Deepwater S-shaped pipe laying self-adaptive tensioning system and method based on dynamic compensation and cooperative control

Abstract

The invention discloses a deepwater S-shaped pipe laying self-adaptive tensioning system and method based on dynamic compensation and cooperative control, and relates to the technical field of seabed pipe laying. The system is used for solving the problem of pipeline top tension fluctuation caused by movement of the pipe laying ship under the action of waves. According to the technical scheme, the system is characterized in that the system collects data in real time through a motion sensor set, an environment sensor set, a tension sensor, a tilt angle sensor and a speed sensor, a dynamic feed-forward compensation module of a central controller predicts and generates a feed-forward compensation amount according to ship motion, and a cooperative feedback control module generates a feedback control amount according to tension deviation; the instruction synthesis module superposes the two signals and then drives the tensioning device and the stinger posture adjusting system to act cooperatively. The system is mainly used for self-adaptive accurate control of the tensioning force in the deepwater submarine pipeline laying process.

Description

Technical Field

[0001] This invention relates to the field of subsea pipelaying technology. More specifically, this invention relates to an adaptive tensioning system and method for deep-water S-shaped pipelaying based on dynamic compensation and collaborative control. Background Technology

[0002] In deep-water S-shaped pipelaying operations, maintaining stable tension at the top of the pipe is crucial for ensuring laying quality and safety. The pipelaying vessel experiences continuous heave, pitch, and roll motions under the influence of the marine environment. These movements directly cause dynamic responses in the pipe connected to the hull, resulting in significant fluctuations in top tension. Excessive tension fluctuations can lead to pipe buckling, excessive bending, or damage, affecting laying accuracy and posing safety risks.

[0003] Currently, common tensioning systems primarily rely on feedback control strategies. This involves monitoring the actual tension using tension sensors, comparing it to a target value, and adjusting the output force of the tensioning device based on the deviation. However, this method inherently suffers from hysteresis. Because there is a time lag between detecting a tension deviation and the tensioning device executing compensation, the system struggles to provide timely and effective compensation for high-frequency, rapid tension changes caused by ship motion. Especially in harsh sea conditions, where ship motion is intense and frequent, simple feedback control often leads to tension overshoot or under-adjustment, resulting in decreased control quality. Summary of the Invention

[0004] One object of the present invention is to solve at least the above-mentioned problems and to provide at least the advantages that will be described later.

[0005] To achieve these objectives and other advantages according to the present invention, a deep-water S-shaped pipelaying adaptive tensioning system based on dynamic compensation and cooperative control is provided, comprising: Tensioning devices are used to apply controlled tension to the pipes on pipelaying vessels; A motion sensor array, deployed on the pipelaying vessel, is used to collect the vessel's motion data in real time, including heave, pitch, and roll data. An environmental sensor array is used to collect real-time data on the ship's heading, speed, and surface currents. Tension sensors are used to monitor the top tension of pipes in real time. Tilt sensors are installed on the support frame to monitor the tilt angle of the support frame; Speed ​​sensors are used to monitor the pipeline's deployment speed in real time; The rack attitude adjustment system is used to drive and adjust the pitch angle of the rack. The central controller has its signal input terminal connected to the motion sensor group, environmental sensor group, tension sensor, tilt sensor and speed sensor, and its signal output terminal connected to the tensioning device and the support frame attitude adjustment system. The central controller includes: The dynamic feedforward compensation module is used to predict the changes in the shape of the pipe suspension line caused by the movement of the pipe-laying vessel and the resulting fluctuations in tension force based on the motion data of the pipe-laying vessel collected in real time by the motion sensor group, and to generate a tension force feedforward compensation amount to offset the fluctuations. The collaborative feedback control module is used to generate a tension force feedback control quantity based on the deviation between the top tension force of the pipe collected by the tension sensor and the preset target tension force, using an adaptive control algorithm. The instruction synthesis module is used to superimpose the tension force feedforward compensation amount and the tension force feedback control amount to generate a comprehensive control instruction and send it to the tensioning device to drive its operation.

[0006] Preferably, the method for the dynamic feedforward compensation module to generate the tension feedforward compensation amount includes: Calculate the tension feedforward compensation amount : in, The equivalent mass is calculated based on the pipe specifications and underwater weight. The acceleration of the pipelaying vessel's heave motion. and These are the angular accelerations of pitch and roll, respectively. and These are the angular displacements of pitch and roll, respectively. and These are the equivalent arm lengths from the tensioning device to the center of the ship's pitch and roll, respectively.

[0007] Preferably, the collaborative feedback control module employs a fuzzy adaptive PID controller with a proportional coefficient K. p Integral coefficient K i and differential coefficient K d Based on the dynamic changes of the tension deviation e and its rate of change ec, online tuning is performed using fuzzy inference rules to adapt to control requirements under different sea conditions.

[0008] Preferably, the central controller further includes: The collaborative optimization module is used to calculate and generate collaborative control commands when the change amplitude or rate of the comprehensive control commands generated by the command synthesis module exceeds a preset threshold, and send them to the support frame attitude adjustment system to drive the support frame to make corresponding attitude adjustments, so as to assist the tensioning device in maintaining the stability of the tension force at the top of the pipe, thereby reducing the requirements for the dynamic performance and wear of the tensioning device.

[0009] Preferably, the central controller further includes: The signal filtering and fault diagnosis module is used to perform fusion filtering on the raw data from various sensors using the Kalman filtering algorithm to eliminate abnormal jump signals, and to perform consistency verification on the signals of each sensor. When the data of a certain sensor continuously deviates from the predicted value of the mathematical model based on the data of other sensors, the sensor is determined to be faulty or the data is abnormal, and a redundancy fault-tolerant control strategy based on the remaining valid sensor data is activated.

[0010] Preferably, the equivalent mass It is based on the surface current data collected by the environmental sensor group, the tilt angle of the support frame collected by the tilt sensor, and the pipeline deployment speed collected by the velocity sensor. The variables are updated in real time and are realized by querying a pre-set database containing underwater equivalent mass data of the pipeline under different surface current data, support frame tilt angle, and pipeline deployment speed conditions.

[0011] Preferably, the central controller further includes: The human-machine interface is used to display the top tension of the pipeline, the motion data of the pipelaying vessel, the tension feedforward compensation and tension feedback control, and system alarm information. It is also used to receive the preset target tension and boundary limit values ​​of the control parameters input by the operator to prevent the central controller from outputting commands that exceed the physical limits of the tensioning device.

[0012] The present invention also provides an adaptive tensioning method for deep-water S-shaped pipelaying based on the above system, comprising the following steps: S1: The motion data of the pipelaying vessel is collected through the motion sensor group, the bow direction, speed and surface current data are collected through the environmental sensor group, the top tension of the pipe is collected through the tension sensor, the tilt angle of the support frame is collected through the tilt sensor, and the deployment speed of the pipe is collected through the speed sensor. S2: The dynamic feedforward compensation module calculates the tension force feedforward compensation amount based on the hull motion data. ; S3: The collaborative feedback control module generates a tension feedback control quantity based on the deviation between the top tension force of the pipeline and the target tension force. ; S4: The instruction synthesis module will... and Superimposed, resulting in integrated control commands. = + And send it to the tensioning device; S5: The tensioning device executes the integrated control command. Apply precise tension to the cable.

