is as follows: Among them, the ultimate shrinkage strain The value ranges from 0.0003 to 0.0008. This value was obtained by conducting a 180-day free shrinkage test on the same batch of ALC boards under standard curing conditions; the shrinkage time constant. The value ranges from 120 to 240 hours, and its specific value is determined based on the production formula and autoclaving process of the ALC board, and is obtained by fitting the drying shrinkage curve using the nonlinear least squares method; humidity sensitivity coefficient The unit of measurement is per percentage of relative humidity, and its value ranges from 0.00005 to 0.0002 per percentage of relative humidity. It is obtained by linear regression after measuring the equilibrium shrinkage strain of ALC sheets under different constant relative humidity environments; the current ambient relative humidity... Real-time data collected by temperature and humidity sensors, baseline relative humidity. The humidity level is set at 65%, representing the standard indoor humidity. The thermal expansion deformation model of the integrated aluminum insulation panel is as follows: ,β Al This is the linear coefficient of thermal expansion, and its value is determined based on the material of the aluminum insulation plate used. For commonly used aluminum alloys, such as 6063 aluminum alloy, the value is 2.35 × 10⁻⁶. 5 per degree Celsius; γ is the nonlinear thermal expansion correction coefficient, with a value range of 1.0 × 10⁻⁶. 8 Up to 5.0×10 8 The temperature per square degree Celsius was obtained through thermal cycling tests. This was achieved by conducting thermal cycling tests on the integrated aluminum insulation panel within a temperature range of -20°C to 80°C, measuring the strain-temperature curve, and then performing a secondary fitting. The reference temperature was also obtained. The temperature is set to 20 degrees Celsius, representing the standard ambient temperature; the formula for calculating the target gap width is: The initial design joint width W0 is determined based on the building's exterior wall construction drawings, with a range of 5 mm to 15 mm; the width of a single panel... These are actual measured values; position deviation parameters. The vertical distance deviation of the main building structure relative to the baseline, collected by the laser rangefinder, is directly substituted into the model. This deviation value carries a positive or negative sign. Through the aforementioned quantifiable parameter definition and calibration method, the prediction model unit can accurately calculate the target gap width of the ALC panels and insulated aluminum panels for specific engineering batches under the current environmental temperature and humidity conditions and the deviation of the main structure. By using a humidity sensitivity coefficient and a nonlinear correction coefficient, the time dependence and humidity dependence of the ALC panel's shrinkage are decoupled. At the same time, the second-order nonlinear effect of the aluminum panel's thermal expansion is incorporated into the calculation, improving the prediction accuracy of the target gap width under wide temperature and humidity conditions. The deviation between the target gap width output by the model unit and the current gap width measured by the laser rangefinder is fed into the MPC control unit after passing through the deviation comparison unit. Due to the improved model accuracy, the amplitude and fluctuation range of the gap deviation value are further reduced, thereby reducing the burden on subsequent control algorithms and avoiding overshoot or oscillation caused by model mismatch.

