A method and system for dynamic correction installation of air-float foundation for barrel type foundation

Abstract

This application relates to the field of marine engineering technology and discloses a method and system for dynamic correction installation of a barrel-type foundation using air flotation. The method includes the following steps: acquiring real-time attitude information of the barrel-type foundation; predicting external hydrodynamic disturbances acting on the barrel-type foundation; generating coordinated control commands based on the real-time attitude information and the predicted external hydrodynamic disturbances; and driving an internal dynamic torque compensation system to counteract the high-frequency dynamic tilt caused by the external hydrodynamic disturbances, and driving a zoned air pressure control system to correct the static or low-frequency tilt of the barrel-type foundation according to the coordinated control commands. This invention applies an adaptive model predictive control algorithm, which can identify changes in system dynamic parameters online and update the control model in real time, ensuring that the control strategy remains optimal. This overcomes the problem of existing technologies using fixed-parameter controllers, where control performance deteriorates sharply or even fails due to changes in operating conditions during the barrel-type foundation sinking process.

Description

Technical Field

[0001] This invention relates to the field of marine engineering technology, specifically to a method and system for dynamic correction installation of a barrel-type foundation using air flotation. Background Technology

[0002] Precast concrete bucket foundations for waterway engineering can weigh 5000-5500 tons, making hoisting difficult. They typically employ air flotation transportation and degassing / sinking / positioning processes. Traditional bucket foundation installation methods in air flotation transportation environments usually rely on passive control strategies, i.e., leveling is achieved by inflating or deflating air after tilting is detected. Existing systems mainly use adjusting the air pressure in each compartment within the bucket to counteract external hydrodynamic disturbances. This method is often slow to react and cannot respond promptly to high-frequency disturbances, leading to foundation instability.

[0003] Current technologies that rely on a single sensor for attitude monitoring do indeed present several problems. For example, inertial measurement units (IMUs) are susceptible to drift and cannot consistently provide accurate real-time attitude data. This is clearly unreliable for precise operations that require handling in complex water flow environments.

[0004] Moreover, existing inflation and deflation systems typically lack flexibility. Static pressure settings result in poor adaptability to environmental changes. When sea conditions are rough or sudden dynamic disturbances occur, the pressure level cannot be adjusted in time, directly leading to excessive tilting of the base and potentially causing capsizing.

[0005] Regarding energy management, many systems waste the kinetic energy generated during braking as heat when performing control operations. This reduces energy efficiency and impacts the system's economics.

[0006] Furthermore, some control systems employ relatively simple algorithmic models, lacking the ability to adapt to complex environments, such as basic PID control. Consequently, when faced with constantly changing external conditions and underlying states, the system often becomes uncontrollable. While such systems may operate well under certain ideal conditions, their stability and reliability are significantly compromised in harsh marine environments.

[0007] Therefore, existing technologies have many shortcomings in dynamic control, attitude tracking, and energy efficiency management. To overcome these limitations, current technical solutions urgently need a more flexible, efficient, and intelligent control system to improve the safety and stability of barrel foundations in complex marine environments. This is precisely the problem that this invention aims to solve. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a barrel-based air-float dynamic correction installation method and system, which solves the problems of lagging response to high-frequency dynamic disturbances, insufficient attitude monitoring accuracy, and low energy efficiency of traditional control systems in complex marine environments.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for dynamic correction installation of a barrel-type foundation using air flotation, comprising the following steps:

[0010] S1: Obtain the real-time attitude information of the barrel foundation;

[0011] S2: Predict the external hydrodynamic disturbances acting on the bucket foundation;

[0012] S3: Generate cooperative control commands based on the real-time attitude information and the predicted external hydrodynamic disturbances;

[0013] S4: According to the coordinated control command, drive the internal dynamic torque compensation system to counteract the high-frequency dynamic tilt caused by the external hydrodynamic disturbance, and drive the zoned air pressure control system to correct the static or low-frequency tilt of the bucket foundation.

[0014] Preferably, obtaining the real-time attitude information of the barrel foundation specifically involves:

[0015] High-frequency attitude data is acquired through an inertial measurement unit (IMU), and low-frequency attitude data is acquired through a global navigation satellite system (GNSS). The high-frequency and low-frequency attitude data are then fused using a Kalman filter algorithm to obtain the real-time attitude information.