[0013] Preferably, in step S4, when the integrated control command... When the rate or magnitude of change exceeds a preset threshold, the collaborative optimization module generates an auxiliary control command and sends it to the support frame attitude adjustment system to adjust the tilt angle of the support frame. Adaptive adjustments to meet ,in To adjust the force output of the tensioning device to meet actual needs after adjustment. To adjust the coefficients, a portion of the tension control task is allocated to the support frame system, thus mitigating the violent movements of the tensioning device.

[0014] Preferably, during the entire control process, the signal filtering and fault diagnosis modules operate in parallel, performing real-time filtering and validity verification of sensor data. Once data anomalies or sensor malfunctions are detected, redundant data sources are activated and the system smoothly transitions to fault-tolerant control mode, while simultaneously issuing an alarm through the human-machine interface.

[0015] The present invention has at least the following beneficial effects: This invention effectively suppresses pipeline tension fluctuations caused by the heave, pitch, and roll motions of the pipelaying vessel by combining dynamic feedforward compensation with collaborative feedback control. The feedforward compensation module predictively generates compensation based on real-time collected hull motion data, preemptively offsetting most disturbances; the feedback control module precisely corrects the residual deviations after feedforward compensation. This composite control strategy improves the response speed and accuracy of tension control. This invention employs sensor data fusion filtering and fault diagnosis mechanisms to improve the system's reliability in complex marine environments. Through real-time verification and fault-tolerant processing of multi-source sensor signals, it can maintain basic system functions even when individual sensors malfunction, reducing the risk of the entire control system shutting down due to the failure of a single data source and enhancing operational continuity. This invention achieves a rational allocation of control tasks by introducing a coordinated optimization of the support frame attitude adjustment system and the tensioning device. When tension requirements change drastically, the system can coordinate the actions of both, reducing the dynamic load on the tensioning device, helping to reduce its mechanical wear and energy consumption. At the same time, it expands the system's adaptability to different sea conditions, providing a more stable and reliable control guarantee for deep-water S-shaped pipelaying operations.

[0016] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0017] The present invention will be further described in detail below with reference to embodiments, so that those skilled in the art can implement it based on the description.

[0018] This invention provides an adaptive tensioning system for deep-water S-shaped pipelaying based on dynamic compensation and collaborative control, comprising: Tensioning devices are used to apply controlled tension to the pipes on pipelaying vessels; A motion sensor array, deployed on the pipelaying vessel, is used to collect the vessel's motion data in real time, including heave, pitch, and roll data. An environmental sensor array is used to collect real-time data on the ship's heading, speed, and surface currents. Tension sensors are used to monitor the top tension of pipes in real time. Tilt sensors are installed on the support frame to monitor the tilt angle of the support frame; Speed ​​sensors are used to monitor the pipeline's deployment speed in real time; The rack attitude adjustment system is used to drive and adjust the pitch angle of the rack. The central controller has its signal input terminal connected to the motion sensor group, environmental sensor group, tension sensor, tilt sensor and speed sensor, and its signal output terminal connected to the tensioning device and the support frame attitude adjustment system. The central controller includes: The dynamic feedforward compensation module is used to predict the changes in the shape of the pipe suspension line caused by the movement of the pipe-laying vessel and the resulting fluctuations in tension force based on the motion data of the pipe-laying vessel collected in real time by the motion sensor group, and to generate a tension force feedforward compensation amount to offset the fluctuations. The collaborative feedback control module is used to generate a tension force feedback control quantity based on the deviation between the top tension force of the pipe collected by the tension sensor and the preset target tension force, using an adaptive control algorithm. The instruction synthesis module is used to superimpose the tension force feedforward compensation amount and the tension force feedback control amount to generate a comprehensive control instruction and send it to the tensioning device to drive its operation.

[0019] Specifically, the motion sensor array can be an inertial measurement unit integrating a triaxial accelerometer and a gyroscope, installed at the center of the deck of the pipelaying vessel, to collect heave, pitch, and roll data. The accelerometer used to measure heave acceleration has a range of ±5 m / s². 2The gyroscope used to measure angular velocity has a range of ±50 deg / s, and the angular acceleration can be calculated by differentiating the measured angular velocity. The inclinometer used to measure the ship's attitude angles, or the angular displacement obtained by integrating the gyroscope, has a range of ±30°. The motion sensor array monitors the six degrees of freedom motion of the ship in real time.

[0020] The environmental sensor array may include a GPS receiver, an electromagnetic current meter, and a wind direction sensor. The GPS receiver and wind direction sensor can be integrated and mounted on the upper part of the bow mast to obtain a wide field of view for satellite signal reception and unobstructed wind field data. The GPS receiver provides ship speed data with an accuracy better than 0.1 knots and bow heading data with an accuracy better than 1°. The wind direction sensor has a measurement range of 0–60 m / s and an accuracy of ±0.5 m / s. The electromagnetic current meter needs to be installed in the underwater part of the hull, typically near the bowpost or keel in a location with stable water flow and minimal interference from hull disturbances. Its sensor probe is submerged approximately 1–2 meters below the water surface to measure the velocity (range 0–3 m / s) and direction of the current relative to the surface ocean current. The environmental sensor array collects external environmental parameters.

[0021] A resistance strain gauge mechanical sensor can be selected as the tension sensor. It is installed at the tension point at the top of the pipe, with a range of 0-2000 kN and an accuracy of ±0.5% FS. The tension sensor detects the tension at the top of the pipe.

[0022] The tilt sensor can be a dual-axis inclinometer, fixed at the hinge of the support frame, with a range of ±90° and an accuracy of 0.01°. The tilt sensor records the angle of the support frame.

[0023] A rotary encoder can be used as the speed sensor, mounted on the drive shaft of the pipe delivery mechanism, with a measurement range of 0~1m / s and a resolution of 1 mm / s. The speed sensor tracks the pipe delivery speed.

[0024] The housing of these sensors can be made of 316 stainless steel to adapt to the marine environment; the signal transmission uses shielded cables to connect to the central controller, and during installation, it is necessary to ensure that the sensors are rigidly connected to the hull structure to avoid vibration interference.

[0025] All sensor data, after analog-to-digital conversion, is transmitted to the central controller at a sampling frequency of 100 Hz. Parameter setting methods are based on sensor calibration certificates; for example, tension sensors require periodic static calibration using standard weights. Environmental sensor data is integrated via a marine instrument protocol (such as NMEA 0183). Functional testing can be conducted in still water conditions at the dock, verifying sensor output consistency through simulated ship motion and data acquisition software. The experimental subjects are either a pipelaying vessel model or a full-scale vessel. Experimental methods include comparing sensor readings with reference instrument data, performing repeatability tests, and conducting error analysis.