[0007] As a further technical solution, the model predictive control algorithm executed inside the MPC control unit includes a formal expression of the control objective function, which specifically configures the complete implementation steps of prediction time domain, control time domain, weight matrix numerical values, and constraint quadratic programming solution; specifically, prediction time domain N p The value is set to 20 control cycles, and the control time domain N c The control cycle is set to 5 control cycles, each lasting 1 second, consistent with the sampling cycle of the temperature and humidity sensor. The error weight matrix Q ranges from 1 to 100, and the control increment weight matrix R ranges from 0.1 to 10. These weight values ​​are determined through offline simulation and debugging to achieve a balance between the accuracy of the plate seam tracking and the smoothness of the hanger displacement. The model predictive control algorithm first establishes a discrete state-space model of the controlled object. This model uses the current displacement compensation amount at each hanging point as the state variable, the displacement compensation increment Δu as the control input, and the plate seam deviation value e as the output. In each control cycle, the MPC control unit obtains the current plate seam deviation value e output by the deviation comparison unit as initial feedback, and then calculates the feedback based on the most recent N... p Historical joint deviation data within a period and N c The displacement compensation increment sequence of each cycle is used to construct the predictive output equation; then, the control objective function is... The problem is transformed into a standard quadratic programming form, where the inequality constraints are displacement travel constraints of the servo attachment actuator. Specifically, the total displacement compensation for each attachment point must not exceed the ±20 mm travel range of the lead screw, and the absolute value of the single-step displacement compensation increment Δu must not exceed 0.5 mm. The MPC control unit calls an embedded real-time quadratic programming solver, such as a gradient projection algorithm based on the effective set method, to calculate the optimal displacement compensation increment sequence [Δu(0), Δu(1), ..., Δu(N)] that minimizes the objective function J. c-1)]; After the solution is completed, the MPC control unit takes only the first element Δu(0) in the sequence and adds it to the displacement compensation amount output in the previous cycle to obtain the optimal displacement compensation amount for each attachment point in the current cycle. The optimal displacement compensation amount is then converted into the number of pulses according to the equivalent of 0.005 mm per pulse and output as a drive command to the servo attachment actuator. During the solution process, the MPC control unit reads the actual displacement of each attachment point fed back by the displacement encoder in real time and dynamically updates the upper and lower bounds of the remaining travel in the constraint conditions. If the remaining positive travel of a certain attachment point is less than 1 mm or the remaining negative travel is less than 1 mm, the control increment of the attachment point is forced to be increased in the secondary programming. Both the upper and lower limits are set to 0, and the hanging point is marked as being in a travel lock-up state in the generated drive instruction. At the same time, an alarm signal is generated and uploaded to the upper monitoring system of the PLC controller. The model predictive control algorithm is programmed and implemented in the PLC controller. The MPC control unit predicts the trend of the panel gap deviation in multiple future control cycles and solves the optimal control sequence with hard constraints online, which improves the response speed and stability of the panel gap adjustment. Especially when multiple hanging points work together, the model predictive control algorithm can automatically allocate the displacement compensation of each hanging point, avoiding the failure of the entire exterior wall panel system caused by a certain hanging point reaching the travel limit too early. The travel lock-up and alarm functions are integrated.

[0008] As a further technical solution, the PLC controller also integrates an online model correction unit, which is implemented as follows: The online model correction unit is electrically connected to the prediction model unit and the deviation comparison unit, and internally runs a recursive least squares algorithm to update the drying time constant τ in the ALC board drying deformation model online. dry The nonlinear correction coefficient γ in the thermal expansion deformation model of the integrated thermal insulation aluminum plate; the initial parameters of the recursive least squares algorithm are set as follows: the initial value of the parameter estimation vector θ is taken as [τ dry0 ,γ0], where τ dry0 The factory default value is 150 hours, and γ0 is the factory default value of 2.5 × 10⁻⁶ hours. -8For every square degree Celsius; the initial value of the covariance matrix P is the identity matrix multiplied by 1000 to reflect the uncertainty of the initial parameters; the forgetting factor λ of the recursive least squares algorithm is set to 0.995, enabling the least squares algorithm to slowly forget historical data and adapt to the slow time-varying characteristics of the model parameters; the triggering condition for the online correction unit of the model is: the absolute value of the 10 consecutive plate gap deviation values ​​output by the deviation comparison unit is greater than 0.3 mm, and the actual plate gap width and the target plate gap width corresponding to these 10 deviation values ​​are recorded by the system as sample pairs; once the triggering condition is met, the online correction unit of the model takes these 10 sample pairs as a batch of data and performs a recursive least squares update for each sample pair in turn; the specific update steps are: for each sample pair, first calculate the target plate gap width predicted by the model based on the current parameter estimate, and then calculate the prediction error; then calculate the gain vector based on the current covariance matrix and the forgetting factor; then update the parameter estimate vector, i.e., τ dry And the value of γ; finally update the covariance matrix; after processing each sample pair, the online correction unit of the model will update τ. dry γ is immediately transmitted to the prediction model unit, replacing the original corresponding parameters for the calculation of the target plate gap width in the next control cycle; after processing all 10 sample pairs, the online model correction unit waits for the next 10 consecutive large deviations before triggering an update again; to prevent model parameters from diverging due to accidental perturbations, the online model correction unit also adjusts τ. dry The update value of γ is set to limit the amplitude: τ dry Limited to its original range of 120 to 240 hours, γ is limited to 1.0 × 10⁻⁶. -8 Up to 5.0×10 -8 Between each square degree Celsius; through the recursive least squares algorithm and its specific parameter configuration, the system has the ability to automatically calibrate the prediction model during long-term operation; for example, when the actual drying shrinkage time constant of the ALC board deviates from the factory preset value due to material batch differences, the online correction unit of the model can gradually correct τ by continuously monitoring the board gap deviation. dry The value of the parameter ensures that the calculated value of the target joint width is consistent with the actual required value. This system achieves non-disruptive online adaptive correction, which not only does not affect the normal rhythm of the exterior wall assembly construction, but also improves the robustness of the control system to material inconsistencies and long-term environmental drift. The closed-loop connection between the online model correction unit, the prediction model unit, and the deviation comparison unit constitutes a two-layer adaptive control architecture: the inner layer is the MPC control unit, which adjusts the hanging point displacement at the millisecond level; the outer layer is the online model correction unit, which adjusts the model parameters on an hourly or daily time scale. This gives the control system the dual advantages of fast response and long-term adaptability, thereby maintaining high-precision control of the joint width throughout the entire life cycle of the building.