[0016] Preferably, the predicted external hydrodynamic disturbances acting on the bucket foundation specifically include:

[0017] The upstream water flow information is measured in real time by an acoustic Doppler current profiler installed on the upstream face of the barrel foundation, and a prediction model is established based on the water flow information to obtain the predicted external hydrodynamic disturbance.

[0018] Preferably, the generation of collaborative control instructions specifically includes:

[0019] The model predictive control algorithm is used to generate the cooperative control command by taking the real-time attitude information and the predicted external hydrodynamic disturbance as inputs within a rolling finite time window and solving a constrained optimization problem.

[0020] Preferably, the system dynamics model used in the model predictive control algorithm can perform online parameter identification and adaptive updates based on the real-time attitude information of the barrel foundation and the error of the model prediction output.

[0021] Preferably, the internal dynamic torque compensation system includes a compensation mass block disposed inside the barrel foundation and a servo actuator that drives the compensation mass block to move at high speed.

[0022] The internal dynamic torque compensation system specifically involves controlling the servo actuator to drive the compensation mass block to generate an inertial torque opposite to the high-frequency dynamic tilt direction.

[0023] Preferably, when the servo actuator decelerates and brakes the compensation mass block, it converts kinetic energy into electrical energy through regenerative braking and stores it in the energy recovery unit.

[0024] Preferably, before acquiring real-time attitude information, system initialization is performed, specifically as follows:

[0025] Before installation, fine-tune the initial position of the compensation mass block in the internal dynamic torque compensation system to calibrate and compensate for the initial unbalanced center of mass of the bucket foundation.

[0026] Preferably, the real-time attitude information, external hydrodynamic disturbances, and collaborative control command data are integrated to generate a digital twin file for evaluating installation quality.

[0027] A barrel-type foundation air-float dynamic correction installation system includes:

[0028] The information acquisition module is used to acquire the real-time attitude information of the barrel foundation;

[0029] The disturbance prediction module is used to predict external hydrodynamic disturbances acting on the bucket foundation.

[0030] The cooperative control module is used to generate cooperative control commands based on the real-time attitude information and the predicted external hydrodynamic disturbances.

[0031] An execution module, connected to the collaborative control module, includes:

[0032] An internal dynamic torque compensation system;

[0033] A zoned air pressure control system;

[0034] The execution module is used to drive the internal dynamic torque compensation system to counteract high-frequency dynamic tilt according to the cooperative control command, and to drive the zoned air pressure control system to correct static or low-frequency tilt.

[0035] This invention provides a method and system for dynamic alignment correction installation of a barrel-type foundation using air flotation. It offers the following advantages.

[0036] 1. This invention achieves advanced prediction of external hydrodynamic disturbances through an acoustic Doppler current profiler and a predictive model. This feedforward control mechanism allows the system to respond before the disturbance arrives. Existing technologies typically passively wait for tilting to occur before making corrections, resulting in a naturally delayed response and difficulty in effectively controlling attitude in complex water flows.

[0037] 2. This invention innovatively sets up an internal dynamic torque compensation system and a zoned air pressure control system. The two are uniformly scheduled by a collaborative control algorithm with a clear division of labor. High-frequency dynamic tilt is instantly canceled by the internal system, and low-frequency static tilt is stably corrected by the air pressure control system. Compared with the existing technology that only relies on a single air pressure control system for correction, this invention solves the fundamental defects of slow response speed and inability to cope with high-frequency dynamic disturbances.

[0038] 3. This invention applies an adaptive model predictive control algorithm, which can identify changes in system dynamic parameters online and update the control model in real time. This ensures that the control strategy remains optimal. It overcomes the problem of existing technologies using fixed parameter controllers, where control performance drops sharply or even fails due to changes in operating conditions during the sinking of the bucket foundation.

