Method and system for controlling dynamic sliding shipment of large-scale structure based on self-adaptive MPC

Abstract

The invention discloses a control method and system based on self-adaption MPC large structure dynamic sliding shipment, the system comprises a sensor module, a computer system and a central controller, a pressure sensor is arranged between the upper end of a hydraulic sliding shoe and a base plate of an offshore oil and gas platform module and used for collecting the pressure value of a structure supporting point; the total station is arranged around a ship deck and used for monitoring the height change of each sliding shoe relative to a wharf; the liquid level sensors are arranged at four corners of the hull and used for collecting draft data of the four corners of the hull. The inclinometers are mounted on a bow and two sides of the barge and are used for collecting six-degree-of-freedom inclination angles of the barge; the computer system carries out calculation through a self-adaptive MPC control algorithm to obtain an optimal control strategy; and the central controller dynamically adjusts the ballast water distribution and the hydraulic sliding shoe height of the ship based on the optimal control strategy. Therefore, the ship loading safety, continuity and efficiency can be remarkably improved, the ship loading time is shortened, and the damage risk of the structure is reduced.

Description

Technical Field

[0001] The invention relates to a control method and system for dynamic sliding loading of a large structure based on adaptive MPC, and relates to the technical field of marine engineering. Background Art

[0002] As the weight and size of large structures in offshore engineering continue to increase, the traditional skid loading method faces many challenges. As a common method for loading large structures, skid loading is widely used in loading offshore platforms and other heavy structures.

[0003] However, the traditional skidding loading method has some defects that cannot be ignored: first, the height difference between the barge and the dock cannot be adapted to in real time during the sliding process, which makes it difficult for the barge and the dock rails to remain flush, resulting in the risk of instability or even damage to the structure during loading; second, the traditional skidding loading method reduces the longitudinal and transverse inclination of the barge through step-by-step sliding and ballast water adjustment. However, due to complex sea conditions and calculation errors, the dock and barge deck planes cannot completely overlap. Step-by-step ballasting results in a discontinuous loading process, which increases operational complexity and time costs.

[0004] In summary, current research focuses on improving the sliding shoe system, ignoring the need for dynamic adjustment of the barge's posture, making it difficult to effectively solve the problems caused by step-by-step ballasting, resulting in low loading efficiency and high time cost. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, in view of the above problems, the purpose of the present invention is to provide a control method and system for dynamic sliding loading of large structures based on adaptive MPC, which can significantly improve the safety, continuity and efficiency of loading, reduce loading time and the risk of structural damage.

[0006] In the first aspect, the present invention provides a control system for dynamic sliding loading of large structures based on adaptive MPC, the system comprising a sensor module, a computer system and a central controller, the sensor module comprising a pressure sensor, a total station, a liquid level sensor and an inclinometer; the pressure sensor is arranged between the upper end of the hydraulic skid and the offshore oil and gas platform module pad, for collecting the pressure value of the structure support point; the total station is arranged around the ship deck, for monitoring the height change of each skid relative to the dock; the liquid level sensor is arranged at the four corners of the hull, for collecting the draft depth data of the four corners of the hull; the inclinometer is installed on the bow and both sides of the barge, for collecting the six degrees of freedom tilt angle of the ship; the computer system obtains the optimal control strategy through adaptive MPC control algorithm calculation based on the data collected by the sensor module; the central controller dynamically adjusts the ballast water distribution and hydraulic skid height of the ship based on the optimal control strategy.

[0007] In a possible preferred embodiment, the computer system includes an MPC module and an adaptive control module;

[0008] The MPC module establishes a dynamic system model based on the data collected by the sensor module and the externally collected data, and generates a control strategy in real time by adjusting the parameters of the system model through the Kalman filter, including the ballast water adjustment amount and the skid height adjustment amount;

[0009] The adaptive control module monitors the attitude error and external disturbance of the system in real time, and dynamically adjusts the weight matrix of the MPC module so that the MPC module generates an updated optimal control strategy.

[0010] In a possible preferred embodiment, the central controller includes a ballast water regulating module and a hydraulic skid module;

[0011] The ballast water regulating module is used to send a regulating instruction to the ballast water pump and valve control unit according to the ballast water regulating amount generated by the MPC module, and adjust the water volume distribution of each ballast tank by controlling the start / stop of the ballast water pump and the opening / closing of the valve;

[0012] The hydraulic shoe module is used to send instructions to the hydraulic cylinder and the solenoid valve of the hydraulic shoe according to the shoe height adjustment amount generated by the MPC module, so as to control the extension and retraction of the hydraulic shoe.

[0013] In a second aspect, the present invention further provides a control method for a control system of a large-scale structure dynamic slip loading based on an adaptive MPC, comprising:

[0014] The pressure sensor, total station, level sensor and inclinometer send the collected data to the computer system;

[0015] Before loading, external data is collected and sent to a computer system, wherein the external data includes geometric characteristic data of the structure and material characteristic data;

[0016] The computer system initializes the data of the pre-shipping mode based on the data collected by the sensor module and the geometric characteristics and material characteristics data of the structure;

[0017] When a large structure approaches a barge, it enters the forward loading mode, and the computer system generates the optimal control strategy in real time based on the collected sensor data and external data;

[0018] The central controller adjusts the ballast water volume and skid height in real time according to the generated optimal control strategy, so that the platform module can slide completely onto the barge.