[0009] As a further technical solution, the laser rangefinder has a measurement range of 0 to 50 mm, a measurement accuracy of ±0.05 mm, and a data output frequency of 20 Hz. The laser rangefinder's beam axis is perpendicular to the surface of the board seam, and three measuring points are set at the board seam position corresponding to each hanging point, located at the upper, middle, and lower ends of the board seam respectively. The current board seam width is taken as the arithmetic mean of the measurements from the three measuring points. This arithmetic mean can eliminate measurement errors caused by local warping of the board or installation tilt. The position deviation parameter is the vertical distance deviation of each measuring point relative to the reference baseline, in millimeters, retained to two decimal places. Through the arrangement of measuring points and data processing methods, the system obtains a high-precision, highly interference-resistant board seam width feedback signal.

[0010] As a further technical solution, the servo mounting actuator includes a servo motor, a reducer, a ball screw, and a displacement encoder. The servo motor is a three-phase permanent magnet synchronous servo motor with a rated torque of 1.2 N·m, receiving the speed and angle from the drive commands output by the MPC control unit. The reducer has a reduction ratio of 10:1, a ball screw lead of 5 mm, and a stroke of ±20 mm. The displacement encoder is an absolute magnetic encoder with a resolution of 0.01 mm, providing real-time feedback of the actual displacement of the mounting point to the MPC control unit, forming a closed-loop position control. The optimal displacement compensation in the drive command is output in the form of pulse count, with each pulse corresponding to a displacement of 0.005 mm. Through the meticulous design of this actuator, the optimal displacement compensation calculated by the MPC control unit can be converted into actual mechanical displacement with an extremely high resolution of 0.005 mm, ensuring micron-level accuracy in adjusting the gap width.

[0011] This invention provides a PLC-based assembly control system for ALC panels and integrated thermal insulation aluminum panels used in exterior wall construction. It offers the following advantages: 1. This invention, through the collaboration of a multi-source sensor group and a prediction model unit, combined with a nonlinear deformation model of ALC plate shrinkage and aluminum plate thermal expansion, can calculate the target plate gap width in real time. Compared with open-loop or semi-open-loop control, it further improves the initial prediction accuracy of the plate gap width and avoids blind compensation caused by unknown material deformation characteristics.

[0012] 2. This invention further utilizes the MPC control unit to minimize the plate seam tracking error, solves the optimal displacement compensation amount with stroke constraints online, and forms a closed-loop dynamic compensation by combining the high-frequency feedback of the laser rangefinder. Even when encountering transient conditions such as drastic changes in temperature and humidity, it can respond and adjust accurately.