[0039] 4. This invention adopts a multi-sensor fusion scheme based on Kalman filtering, which integrates the high dynamic characteristics of the inertial measurement unit with the long-term stability of the global navigation satellite system, and obtains high-precision, drift-free real-time attitude information. This scheme provides a reliable data foundation for the accurate decision-making of the entire control system, and avoids the problem of excessive final installation error caused by insufficient accuracy or drift of a single sensor in the prior art.

[0040] 5. The present invention integrates an energy recovery unit in the internal dynamic torque compensation system. During the braking process, the servo actuator converts kinetic energy into electrical energy and stores it for use in the next acceleration. Compared with the prior art, which dissipates braking energy in the form of heat, the present invention significantly reduces system energy consumption and peak power, and improves the system's operating endurance and economy. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the method steps of the present invention;

[0042] Figure 2 This is a schematic diagram of the overall system architecture of the present invention;

[0043] Figure 3 This is a schematic diagram of the internal dynamic torque compensation system of the present invention;

[0044] Figure 4 This is a schematic diagram of the zoned air pressure control system of the present invention;

[0045] Figure 5 This is a schematic diagram of the control algorithm flow of the present invention;

[0046] Figure 6 This is a schematic diagram of the structure of the compensating mass block and the servo actuator that drives the compensating mass block to move at high speed according to the present invention. Detailed Implementation

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Please see the appendix Figure 1-5 ,in Figure 4 This is just an illustration; there can actually be several chambers, and each chamber executes the same operating logic as chamber 1.

[0049] This invention provides a method for dynamic alignment correction installation of a barrel-type foundation using air flotation, comprising the following steps:

[0050] S1: Obtain the real-time attitude information of the barrel foundation;

[0051] Specifically, the physical structure of the system includes an information acquisition module, which, exemplarily, is integrated on top of or inside a barrel-shaped foundation. This information acquisition module includes an inertial measurement unit (IMU), a global navigation satellite system (GNSS) receiver, and a data fusion processor. Both the IMU and the GNSS receiver are communicatively connected to the data fusion processor.

[0052] For example, the inertial measurement unit (IMU) is preferably an industrial-grade fiber optic or microelectromechanical system (MEMS) IMU, which is mounted near the geometric center of the top end cap of the barrel foundation. This IMU is capable of measuring and outputting raw data of the barrel foundation's triaxial angular velocity and triaxial acceleration at a high update frequency. This high-frequency characteristic ensures the capture of any instantaneous, high-frequency attitude changes caused by external disturbances such as water flow impacts.

[0053] For example, the Global Navigation Satellite System (GNSS) receiver is preferably a receiver supporting real-time dynamic differential technology, and its antenna is also mounted on top of the barrel foundation to ensure signal reception quality. This GNSS receiver can provide absolutely accurate three-dimensional position, velocity, and heading information at a relatively low update frequency. Its output attitude information does not have drift errors accumulated over time, exhibiting high absolute accuracy.

[0054] The data fusion processor addresses the limitations of single-sensor technology, namely the drift error that accumulates over time in inertial measurement units (IMUs) and the insufficient update frequency of global navigation satellite systems (GNSS) to meet real-time control requirements. To solve this, the data fusion processor embeds and runs a data fusion algorithm to combine the advantages of both sensors.

[0055] In this embodiment, the data fusion algorithm employs the extended Kalman filter algorithm. This algorithm establishes the swing motion of the barrel foundation as a nonlinear system model and achieves optimal fusion of high-frequency IMU data and low-frequency global navigation satellite system data through a continuous prediction-update loop.

[0056] First, establish the system's state vector. It contains physical quantities that need to be estimated in real time. The state vector is defined, for example, as follows:

[0057] ;

[0058] In its formula, These represent the tilt angles of the barrel foundation about the X-axis (roll) and Y-axis (pitch), respectively. These represent the attitude angular velocities of the barrel foundation around the X-axis and Y-axis, respectively. This indicates the transpose operation.

[0059] The execution flow of the extended Kalman filter algorithm includes the following steps:

[0060] The first step is the prediction step. This step uses the optimal state estimate from the previous time step and the current IMU measurement to predict the state at the current time step. This step is performed continuously at a high frequency (e.g., 100Hz) using the IMU.