[0019] In a possible preferred embodiment, the computer system performs data initialization of the pre-shipping mode based on the data collected by the sensor module and the geometric characteristics and material characteristics data of the structure, including:

[0020] Ballast tank initialization: When the large structure begins to slide onto the barge, the computer system sets the initial water volume W of each ballast tank according to the loading plan. i0 The ballast water regulating module in the central controller controls the ballast water pump to inject the accurately calculated amount of water into each ballast tank one by one, ensuring that each ballast tank reaches the initially set water volume, so that the ship deck is flush with the dock slideway to maintain the initial balance state of the barge; at this time, the pressure sensor and total station continuously monitor and feedback data to ensure the smooth connection of the slideway;

[0021] Shoe height setting: The hydraulic shoe module of the central controller makes the hydraulic cylinders under multiple supporting points of the large structure lift the structure to the specified height H according to the instructions of the computer system. initial The hydraulic cylinder of each sliding shoe controls the increase or decrease of oil pressure through a solenoid valve, thereby achieving precise height adjustment. At this time, the total station measures the height in real time and uses it as the ideal height for position compensation.

[0022] In a possible preferred embodiment, when the large structure approaches the barge, it enters the loading mode, and the computer system generates an optimal control strategy in real time based on the collected sensor data and the collected external data, including:

[0023] The MPC module in the computer system is dynamically built based on the collected sensor data and external data, and generates a control strategy module in real time, including the ballast water adjustment amount and the skid height adjustment amount.

[0024] In a possible preferred embodiment, the MPC module in the computer system is dynamically built based on the collected sensor data and the collected external data, and the control strategy module is generated in real time. The specific process is:

[0025] The MPC module establishes the following dynamic system model:

[0026] x(t+1)=Αx(t)+Bu(t)+Cd(t)

[0027] Where: x(t) represents the system state vector, including the roll angle θ x (t), trim angle θy(t), u(t) is the control input vector, including ballast water adjustment ΔW(t) and shoe height adjustment Δh(t), d(t) represents external disturbance, A, B, C are the state transfer matrices of the system;

[0028] The Kalman filter adjusts the parameters A, B, and C of the dynamic system model in real time to ensure that it reflects the current actual working conditions;

[0029] The adaptive control module is responsible for adjusting the weight matrices Q and R in the MPC optimization problem in real time, using the least squares method to estimate the changes in these weight matrices, and by optimizing the weight matrices Q and R, minimizing the deviation between the future state and the desired state, as well as the amplitude of the control input;

[0030] After adjusting Q and R, the adaptive control module applies the new weight values ​​to the MPC optimization problem and generates an updated control strategy u(t);

[0031] Based on the newly generated control strategy, the calculated optimal control strategy u(t) includes the ballast water adjustment amount ΔW(t) and the skid height adjustment amount Δh(t).

[0032] In a possible preferred embodiment, the Kalman filter adjusts the parameters A, B, and C of the dynamic system model in real time to ensure that they reflect the current actual working conditions. The Kalman filter is used for online parameter estimation. The specific process is as follows:

[0033] ∧

[0034] Predict the current state vector x(t+1|t) and covariance matrix P(t+1|t);

[0035] Update the predicted state using the current measurement value and adjust the state transfer matrix;

[0036] The MPC module predicts the system behavior in the next N time steps based on the above system model;

[0037] The MPC module generates the optimal control strategy by solving the following optimization problem:

[0038]

[0039] Constraints:

[0040] u min ≤u(t+k-1)≤u max

[0041]

[0042]

[0043] Where: x ref (t+k) is the reference trajectory or desired state, Q and R are the weight matrices of state error and control input, respectively, and u min and u max is the upper and lower limits of the control input, Wi (t) is the weight of ballast water in the ith ballast tank at time t, y i is the longitudinal position of the i-th ballast tank relative to the fore and aft direction of the ship, F j (t) is the external force acting on the ship at time t, Y j is the jth external force F j (t)) The longitudinal position of the point of action relative to the fore and aft direction of the ship.

[0044] In a possible preferred embodiment, the central controller adjusts the ballast water volume in real time according to the generated optimal control strategy, including:

[0045] According to the optimal control strategy generated by the MPC module, the ballast water regulation module obtains the ballast water regulation amount ΔW i (t), first select the ballast tanks that have the greatest impact on the ship's attitude and adjust them to minimize the heel and trim;

[0046] The ballast water regulation module adjusts the amount ΔW i (t) The ballast water pump control unit sends a command to increase or decrease the amount of water to the target ballast tank. When the ballast water pump performs the adjustment operation, the water level changes of each ballast tank are monitored in real time to ensure that the adjustment process meets expectations;

[0047] During the adjustment process, the computer system uses the liquid level sensor to collect the current water level in real time and calculate the deviation between the current water level and the target water level. If the deviation is found to exceed the preset threshold, the adaptive module first detects and triggers the adjustment mechanism, and then passes the updated weight matrix to the MPC module. After receiving the adaptively adjusted weights, the MPC module will generate a new control strategy based on the new optimization goal to adjust the water level and posture to ensure system stability and control accuracy, so as to ensure that the final water volume reaches the set value W. i (t+1):

[0048] W i (t+1)=W i (t)+ΔW i (t).