[0026] The aforementioned sensors can provide high-precision, multi-source real-time data, laying the foundation for dynamic compensation and feedback control, ensuring rapid and accurate system response, and reducing control deviations caused by data delays or errors.

[0027] The tensioning device can be a hydraulic winch system, consisting of a hydraulic motor, a braking mechanism and a tension roller, installed on the pipeline path on the aft deck of the pipelaying vessel. The maximum tension force can reach 3000 kN and the response time is less than 500 ms. In terms of materials, the hydraulic cylinder body can be made of high-strength alloy steel and the seals can be made of nitrile rubber.

[0028] The attitude adjustment system of the support frame can be driven by an electric push rod or a hydraulic cylinder and installed at the connection between the support frame and the hull. The pitch angle adjustment range is -10° to +45°, and the adjustment speed is 0.1° / s. The push rod material can be aluminum alloy, and the structural components are Q235 carbon steel. The assembly position must ensure that the tensioning device is aligned with the pipeline axis, and the hinge points of the support frame adjustment system are well lubricated to avoid jamming.

[0029] The tensioning device, based on the comprehensive control commands from the central controller, adjusts the hydraulic pressure via a proportional valve to output precise tension. Upon receiving the collaborative optimization command, the support frame attitude adjustment system drives a push rod to change the support frame's tilt angle, assisting in tension stabilization.

[0030] The central controller can be an industrial PLC or an embedded industrial computer, installed in the control room of the pipelaying vessel; the dynamic feedforward compensation module uses a microprocessor to implement algorithm calculations, the collaborative feedback control module integrates a fuzzy adaptive PID controller, and the instruction synthesis module uses adder logic. The threshold settings are as follows: the threshold for the change amplitude of the comprehensive control instruction is set to 10% of the maximum tension force (i.e., 300 kN), and the threshold for the change rate is set to 50 kN / s.

[0031] The dynamic feedforward compensation module calculates the tension force feedforward compensation amount using the following formula based on motion sensor data; the collaborative feedback control module adjusts the PID parameters online using fuzzy rules based on tension deviation and rate of change to generate the tension force feedback control quantity; the instruction synthesis module superimposes the two and outputs a comprehensive instruction to the execution system. The parameter setting method includes updating the equivalent mass M in the feedforward formula by querying a database, which is preset based on ocean current, inclination angle, and velocity data; the fuzzy PID rule table is set based on sea state experience.

[0032] Furthermore, the method for the dynamic feedforward compensation module to generate the tension feedforward compensation amount includes: Calculate the tension feedforward compensation amount : in, The equivalent mass is calculated based on the pipe specifications and underwater weight. The acceleration of the pipelaying vessel's heave motion. and These are the angular accelerations of pitch and roll, respectively. and These are the angular displacements of pitch and roll, respectively. and These are the equivalent arm lengths from the tensioning device to the center of the ship's pitch and roll, respectively.

[0033] Specifically, based on the measurement range of the accelerometers in the motion sensor array, the acceleration of the pipelaying vessel's heave motion is... The value range can be -5 to +5 m / s 2 Based on the measurement range of the gyroscope in the motion sensor array, pitch... and sway The angular displacement can range from -30° to +30°, while the angular acceleration of pitch and roll... and The effective range reaches approximately -0.5 to +0.5 rad / s. 2 Equivalent arm length and These are fixed values ​​determined based on the ship's design drawings, for example... The value can be 50~120m. The value can be taken as 15~30m. The real-time motion data required for calculation is provided by a motion sensor array mounted on the hull. This sensor array can be an inertial measurement unit (IMU) integrating a three-axis accelerometer and a gyroscope. Its housing material can be aluminum alloy to reduce weight, and the internal sensing elements are silicon-based microelectromechanical systems (MEMS). The IMU is usually installed near the ship's center of gravity or at the location where the motion response is most representative, such as on the rigid structure of the main deck, and is bolted to ensure synchronization with the ship's movement.

[0034] The dynamic feedforward compensation module reads raw acceleration and angular velocity data from the IMU at a frequency of 100Hz. First, the angular acceleration is obtained by numerically differentiating the angular velocity signal. and Simultaneously, the acceleration signal in the heave direction is directly read to obtain... Secondly, the module retrieves the preset equivalent arm length from memory. and Subsequently, the aforementioned real-time data and the equivalent mass M are substituted into the formula for calculation, ultimately outputting the tension force feedforward compensation amount F. ffThe parameter settings depend on the initial calibration of the motion sensor, which can be achieved by zero-point calibration of the sensor while it is stationary at the dock. Functional testing can be conducted on a ship motion simulation platform, verifying the accuracy of calculations under different simulated sea conditions by comparing the formula output values ​​with the theoretical calculation values.

[0035] The initial value of the equivalent mass M can be determined based on pipe specifications (such as outer diameter and wall thickness) and material density (e.g., the density of steel is approximately 7850 kg / m³). 3 The weight of the pipe in air is calculated, and then the buoyancy of the pipe in water and the weight of the medium it may carry are considered to calculate the underwater weight, which is then converted into an equivalent mass. Dynamic updates are achieved by querying a pre-set database containing data on the equivalent underwater mass of the pipe under different surface current conditions, stator tilt angles, and pipe deployment speeds. Surface current data is provided by electromagnetic current meters, with a velocity range of 0–3 m / s; stator tilt angles are measured by tilt sensors, ranging from -10° to +45°; and pipe deployment speeds are measured by rotary encoders, ranging from 0–1 m / s. The central controller can update the currently used M value based on these real-time environmental parameters using a lookup table or interpolation algorithm.

[0036] The dynamic feedforward compensation module can be a software function block running within a central controller (such as an industrial programmable logic controller (PLC) or industrial computer), for example, written using Structured Text (ST) or Function Block Diagram (FBD) language conforming to the IEC 61131-3 standard. The processor responsible for performing calculations can be a multi-core CPU to ensure that all floating-point operations are completed within a specified control cycle (e.g., 10ms). The memory required for module operation can be Dynamic Random Access Memory (DRAM), and the memory storing the lookup table can be Flash Memory. This software module is integrated into the controller's operating system and exchanges data with other modules responsible for data acquisition and instruction output via the controller's backplane bus.

[0037] At the start of each control cycle, the dynamic feedforward compensation module acquires the sensor dataset from the shared data area. Then, the module performs calculations according to the aforementioned formulas and query logic. After the calculations are complete, the results are sent to the input buffer of the instruction synthesis module.

[0038] The above technical solution introduces a physical model formula that includes the ship's multi-degree-of-freedom motion terms and dynamically updates the equivalent mass M of the key parameter. This allows the feedforward compensation to more accurately predict the impact of the ship's motion on the pipeline tension, thereby issuing compensation commands in advance, reducing the burden on the feedback control module, and helping to improve the control accuracy and response speed of the entire tensioning system when facing complex marine environments.