[0061] The state prediction equation is:

[0062] ;

[0063] In its formula, Indicates at time The predicted value of the state, based on time. Information, Indicates at time The estimated value of the state, Indicates at time The control input or manipulation quantity, Represents a function.

[0064] The prediction covariance equation is:

[0065] ;

[0066] In its formula, Indicates the state at time [time]. The covariance matrix, given the condition based on time. Information, State transition matrix, Indicates at time The state covariance matrix is ​​given based on time... Information for estimating the uncertainty of the state. State transition matrix transpose, Process noise covariance matrix.

[0067] The second step is the update step. This step is triggered when low-frequency measurement data from GNSS is received. This high-precision absolute measurement is used to correct the drift generated in the prediction step.

[0068] First, calculate the Kalman gain. :

[0069] ;

[0070] In its formula, Indicates at time Kalman gain, Indicates the state at time [time]. The covariance matrix, given the condition based on time. Information, Indicates at time The observation matrix Representation matrix transpose, Indicates at time The observation noise covariance matrix, This is a summed matrix representing the uncertainty of measurement prediction.

[0071] The state estimate is then updated to obtain the time step. The updated state estimation vector :

[0072] ;

[0073] In its formula, Indicates at time The updated state estimate vector, Indicates at time The predicted value of the state, based on time. Information, Indicates at time Kalman gain, Indicates at time Observed measurements Describes the observation model, where It is to estimate the state Mapped to a nonlinear function of the observation space, This represents the observation residual, i.e., at time [time value missing]. Observed measurements Compared with predicted measurements The difference between them It is the observation function, which maps the predicted pose in the state vector to the measurement space.

[0074] Finally update the state estimate covariance matrix. :

[0075] ;

[0076] In its formula, Indicates at time The state covariance matrix, given the condition based on time step. Observational information, identity matrix Indicates at time Kalman gain, Observation matrix Indicates the state at time [time]. The covariance matrix, given the condition based on time. Information.

[0077] By repeatedly executing the above steps, the data fusion processor can output a real-time time value. The updated state estimation vector The attitude information contained in this vector maintains the high update rate of the IMU and eliminates accumulated drift through periodic correction of the GNSS, thus obtaining a high-precision, high-frequency, drift-free barrel-based real-time attitude information.

[0078] The method for obtaining the real-time attitude information of the barrel foundation can provide stable and reliable data input for subsequent model predictive control algorithms. It is a necessary prerequisite for achieving high performance of the entire closed-loop control system and has the technical effect of ensuring that the control system can accurately perceive the attitude of the foundation.

[0079] S2: Predict the external hydrodynamic disturbances acting on the bucket foundation;

[0080] Specifically, the purpose of this step is to enable the control system to have feedforward control capabilities, so that it can proactively respond to upcoming environmental disturbances, which is achieved through a disturbance prediction module.

[0081] The disturbance prediction module includes an acoustic Doppler current profiler and a prediction model processor. The acoustic Doppler current profiler is communicatively connected to the prediction model processor to transmit measurement data to the processor for analysis and prediction.

[0082] For example, the acoustic Doppler current profiler is mounted on the outer wall of the bucket foundation's upstream face. This mounting position allows it to continuously and non-contactly measure a distance in front of it; more specifically, its output is a three-dimensional water flow velocity vector of multiple discrete depth cells.

[0083] The predictive model processor receives and processes the flow velocity data measured by the acoustic Doppler current profiler and runs a predictive algorithm to output real-time, rolling predictions of future disturbances. This processing flow further includes three consecutive sub-steps: data preprocessing, hydrodynamic moment conversion, and time series prediction.

[0084] First, in the data preprocessing sub-step, the prediction model processor filters the raw velocity vector data received from the acoustic Doppler current profiler to remove measurement noise. For example, a median filter or a Kalman filter can be used to improve the signal-to-noise ratio of the data. Subsequently, the filtered velocity vector is transformed from the acoustic Doppler current profiler's own coordinate system to the volume coordinate system of the barrel foundation to unify the subsequent calculation reference.