[0049] In a possible preferred embodiment, the central controller adjusts the height of the skid shoe in real time according to the generated optimal control strategy, including:

[0050] According to the optimal control strategy generated by the MPC module, the hydraulic shoe module obtains the shoe height adjustment amount to control the working state of the hydraulic pump and adjust the extension and retraction amount of the shoe;

[0051] The total station transmits the detected slide rail height change data to the computer system, and the calculated displacement compensation amount Δh is fed back to the adaptive control module. After receiving the displacement compensation amount Δh, the adaptive control module analyzes the height difference between the current slide rail and the slide shoe according to the feedback data;

[0052] If the height difference exceeds the preset threshold or a deviation occurs, the adaptive module will update the optimization weight matrix of the MPC module at this time. Based on the updated weights and the data fed back by the total station, the MPC module will recalculate the optimal control strategy and generate a new adjustment amount Δh for the extension and retraction of the sliding shoe. After the hydraulic sliding shoe module receives the latest control instruction from the MPC module, it controls the extension and retraction of the hydraulic cylinder according to the new height adjustment amount Δh. The sliding shoe height adjustment is performed based on the adjustment amount Δh generated by the MPC module:

[0053] Δh i (t+1)=h i (t)+Δh i (t+1).

[0054] The present invention adopts the above technical solution, and has the following characteristics:

[0055] 1. Improve loading efficiency: The present invention realizes the smooth loading of ships and large structures by integrating dynamic adjustment of ballast water and hydraulic skids, responding to changes in ship posture and external environment in real time. Compared with the traditional step-by-step ballast method, the present invention uses an adaptive MPC algorithm to make ballast water adjustment and skid height adjustment more accurate, ensuring the continuity and efficiency of the loading process, and greatly improving the overall loading efficiency.

[0056] 2. Enhanced loading stability: The present invention combines multiple sensors such as pressure sensors, inclinometers, liquid level sensors and total stations to monitor the status changes of ships and structures in real time. Through intelligent analysis and dynamic adjustment of computer systems and central controllers, it can quickly respond to deviations and external disturbances that occur during the loading process, ensure the stability of the loading process, and significantly reduce loading risks.

[0057] 3. Save labor costs: The present invention realizes highly automated control and intelligent regulation, reducing the need for human intervention. Through real-time data monitoring and dynamic control, the loading process can be carried out more continuously and efficiently, thereby shortening the loading time, further reducing labor costs and operational complexity, and improving operational safety.

[0058] In summary, the present invention improves many problems existing in traditional sliding loading methods, including unstable loading, low efficiency and complex operation, and aims to achieve stability, continuity and adaptability in the dynamic loading process of large structures. By real-time monitoring and dynamic adjustment of the loading status, the loading process is made safer and more efficient, and can adapt to the loading needs under different marine environmental conditions. It can be widely used in the loading of offshore platforms and other heavy structures, and realize dynamic sliding loading operations on large offshore platform modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Throughout the accompanying drawings, the same reference numerals are used to represent the same components. In the accompanying drawings:

[0060] Figure 1 It is a schematic diagram of the control system structure of the present invention.

[0061] Figure 2 It is a schematic diagram of the installation of the total station of the present invention.

[0062] Figure 3 It is a schematic diagram of the control method of the present invention.

[0063] Figure 4 It is a schematic diagram of the application of the control system of the present invention. DETAILED DESCRIPTION

[0064] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0065] Although the terms first, second, third, etc. can be used in the text to describe multiple elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms can only be used to distinguish an element, component, region, layer or section from another region, layer or section. Unless the context clearly indicates, terms such as "first", "second" and other numerical terms do not imply order or sequence when used in the text. Therefore, the first element, component, region, layer or section discussed below can be referred to as the second element, component, region, layer or section without departing from the teaching of the example embodiments.

[0066] For ease of description, spatially relative terms may be used herein to describe the relationship of one element or feature relative to another element or feature as shown in the figures, such as "inside", "outside", "inner side", "outer side", "below", "above", etc. Such spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation depicted in the figures.

[0067] Since the existing technologies mostly focus on improving the sliding shoe system, they ignore the dynamic adjustment requirements for the barge posture, making it difficult to effectively solve the problems caused by step-by-step ballast loading, resulting in low loading efficiency and high time cost. The control method and system for dynamic sliding loading of large structures based on adaptive MPC provided by the present invention include: a pressure sensor, a total station, a liquid level sensor and an inclinometer send the collected data to a computer system; collect external data before loading and send it to a computer system, wherein the external data includes the geometric property data and material property data of the structure; the computer system initializes the data of the pre-loading mode based on the data collected by the sensor module and the geometric property data and material property data of the structure; when the large structure approaches the barge, it enters the loading mode, and the computer system generates the optimal control strategy in real time based on the collected sensor data and the collected external data; the central controller adjusts the ballast water volume and the sliding shoe height in real time based on the generated optimal control strategy, so that the platform module slides completely onto the barge. Therefore, the present invention integrates multiple sensors to collect data on changes in ship attitude, structural stress and external environment in real time, and uses an adaptive MPC control system to dynamically adjust the ship's ballast water distribution and the height of the hydraulic shoe, which can be adjusted according to real-time data during the loading process, so that the height difference between the barge and the dock slideway is kept within a safe range and the ship's attitude is maintained stable. The dynamic adjustment capability of the present invention significantly improves the safety, continuity and efficiency of loading, and reduces the loading time and the risk of structural damage.

[0068] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0069] Embodiment 1: Figure 1 As shown, the control system for dynamic sliding loading of large structures based on adaptive MPC provided in this embodiment includes a sensor module, a computer system and a central controller. The sensor module includes a pressure sensor, a liquid level sensor, a total station and an inclinometer.