[0013] 3. Based on the online model correction unit, this invention uses the recursive least squares method to update the drying shrinkage time constant and nonlinear thermal expansion coefficient in real time, enabling the control system to have long-term adaptive learning capabilities. It can continuously correct model mismatch caused by material batch differences and environmental drift, fundamentally solving the defect of frequent manual calibration and providing intelligent protection for the stability of panel joints throughout the entire life cycle of the exterior wall. Attached Figure Description

[0014] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 This is a system framework diagram as described in this invention. Detailed Implementation

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0016] like Figure 1 As shown, this invention proposes a PLC-based assembly control system for ALC panels and integrated thermal insulation aluminum panels for exterior walls.

[0017] This embodiment takes the ALC panel and integrated aluminum insulation panel composite exterior wall system used in the construction of a high-rise building as an example to describe in detail the specific working process, parameter settings, data interaction and control logic of the PLC-based ALC panel and integrated aluminum insulation panel exterior wall assembly control system.

[0018] During the construction preparation phase, a multi-source sensor array is first installed at the mounting points of the exterior wall panels to be installed. A SHT35 digital temperature and humidity sensor with a temperature resolution of 0.2 degrees Celsius and a default sampling period of 1 second is selected and installed 50 mm away from the ALC panel surface in a ventilated area to avoid local microclimate interference. A resistance strain gauge sensor is used, attached to the center area of ​​the back of the ALC panel for real-time acquisition of shrinkage strain. A laser displacement sensor with a range of 0 to 50 mm, an accuracy of ±0.05 mm, and a data output frequency of 20 Hz is selected, with its beam axis perpendicular to the panel seam surface. Three measuring points are set at the panel seam position corresponding to each mounting point, located at the upper, middle, and lower ends of the seam. The measured values ​​from the three points are processed by the arithmetic mean module in the PLC controller to obtain the current seam width. Simultaneously, the vertical distance deviation of each measuring point relative to the baseline is recorded as the position deviation parameter δ. positionThe unit is millimeters, with two decimal places; the baseline is a horizontal control line pre-marked on the main structure of the building.

[0019] The PLC controller is electrically connected to a multi-source sensor group and internally integrates a prediction model unit, a deviation comparison unit, and an MPC control unit, all connected sequentially, as well as a model online correction unit. After the system is powered on, initialization is performed first: the prediction model unit loads preset parameters for the ALC plate shrinkage deformation model and the thermal expansion deformation model of the integrated aluminum insulation plate. Taking the ALC plate used in this embodiment as an example, the ultimate shrinkage strain ε... ∞ Take 0.0005, and set the drying time constant τ. dry Take 180 hours, humidity sensitivity coefficient α dry Take 0.0001 per percentage of relative humidity, the reference relative humidity RH ref Set at 65%; the linear thermal expansion coefficient β of the integrated thermal insulation aluminum plate Al Take 2.35×10 -5 The nonlinear thermal expansion correction factor γ is taken as 2.5 × 10⁻⁶ per degree Celsius. -8 per square degree Celsius, reference temperature T ref The initial design temperature is 20 degrees Celsius; the initial design gap width W0 is 10 mm, and the width of a single board is L. panel The length is 600 mm; these parameters are stored in the PLC controller.