[0085] Secondly, in the hydrodynamic moment conversion sub-step, the prediction model processor converts the preprocessed upstream velocity profile data into the total disturbance moment that will act on the barrel foundation. This conversion is based on fluid mechanics principles, such as the Morrison equations, to calculate the force exerted by the fluid on slender rods.

[0086] Hydrodynamic forces acting on a cylinder of unit length This can be represented as a linear superposition of resistance and inertial force. By dividing the submerged portion of the bucket foundation into multiple micro-segments along the depth direction, and using the water flow velocity at the corresponding depth measured by an acoustic Doppler current profiler, the hydrodynamic force on each micro-segment is calculated. Finally, by integrating the hydrodynamic forces generated by all micro-segments along the entire submerged depth and multiplying by the corresponding lever arm, the total external hydrodynamic disturbance torque acting on the swing center point of the bucket foundation can be obtained. The calculation process is repeatedly executed at high frequency in the processor, forming a time series of disturbance torques. Next, in the time series prediction sub-step, the prediction model processor analyzes and predicts the generated hydrodynamic torque time series. In this embodiment, an autoregressive integral moving average model is preferably used for prediction because it can effectively capture the randomness and time correlation exhibited by wave and water flow. Before applying the model, it is necessary to first identify the model to determine its optimal order. This process may include performing stationarity tests on historical data, such as using the augmented Dickey-Fowler test. Then, by analyzing the autocorrelation function and partial autocorrelation function plots of the sample data, the autoregressive order of the model is initially determined. and moving average order To obtain the optimal model, the Akaike information criterion or Bayesian information criterion can be used for further evaluation, selecting the order combination that minimizes the criterion function value. .

[0087] A general ARIMA The mathematical expression of the model is:

[0088] ;

[0089] In its formula, Indicates time The predicted or estimated value, The original disturbance torque time series after After the order difference, in time The value, yes The predicted value, and These are the autoregressive and moving average coefficients of the model, respectively. The order of the model identified above. Indicates the difference order. For constant terms, This is the white noise error term. Indicating the past time point The weighted sum, Indicating the past The weighted sum of the error (white noise) terms at each time point.

[0090] In actual operation, the model makes predictions in a rolling manner. That is, in each control cycle, the model is updated using the latest disturbance moment observations, and the disturbance moment sequence within a finite prediction time domain N is predicted forward, denoted as... ,in To adapt to slow changes in sea state, the parameters of the model can also be set to be periodically re-identified and updated.

[0091] Finally, the predicted disturbance torque sequence is sent in real time to the subsequent cooperative control module, serving as a key input for its feedforward control and rolling optimization. This implementation, through real-time measurement of upstream water flow and advanced prediction of disturbance torque, enables the entire dynamic correction installation system to transform from a traditional passive response to an active defense. This significantly improves the system's resistance to sudden disturbances, effectively reduces attitude control deviations, and thus ensures the accuracy and safety of the installation process.

[0092] S3: Generate cooperative control commands based on the real-time attitude information and the predicted external hydrodynamic disturbances;

[0093] Specifically, this step is executed by a collaborative control module, which serves as the decision-making core of the system and aims to generate an optimal control strategy that can simultaneously cope with high-frequency dynamic disturbances and low-frequency static tilt.

[0094] The collaborative control module, exemplarily, is a high-performance industrial computing platform. Logically, this platform serves as the central processing unit of the system. Its input terminals are connected to the information acquisition module and the disturbance prediction module to receive real-time status and future disturbance information; its output terminal is connected to the execution module to send control commands.

[0095] The core algorithm of this module is preferably an adaptive model predictive control algorithm. The reason for adopting this algorithm is that its predictive control framework can naturally utilize the feedforward information provided by the disturbance prediction module, and its optimization-based method can explicitly handle various physical constraints existing in the system.

[0096] The implementation of adaptive model predictive control (EMC) first involves establishing and maintaining a mathematical model that accurately describes the dynamic behavior of the bucket foundation. This model is exemplarily represented in discrete-time state-space form:

[0097] ;

[0098] In its formula, Indicates at time The system state vector, Indicates at time The state transition matrix, Indicates at time The control input matrix, Indicates at time The current state vector, Indicates at time The control input, This represents the disturbance impact matrix, used to describe the system's response to external disturbances. Indicates at time The perturbation influence vector.

