Pccp pipe double lifting point adaptive dynamic balance lifting precision centering control method

Abstract

The application relates to the technical field of pipeline hoisting construction control, and discloses a PCCP pipeline double-hoisting-point self-adaptive dynamic balance hoisting precision centering control method, which comprises the following steps: collecting infrared laser centering deviation data; generating a six-degree-of-freedom centering error vector; fusing IMU data to decouple dynamic error and static deviation; generating a centering compensation control vector; performing hierarchical fine adjustment control output; confirming centering in place and locking an execution mechanism; and performing online correction on kinematic mapping model parameters.

Description

Technical Field

[0001] This invention relates to the field of pipeline hoisting construction control technology, specifically to a precise centering control method for PCCP pipeline dual-lifting-point adaptive dynamic balance hoisting. Background Technology

[0002] PCCP (prestressed concrete cylinder pipe) pipes require high precision in spigot and socket fitting. The outer diameter of the pipe is usually 1.2 to 4m and the length is about 5m. When the pipe is installed in the trench, it is necessary to ensure that the axial centering is precisely aligned with the spigot and socket.

[0003] In current hoisting operations, the axial alignment and socket alignment of pipes during trench installation rely on manual line laying and visual estimation. This is susceptible to variations in lighting conditions, viewing angle, and the experience of the workers, making it difficult to guarantee positioning accuracy. Furthermore, the slight swaying of the suspended pipe causes continuous drift in the visual alignment reference, making it difficult to consistently capture the socket centerline. Existing control methods lack non-contact automatic measurement tools, making it impossible to obtain real-time spatial deviations between the pipe axis and the socket center of the installed pipe section. The sequential process of manual adjustment and observation further reduces installation efficiency, resulting in both socket alignment accuracy and installation efficiency failing to meet engineering requirements. Summary of the Invention

[0004] This invention provides a precise alignment control method for PCCP pipeline dual-lifting-point adaptive dynamic balance lifting, which solves the technical problems in related technologies where dynamic swing interference and static position deviation are coupled during PCCP pipeline lifting, resulting in insufficient pipeline alignment accuracy and suboptimal distribution of actuator adjustment.

[0005] This invention discloses a precise alignment control method for PCCP pipeline dual-suspension point adaptive dynamic balance hoisting, including the following steps: collecting infrared laser alignment deviation data: a dual-axis infrared laser emitter continuously projects laser rays along the pipeline axis to a target reflector plate pre-placed at the center of the socket end face of the installed pipe section, collects the real-time coordinates of the laser points on the target reflector plate, and obtains the horizontal deviation value and the vertical deviation value. Generate a six-degree-of-freedom centering error vector: Based on the distance distribution from the pipe end face to the socket end face measured by the infrared ranging sensor array symmetrically arranged at the four directions of the circumference of the suspension points at both ends of the pipe, calculate the pitch angle and yaw angle, and combine them with the horizontal deviation value and the vertical deviation value to generate a six-degree-of-freedom centering error vector. Fusion IMU data decoupling of dynamic error and static deviation: The real-time attitude data output by the inertial measurement unit on the dual-suspension point hoist is fused with the six-degree-of-freedom centering error vector to separate the dynamic swing error component and the static position deviation component, and output a stable centering error estimate. Generate centering compensation control vector: Based on the stable centering error estimate, the kinematic mapping model is used to calculate the differential speed adjustment of the dual-lifting-point lifting motor, the differential displacement adjustment of the lateral hydraulic cylinder, and the angle compensation of the slewing mechanism to generate the centering compensation control vector. Graded fine-tuning control output: Based on the modulus of the stable centering error estimate, the coarse-tuning mode and fine-tuning mode are dynamically switched. In the fine-tuning mode, the error integral term is introduced to eliminate the steady-state residual, and the centering compensation control vector is output to the dual-suspension-point actuator. Centering confirmation and actuator locking: Determine whether the magnitude of the stable centering error estimate is continuously lower than the centering qualification threshold within a preset number of consecutive cycles. If the condition is met, output the centering confirmation signal and lock the position of the actuator to trigger the pipeline to be lowered into the tank. Online calibration of kinematic mapping model parameters: Using the stable centering error estimates and centering compensation control vector data of each control cycle in historical operations, the kinematic mapping model parameters are calibrated online to compensate for the system errors introduced by the mechanical wear of the lifting device and the nonlinearity of the hydraulic system.