[0020] During continuous operation, the temperature and humidity sensor collects ambient temperature (T) and relative humidity (RH) at a 1-second cycle. curSimultaneously, the temperature and humidity sensor internally calculates the rate of temperature change in real time. The calculation method is as follows: using the ambient temperature values ​​of three consecutive sampling points before the current moment, the slope is obtained through least-squares linear fitting as the rate of temperature change, with the unit being degrees Celsius per minute. For example, if the temperatures of three consecutive sampling points at a certain moment are 22.1 degrees Celsius, 22.3 degrees Celsius, and 22.6 degrees Celsius, the fitting slope is 0.25 degrees Celsius per minute, and the absolute value is less than 0.5 degrees Celsius per minute, which is determined to be a steady-state temperature condition. Another example is when a sudden downpour occurs, and the temperatures of the three sampling points drop sharply from 32.5 degrees Celsius to 30.8 degrees Celsius and 29.2 degrees Celsius, the fitting slope is -1.65 degrees Celsius per minute, and the absolute value is greater than or equal to 0.5 degrees Celsius per minute. If the temperature is detected as transient, the sampling period of the temperature and humidity sensor is immediately switched to 0.2 seconds, and the strain sensor is simultaneously triggered to increase its sampling frequency from the default 1 Hz to 10 Hz. Under transient conditions, the system continuously monitors the rate of temperature change. Only when the absolute value of the rate of temperature change calculated in each of the three consecutive sampling periods falls to less than 0.5 degrees Celsius per minute is the transient condition considered to have been exited. At this time, the sampling period of the temperature and humidity sensor returns to 1 second, and the sampling frequency of the strain sensor returns to 1 Hz. For example, after a rainstorm, the temperature gradually stabilizes, and the rates of change calculated in three consecutive periods are 0.4, 0.3, and 0.2 degrees Celsius per minute, respectively, all less than 0.5, thus the transient condition is considered to have been exited.

[0021] The prediction model unit receives ambient temperature T and current relative humidity RH in real time. cur ε, shrinkage strain dry and position deviation parameter δ position First, calculate the shrinkage strain ε at the current time t based on the ALC plate shrinkage deformation model. dry The model expression is: Where t is the cumulative time since the board leaves the factory, for example, t = 720 hours; the calculation yields: Therefore That is, a negative value for drying shrinkage strain indicates shrinkage; at the same time, the thermal expansion strain is calculated based on the thermal expansion deformation model of the integrated thermal insulation aluminum plate. Assuming the current ambient temperature T is 35 degrees Celsius, then TT ref =15, so ε thermal =0.0003525+0.000005625=0.000358125; then, the target board gap width is obtained from the formula. Let δ position A reading of +1.2 mm from the laser rangefinder indicates that the main structure has shifted outwards. Millimeters; rounded to three decimal places, the result is 10.809 millimeters.

[0022] The deviation comparison unit receives the target gap width of 10.809 mm output by the prediction model unit and the current gap width returned by the laser rangefinder. Assuming that the measured values ​​of the three measuring points of the laser rangefinder are 10.5 mm, 10.6 mm, and 10.7 mm, and the arithmetic mean is 10.6 mm, then the gap deviation value is 10.809 minus 10.600, which equals positive 0.209 mm. That is, the current gap is too narrow and needs to be widened.

[0023] The MPC control unit receives the gap deviation value as the tracking error e; the MPC control unit is internally configured with a prediction time domain N. p =20 control cycles, control time domain N c =5 control cycles, each control cycle is 1 second; the error weight matrix Q is 10, and the control increment weight matrix R is 0.5; the MPC control unit first establishes a discrete state-space model: the state variable x(k) is the current displacement compensation amount of each hanging point, the control input Δu(k) is the displacement compensation increment, and the output y(k) = plate gap deviation e(k); the prediction equation is recursively derived based on historical data; in each control cycle, the MPC control unit reads the actual displacement of each hanging point fed back by the displacement encoder and dynamically updates the constraints: the total stroke of the ball screw is ±20 mm, and the absolute value of the single-step displacement compensation increment Δu must not exceed 0.5 mm; assuming the current actual displacement of the hanging point is If the current positive travel is +5 mm, then the remaining positive travel is 15 mm, and the remaining negative travel is -25 mm. Note that the absolute value of the negative travel is 25 mm, but it is limited to -20 mm. The travel range is [-20, 20]. If the current travel is 5 mm, then the remaining positive travel is 15 mm, and the remaining negative travel is 25 mm. However, the negative direction can only move up to -20 mm, so it can move 25 mm in the negative direction, but it is limited by the lower limit of the travel of -20 mm. The actual usable negative displacement is 5 - (-20) = 25 mm. However, the judgment of the remaining travel being less than 1 mm is as follows: if the remaining positive travel is less than 1 mm, that is, the current displacement is greater than 19 mm, or the remaining negative travel is less than 1 mm, that is, the current displacement is less than -19 mm, then locking is triggered. In this embodiment, the current travel is 5 mm, and there is enough remaining travel, so locking is not triggered.