[0070] The hydraulic slipper is pre-installed with pressure sensors, solenoid valves and data acquisition devices. The pressure sensor is fixed between the upper end of the hydraulic slipper and the module pad of the offshore oil and gas platform. The hydraulic cylinder of each hydraulic slipper is connected to the solenoid valve. Multiple pressure sensors and multiple solenoid valves are fixed in the set area. The data acquisition device can be used to collect the pressure value of the support point of the structure in real time.

[0071] like Figure 2 As shown, four measuring poles are set around the ship deck, and total stations 1-4 are installed on the measuring poles at the edge of the dock. The total stations measure the height changes of each sliding shoe relative to the dock. The data of the total stations are sent to the computer system to calculate the adjustment requirements of the sliding shoes in real time.

[0072] Liquid level sensors are arranged at the four corners of the hull to collect draft depth data at the four corners of the hull to monitor the ship's posture and draft changes.

[0073] Inclinometers are installed on the bow and sides of the barge to collect the ship's six-degree-of-freedom tilt angle data, including surge, sway, heave, roll, pitch and pitch.

[0074] The data collected by the above-mentioned sensor modules can be transmitted to the computer system through the wireless communication module. The computer system calculates the optimal control strategy based on the collected data through the adaptive MPC control algorithm, including the ballast water adjustment amount and the skid height adjustment amount.

[0075] The central controller dynamically adjusts the ship's ballast water distribution and the height of the hydraulic skids based on the optimal control strategy sent by the computer system.

[0076] In a preferred embodiment of the present invention, the computer system includes an MPC module and an adaptive control module, wherein:

[0077] The MPC module uses real-time monitored system status data (such as roll angle, pitch angle and height difference) and external disturbances (such as changes in sea conditions and changes in the center of gravity of the structure) to establish a dynamic system model, and uses the Kalman filter to make predictions and adjustments. The MPC module can stably track the state in a noisy and uncertain environment and adjust the control strategy in advance. Based on the predicted state, the module solves the optimization problem to generate the optimal control strategy to minimize the deviation between the current state and the target state, and constrain the control input (such as ballast water and skid height adjustment) to ensure the safe and stable operation of the system.

[0078] The adaptive control module monitors the system's attitude error and external disturbances (such as sea state fluctuations) in real time, and dynamically adjusts the weight matrix in the MPC module to prioritize the correction of attitude deviations. When external disturbances are significant, the adaptive control module will activate the emergency response mechanism and adjust the control strategy to ensure the safety range of ballast water and skid height, thereby enhancing the system's adaptability and stability in complex environments.

[0079] In a preferred embodiment of the present invention, the central controller includes a ballast water regulating module and a hydraulic shoe module, and the functions of the ballast water regulating module and the hydraulic shoe module are integrated inside the central controller. These modules run on the central controller as part of the control program and are responsible for generating and sending control instructions according to MPC and adaptive control strategies. The central controller is connected to various sensors and actuators (ballast water pumps, solenoid valves, hydraulic cylinders, etc.) through a communication module (such as a wireless or wired network) to pass instructions to hardware devices.

[0080] Ballast water regulation module: used to send regulation instructions to the ballast water pump and valve control unit according to the ballast water regulation amount generated by the MPC module. This module adjusts the water distribution in each ballast tank by controlling the start / stop of the ballast water pump and the opening / closing of the valve, thereby affecting the attitude and balance of the ship. It ensures that the ship maintains a preset balance state during the loading process.

[0081] Hydraulic Shoe Module: It is used to send instructions to the hydraulic cylinder and solenoid valve of the hydraulic shoe according to the height adjustment of the shoe generated by the MPC module to control the extension and retraction of the hydraulic shoe. The module controls the lifting and lowering of the shoe by adjusting the pressure of the hydraulic pump and the opening and closing of the solenoid valve to keep the slideway flush with the hull, thereby stabilizing the ship's posture and reducing the risk of tilting or shaking during loading.

[0082] Embodiment 2: Figure 3 , Figure 4 As shown, the control method for dynamic sliding loading of large structures based on adaptive MPC provided in this embodiment includes:

[0083] S1. Data collection.

[0084] In this embodiment, when the large structure is completely onshore, a pressure sensor, a solenoid valve and a data acquisition device are pre-installed on the hydraulic shoe of the hydraulic shoe module. The pressure sensor is fixed between the upper end of the hydraulic shoe and the pad of the offshore oil and gas platform module. The hydraulic cylinder of each hydraulic shoe is connected to the solenoid valve. Multiple pressure sensors and multiple solenoid valves are fixed in the set area. The data acquisition device collects the pressure value of the support point of the structure in real time. The total station is installed at the key positions of the dock and the ship to measure the displacement and height change of the slide in real time. The data of the total station is directly transmitted to the computer system for real-time calculation of the adjustment requirements of the shoe. The liquid level sensor is arranged at the four corners of the hull to collect the draft depth data of the four corners of the hull for monitoring the attitude and draft changes of the ship. The inclinometer is installed on the bow and both sides of the barge to collect the six degrees of freedom tilt angle data of the ship, including sway, sway, heave, roll, pitch and bow. The above data are all transmitted to the computer system through the wireless communication module.

[0085] S2. Collect data.

[0086] In this embodiment, the system collects data such as local tidal changes, platform module weight, dock elevation, structure weight, size, shape and material before loading. These data are uploaded to the computer system through the sensor network and data input terminal, where the above data may include initial ballast tank settings, initial height of hydraulic shoes, etc. This is an example and is not limited to this.

