Electronic anchor control method based on double-loop active disturbance rejection control

Abstract

This invention proposes an electronic anchor control method based on dual-loop active disturbance rejection control. The method includes: deploying a sensor group on the top-flow turbine, the sensor group including a GNSS receiver, a geomagnetic sensor, and an inertial sensor; collecting sensor data from the top-flow turbine; performing coordinate system transformation and alignment on the sensor data to construct a system observation vector for state estimation; establishing a hybrid state-space model; obtaining the actual control thrust of the previous control cycle as a known input; processing the system observation vector using an extended Kalman filter algorithm; performing recursive state estimation on the hybrid state-space model; separating the high-frequency motion state; and outputting the low-frequency position and low-frequency velocity of the top-flow turbine at the current moment; using the current low-frequency position and low-frequency velocity as input to the dual-loop control architecture. This method improves the ship's attitude and position stability and enables real-time compensation for disturbances such as wind, waves, and currents.

Description

Technical Field

[0001] This invention proposes an electronic anchor control method based on dual-loop active disturbance rejection control, which relates to the field of water conservancy engineering. Background Technology

[0002] An electronic anchor is a technology that utilizes a ship's own dynamic positioning system to achieve fixed-point holding. Unlike traditional mechanical anchors, electronic anchors control the ship's propellers to generate thrust that resists environmental disturbances, allowing the ship to maintain a preset position in a specific sea area, achieving virtual mooring without the need for anchoring. This technology is widely used in offshore wind power operation and maintenance, deep-sea aquaculture, scientific research operations, and emergency escape scenarios, offering advantages such as not damaging the seabed environment, high positioning accuracy, and rapid relocation.

[0003] The traditional control algorithms currently used in top-flow turbines have poor performance: electronic anchoring using PID control algorithms has weak adaptability, fixed PID parameters, and difficulty in automatically adjusting according to changes in airflow, load, etc., resulting in poor system robustness and insufficient environmental adaptability; it can only achieve basic positioning and cannot meet the multi-dimensional control requirements such as attitude control and heading maintenance. Summary of the Invention

[0004] This invention provides an electronic anchor control method based on dual-loop active disturbance rejection control to solve the aforementioned problems: This invention proposes an electronic anchor control method based on dual-loop active disturbance rejection control, the method comprising: Step 1: Deploy a sensor group on the top flow machine. The sensor group includes a GNSS receiver, a geomagnetic sensor, and an inertial sensor. Collect data from the sensor group on the top flow machine, perform coordinate system transformation and alignment on the sensor group data, and construct a system observation vector for state estimation. Step 2: Establish a hybrid state-space model, obtain the actual control thrust of the previous control cycle as a known input, process the system observation vector through the extended Kalman filter algorithm, perform recursive state estimation on the hybrid state-space model, separate the high-frequency motion state, and output the low-frequency position and low-frequency speed of the top flow machine at the current moment. Step 3: Using the current low-frequency position and low-frequency velocity as input, the system enters a dual-loop control architecture, which includes an outer loop and an inner loop. The outer loop, as the position loop, calculates the position deviation between the low-frequency position and the preset target anchor point, and generates target velocity commands in the north and east directions based on the position deviation. The inner loop, as the velocity loop, uses an active disturbance rejection controller to track the target velocity commands and compensate for environmental disturbances, calculates the control thrust in the north and east directions at the current moment, sends the control thrust to the actuator, and simultaneously feeds it back to Step 2 for state estimation in the next control cycle. The active disturbance rejection controller in the inner loop includes an extended state observer.

[0005] Preferably, the process of acquiring sensor data from the top-flow machine, performing coordinate system transformation and alignment on the sensor data, and constructing a system observation vector for state estimation includes: Data from the GNSS receiver, geomagnetic sensor, and inertial sensor are collected synchronously at a preset sampling frequency. Error compensation is performed on the data from the geomagnetic sensor and inertial sensor using pre-stored calibration parameters, and interpolation and rejection processing is performed on the data from the GNSS receiver. The data from the GNSS receiver, after interpolation and elimination processing, is converted into planar position coordinates in a local navigation coordinate system with a preset anchor point as the origin. The angular velocity data from the geomagnetic sensor after error compensation and the inertial sensor are fused and calculated to obtain the real-time heading angle and turning angular velocity of the top flow machine in the local navigation coordinate system; Based on the pre-measured installation position deviation of the GNSS receiver relative to the center of gravity of the top-flow aircraft, the planar position coordinates are transformed to the center of gravity of the top-flow aircraft based on the rotation matrix and the real-time heading angle to obtain the corrected center of gravity position coordinates. The corrected center of gravity coordinates, real-time heading angle, and the ship's longitudinal velocity, lateral velocity, and yaw rate derived from the sensor array are combined in a preset order to construct the system observation vector.