[0006] Furthermore, the six-degree-of-freedom alignment error vector includes six components: horizontal offset, vertical offset, pitch angle deviation, yaw angle deviation, axial spacing deviation, and roll angle deviation. The four-directional infrared ranging sensor array is arranged in the four directions of up, down, left, and right in the circumferential direction of the suspension points at both ends of the pipeline. The pitch angle and yaw angle are calculated based on the difference in relative directional distance.

[0007] Furthermore, the fusion solution employs Kalman filtering, using the attitude quaternion time series output by the inertial measurement unit as the basis for state prediction of the process model, and using laser alignment deviation data and ranging angle data as observations. Iterative updates are performed within each control cycle to output a stable alignment error estimate after removing dynamic sway components.

[0008] Furthermore, the kinematic mapping model establishes a linear mapping matrix between the adjustment amounts of each degree of freedom of the dual-suspension actuator and the pose changes of the six degrees of freedom of the pipeline. With the stable centering error estimate as input, the centering compensation control vector is calculated through the pseudo-inverse matrix of the mapping matrix to achieve the optimal allocation of the adjustment amounts of each degree of freedom of the actuator. Among them, the horizontal offset is decomposed into the differential displacement adjustment of the lateral hydraulic cylinders of the left and right lifting points, the pitch angle deviation is converted into the differential speed adjustment of the lifting motors of the two lifting points, and the yaw angle deviation is mapped into the angle compensation of the slewing mechanism.

[0009] Furthermore, in the graded fine-tuning control output, when the magnitude of the stable centering error estimate is greater than the coarse-tuning threshold, the coarse-tuning mode is adopted to drive the actuator to quickly reduce the deviation with a larger step size. When the magnitude of the stable centering error estimate is not greater than the coarse adjustment threshold but is greater than the fine adjustment threshold, switch to fine adjustment mode to approach the target centering position with high precision in small steps. In fine-tuning mode, the stable centering error estimate is accumulated and integrated according to the control cycle, and the error integral term is superimposed on the centering compensation control vector until the magnitude of the stable centering error estimate drops below the fine-tuning threshold.

[0010] Furthermore, the online calibration of the kinematic mapping model parameters adopts the recursive least squares method, taking the stable centering error estimate and the centering compensation control vector data pair of each control cycle in the historical operation as input. After each new operation is completed, the mapping matrix parameters are updated incrementally, without the need to reprocess all historical data, so that the mapping matrix parameters continuously approach the real system characteristics with the cumulative number of operations.

[0011] Furthermore, each column of the mapping matrix corresponds to the pipeline pose change vector caused by a single actuator unit adjustment. The initial value is derived through the geometric relationship of the dual-suspension point geometry and is continuously updated in the online correction step of the kinematic mapping model parameters.

[0012] This invention replaces manual line laying and visual judgment with infrared laser target shooting and a four-directional infrared ranging array, realizing non-contact quantitative measurement of the axis deviation of the socket, eliminating subjective errors and environmental dependence introduced by manual estimation; by fusing and decoupling IMU attitude data and the six-degree-of-freedom alignment error vector, the dynamic sway component of the pipeline is separated from the measurement data, ensuring the stability of the measurement data under pipeline suspension and swaying conditions; the graded fine-tuning control achieves rapid convergence in coarse-tuning mode when the error is large, and approaches with high precision in fine-tuning mode after the error enters the fine-tuning range, and eliminates steady-state residuals through the error integral term. It balances convergence efficiency during large deviations with centering accuracy during small deviations; automatic centering confirmation and actuator locking form a fully automatic closed loop from centering measurement to safe positioning, replacing the subjective operation of manual judgment for alignment; by correcting kinematic mapping model parameters online using historical data, it continuously compensates for system errors introduced by mechanical wear of the lifting device and nonlinearity of the hydraulic system, solving the technical problems of existing methods lacking non-contact automatic measurement means and being unable to obtain the spatial deviation between the pipe axis and the socket center in real time, thus achieving the technical effect of improving the centering accuracy of the socket and the degree of automation of installation operations. Attached Figure Description