[0039] Furthermore, the collaborative feedback control module employs a fuzzy adaptive PID controller with a proportional coefficient K. p Integral coefficient K i and differential coefficient K d Based on the dynamic changes of the tension deviation e and its rate of change ec, online tuning is performed using fuzzy inference rules to adapt to control requirements under different sea conditions.

[0040] Specifically, the input variables of the fuzzy adaptive PID controller are the tension deviation e (i.e., the difference between the target tension and the actual top tension) and its rate of change ec. The tension deviation e can be set according to actual operational requirements, for example, ±10% of the tension sensor range (0-2000 kN), i.e., ±200 kN. The rate of change ec can be calculated by measuring the difference between the deviation of the current control cycle and the previous cycle, and its effective range can be set to ±50 kN / s. The output of the fuzzy adaptive PID controller is the proportional coefficient K of the PID controller. p Integral coefficient K i and differential coefficient K d The adjustment amount. This fuzzy adaptive PID algorithm can be implemented as a functional module within the central controller in the software environment of an industrial programmable logic controller (PLC) or industrial computer (IPC). The processor running this software can be a multi-core CPU, its printed circuit board (PCB) substrate can be FR-4, and the components are commercial-grade chips packaged with surface mount technology (SMT).

[0041] The fuzzy adaptive PID controller begins its operation with data acquisition. Each control cycle (e.g., 10ms), the module reads real-time pipeline top tension data from shared memory and compares it with a preset target tension to calculate the current deviation *e*. Subsequently, the deviation change rate *ec* is obtained by calculating the difference between the current and previous deviations and dividing it by the control cycle time. These two precise numerical values ​​serve as inputs to the fuzzy inference system. The threshold settings for parameters *e* and *ec* are based on the analysis of the system's dynamic response characteristics; for example, if |e| consistently exceeds 150 kN, a high-level system alarm may be triggered.

[0042] The fuzzy inference rules of the fuzzy adaptive PID controller convert the precise input quantities e and ec into fuzzy linguistic values. In these rules, the universes of discourse (basic domains) of e and ec can be divided into seven fuzzy subsets: negative large (NB), negative medium (NM), negative small (NS), zero (ZE), positive small (PS), positive medium (PM), and positive large (PB). The membership functions can be triangular or trapezoidal functions. For example, for the universe of discourse of e [-200, 200] kN, the vertex of the membership function for its "zero (ZE)" subset can be set at 0 kN, with a width covering [-30, 30] kN. Furthermore, the fuzzy adaptive PID controller has a pre-built fuzzy rule base, which contains several rules based on expert experience. if - then Rules used to describe e, ec, and the output quantity ΔK p ΔK i ΔK d The non-linear relationship between them. The rule base can contain 49 (7x7) rules, for example: "If e is PB and ec is ZE, then ΔK..." p It is PB, ΔK i It's NB, ΔK d "PS", etc. The inference process of the fuzzy adaptive PID controller adopts Mamdani-type fuzzy inference, and the centroid method can be used for defuzzification to convert the fuzzy output into precise PID parameter adjustment. These fuzzy sets, rules, and inference mechanisms can be implemented through software code and loaded into memory from the configuration file when the fuzzy adaptive PID controller starts. Output ΔK p ΔK i ΔK d Used for real-time correction of the initial parameters of the PID controller. The correction formula can be: K p-online =K p-initial + ΔK p K i-online and K d-online Similarly, the initial parameter K... p-initial K i-initial K d-initial It is a set of baseline values ​​based on the initial preset of the system's rough model.

[0043] Next, the fuzzy adaptive PID controller calculates the current tension feedback control quantity based on the online tuned parameters using a positional or incremental PID algorithm. The entire parameter tuning and calculation process is completed within each control cycle.

[0044] The above technical solution automatically adjusts the parameters of the PID controller through a fuzzy inference mechanism, enabling the feedback control system to adapt to the dynamic characteristics of the pipeline tensioning system under different sea conditions. This improves the problems of slow response or excessive overshoot that may occur in fixed-parameter PID controllers in complex nonlinear systems, thereby enhancing the control quality and robustness of the system.

[0045] Furthermore, the central controller also includes: The collaborative optimization module is used to calculate and generate collaborative control commands when the change amplitude or rate of the comprehensive control commands generated by the command synthesis module exceeds a preset threshold, and send them to the support frame attitude adjustment system to drive the support frame to make corresponding attitude adjustments, so as to assist the tensioning device in maintaining the stability of the tension force at the top of the pipe, thereby reducing the requirements for the dynamic performance and wear of the tensioning device.

[0046] Specifically, the collaborative optimization module monitors the integrated control commands output by the instruction synthesis module. Whether the magnitude or rate of change exceeds a preset threshold. For example, in one embodiment, the collaborative optimization module monitors the integrated control commands output by the instruction synthesis module. Whether the change in amplitude exceeds a preset threshold. The change amplitude threshold can be set as a fixed percentage of the rated tension of the tensioning device, for example, 10% of the rated tension of 3000 kN, which is 300 kN. This means that when When the absolute value of the variable changes by more than 300 kN within a single control cycle, collaborative optimization may be triggered. In another embodiment, the collaborative optimization module monitors the integrated control commands output by the instruction synthesis module. Whether the rate of change exceeds a preset threshold, which can be set to 500 kN / s, is used to determine the severity of the sudden change in tension command. The specific values ​​of these thresholds can be adjusted according to pipeline specifications, sea state, and the dynamic response capability of the tensioning device, and are stored in the controller's non-volatile memory. The monitoring function is implemented by a software algorithm running within the collaborative optimization module, which, as a parallel task in the central controller software architecture, continuously monitors the output buffer of the command synthesis module.

[0047] In each control cycle (e.g., 10ms), the collaborative optimization module reads the latest integrated control instructions from the shared data area. ( k ) and its value in the previous period ( k- 1). Subsequently, calculate the change Δ of the current instruction. = | ( k )- ( k- 1) | and the rate of change Δ / Δt, where Δt is the control period. These two calculated values ​​are compared with preset amplitude and rate thresholds. Whenever either condition is met, such as the rate of change instantaneously exceeding 500 kN / s, the collaborative optimization transitions from monitoring to command calculation. This logic ensures that the system only activates the auxiliary coordination mechanism when the tensioning device faces drastic movement demands.

[0048] When the triggering conditions are met, the collaborative optimization module calculates an auxiliary control command to adjust the pitch angle of the support frame based on the current tension deviation, hull motion state, and pipeline deployment dynamics. The goal of this command is to induce an adaptive adjustment in the support frame's tilt angle. This partially offsets tension fluctuations and reduces the burden on the tensioning device. Adjustment amount The calculation can be based on a simplified torque balance model, which can range from -3° to +3°. Adjustment factor. k This could be an empirical value obtained through system identification, such as 50 kN / °, meaning that a 1° change in the inclination angle of the support frame is equivalent to a change of approximately 50 kN in the tension at the top of the pipe. (Calculated...) The instructions are encapsulated into data frames and sent to the driver of the rack attitude adjustment system via the internal communication bus of the controller.