[0099] The cooperative control command vector This embodies the cooperative control concept of the present invention. It is a composite vector, which includes high-frequency control components used to control the internal dynamic torque compensation system. and low-frequency control components used to control the zoned air pressure control system. .

[0100] To address the time-varying dynamic characteristics of bucket foundations during settlement due to factors such as embedment depth and soil condition changes, the collaborative control module further includes an online parameter identification unit. This unit ensures the accuracy of the parameter matrix in the state-space model. and It can perform online adaptive updates.

[0101] The online parameter identification unit, for example, employs a recursive least squares algorithm with a forgetting factor. This algorithm recursively corrects the model parameters by continuously comparing the predicted state of the model with the actual state output by the information acquisition module, using the prediction error between the two.

[0102] The specific implementation of the recursive least squares algorithm first rewrites the state-space equations into a linear regression form. Then, in each control cycle, it iterates the vector containing the model parameters based on the newly acquired data. The update process is performed. Its core update law can be described by the following set of formulas:

[0103] ;

[0104] ;

[0105] ;

[0106] In its formula, Indicates at time The gain matrix, Indicates at time The error covariance matrix, Indicates at time matrix transpose, It is a scalar constant. This reflects the combined effect of measurement uncertainty and state uncertainty. Indicates at time Updated estimates, Indicates at time Estimation of state or parameters, Indicates at time The current observations or measurement data, Indicates through the observation matrix For the previous estimate The transformation yields the predicted value. Indicates measurement residuals, Indicates at time The updated error covariance matrix, Used to adjust the size of the covariance Indicates based on the previous covariance The updated result, minus the result obtained through the gain matrix and measurement matrix Uncertainty following the impact.

[0107] Subsequently, in each control cycle, the cooperative control module constructs and solves a finite-time rolling optimization problem based on the updated adaptive model. This problem aims to compute the future prediction time domain... The optimal control sequence within the given range. The objective function of this optimization problem is... It can be represented as:

[0108] ;

[0109] In its formula, The objective function to be minimized is... , Describe the objective function. Indicates the control input sequence. Indicates the prediction time domain, Indicates at time State prediction, Indicates the reference state value. This represents the weighted sum of squares of the state error. Indicates at time The control input, This represents the weighted sum of squares of the control inputs.

[0110] The solution process for this optimization problem is subject to a series of physical constraints, including state constraints, input constraints, and input rate of change constraints.

[0111] The constrained optimization problem is typically a quadratic programming problem. The cooperative control module integrates a high-efficiency quadratic programming numerical solver suitable for real-time systems, used to quickly solve the problem online within each control cycle and obtain the optimal future control input sequence. .

[0112] Finally, based on the rolling time-domain principle of model predictive control, the system does not execute the entire calculated optimal control sequence. Instead, it extracts only the first element of the sequence, i.e. This serves as the final coordinated control command generated at the current moment. This command vector is then sent to the execution module for decomposition and physical implementation.

[0113] This implementation employs adaptive model predictive control, enabling forward-looking decision-making based on future predictions and online adaptation to system changes while respecting physical constraints. This method generates optimal and safe cooperative control commands, thereby achieving precise and efficient cooperative control of both high-frequency and low-frequency disturbances.

[0114] S4: According to the coordinated control command, drive the internal dynamic torque compensation system to counteract the high-frequency dynamic tilt caused by the external hydrodynamic disturbance, and drive the zoned air pressure control system to correct the static or low-frequency tilt of the bucket foundation.

[0115] Specifically, this step is accomplished by a physically integrated execution module, which is responsible for efficiently and accurately translating the abstract digital instructions generated by the collaborative control module into physical actions that can stabilize the basic attitude of the barrel.

[0116] The execution module physically includes an internal dynamic torque compensation system and a zoned air pressure control system. The input of the execution module is connected to the output of the collaborative control module to receive real-time generated collaborative control commands.