[0013] Figure 1 This is a flowchart of the PCCP pipeline dual-suspension-point adaptive dynamic balance hoisting precise centering control method provided in the embodiments of the present invention. Detailed Implementation

[0014] Example 1 PCCP (Prestressed Concrete Cylinder Pipe) pipes require high precision in spigot and socket fitting, with pipe outer diameters typically ranging from 1.2 to 4 meters and lengths around 5 meters. In current hoisting operations, axial alignment and spigot / socket alignment during pipe installation rely on manual line laying and visual estimation, which are susceptible to errors due to lighting, viewing angle, and the experience of the workers. Slight swaying of the suspended pipe causes continuous drift in the visual alignment reference, making it difficult to consistently capture the spigot / socket centerline. The sequential process of manual adjustment, observation, and readjustment results in low installation efficiency. Existing control methods lack non-contact automatic measurement tools, making it impossible to obtain real-time spatial deviations between the pipe axis and the center of the socket of the installed pipe section.

[0015] This embodiment provides a precise centering control method for PCCP pipeline dual-lifting-point adaptive dynamic balance lifting. The required hardware environment includes: a dual-axis infrared laser emitter fixed to the end of the lifting device, a target reflector pre-positioned at the center of the socket end face of the installed pipe section, an infrared ranging sensor array symmetrically arranged in four directions around the lifting points at both ends of the pipeline, an inertial measurement unit (IMU) mounted on the dual-lifting-point lifting device, and a lifting motor, a lateral hydraulic cylinder, and a slewing mechanism to control the dual lifting points.

[0016] According to an embodiment of the present invention, a precise centering control method for PCCP pipeline dual-lifting-point adaptive dynamic balance lifting is provided, such as... Figure 1 As shown, it includes the following steps: Step 1: Collect infrared laser alignment deviation data.

[0017] During the hoisting and positioning process, the dual-axis infrared laser emitter continuously projects laser beams along the axial direction of the pipeline onto the target reflector. The center of the target reflector corresponds to the reference point of the socket axis. The real-time coordinates of the laser-marked points on the target reflector are continuously collected to obtain the horizontal and vertical deviation values, which serve as direct measurement inputs for the spatial alignment error between the pipeline axis and the socket centerline.

[0018] Step 2: Generate a six-degree-of-freedom alignment error vector.

[0019] An infrared ranging sensor array is symmetrically arranged at the four circumferential positions (up, down, left, and right) of the suspension points at both ends of the pipe to measure the distance distribution between the pipe end face and the socket end face in real time. Based on the distance differences in the four directions, the pitch angle and yaw angle of the pipe end face relative to the socket end face are calculated. The pitch angle and yaw angle are combined with the horizontal deviation value and vertical deviation value obtained in step 1 to generate a six-degree-of-freedom alignment error vector.

[0020] in, Indicates transpose. , These are the horizontal offset and the vertical offset, respectively. , These are pitch angle deviation and yaw angle deviation, respectively. This refers to the axial spacing deviation. This refers to the roll angle deviation.

[0021] Step 3: Decouple dynamic error and static bias by fusing IMU data.

[0022] Obtain the real-time quaternion of the pipe's attitude output from the IMU on the dual-point lifting device, and calculate the pipe's current spatial attitude angle. Compare the IMU attitude data with the six-DOF alignment error vector. Perform fusion calculations to distinguish the dynamic error components caused by the pipe's own oscillation. Static position deviation component relative to the installation reference of the pipeline Output the stable centering error estimate after decoupling:

[0023] in, This is the six-degree-of-freedom centering error vector. For dynamic error components, This is the estimated value of the stable centering error after removing the dynamic oscillation component.