[0049] The collaborative optimization module operates through continuous analysis and decision-making. It first quickly calculates the required adjustment amount of the hosting rack. The calculation principle is to ensure that the actual output demand of the tensioning device after adjustment is met. Satisfying Relationship: By introducing This transforms some of the tension control tasks, which originally required a rapid response from the tensioning device, into relatively gentle attitude adjustment tasks performed by the support frame attitude adjustment system. The module needs to ensure that the calculated... Within the mechanical limits (e.g., -10° to +45°) of the tensioning device's attitude adjustment system. Functional testing can be conducted in a simulation environment, simulating severe tension fluctuations under extreme sea conditions to verify whether the collaborative optimization logic can effectively smooth the control commands of the tensioning device.

[0050] The auxiliary control commands (i.e., target tilt angle or tilt adjustment amount) generated by the collaborative optimization module are sent to the gantry attitude adjustment system. This system can use electric actuators or hydraulic cylinders as actuators. One end of the actuator is connected to the hull base via a universal joint, and the other end is connected to the gantry support structure. The actuator's motor driver or hydraulic servo valve receives command signals from the central controller. The execution of the commands involves the linkage of multiple components. The central controller sends an analog signal (e.g., 4-20 mA) or a digital bus signal (e.g., CANopen) representing the target angle to the gantry attitude adjustment system. This system drives the actuators (e.g., extending or retracting the electric actuators), thereby changing the pitch angle of the gantry relative to the hull. Tilt sensors mounted on the gantry feed the actual angle back to the central controller in real time, forming a closed-loop position control to ensure the accuracy of the angle adjustment. Simultaneously, the tensioning device adjusts according to the adjusted commands... The entire collaborative process requires high-precision timing synchronization and data consistency among the various modules within the central controller (dynamic feedforward, feedback control, instruction synthesis, and collaborative optimization).

[0051] The above technical solution introduces a collaborative optimization mechanism. When a drastic change in tension control demand is detected, the control task is intelligently allocated to the tensioning device and the attitude adjustment system of the support frame. The attitude adjustment of the support frame is used to share the dynamic load, thereby effectively suppressing the amplitude and frequency of the tensioning device's movement. This helps to reduce its mechanical wear and energy consumption, and improves the system's adaptability and overall reliability in harsh sea conditions.

[0052] Furthermore, the central controller also includes: The signal filtering and fault diagnosis module is used to perform fusion filtering on the raw data from various sensors using the Kalman filtering algorithm to eliminate abnormal jump signals, and to perform consistency verification on the signals of each sensor. When the data of a certain sensor continuously deviates from the predicted value of the mathematical model based on the data of other sensors, the sensor is determined to be faulty or the data is abnormal, and a redundancy fault-tolerant control strategy based on the remaining valid sensor data is activated.

[0053] Specifically, the signal filtering and fault diagnosis module performs fusion filtering on the raw data from various sensors. This module can employ the Kalman filter algorithm, the core of which estimates the optimal state of the system through two steps: prediction and update. For the pipelaying vessel's motion data, the system state variables can include position, velocity, acceleration, and attitude angle, forming a state vector. The process noise covariance matrix Q and the measurement noise covariance matrix R in the Kalman filter algorithm are key parameters that need to be preset. The Q matrix can be set based on the assessment of the uncertainty of the hull motion model, while the R matrix is ​​initialized according to the factory accuracy specifications of each sensor. For example, the measurement noise variance of the accelerometer can be set to 0.01 (m / s²).2 ) 2 The algorithm runs as a software program on the processor of the central controller. The processor can be an industrial-grade multi-core CPU that meets the IEC 61508 standard. Its printed circuit board substrate is FR-4, and its running memory is DDR4 SDRAM.

[0054] The data filtering process is executed cyclically. In each control cycle, the signal filtering and fault diagnosis module reads the raw data from all sensors. First, a prediction step is performed, using the state estimate from the previous moment and the ship's kinematics model to predict the current system state (e.g., the expected attitude and acceleration of the hull). Then, an update step is performed, comparing the actual sensor measurements with the predicted values ​​and calculating a weighted optimal state estimate using Kalman gain. This gain is automatically adjusted; when a sensor's data has high noise, its reliability is reduced. Through this process, anomalous jump signals are effectively suppressed, and the output is smooth, more accurate fused data, which is then used by subsequent modules.

[0055] The signal filtering and fault diagnosis module establishes a mathematical model for the system, such as a simplified dynamic model of the pipelaying vessel-pipeline coupling based on the Newton-Euler equations. This model uses reliable sensor data (such as hull motion data filtered by Kalman) as input to predict the readings of other sensors (such as tension sensors). Consistency verification is achieved by calculating the residual (difference) between the measured values ​​of the sensors and the predicted values ​​of the model. A residual threshold is set for diagnosis; for example, when the residual between the measured value and the predicted value of the tension sensor continuously exceeds a preset threshold (such as 50 kN) for 5 consecutive control cycles, the tension sensor data is determined to be abnormal. The signal filtering and fault diagnosis module receives all filtered sensor data in real time and inputs it into the mathematical model to generate predicted values ​​for each physical quantity. Subsequently, it calculates the residual sequence between the measured value and the predicted value of each sensor. The module continuously monitors these residuals, and once it detects that the residual of a sensor significantly and continuously deviates from zero (exceeding its preset threshold), it triggers a fault flag. The diagnostic logic can also be more complex, for example, combining the mean and variance of the residuals for a comprehensive judgment. Once a fault is detected, the module will record the fault code, timestamp, and specific parameters through the human-machine interface, and issue an alarm to the system.

[0056] When a sensor is determined to be faulty, the signal filtering and fault diagnosis module immediately activates fault-tolerant mode. Its strategy is to reconstruct or estimate the information of the faulty sensor based on the remaining valid sensor data. For example, if a tension sensor fails, it can switch to using an estimated tension value calculated from motion sensor data and a pipe dynamics model as the feedback signal. The switching process needs a smooth transition, which can be achieved using a weighted average method, gradually transitioning from the measured value to the estimated value over several control cycles to avoid abrupt changes in control commands. The software module responsible for executing this strategy is also integrated into the central controller; it needs access to the health status flags of all sensors and backup data sources. The fault-tolerant control process is fast and stable. After the fault diagnosis conclusion is generated, the control switching logic is activated. This logic first blocks the data path of the faulty sensor and then selects a preset redundancy scheme. For example, when a tension sensor fails, it calls the feedforward tension estimate calculated based on the ship's motion acceleration and the equivalent mass of the pipe, and corrects it by combining factors such as the inclination angle of the support frame, using it as a substitute input for the cooperative feedback control module. The entire switching process should be completed within a short time to ensure uninterrupted operation of the control system. Meanwhile, the human-machine interface will continuously display that the system is in fault-tolerant operation and prompt for maintenance needs. Functional testing can be performed by deliberately disconnecting a sensor signal or injecting fault data on a hardware-in-the-loop simulation platform to observe whether the system can correctly diagnose and seamlessly switch to fault-tolerant mode, thereby verifying its reliability.