[0117] The execution process of this step first includes the coordination control command. The instruction is a composite vector, internally allocated by a model predictive control algorithm to optimally distribute high-frequency and low-frequency control tasks. An instruction allocation unit, exemplarily a software function of the cooperative control module or a front-end controller of the execution module, decomposes the instruction vector into high-frequency control instructions. and low-frequency control commands Subsequently, the high-frequency control command It is sent to the internal dynamic torque compensation system. The system is exemplarily installed in the internal drying chamber on top of the barrel foundation, and its core includes a compensation mass block made of high-density alloy and a servo actuator consisting of two sets of orthogonally arranged, high-acceleration linear servo motors.

[0118] The high-frequency control command For example, a two-dimensional vector is used to specify the target acceleration of the compensation mass block in the XY plane. Upon receiving this command, the controller of the servo actuator drives the compensation mass block to generate the corresponding acceleration through its internal high-speed closed-loop position and current controller. According to Newton's third law, a moving mass will exert an equal and opposite reaction force on its base, i.e., a barrel foundation. , This is the mass of the compensating mass block. The reaction force acts on the installation position of the internal dynamic torque compensation system, thereby generating a compensating torque on the rotation center of the bucket foundation:

[0119] ;

[0120] In its formula, Indicates time The compensation torque, From the rotation center of the barrel foundation to the internal dynamic torque compensation system The lever arm vector at the center, Indicates time Response force.

[0121] The compensating torque Its response speed can reach the millisecond level, which can accurately and instantaneously cancel the high-frequency external disturbance torque predicted by the disturbance prediction module, thereby maintaining the dynamic stability of the foundation under high-frequency disturbances.

[0122] Furthermore, to improve the system's energy efficiency and sustainable operation, the internal dynamic torque compensation system also includes an energy recovery unit. This unit is exemplarily a supercapacitor module and is connected to the DC bus of the servo actuator via a bidirectional DC-DC converter.

[0123] When the servo actuator decelerates and brakes the compensating mass block, its regenerative braking function is activated. The servo motor then operates in generator mode, efficiently converting the kinetic energy of the compensating mass block into electrical energy, which is then used to charge the supercapacitor module via the bidirectional DC-DC converter.

[0124] When the servo actuator needs to drive the mass block to accelerate again, the bidirectional DC-DC converter draws stored electrical energy from the supercapacitor module to supplement or replace the main power supply. This energy recycling significantly reduces the system's peak power demand from the main power supply and its total energy consumption.

[0125] At the same time, the low-frequency control command The pressure is sent to the partitioned air pressure control system. This system includes multiple independent sealed chambers divided at the top of the barrel foundation by sealing partitions, and inverter-controlled air compressors and electrically controlled exhaust valves connected to each chamber. Each chamber is also equipped with a high-precision pressure sensor for closed-loop control.

[0126] The low-frequency control command For example, a vector is used, whose elements are the air pressure setpoints for each chamber. After receiving the corresponding air pressure setpoint, the frequency converter of each chamber compares the setpoint with the real-time feedback value of the pressure sensor through its internal PID controller. When the setpoint is greater than the measured value, the air compressor operating frequency is automatically adjusted to inflate; when the setpoint is less than the measured value, the exhaust valve is opened to exhaust air, thereby precisely controlling the air pressure in each independent chamber.

[0127] By generating differentiated air pressures between different chambers, the system can apply a net corrective torque to the barrel foundation. This torque acts gently but with tremendous force, specifically designed to correct static or low-frequency overall tilt of the barrel foundation caused by factors such as uneven seabed geology or continuous, gently varying ocean currents.

[0128] By employing the aforementioned high- and low-frequency coordinated execution method, this invention enables targeted and efficient control of tilt disturbances of different natures.

[0129] The internal dynamic torque compensation system acts as an agile dynamic stabilizer, while the zoned air pressure control system functions as a powerful attitude adjuster. This method offers advantages such as fast dynamic response, stable static correction, and low energy consumption, comprehensively ensuring the installation accuracy and operational safety of the bucket foundation in complex marine environments.

[0130] A barrel-type foundation air-float dynamic correction installation system includes:

[0131] The information acquisition module is used to acquire the real-time attitude information of the barrel foundation;

[0132] The disturbance prediction module is used to predict external hydrodynamic disturbances acting on the bucket foundation.