[0087] S3. Data initialization of pre-shipping mode.

[0088] In this embodiment, the computer system performs data initialization of the pre-shipping mode based on the collected data and in combination with the geometric characteristics and material characteristics of the structure, including:

[0089] S31. Ballast tank initialization.

[0090] In this embodiment, when the large structure begins to slide onto the barge, the computer system sets the initial water volume W of each ballast tank according to the loading plan. i0 The ballast water regulation module in the central controller controls the ballast water pump to inject the accurately calculated amount of water into each ballast tank one by one, ensuring that each ballast tank reaches the initial set water volume, so that the ship deck is flush with the dock slideway to maintain the initial balance of the barge. At this time, the pressure sensor and total station continuously monitor and feedback data to ensure the smooth connection of the slideway.

[0091] S32, Shoe height setting.

[0092] In this embodiment, the hydraulic shoe module of the central controller causes the hydraulic cylinders under the multiple supporting points of the large structure to lift the structure to a specified height H according to the instructions of the computer system. initial, the hydraulic cylinder of each shoe controls the increase or decrease of oil pressure through the solenoid valve, thereby achieving precise height adjustment. The total station measures this height in real time and uses it as the ideal height for position compensation. The computer system will store this initial data and refer to it in subsequent processes.

[0093] S4. When the large structure approaches the barge, the system enters the loading mode, and the MPC module generates control strategies in real time based on the collected sensor data and external data.

[0094] In this embodiment, the MPC module generates a control strategy in real time based on the collected sensor data and the collected external data, including:

[0095] S41. Pressure monitoring.

[0096] In this embodiment, the pressure sensor detects the pressure value of the supporting point of the structure in real time and transmits the measured data to the computer system through a communication cable. The computer system dynamically models and analyzes the data based on the MPC (Model Predictive Control) algorithm to generate a control strategy. The central controller issues instructions based on the strategy to adjust the opening and closing state of the solenoid valve to maintain the pressure balance and height adjustment requirements of the system, thereby ensuring the stability of the ship's attitude and the safety of the loading process.

[0097] S42, the MPC module generates a control strategy module in real time based on the collected sensor data and the collected external data, including the ballast water adjustment amount and the skid height adjustment amount. The specific process is as follows:

[0098] S421, Dynamic System Modeling and Control Input

[0099] When the large structure begins to slide to the barge, the pressure sensor installed on each skid collects data at preset time intervals and sends the data to the computer system in real time. The MPC module begins to dynamically adjust the ballast water, and the computer system receives the state vector of the sensor: x(t), including the ship's heel angle, pitch angle, height difference and other information; external disturbance vector: d(t), such as changes in sea conditions and changes in the center of gravity of the structure. The central controller controls the input: u(t), including the ballast water adjustment amount and the skid height adjustment amount.

[0100] Based on the above real-time monitoring data, the MPC module establishes the following dynamic system model:

[0101] x(t+1)=Αx(t)+Bu(t)+Cd(t)

[0102] Where: x(t) represents the system state vector, including the roll angle θ x(t), trim angle θy(t), u(t) is the control input vector, including ballast water adjustment ΔW(t) and shoe height adjustment Δh(t). d(t) represents external disturbances, such as changes in sea conditions, changes in the center of gravity of the structure, etc. A, B, and C are the state transfer matrices of the system.

[0103] S422, Status Prediction

[0104] In this embodiment, during the dynamic loading process, the system uses a Kalman filter to estimate the state vector and system model parameters in real time. The Kalman filter can handle noise and uncertainty, predict the current system state at each time step, and update the prediction model. The output of the Kalman filter includes the updated system state vector x(t), the control input vector u(t), and the state covariance matrix P(t), ensuring that the system predicts and controls the ship's attitude more accurately.

[0105] Furthermore, the Kalman filter adjusts the parameters A, B, and C of the system model in real time to ensure that it reflects the current actual working conditions. The Kalman filter is used for online parameter estimation. The specific process is as follows:

[0106] ∧

[0107] (1) State prediction: Predict the state vector x(t+1|t) and covariance matrix P(t+1|t) at the current moment.

[0108]

[0109] P(t+1|t)=ΑP(t|t-1)A T +Q

[0110] (2) Measurement update: Use the current measurement value to update the predicted state and adjust the state transfer matrix.

[0111] K(t+1)=P(t-1|t)H T [HP(t+1|t)H T +R] -1

[0112]

[0113] P(t+1|t+1)=(IK(t+1)H)P(t+1|t)

[0114] Where P is the state covariance matrix, K(t+1) is the Kalman gain matrix, z(t+1) is the actual measurement value, such as the current ship's heel angle, pitch angle and height difference, H is the measurement matrix, mapping the state vector to the measurement space, and R is the measurement noise covariance matrix.

[0115] (3) Predicting future states: The MPC module predicts the system behavior in the next N time steps based on the above system model:

[0116]

[0117] in: It represents the prediction of the system state for the next k steps at time t. N is the length of the prediction time domain, which is usually selected as a number of time steps.

[0118] (4) Generate optimal control strategy: The MPC module generates the optimal control strategy by solving the following optimization problem:

[0119]

[0120] Constraints:

[0121] u min ≤u(t+k-1)≤u max

[0122]

[0123]

[0124] Where: x ref (t+k) is the reference trajectory or desired state. Q and R are the weight matrices of state error and control input, respectively. min and u max It is the upper and lower limits of the control input. i (t) is the weight of ballast water in the ith ballast tank at time t. i is the longitudinal position of the i-th ballast tank relative to the fore and aft direction of the ship. j (t) is the external force acting on the ship at time t, Y j is the jth external force F j (t)) The longitudinal position of the point of action relative to the fore and aft direction of the ship.