[0024] It should be noted that the above fusion calculation uses Kalman filtering, and its input is the attitude quaternion time series output by the IMU and the six-degree-of-freedom alignment error vector. The time series data is output as a stable midpoint error estimate. Kalman filtering uses the attitude change rate of the IMU as the basis for state prediction of the process model, and uses laser alignment deviation data and ranging angle data as observations. It iteratively updates the data in each control cycle, thereby continuously outputting a stable output under the condition of pipe suspension and swaying. .

[0025] Step 4: Generate the centering compensation control vector.

[0026] Based on the stable centering error estimate The adjustment amount of the dual-suspension actuator is calculated using a kinematic mapping model. The kinematic mapping model establishes the mapping relationship between the displacement of the dual-suspension actuator and the six-degree-of-freedom pose of the pipeline, with the input being... The output is the centering compensation control vector:

[0027] in, Indicates transpose. , These are the differential displacement adjustment amounts of the left and right lifting point lateral hydraulic cylinders, respectively. , These refer to the differential speed adjustment of the lifting motors at the two lifting points. This refers to the angular compensation amount for the slewing mechanism of the spreader. Specifically, it refers to the horizontal offset. Decomposed into , Differential displacement; pitch angle deviation Transform into , Differential speed; yaw angle deviation Mapped to the angle compensation amount of the rotary mechanism .

[0028] It should be noted that the above kinematic mapping model is based on a dual-suspension-point geometry and establishes a linear mapping matrix between the adjustment amounts of each degree of freedom of the actuator and the changes in the pipe's pose. ,satisfy:

[0029] in, For centering compensation control vector, For mapping matrix The pseudo-inverse matrix, To stabilize the centering error estimate, Used to optimally allocate between the degrees of freedom of the actuator and the error components. Mapping matrix Each column corresponds to the pipeline pose change vector caused by the unit adjustment of a single actuator. The initial value is obtained through geometric derivation and is continuously updated in step 7 using historical operation data.

[0030] Step 5: Fine-tune the output control in stages.

[0031] Based on the magnitude of the centering error vector Dynamically determine the control mode. When When using coarse adjustment mode, the actuator is driven with a larger step size to quickly reduce the deviation; when and At this time, it switches to fine-tuning mode to approach the target centering position with high precision using small steps, and introduces an integral term to eliminate steady-state residuals; among which To coarsely adjust the threshold, To fine-tune the threshold, the centering compensation control vector will be adjusted. The current mode parameters are output to the dual-point lifting motor, the horizontal hydraulic cylinder, and the slewing mechanism, driving the pipeline to converge toward the precise centering target position.

[0032] In this embodiment of the application, in order to eliminate steady-state centering residuals, the error integral term is accumulated in fine-tuning mode:

[0033] in, This is the current control cycle number. For the first The error integral term for each control cycle, For the first The estimated value of the stable alignment error for each control cycle. To determine the control cycle duration, the error integral term is superimposed onto the centering compensation control vector. This enables the implementing agency to continuously compensate for systemic biases during the small deviation phase, until... Reduced to the fine-tuning threshold the following.

[0034] Step 6: Confirm the location of the center and lock the execution agency.

[0035] In each control cycle, laser alignment deviation data and ranging angle data are collected in real time to determine... Whether it remains below the acceptable threshold .when continuous Each control cycle satisfies At that time, an automatic centering confirmation signal is output, in which... This is the preset number of consecutive qualified cycles. The alignment threshold is set. Simultaneously, the current position of the dual-lifting-point actuators is locked to prevent displacement of the pipeline during lowering. An alignment confirmation signal is output to the lifting control system, triggering the pipeline lowering into the trench.

[0036] Step 7: Online calibration of kinematic mapping model parameters.