[0057] The above technical solution improves data quality through advanced filtering algorithms and establishes an effective fault diagnosis and fault tolerance mechanism, enabling the system to maintain basic control functions when individual sensors fail, thereby enhancing the survivability and operational continuity of the entire tensioning system in harsh marine environments.

[0058] Furthermore, the equivalent mass It is based on the surface current data collected by the environmental sensor group, the tilt angle of the support frame collected by the tilt sensor, and the pipeline deployment speed collected by the velocity sensor. The variables are updated in real time and are realized by querying a pre-set database containing underwater equivalent mass data of the pipeline under different surface current data, support frame tilt angle, and pipeline deployment speed conditions.

[0059] Specifically, the central controller's data acquisition unit cyclically reads the output values ​​of each sensor at a fixed sampling frequency (e.g., 100Hz). The raw data is first sent to the signal filtering and fault diagnosis module for preprocessing, including using a low-pass filter to remove high-frequency noise and verifying the physical rationality of the data (e.g., whether the velocity value is within possible mechanical limits). The valid data, after verification and timestamp alignment, is stored in a shared memory area for database queries. These parameters collectively reflect the changes in hydrodynamic loads (such as drag force and added mass effects) experienced by the pipeline in water, and are crucial for accurately calculating the equivalent mass. The foundation.

[0060] The database storing the underwater equivalent mass data of the pipeline exists in the form of a lookup table, with its dimensions corresponding to three input parameters: surface current velocity, stator tilt angle, and pipeline deployment speed. The database can be pre-installed in the non-volatile memory of the central controller, such as a solid-state drive or Flash memory chip, using floating-gate memory cells as the storage medium. The database is constructed from extensive prior hydrodynamic analysis, computational fluid dynamics simulations, and tank model test data. For example, the current velocity can be divided into intervals of 0.1 m / s, ranging from 0 to 3 m / s; the stator tilt angle can be divided into intervals of 1°, ranging from -10° to +45°; and the pipeline deployment speed can be divided into intervals of 0.05 m / s, ranging from 0 to 1 m / s. Each combination of three-dimensional parameters (current velocity, tilt angle, deployment speed) corresponds to a pre-calculated equivalent mass. value.

[0061] The database query mechanism works by performing real-time interpolation calculations. When updates are needed... When calculating the value, the three filtered parameter values ​​are retrieved from shared memory. Since the measured parameter values ​​may not exactly fall on discrete nodes in the database, a three-dimensional linear interpolation algorithm can be used. This algorithm calculates the weights of the current parameter point relative to its eight surrounding grid nodes based on its position in the three-dimensional grid, and then calculates a weighted average of the values ​​stored in these eight nodes. The value is ultimately used to obtain a continuous equivalent mass that precisely matches the current operating conditions. This interpolation method guarantees the output... The value changes smoothly with the input parameters, avoiding abrupt changes in control commands caused by table lookups. The accuracy of the database itself can be verified by comparing the retrieved values ​​under known operating conditions (such as still water or zero velocity). The values ​​were verified against the theoretically calculated values.

[0062] The real-time update process for the equivalent mass M can be managed by a dedicated background task within the central controller. Its update frequency can be set to 1-10Hz, lower than the core control loop (100Hz) because changes in environmental parameters and pipeline motion are relatively slow. The update cycle threshold can be set to 1 second. At the beginning of each update cycle, this task checks for new valid sensor data packets. If available, a database query is triggered. The retrieved new... The new value will replace the old value used previously and will be immediately called by the dynamic feedforward compensation module for calculating the tension feedforward compensation amount in the next control cycle.

[0063] The real-time update process ensures the accuracy of feedforward compensation. During system initialization, A default value is assigned (e.g., a value corresponding to still water conditions). Once operations begin, environmental and speed sensors start providing data, and the update process is automatically initiated. For example, when a ship enters a strong current area, the current speed increases, and the retrieved data... The value will increase accordingly due to the added mass effect, thus increasing the tension feedforward compensation. It can more accurately reflect the impact of ocean currents. The entire update process is automatic and closed-loop, requiring no manual intervention. Functional testing can be performed in marine engineering simulation software, verifying the system's query and update capabilities by setting different ocean current, tilt angle, and velocity scenarios. The accuracy and real-time nature of the values ​​ensure that the feedforward compensation model always matches the current operating environment.

[0064] The above technical solution introduces a real-time query mechanism based on multi-source environment and motion parameters to improve the equivalent mass. It can dynamically respond to changes in actual operating conditions, thereby significantly improving the calculation accuracy of dynamic feedforward compensation, enabling the system to more accurately predict and offset tension fluctuations caused by environmental changes, and improving the adaptability and accuracy of control.

[0065] Furthermore, the central controller also includes: The human-machine interface is used to display the top tension of the pipeline, the motion data of the pipelaying vessel, the tension feedforward compensation and tension feedback control, and system alarm information. It is also used to receive the preset target tension and boundary limit values ​​of the control parameters input by the operator to prevent the central controller from outputting commands that exceed the physical limits of the tensioning device.

[0066] Specifically, the human-machine interface is used to display key system status parameters and alarm information in real time. The displayed content may include real-time values ​​and historical trend curves of the top tension force of the pipeline, the heave and displacement of the pipelaying vessel, real-time data and waveforms of the pitch and roll angles, and the tension feedforward compensation amount F output by the dynamic feedforward compensation module. ff The numerical value of the tension feedback control quantity F output by the collaborative feedback control module. fb The display shows the numerical values ​​and a list of system alarm information (such as sensor failure, communication interruption, tension over-limit, etc.). The hardware for this display function can be an industrial touchscreen monitor, with a screen size ranging from 15 inches to 21 inches and a resolution of at least 1920x1080 pixels. This display is mounted on the control panel in the pipelaying vessel's control room and connects to the central controller's industrial host via VGA or HDMI interfaces and USB interfaces.

[0067] The information display process is dynamically updated. The data server module within the central controller periodically retrieves the latest processed data packets from various functional modules (such as the signal filtering module and control algorithm module). Subsequently, the data server sends this data to the runtime software of the industrial touchscreen according to a predetermined communication protocol (such as OPC UA). The configuration interface software on the touchscreen refreshes the status of various graphic elements (such as numeric display frames, trend curves, and alarm bars) in real time based on the received data. For example, the value of the top tension will be continuously updated, and its trend curve will scroll along the time axis. When the fault diagnosis module of the central controller generates a new alarm message, the message will be immediately added to the top of the alarm list on the touchscreen, possibly accompanied by visual (such as flashing) and audible (such as a buzzer) cues, until the alarm condition disappears and is confirmed by the operator.