[0125] S423, Optimization Problem Solving

[0126] The adaptive control module is responsible for adjusting the weight matrices Q and R in the MPC optimization problem in real time, and using the least squares method to estimate the changes in these weight matrices. By optimizing the weight matrices Q and R, the deviation between the future state and the desired state, as well as the amplitude of the control input, is minimized. The specific process is:

[0127] (1) The formula for estimating these weight matrices using the least squares method is as follows:

[0128]

[0129] Among them, xref (t) is the desired state of the system.

[0130] (2) Deviation detection and analysis: The adaptive control module monitors the ship’s attitude data in real time, such as the heel angle θx(t) and the pitch angle θy. When the actual attitude deviates from the reference trajectory x ref (t), calculate the current attitude error:

[0131] e(t)=x(t)-x ref (t)

[0132] At the same time, the adaptive control module will monitor the changes in external disturbances d(t), including changes in sea conditions, the impact of wind and waves, and the offset of the center of gravity of the structure.

[0133] (3) Weight matrix adjustment: According to the size of the attitude error and the intensity of the external disturbance, the adaptive control module dynamically adjusts the weight matrices Q and R in the MPC optimization problem.

[0134] In this embodiment, the weight related to the attitude error is increased: when the attitude error e(t) exceeds the preset threshold, the adaptive module will increase the weight value related to the attitude control in Q to give priority to reducing the attitude deviation. For example, if the roll angle deviation θ x If the value is larger, Q and θ will increase. x (t)-related weight terms, prompting the MPC strategy to pay more attention to the correction of the heel angle. When the external disturbance d(t) is strong, the adaptive module may adjust the control input weight of R to balance the control strength and system stability. For example, in strong sea conditions, the weight of the control input is increased to avoid over-adjustment leading to system instability.

[0135] S424, strategy optimization and execution: After adjusting Q and R, the adaptive control module will apply the new weight values ​​to the MPC optimization problem and generate an updated control strategy u(t).

[0136] S425. Based on the newly generated control strategy, the calculated optimal control strategy u(t) includes the ballast water adjustment amount ΔW(t) and the skid height adjustment amount Δh(t).

[0137] S5, the central controller adjusts the ballast water volume and skid height in real time to ensure the stability of the loading process, including:

[0138] In this embodiment, ballast water regulation and hydraulic slipper operation include:

[0139] S51, ballast water regulation: According to the optimal control strategy generated by the MPC module, the ballast water regulation module obtains the ballast water regulation amount ΔW i (t), the ballast tanks that have the greatest impact on the ship's attitude are preferentially adjusted to minimize heel and trim.

[0140] S52, execution of ballast water adjustment command: the ballast water adjustment module adjusts the adjustment amount ΔW i (t) is transmitted to the corresponding ballast water pump control unit, which issues instructions to increase or decrease the water volume to the target ballast tank. The control instructions include specific operations to start the ballast water pump, valve switch control, etc. When the ballast water pump performs the adjustment operation, the system monitors the water level changes of each ballast tank in real time to ensure that the adjustment process meets expectations.

[0141] S53, Feedback adjustment: During the adjustment process, the computer system uses the liquid level sensor in the ballast tank to collect the current water level in real time and calculate the deviation between the current water level and the target water level. If the deviation is found to exceed the preset threshold, the adaptive module first detects and triggers the adjustment mechanism, and then passes the updated weight matrix to the MPC module. After receiving the adaptively adjusted weights, the MPC module will generate a new control strategy based on the new optimization goal to adjust the water level and posture to ensure system stability and control accuracy, so as to ensure that the final water volume reaches the set value W i (t+1):

[0142] W i (t+1)=W i (t)+ΔW i (t).

[0143] S54. According to the change of pressure data, the hydraulic shoe module controls the working state of the hydraulic pump and adjusts the extension and contraction amount Δh(t) of the shoe to achieve synchronous lifting or lowering to ensure the stability of the ship's attitude.

[0144] In this embodiment, the total station transmits the detected slide rail height change data to the computer system, and the calculated displacement compensation amount Δh will be fed back to the adaptive control module. After receiving the displacement compensation amount Δh, the adaptive control module will analyze the height difference between the current slide rail and the slide shoe based on the feedback data. If the height difference exceeds the preset threshold or a deviation occurs, it indicates that the height adjustment of the slide shoe needs to be further optimized. The adaptive module will update the optimization weight matrix of the MPC module at this time. According to the updated weights and the data fed back by the total station, the MPC module will recalculate the optimal control strategy and generate a new slide shoe extension and retraction adjustment amount Δh. After the hydraulic slide shoe module receives the latest control instruction from the MPC module, it controls the extension and retraction of the hydraulic cylinder according to the new height adjustment amount Δh. In this way, the slide shoe height can be fine-tuned in real time to ensure that the slide rail and the slide shoe remain flush during the dragging process, and to prevent the structure from tilting or being damaged during the sliding process. The slide shoe height adjustment is performed based on the adjustment amount Δh generated by the MPC:

[0145] Δh i (t+1)=h i (t)+Δh i(t+1)