[0024] The MPC control unit will use the objective function The problem is transformed into a quadratic programming problem. The constraints include: for each prediction step, the predicted value of the plate gap deviation satisfies the dynamic equation; the control increment sequence satisfies the upper and lower bound constraints; the total displacement compensation after accumulation does not exceed the travel boundary; the MPC calls the embedded effective set method quadratic programming solver to calculate the optimal sequence [Δu(0),Δu(1),Δu(2),Δu(3),Δu(4)]; the solution yields Δu(0) = 0.15 mm; then the optimal displacement compensation of each hanging point in the current cycle is 5 mm plus 0.15 mm from the previous cycle, which equals 5.15 mm; the MPC control unit converts the optimal displacement compensation of 5.15 mm into 1030 pulses according to 0.005 mm per pulse, and outputs it as a drive command to the servo hanging actuator.

[0025] The servo actuator includes a three-phase permanent magnet synchronous servo motor with a rated torque of 1.2 N·m, a 10:1 reduction ratio reducer, a 5 mm lead ball screw, and an absolute magnetic encoder with a resolution of 0.01 mm. The number of pulses in the drive command controls the rotation of the servo motor, which drives the ball screw through the reducer, causing the attachment point to move outward by 0.15 mm. The displacement encoder provides real-time feedback of the actual displacement to the MPC control unit, forming a position closed loop to ensure that the actual displacement reaches 5.15 mm.

[0026] After adjustment, the laser rangefinder collects the adjusted gap width in real time at a frequency of 20 Hz. The new measured values ​​at the three measuring points are 10.75 mm, 10.85 mm, and 10.95 mm, respectively, with an average value of 10.85 mm, which is fed back to the deviation comparison unit. At this time, the target gap width is still 10.809 mm, and the new deviation value is -0.041 mm. The deviation has been significantly reduced, and the next control cycle begins.

[0027] During long-term operation, the online model correction unit continuously monitors the absolute value of the gap deviation output by the deviation comparison unit. Assuming that within a certain period, due to the actual shrinkage rate of the ALC board being slower than the factory preset value, the absolute values ​​of the gap deviation for 10 consecutive times are 0.31, 0.32, 0.35, 0.33, 0.36, 0.34, 0.38, 0.37, 0.35, and 0.33 mm, all greater than 0.3 mm, triggering online model correction. The online model correction unit forms sample pairs with the actual gap widths after these 10 adjustments and the corresponding target gap widths; the recursive least squares algorithm initializes the parameter vector θ=[τ]. dry ,γ] T The initial values ​​are [180, 2.5e-8], the covariance matrix P is initially a 1000×identity matrix, and the forgetting factor λ = 0.995; for each sample pair, the algorithm performs the following iterations: First, calculate the prediction output under the current parameters: Predicted target gap width. ; due to τ dry γ needs to be updated, other parameters are known; calculate the prediction error e. target =W actual - W target Then calculate the gain vector. Where Φ is the regression vector, It can numerically calculate partial derivatives and update the parameter vector θ. new =θ old +Ke target Update the covariance matrix Process 10 sample pairs sequentially, updating τ immediately after processing each pair. dry And γ are transmitted to the prediction model unit to replace the original parameters; after processing, τ dryThe update was set to 165 hours, and γ was updated to 3.0e-8 degrees Celsius per square degree, both within the limit range. In subsequent control cycles, the prediction model unit used the new parameters to calculate the target plate gap width, and the average plate gap deviation was reduced to below 0.1 mm.

[0028] During the entire control process, if the displacement encoder of a certain attachment point reports an actual displacement of +19.2 mm and the remaining positive stroke of 0.8 mm is less than 1 mm, the MPC control unit will forcibly limit the maximum single-step displacement compensation of the attachment point to 0 mm before calculation, and mark the attachment point as a stroke lock-up state in the drive command. At the same time, an alarm signal will be generated to the monitoring screen to prompt the operator that the attachment point is close to the limit and manual intervention or adjustment of the reference baseline is required.