[0068] The human-machine interface allows operators to set key control parameters. Operators can input preset target tension forces via a virtual keyboard or soft buttons on the touchscreen; these values ​​can be set according to pipe specifications and operating water depth, for example, adjustable within a range of 500 kN to 2000 kN. Simultaneously, boundary limits for control parameters can be set, such as the proportional coefficient K of the fuzzy adaptive PID controller. p The maximum allowable adjustment can be set to 150% of the initial value, with the integral coefficient K. i The lower limit of the allowable adjustment can be set to 50% of the initial value. The input device mainly relies on the capacitive or resistive touch layer of the touchscreen itself. To ensure reliable operation in the vibration environment of the ship, an additional keyboard with physical buttons and a trackball can be provided as backup input devices. These backup devices are connected to the industrial control host via USB interface and fixed to the control console.

[0069] The parameter setting process includes authorization and data verification. Operators must first enter their username and password through the login interface to gain the necessary permissions before accessing the parameter setting screen. When a new target tension or parameter boundary value is input, logical verification is performed first, such as checking whether the input target tension is within the system's allowed physical range (e.g., 0-2500 kN). If the input value is valid, the new parameter is written to the designated parameter area in the central controller's non-volatile memory (e.g., EEPROM chip) via internal communication. During operation, each functional module of the central controller (e.g., the collaborative feedback control module) reads the latest setpoint from this parameter area. All parameter modifications are recorded in the operation log, including the modification time, modifier, and values ​​before and after the modification.

[0070] The instruction boundary limitation function of the human-machine interface aims to prevent the central controller from outputting instructions that exceed the physical limits of the tensioning device. This function is implemented through software logic. When the instruction synthesis module calculates the comprehensive control instruction... Then, before sending it to the tensioning device, it is compared with preset boundary limits. These boundary limits include the upper limit of the absolute value of the tensioning force command and the upper limit of the rate of change. The upper limit of the absolute value can be set to 95% of the maximum capacity of the tensioning device; for example, for a system with a maximum tension of 3000 kN, the upper limit of the command can be set to 2850 kN. The upper limit of the rate of change can be set to 800 kN / s to prevent impact on the tensioning device's transmission mechanism.

[0071] The boundary constraint function operates through mandatory safety checks. In each control cycle, the instruction synthesis module outputs... Then, the boundary restriction module will immediately intervene. If If the absolute value exceeds the set upper limit (e.g., 2850 kN), the module will force its output value to be limited to the upper limit (i.e., output 2850 kN). Similarly, if If the instantaneous rate of change (the change relative to the previous cycle divided by the cycle time) exceeds a set upper limit (e.g., 800 kN / s), the module will limit the slope of the current output command, ensuring a smooth transition to the target value at a rate not exceeding this limit. This process requires no operator intervention and is a low-level safety protection mechanism. Simultaneously, once a limiting event occurs, the system will send a high-priority alarm message to the human-machine interface to alert the operator. The reliability of this function can be verified through injection testing, for example, by intentionally setting a control scenario in simulation mode that would lead to exceeding limits, to verify whether the system output is correctly limited.

[0072] The above technical solution provides operators with an intuitive channel to monitor the system's operating status and make necessary interventions. At the same time, by limiting parameter boundaries, it prevents control commands from exceeding the physical capabilities of the equipment, thereby enhancing the convenience and safety of the entire system operation.

[0073] The present invention also provides an adaptive tensioning method for deep-water S-shaped pipelaying based on the above system, comprising the following steps: S1: The motion data of the pipelaying vessel is collected through the motion sensor group, the bow direction, speed and surface current data are collected through the environmental sensor group, the top tension of the pipe is collected through the tension sensor, the tilt angle of the support frame is collected through the tilt sensor, and the deployment speed of the pipe is collected through the speed sensor. S2: The dynamic feedforward compensation module calculates the tension force feedforward compensation amount based on the hull motion data. ; S3: The collaborative feedback control module generates a tension feedback control quantity based on the deviation between the top tension force of the pipeline and the target tension force. ; S4: The instruction synthesis module will... and Superimposed, resulting in integrated control commands. = + And send it to the tensioning device; S5: The tensioning device executes the integrated control command. Apply precise tension to the cable.

[0074] The above technical solution organically combines multi-source information acquisition, feedforward prediction and compensation, feedback correction and precise execution through systematic steps, forming a pipe-laying tensioning method that can adaptively cope with hull motion and environmental changes. This helps to maintain stable tension during the pipeline laying process and meets the process requirements of deep-water S-shaped pipe laying.

[0075] Furthermore, in step S4, when the integrated control command... When the rate or magnitude of change exceeds a preset threshold, the collaborative optimization module generates an auxiliary control command and sends it to the support frame attitude adjustment system to adjust the tilt angle of the support frame. Adaptive adjustments to meet ,in To adjust the force output of the tensioning device to meet actual needs after adjustment. To adjust the coefficients, a portion of the tension control task is allocated to the support frame system, thus mitigating the violent movements of the tensioning device.

[0076] The above technical solution optimizes the system's load distribution strategy by introducing auxiliary adjustment of the support frame's attitude. When fluctuations in the integrated control commands exceed a preset threshold, the collaborative optimization module generates commands to drive the support frame to make adaptive angle adjustments, thereby actively changing the pipe's suspension shape and sharing some of the dynamic tension load that would otherwise be borne solely by the tensioning device. This collaborative effect reduces the extreme requirements on the tensioning device's response speed and amplitude. By distributing some control tasks to the support frame's attitude adjustment system, drastic changes in the tensioning device's output force can be effectively mitigated, preventing accelerated mechanical wear or overheating due to frequent, large-amplitude movements. This allows the entire tensioning system to maintain a more stable operating state in harsh sea conditions, reducing the impact of sudden starts and stops on the mechanical structure. Furthermore, this method enhances adaptability to different operating conditions. By adjusting the coefficients... By coupling tension control with attitude adjustment, the system can flexibly allocate control tasks between the tensioning device and the support frame according to actual needs. This allocation strategy not only alleviates the working pressure on the core tensioning equipment but also expands the system's control freedom, providing a more robust control method for maintaining stable pipeline laying tension in complex and variable deep-sea environments.

[0077] Furthermore, throughout the entire control process, the signal filtering and fault diagnosis modules operate in parallel, performing real-time filtering and validity verification of sensor data. Once data anomalies or sensor malfunctions are detected, redundant data sources are activated and the system smoothly transitions to fault-tolerant control mode, while simultaneously issuing an alarm through the human-machine interface.