[0146] In summary, the parameters of the state transfer matrix A, the control input matrix B, and the external disturbance matrix C are adjusted in real time. By adjusting the weight matrices Q and R, the attitude deviation is reduced first, and the strength of the control input is balanced to ensure the stability of the ship's attitude. According to the data fed back by the sensor, the control strategy is adjusted in real time to optimize the operation of the ballast water pump and the hydraulic shoe to ensure the ship's attitude and stability during the loading process. The adaptive control module monitors the changes in sea conditions in real time and adjusts the control strategy of the MPC to ensure that the ship's attitude can remain stable under tidal changes or other external disturbances. After the adjustment is completed, the system confirms the final water volume in the ballast tank and feeds the results back to the MPC module and the adaptive control module for state prediction and control strategy optimization for the next time step. The system monitors the data of the pressure sensor and the liquid level sensor in real time, feeds back the actual execution results to the MPC module and the adaptive module, and further adjusts the prediction model and control strategy to ensure the continuous stability of the ship's attitude.

[0147] S6. After the platform module is completely slid onto the barge, the posture status is detected and confirmed by the liquid level sensor and total station.

[0148] In this embodiment, the posture state is detected and confirmed: the level sensor detects the draft depth of the four corners of the barge, and the total station records the height difference between the structure and the barge. The system summarizes these data to confirm whether the overall posture is balanced to prevent the risk of tilting or shifting of the structure during marine transportation.

[0149] Further, confirmation and locking: through multiple feedbacks from the level sensor and total station, the system confirms that the draft and posture of the barge have reached the preset equilibrium state, and the structure and the barge have achieved a stable combination. At this time, the central controller will lock all control devices and finally complete the fixation of the structure. After the adjustment is completed, the system will record the final ballast water volume, skid height and pressure sensor data in the database and generate a detailed loading report. The system automatically saves all data generated during the loading process and uses it in future loading tasks to optimize the control strategy.

[0150] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In the description of this specification, the description of reference terms "a preferred embodiment", "further", "specifically", "in the present embodiment", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples without contradiction.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control system for dynamic slip loading of large structures based on adaptive MPC, characterized in that: The system includes a sensor module, a computer system and a central controller, wherein the sensor module includes a pressure sensor, a total station, a liquid level sensor and an inclinometer; The pressure sensor is arranged between the upper end of the hydraulic shoe and the module pad of the offshore oil and gas platform, and is used to collect the pressure value of the supporting point of the structure; The total station is arranged around the deck of the ship to monitor the height change of each skid relative to the dock; The liquid level sensors are arranged at the four corners of the hull to collect the draft depth data at the four corners of the hull; The inclinometers are installed on the bow and sides of the barge to collect the six-degree-of-freedom tilt angles of the ship; The computer system calculates the optimal control strategy through an adaptive MPC control algorithm based on the data collected by the sensor module; The central controller dynamically adjusts the ballast water distribution and hydraulic skid height of the ship based on the optimal control strategy.

2. The control system for dynamic slip loading of large structures based on adaptive MPC according to claim 1 is characterized in that: The computer system includes an MPC module and an adaptive control module; The MPC module establishes a dynamic system model based on the data collected by the sensor module and the externally collected data, and generates a control strategy in real time by adjusting the parameters of the system model through the Kalman filter, including the ballast water adjustment amount and the skid height adjustment amount; The adaptive control module monitors the attitude error and external disturbance of the system in real time, and dynamically adjusts the weight matrix of the MPC module so that the MPC module generates an updated optimal control strategy.

3. The control system for dynamic slip loading of large structures based on adaptive MPC according to claim 2 is characterized in that: The central controller includes a ballast water regulating module and a hydraulic skid module; The ballast water regulating module is used to send a regulating instruction to the ballast water pump and valve control unit according to the ballast water regulating amount generated by the MPC module, and adjust the water volume distribution of each ballast tank by controlling the start / stop of the ballast water pump and the opening / closing of the valve; The hydraulic shoe module is used to send instructions to the hydraulic cylinder and the solenoid valve of the hydraulic shoe according to the shoe height adjustment amount generated by the MPC module, so as to control the extension and retraction of the hydraulic shoe.

4. A control method for a control system for dynamic slip loading of large structures based on adaptive MPC according to any one of claims 1 to 3, characterized in that: include: The pressure sensor, total station, level sensor and inclinometer send the collected data to the computer system; Before loading, external data is collected and sent to a computer system, wherein the external data includes geometric characteristic data of the structure and material characteristic data; The computer system initializes the data of the pre-shipping mode based on the data collected by the sensor module and the geometric characteristics and material characteristics data of the structure; When a large structure approaches a barge, it enters the forward loading mode, and the computer system generates the optimal control strategy in real time based on the collected sensor data and external data; The central controller adjusts the ballast water volume and skid height in real time according to the generated optimal control strategy, so that the platform module can slide completely onto the barge.

5. The control method according to claim 4, characterized in that: The computer system performs data initialization of the pre-shipping mode based on the data collected by the sensor module and combined with the geometric characteristics and material characteristics data of the structure, including: Ballast tank initialization: When the large structure begins to slide onto the barge, the computer system sets the initial water volume W of each ballast tank according to the loading plan. i0 The ballast water regulating module in the central controller controls the ballast water pump to inject the accurately calculated amount of water into each ballast tank one by one, ensuring that each ballast tank reaches the initially set water volume, so that the ship deck is flush with the dock slideway to maintain the initial balance state of the barge; at this time, the pressure sensor and total station continuously monitor and feedback data to ensure the smooth connection of the slideway; Shoe height setting: The hydraulic shoe module of the central controller makes the hydraulic cylinders under multiple supporting points of the large structure lift the structure to the specified height H according to the instructions of the computer system. initial The hydraulic cylinder of each sliding shoe controls the increase or decrease of oil pressure through a solenoid valve, thereby achieving precise height adjustment. At this time, the total station measures the height in real time and uses it as the ideal height for position compensation.