[0133] The cooperative control module is used to generate cooperative control commands based on the real-time attitude information and the predicted external hydrodynamic disturbances.

[0134] An execution module, connected to the collaborative control module, includes:

[0135] An internal dynamic torque compensation system;

[0136] A zoned air pressure control system;

[0137] The execution module is used to drive the internal dynamic torque compensation system to counteract high-frequency dynamic tilt according to the cooperative control command, and to drive the zoned air pressure control system to correct static or low-frequency tilt.

[0138] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic alignment correction installation of a barrel-type foundation using air flotation, characterized in that, Includes the following steps: S1: Obtain the real-time attitude information of the barrel foundation; S2: Predict the external hydrodynamic disturbances acting on the bucket foundation; S3: Generate cooperative control commands based on the real-time attitude information and the predicted external hydrodynamic disturbances; S4: According to the coordinated control command, drive the internal dynamic torque compensation system to counteract the high-frequency dynamic tilt caused by the external hydrodynamic disturbance, and drive the zoned air pressure control system to correct the static or low-frequency tilt of the bucket foundation. The real-time attitude information of the barrel foundation is obtained as follows: High-frequency attitude data is acquired through an inertial measurement unit, and low-frequency attitude data is acquired through a global navigation satellite system. The high-frequency and low-frequency attitude data are then fused using a Kalman filter algorithm to obtain the real-time attitude information. The predicted external hydrodynamic disturbances acting on the bucket foundation specifically refer to: The upstream water flow information is measured in real time by an acoustic Doppler current profiler installed on the upstream face of the barrel foundation, and a prediction model is established based on the water flow information to obtain the predicted external hydrodynamic disturbance. The specific method for generating collaborative control instructions is as follows: The model predictive control algorithm is adopted. Within a rolling finite time window, the real-time attitude information and the predicted external hydrodynamic disturbance are taken as inputs. The cooperative control command is generated by solving a constrained optimization problem. The internal dynamic torque compensation system includes a compensation mass block disposed inside the barrel foundation and a servo actuator that drives the compensation mass block to move at high speed. The internal dynamic torque compensation system specifically involves controlling the servo actuator to drive the compensation mass block to generate an inertial torque opposite to the high-frequency dynamic tilt direction.

2. The method for dynamic correction installation of a barrel-type foundation using air flotation according to claim 1, characterized in that, The system dynamics model used in the model predictive control algorithm can perform online parameter identification and adaptive updates based on the real-time attitude information of the barrel foundation and the error of the model prediction output.

3. The method for dynamic correction installation of a barrel-type foundation using air flotation according to claim 1, characterized in that, When the servo actuator decelerates and brakes the compensation mass block, it converts kinetic energy into electrical energy through regenerative braking and stores it in the energy recovery unit.

4. The method for dynamic correction installation of a barrel-type foundation using air flotation according to claim 1, characterized in that, Before acquiring real-time attitude information, system initialization is performed, specifically as follows: Before installation, fine-tune the initial position of the compensation mass block in the internal dynamic torque compensation system to calibrate and compensate for the initial unbalanced center of mass of the bucket foundation.

5. The method for dynamic correction installation of a barrel-type foundation using air flotation according to claim 1, characterized in that, The real-time attitude information, external hydrodynamic disturbances, and collaborative control command data are integrated to generate a digital twin profile for evaluating installation quality.

6. A barrel-type foundation air-float dynamic correction installation system, applied to the barrel-type foundation air-float dynamic correction installation method as described in any one of claims 1-5, characterized in that, include: The information acquisition module is used to acquire the real-time attitude information of the barrel foundation; The disturbance prediction module is used to predict external hydrodynamic disturbances acting on the bucket foundation. The cooperative control module is used to generate cooperative control commands based on the real-time attitude information and the predicted external hydrodynamic disturbances. An execution module, connected to the collaborative control module, includes: An internal dynamic torque compensation system; A zoned air pressure control system; The execution module is used to drive the internal dynamic torque compensation system to counteract high-frequency dynamic tilt according to the cooperative control command, and to drive the zoned air pressure control system to correct static or low-frequency tilt.

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