[0078] The aforementioned technical solution employs real-time filtering to effectively eliminate abnormal signal jumps caused by environmental interference or electrical noise, providing a high-quality data foundation for subsequent control decisions. Simultaneously, a consistency verification mechanism identifies sensor faults or data anomalies that continuously deviate from the normal range. When a sensor fault is diagnosed, the system automatically activates redundant data sources, smoothly transitioning to fault-tolerant control mode. This seamless switching mechanism ensures that core control functions are maintained even in the event of single or multiple sensor failures, preventing system downtime or malfunctions due to data interruptions. This is particularly suitable for deep-sea pipelaying operations where maintenance costs are high and downtime losses are significant. Furthermore, combined with the real-time alarm function of the human-machine interface, operators are provided with timely system status awareness and fault warnings. Once fault-tolerant control mode is entered, the system immediately issues an alarm, indicating maintenance needs, enabling staff to plan maintenance in advance rather than passively responding to sudden failures. This design combines proactive prevention with passive fault tolerance, enhancing the overall maintainability and operational safety of the system.

[0079] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A deep-water S-shaped pipelaying adaptive tensioning system based on dynamic compensation and collaborative control, characterized in that, include: Tensioning devices are used to apply controlled tension to the pipes on pipelaying vessels; A motion sensor array, deployed on the pipelaying vessel, is used to collect the vessel's motion data in real time, including heave, pitch, and roll data. An environmental sensor array is used to collect real-time data on the ship's heading, speed, and surface currents. Tension sensors are used to monitor the top tension of pipes in real time. Tilt sensors are installed on the support frame to monitor the tilt angle of the support frame; Speed ​​sensors are used to monitor the pipeline's deployment speed in real time; The rack attitude adjustment system is used to drive and adjust the pitch angle of the rack. The central controller has its signal input terminal connected to the motion sensor group, environmental sensor group, tension sensor, tilt sensor and speed sensor, and its signal output terminal connected to the tensioning device and the support frame attitude adjustment system. The central controller includes: The dynamic feedforward compensation module is used to predict the changes in the shape of the pipe suspension line caused by the movement of the pipe-laying vessel and the resulting fluctuations in tension force based on the motion data of the pipe-laying vessel collected in real time by the motion sensor group, and to generate a tension force feedforward compensation amount to offset the fluctuations. The collaborative feedback control module is used to generate a tension force feedback control quantity based on the deviation between the top tension force of the pipe collected by the tension sensor and the preset target tension force, using an adaptive control algorithm. The instruction synthesis module is used to superimpose the tension force feedforward compensation amount and the tension force feedback control amount to generate a comprehensive control instruction and send it to the tensioning device to drive its operation.

2. The deep-water S-shaped pipelaying adaptive tensioning system as described in claim 1, characterized in that, The method for generating the tension force feedforward compensation amount by the dynamic feedforward compensation module includes: Calculate the tension feedforward compensation amount : in, The equivalent mass is calculated based on the pipe specifications and underwater weight. The acceleration of the pipelaying vessel's heave motion. and These are the angular accelerations of pitch and roll, respectively. and These are the angular displacements of pitch and roll, respectively. and These are the equivalent arm lengths from the tensioning device to the center of the ship's pitch and roll, respectively.

3. The deep-water S-shaped pipelaying adaptive tensioning system as described in claim 2, characterized in that, The collaborative feedback control module employs a fuzzy adaptive PID controller with a proportional coefficient K. p Integral coefficient K i and differential coefficient K d Based on the dynamic changes of the tension deviation e and its rate of change ec, online tuning is performed using fuzzy inference rules to adapt to control requirements under different sea conditions.

4. The deep-water S-shaped pipelaying adaptive tensioning system as described in claim 1, characterized in that, The central controller also includes: The collaborative optimization module is used to calculate and generate collaborative control commands when the change amplitude or rate of the comprehensive control commands generated by the command synthesis module exceeds a preset threshold, and send them to the support frame attitude adjustment system to drive the support frame to make corresponding attitude adjustments, so as to assist the tensioning device in maintaining the stability of the tension force at the top of the pipe, thereby reducing the requirements for the dynamic performance and wear of the tensioning device.

5. The deep-water S-shaped pipelaying adaptive tensioning system as described in claim 1, characterized in that, The central controller also includes: The signal filtering and fault diagnosis module is used to perform fusion filtering on the raw data from various sensors using the Kalman filtering algorithm to eliminate abnormal jump signals, and to perform consistency verification on the signals of each sensor. When the data of a certain sensor continuously deviates from the predicted value of the mathematical model based on the data of other sensors, the sensor is determined to be faulty or the data is abnormal, and a redundancy fault-tolerant control strategy based on the remaining valid sensor data is activated.

6. The deep-water S-shaped pipelaying adaptive tensioning system as described in claim 2, characterized in that, The equivalent mass It is based on the surface current data collected by the environmental sensor group, the tilt angle of the support frame collected by the tilt sensor, and the pipeline deployment speed collected by the velocity sensor. The variables are updated in real time and are realized by querying a pre-set database containing underwater equivalent mass data of the pipeline under different surface current data, support frame tilt angle, and pipeline deployment speed conditions.

7. The deep-water S-shaped pipelaying adaptive tensioning system as described in claim 1, characterized in that, The central controller also includes: The human-machine interface is used to display the top tension of the pipeline, the motion data of the pipelaying vessel, the tension feedforward compensation and tension feedback control, and system alarm information. It is also used to receive the preset target tension and boundary limit values ​​of the control parameters input by the operator to prevent the central controller from outputting commands that exceed the physical limits of the tensioning device.

8. A deep-water S-shaped pipelaying adaptive tensioning method based on the system described in any one of claims 1 to 7, characterized in that, Includes the following steps: S1: The motion data of the pipelaying vessel is collected through the motion sensor group, the bow direction, speed and surface current data are collected through the environmental sensor group, the top tension of the pipe is collected through the tension sensor, the tilt angle of the support frame is collected through the tilt sensor, and the deployment speed of the pipe is collected through the speed sensor. S2: The dynamic feedforward compensation module calculates the tension force feedforward compensation amount based on the hull motion data. ; S3: The collaborative feedback control module generates a tension feedback control quantity based on the deviation between the top tension force of the pipeline and the target tension force. ; S4: The instruction synthesis module will... and Superimposed, resulting in integrated control commands. = + And send it to the tensioning device; S5: The tensioning device executes the integrated control command. Apply precise tension to the cable.

9. The deep-water S-shaped pipelaying adaptive tensioning method as described in claim 8, characterized in that, In step S4, when the integrated control command When the rate or magnitude of change exceeds a preset threshold, the collaborative optimization module generates an auxiliary control command and sends it to the support frame attitude adjustment system to adjust the tilt angle of the support frame. Adaptive adjustments to meet ,in To adjust the force output of the tensioning device to meet actual needs after adjustment. To adjust the coefficients, a portion of the tension control task is allocated to the support frame system, thus mitigating the violent movements of the tensioning device.

10. The deep-water S-shaped pipelaying adaptive tensioning method as described in claim 8 or 9, characterized in that, Throughout the control process, the signal filtering and fault diagnosis modules operate in parallel, performing real-time filtering and validity verification of sensor data. Once data anomalies or sensor malfunctions are detected, redundant data sources are activated and the system smoothly transitions to fault-tolerant control mode, while simultaneously issuing an alarm through the human-machine interface.

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