6. The control method according to claim 5, characterized in that: When the large structure approaches the barge, it enters the loading mode. The computer system generates the optimal control strategy in real time based on the collected sensor data and external data, including: The MPC module in the computer system is dynamically built based on the collected sensor data and external data, and generates a control strategy module in real time, including the ballast water adjustment amount and the skid height adjustment amount.

7. The control method according to claim 6, characterized in that: The MPC module in the computer system is dynamically built based on the collected sensor data and external data, and generates the control strategy module in real time. The specific process is as follows: The MPC module establishes the following dynamic system model: x(t+1)=Αx(t)+Bu(t)+Cd(t) Where: x(t) represents the system state vector, including the roll angle θ x (t), trim angle θy(t), u(t) is the control input vector, including ballast water adjustment ΔW(t) and shoe height adjustment Δh(t), d(t) represents external disturbance, A, B, C are the state transfer matrices of the system; The Kalman filter adjusts the parameters A, B, and C of the dynamic system model in real time to ensure that it reflects the current actual working conditions; The adaptive control module is responsible for adjusting the weight matrices Q and R in the MPC optimization problem in real time, using the least squares method to estimate the changes in these weight matrices, and by optimizing the weight matrices Q and R, minimizing the deviation between the future state and the desired state, as well as the amplitude of the control input; After adjusting Q and R, the adaptive control module applies the new weight values ​​to the MPC optimization problem and generates an updated control strategy u(t); Based on the newly generated control strategy, the calculated optimal control strategy u(t) includes the ballast water adjustment amount ΔW(t) and the skid height adjustment amount Δh(t).

8. The control method according to claim 6, characterized in that: The Kalman filter adjusts the parameters A, B, and C of the dynamic system model in real time to ensure that it reflects the current actual working conditions. The Kalman filter is used for online parameter estimation. The specific process is as follows: Predict the current state vector and covariance matrix P(t+1|t); Update the predicted state using the current measurement value and adjust the state transfer matrix; The MPC module predicts the system behavior in the next N time steps based on the above system model; The MPC module generates the optimal control strategy by solving the following optimization problem: Constraints: u min ≤u(t+k-1)≤u max Where: x ref (t+k) is the reference trajectory or desired state, Q and R are the weight matrices of state error and control input, respectively, and u min and u max is the upper and lower limits of the control input, W i (t) is the weight of ballast water in the ith ballast tank at time t, y i is the longitudinal position of the i-th ballast tank relative to the fore and aft direction of the ship, F j (t) is the external force acting on the ship at time t, Y j is the jth external force F j (t)) The longitudinal position of the point of action relative to the fore and aft direction of the ship.

9. The control method according to claim 8, characterized in that: The central controller adjusts the ballast water volume in real time according to the generated optimal control strategy, including: According to the optimal control strategy generated by the MPC module, the ballast water regulation module obtains the ballast water regulation amount ΔW i (t), first select the ballast tanks that have the greatest impact on the ship's attitude and adjust them to minimize the heel and trim; The ballast water regulation module adjusts the amount ΔW i (t) The ballast water pump control unit sends a command to increase or decrease the amount of water to the target ballast tank. When the ballast water pump performs the adjustment operation, the water level changes of each ballast tank are monitored in real time to ensure that the adjustment process meets expectations; During the adjustment process, the computer system uses the liquid level sensor to collect the current water level in real time and calculate the deviation between the current water level and the target water level. If the deviation is found to exceed the preset threshold, the adaptive module first detects and triggers the adjustment mechanism, and then passes the updated weight matrix to the MPC module. After receiving the adaptively adjusted weights, the MPC module will generate a new control strategy based on the new optimization goal to adjust the water level and posture to ensure system stability and control accuracy, so as to ensure that the final water volume reaches the set value W. i (t+1): W i (t+1)=W i (t)+ΔW i (t)。 10. The control method according to claim 8, characterized in that: The central controller adjusts the skid height in real time according to the generated optimal control strategy, including: According to the optimal control strategy generated by the MPC module, the hydraulic shoe module obtains the shoe height adjustment amount to control the working state of the hydraulic pump and adjust the extension and retraction amount of the shoe; The total station transmits the detected slide rail height change data to the computer system, and the calculated displacement compensation amount Δh is fed back to the adaptive control module. After receiving the displacement compensation amount Δh, the adaptive control module analyzes the height difference between the current slide rail and the slide shoe according to the feedback data; If the height difference exceeds the preset threshold or a deviation occurs, the adaptive module will update the optimization weight matrix of the MPC module at this time. Based on the updated weights and the data fed back by the total station, the MPC module will recalculate the optimal control strategy and generate a new adjustment amount Δh for the extension and retraction of the sliding shoe. After the hydraulic sliding shoe module receives the latest control instruction from the MPC module, it controls the extension and retraction of the hydraulic cylinder according to the new height adjustment amount Δh. The sliding shoe height adjustment is performed based on the adjustment amount Δh generated by the MPC module: Δh i (t+1)=h i (t)+Δh i (t+1)。

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