[0037] Record the initial error vector for each pipeline alignment operation. The system generates adjustment sequences and final alignment accuracy data for each control cycle. Historical operational data is used to perform online correction of the kinematic mapping model parameters, compensating for system errors introduced by factors such as spreader mechanical wear and hydraulic system nonlinearity. Updated model parameters are output for use in subsequent control cycles.

[0038] In this embodiment, to improve the accuracy of kinematic mapping model parameter correction, online correction employs recursive least squares method, with the input being the stable alignment error estimate for each control cycle in historical operations. With centering compensation control vector The data pairs are output as the updated kinematic mapping model parameter matrix. The recursive least squares method updates parameters incrementally after each new task, without having to reprocess all historical data, allowing the kinematic mapping model parameters to continuously approach the actual system characteristics as the number of tasks accumulates.

[0039] Steps 1 and 2 replace manual line laying and visual judgment with infrared laser target shooting and a four-directional infrared ranging array, realizing non-contact quantitative measurement of the socket axis deviation and eliminating subjective errors and environmental dependence introduced by manual estimation. Step 3 separates the dynamic sway component of the pipeline from the measurement data by fusing and decoupling IMU attitude data and the six-degree-of-freedom alignment error vector, ensuring the stability of the measurement data under pipeline suspension and swaying conditions and solving the problem of continuous drift of the visual alignment reference due to swaying. The kinematic mapping model in Step 4 directly maps each component of the six-degree-of-freedom alignment error vector to the corresponding actuator adjustment amount, realizing the coordinated compensation of multi-degree-of-freedom errors. The graded fine-tuning control in Step 5 converges quickly in coarse-tuning mode when the error is large, and approaches with high precision in fine-tuning mode after the error enters the fine-tuning range. The steady-state residual is eliminated by the error integral term, solving the contradiction that a single control parameter cannot balance efficiency and accuracy in the large and small deviation stages. Step 6, with its automatic alignment confirmation and actuator locking, forms a fully automated closed loop from alignment measurement to safe positioning, replacing the subjective operation of manual alignment judgment. Step 7 uses historical data to online correct the kinematic mapping model parameters, continuously compensating for system errors introduced by the mechanical wear of the spreader and the nonlinearity of the hydraulic system, thus continuously improving the alignment adjustment accuracy with the cumulative number of operations.

[0040] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for precise centering control during dual-suspension-point adaptive dynamic balance hoisting of PCCP pipelines, characterized in that, Includes the following steps: Collect infrared laser alignment deviation data: The dual-axis infrared laser emitter continuously projects laser rays along the pipeline axis onto the target reflector plate pre-positioned at the center of the socket end face of the installed pipe section, collects the real-time coordinates of the laser points on the target reflector plate, and obtains the horizontal and vertical deviation values. Generate a six-degree-of-freedom centering error vector: Based on the distance distribution from the pipe end face to the socket end face measured by the infrared ranging sensor array symmetrically arranged at the four directions of the circumference of the suspension points at both ends of the pipe, calculate the pitch angle and yaw angle, and combine them with the horizontal deviation value and the vertical deviation value to generate a six-degree-of-freedom centering error vector. Fusion IMU data decoupling of dynamic error and static deviation: The real-time attitude data output by the inertial measurement unit on the dual-suspension point hoist is fused with the six-degree-of-freedom centering error vector to separate the dynamic swing error component and the static position deviation component, and output a stable centering error estimate. Generate centering compensation control vector: Based on the stable centering error estimate, the kinematic mapping model is used to calculate the differential speed adjustment of the dual-lifting-point lifting motor, the differential displacement adjustment of the lateral hydraulic cylinder, and the angle compensation of the slewing mechanism to generate the centering compensation control vector. Graded fine-tuning control output: Based on the modulus of the stable centering error estimate, the coarse-tuning mode and fine-tuning mode are dynamically switched. In the fine-tuning mode, the error integral term is introduced to eliminate the steady-state residual, and the centering compensation control vector is output to the dual-suspension-point actuator. Centering confirmation and actuator locking: Determine whether the magnitude of the stable centering error estimate is continuously lower than the centering qualification threshold within a preset number of consecutive cycles. If the condition is met, output the centering confirmation signal and lock the position of the actuator to trigger the pipeline to be lowered into the tank. Online calibration of kinematic mapping model parameters: Using the stable centering error estimates and centering compensation control vector data of each control cycle in historical operations, the kinematic mapping model parameters are calibrated online to compensate for the system errors introduced by the mechanical wear of the lifting device and the nonlinearity of the hydraulic system.