[0029] This invention achieves fully closed-loop intelligent adaptive control of the joint width between ALC panels and integrated aluminum insulation panels in the exterior wall by employing an adaptive sampling strategy using a multi-source sensor group, a prediction model with complete parameter calibration, an MPC control algorithm with constrained quadratic programming solution, and online model correction using recursive least squares method.

[0030] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A PLC-based assembly control system for ALC panels and integrated thermal insulation aluminum panels for exterior walls, comprising a multi-source sensor group, a PLC controller, and a servo-mounted actuator, characterized in that: The multi-source sensor group includes a temperature and humidity sensor, a strain sensor, and a laser rangefinder. The temperature and humidity sensor is configured to collect the ambient temperature in real time, the strain sensor is configured to collect the shrinkage strain of the ALC board in real time, and the laser rangefinder is configured to collect the current board gap width in real time and obtain the position deviation parameters of the main building structure relative to the reference baseline. The PLC controller is electrically connected to the multi-source sensor group. The PLC controller integrates a prediction model unit, a deviation comparison unit, and an MPC control unit that are connected in sequence. The prediction model unit is configured to receive the ambient temperature, shrinkage strain, and position deviation parameters, and calculate the target plate gap width under the current working conditions based on the preset ALC plate shrinkage deformation model and the thermal expansion deformation model of the integrated thermal insulation aluminum plate. The deviation comparison unit is configured to receive the target gap width and the current gap width transmitted back by the laser rangefinder, compare the two and calculate the gap deviation value. The MPC control unit is configured to receive the plate gap deviation value, execute the model predictive control algorithm, use the hanging point displacement compensation amount as the control variable, use the minimization of the plate gap tracking error as the control objective, and combine the displacement stroke constraint of the servo hanger actuator to calculate the optimal displacement compensation amount corresponding to each hanging point and output the drive command. The servo mounting actuator is electrically connected to the MPC control unit and is configured to drive the corresponding mounting point to generate displacement compensation according to the drive command, so as to adjust the width of the gap between adjacent plates. The laser rangefinder is also configured to collect the adjusted gap width in real time and feed it back to the deviation comparison unit to form adaptive dynamic compensation control.

2. The PLC-based ALC panel and integrated thermal insulation aluminum panel exterior wall assembly control system according to claim 1, characterized in that: The temperature and humidity sensor in the multi-source sensor group is configured to continuously collect the ambient temperature with a temperature resolution of 0.2℃ and a sampling period of 1 second, and calculate the temperature change rate in real time. When the absolute value of the temperature change rate is less than 0.5℃ / min, it is determined to be a steady-state temperature condition, and the sampling period is maintained. When the absolute value of the temperature change rate is greater than or equal to 0.5℃ / min, it is determined to be a transient temperature condition, the sampling period is switched to 0.2 seconds, and the strain sensor is triggered to synchronously increase the sampling frequency to 10Hz, which continues until the temperature change rate falls back below the steady-state threshold.

3. The PLC-based ALC panel and integrated thermal insulation aluminum panel exterior wall assembly control system according to claim 1, characterized in that: In the prediction model unit, the specific mathematical expressions for the ALC plate shrinkage deformation model and the thermal expansion deformation model of the integrated thermal insulation aluminum plate are as follows: ALC plate shrinkage deformation model: Thermal expansion deformation model of integrated thermal insulation aluminum plate: Formula for calculating the target board gap width: Where, ε dry (t) represents the shrinkage strain at time t; ε ∞ For the ultimate shrinkage strain; τ dry α is the drying time constant; dry Humidity sensitivity coefficient; RH ref Reference relative humidity; RH cur The current ambient relative humidity is collected in real time by the temperature and humidity sensors in the multi-source sensor group; ε thermal (T) represents the thermal expansion strain at temperature T; β Al The coefficient of thermal expansion is linear; T is the current ambient temperature, which is collected in real time by the temperature and humidity sensor; T ref The reference temperature is γ; the nonlinear thermal expansion correction coefficient is γ; W0 is the initial design gap width; L panel δ represents the width of a single sheet material; position The position deviation parameter is obtained by directly substituting the vertical distance deviation of the main building structure relative to the reference baseline, which is collected by the laser rangefinder.

4. The PLC-based ALC panel and integrated thermal insulation aluminum panel exterior wall assembly control system according to claim 1, characterized in that: In the MPC control unit, the model predictive control algorithm is specifically configured as follows: the plate gap deviation value output by the deviation comparison unit is used as the tracking error e(k), where k=1,2,…,N p This represents the k-th prediction step in the future; the displacement compensation increment Δu(j) of each anchor point is used as the control variable, where j=0,1,…,N c 1 represents the current and future j-th control step; the dual control objectives are minimizing the plate seam tracking error and ensuring a smooth change in displacement compensation; the control objective function is constructed as follows: In each control cycle, the optimal displacement compensation increment sequence that minimizes J is solved, and the first element Δu(0) in the sequence is superimposed on the displacement compensation of the previous cycle to obtain the optimal displacement compensation of each hanging point in the current cycle. The output is the drive command. Where Q is the error weight matrix and R is the control increment weight matrix.

5. The PLC-based ALC panel and integrated thermal insulation aluminum panel exterior wall assembly control system according to claim 4, characterized in that: The MPC control unit is also configured to: before each calculation of the optimal displacement compensation amount, read the current actual displacement of each attachment point of the servo attachment actuator and update the remaining available stroke of the displacement stroke constraint; if the remaining positive stroke is less than 1 mm or the remaining negative stroke is less than 1 mm, then the single-step maximum displacement compensation amount of the corresponding attachment point is forcibly limited to 0 mm, and the attachment point is marked as stroke lock state in the drive command, while generating an alarm signal.

6. The PLC-based ALC panel and integrated thermal insulation aluminum panel exterior wall assembly control system according to claim 1, characterized in that: The PLC controller also integrates an online model correction unit, which is electrically connected to the prediction model unit and the deviation comparison unit respectively. The configuration is as follows: when the absolute value of the plate gap deviation value is greater than 0.3 mm for 10 consecutive times, the actual plate gap width after the 10 adjustments and the corresponding target plate gap width are formed into a sample pair. The recursive least squares method is used to update the drying shrinkage time constant in the ALC plate drying shrinkage deformation model and the nonlinear correction coefficient in the thermal expansion deformation model of the integrated thermal insulation aluminum plate online. The update step size is once for each sample pair. The updated model parameters immediately replace the original parameters for the calculation of the target plate gap width in the next cycle.

7. The PLC-based ALC panel and integrated thermal insulation aluminum panel exterior wall assembly control system according to claim 1, characterized in that: The laser rangefinder is configured with a measurement range of 0–50 mm, a measurement accuracy of ±0.05 mm, and a data output frequency of 20 Hz. The laser beam axis of the rangefinder is perpendicular to the surface of the board seam, and three measuring points are set at the board seam position corresponding to each hanging point, located at the upper, middle, and lower ends of the board seam, respectively. The current board seam width is taken as the arithmetic mean of the measurements from the three measuring points. The position deviation parameter is the vertical distance deviation of each measuring point relative to the reference baseline, in millimeters, with two decimal places.

8. The PLC-based ALC panel and integrated thermal insulation aluminum panel exterior wall assembly control system according to claim 1, characterized in that: The servo actuator includes a servo motor, a reducer, a ball screw, and a displacement encoder. The servo motor is a three-phase permanent magnet synchronous servo motor with a rated torque of 1.2 N·m, configured to receive the speed and angle from the drive commands output by the MPC control unit. The reducer has a reduction ratio of 10:

1. The ball screw has a lead of 5 mm and a stroke of ±20 mm. The displacement encoder is an absolute magnetic encoder with a resolution of 0.01 mm, providing real-time feedback of the actual displacement of the attachment point to the MPC control unit to form a closed-loop position control. The optimal displacement compensation in the drive commands is output in the form of pulse counts, with each pulse corresponding to a displacement of 0.005 mm.

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