2. The PCCP pipeline dual-lifting-point adaptive dynamic balance lifting precise centering control method according to claim 1, wherein, The six-degree-of-freedom centering error vector includes six components: horizontal offset, vertical offset, pitch angle deviation, yaw angle deviation, axial spacing deviation, and roll angle deviation. The four-directional infrared ranging sensor array is arranged in the four directions of up, down, left, and right in the circumferential direction of the suspension points at both ends of the pipeline. The pitch angle and yaw angle are calculated based on the difference in relative directional distance.

3. The PCCP pipeline dual-lifting-point adaptive dynamic balance lifting precise centering control method according to claim 1, wherein, The fusion solution employs Kalman filtering, using the attitude quaternion time series output by the inertial measurement unit as the basis for state prediction of the process model, and using laser alignment deviation data and ranging angle data as observations. Iterative updates are performed within each control cycle, and the output is a stable alignment error estimate after removing the dynamic sway component.

4. The PCCP pipeline dual-lifting-point adaptive dynamic balance lifting precise centering control method according to claim 1, wherein, The kinematic mapping model establishes a linear mapping matrix between the adjustment amounts of each degree of freedom of the dual-suspension actuator and the pose changes of the six degrees of freedom of the pipeline. The stable alignment error estimate is used as input, and the alignment compensation control vector is calculated through the pseudo-inverse matrix of the mapping matrix to achieve the optimal allocation of the adjustment amounts of each degree of freedom of the actuator. Among them, the horizontal offset is decomposed into the differential displacement adjustment of the lateral hydraulic cylinders of the left and right lifting points, the pitch angle deviation is converted into the differential speed adjustment of the lifting motors of the two lifting points, and the yaw angle deviation is mapped into the angle compensation of the slewing mechanism.

5. The PCCP pipeline dual-lifting-point adaptive dynamic balance lifting precise centering control method according to claim 1, wherein, In the graded fine-tuning control output, when the magnitude of the stable centering error estimate is greater than the coarse-tuning threshold, the coarse-tuning mode is used to drive the actuator to quickly reduce the deviation with a larger step size. When the magnitude of the stable centering error estimate is not greater than the coarse adjustment threshold but is greater than the fine adjustment threshold, switch to fine adjustment mode to approach the target centering position with high precision in small steps. In fine-tuning mode, the stable centering error estimate is accumulated and integrated according to the control cycle, and the error integral term is superimposed on the centering compensation control vector until the magnitude of the stable centering error estimate drops below the fine-tuning threshold.

6. The PCCP pipeline dual-lifting-point adaptive dynamic balance lifting precise centering control method according to claim 1, wherein, The online calibration of the kinematic mapping model parameters adopts the recursive least squares method, which takes the stable centering error estimate and the centering compensation control vector data pair of each control cycle in the historical operation as input. After each new operation is completed, the mapping matrix parameters are updated incrementally, without the need to reprocess all historical data, so that the mapping matrix parameters continuously approach the real system characteristics with the cumulative number of operations.

7. The PCCP pipeline dual-lifting-point adaptive dynamic balance lifting precise centering control method according to claim 4 or 6, wherein, Each column of the mapping matrix corresponds to the pipeline pose change vector caused by a single actuator unit adjustment. The initial value is derived through the geometric relationship of the dual-suspension point geometry and is continuously updated in the online correction step of the kinematic mapping model parameters.

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