[0006] Preferably, a hybrid state-space model is established, including: The hybrid space state model includes a 15-dimensional state vector and a set of state differential equations characterizing the dynamic properties of the state vector.

[0007] Preferably, the 15-dimensional state vector is composed of a six-dimensional high-frequency motion sub-vector, a three-dimensional low-frequency position sub-vector, a three-dimensional low-frequency velocity sub-vector, and a three-dimensional environment sub-vector; the six-dimensional high-frequency motion sub-vector includes high-frequency displacements and their high-frequency velocities in the longitudinal, lateral, and heading directions; the three-dimensional low-frequency position sub-vector includes north coordinates, east coordinates, and heading angle; the three-dimensional low-frequency velocity sub-vector includes longitudinal low-frequency velocity, lateral low-frequency velocity, and heading low-frequency velocity; and the three-dimensional environment sub-vector includes longitudinal environmental force, lateral environmental force, and environmental torque.

[0008] Preferably, the state differential equation is: in, This represents the trend of change of the variables in the 15-dimensional state variables at the current moment. This represents the state matrix of a high-frequency oscillating system. Represents a high-frequency motion state vector. This represents the high-frequency noise input matrix, which determines how random wave energy is converted into specific high-frequency oscillations of the ship's hull. Gaussian white noise representing high-frequency oscillations This represents the low-frequency velocity sub-vector of the ship's hull. This represents the rotation matrix that varies with the heading angle. Represents the environmental force state vector. The matrix representing the inverse of the time constant matrix of the time delay characteristic. This represents low-frequency environmental force variation noise. The matrix representing the inverse of the ship's dynamic mass matrix. This represents the control thrust input vector. denoted as low-frequency motion noise, and D represents the hull linear damping matrix.

[0009] Preferably, the system observation vector is processed by an extended Kalman filter algorithm to perform recursive state estimation on the hybrid state-space model, separate the high-frequency motion state, and output the low-frequency position and low-frequency velocity of the top-flow machine at the current moment, including: The state differential equations of the hybrid state-space model are linearized by calculating the Jacobian matrix. The linearized continuous state equations are discretized using the forward Euler method to obtain the state transition matrix; The prior estimate of the state transition matrix is ​​calculated through the prediction step of the extended Kalman filter. Based on the system observation vector, the prior estimate is corrected through the update step of the extended Kalman filter, and the low-frequency position and low-frequency velocity of the top flow machine are output after filtering out the high-frequency motion state.

[0010] Preferably, the position loop controller employs an improved PID algorithm, which includes an integral inverse calculation anti-saturation strategy: When the output of the PID controller is not saturated, the integral term accumulates normally based on the position deviation. When the output of the PID controller reaches the physical limit amplitude and enters the saturation state, the difference between the saturated output and the unsaturated output is calculated through the negative feedback channel, and this difference is multiplied by the back-calculated gain as a dynamic compensation term, which is introduced to the input of the integrator, thereby proportionally reducing the integral term in the reverse direction. Preferably, the integral back-calculation anti-saturation update equation is: in, This represents the output value of the effective integral term at the current moment, where t represents the current moment and s represents the historical time integration variable from 0 to t. This represents the difference between the target position of the ship and the actual position measured by the sensors. This represents the thrust value of the theoretical unsaturated thrust command. This represents the thrust value of the actual saturation thrust command. Indicates the conventional integral coefficient. This indicates the inverse gain coefficient.

[0011] Preferably, the improved PID algorithm further includes: in, The current time scale coefficient represents the continuous dynamic change of position error. This represents the integral coefficient at the current moment, which changes continuously and dynamically with the position error. The differential coefficients at the current moment represent the continuous dynamic changes with the position error. This represents the minimum value of a ship's restoring force. This indicates the maximum value of the ship's restoring force. This indicates the maximum value of the ship's damping force. This represents the minimum value of the ship's damping force (braking force). This represents the maximum integral gain. This represents the attenuation factor of the first Gaussian curve. This represents the attenuation factor of the second Gaussian curve. This represents the decay rate of the third Gaussian curve. Indicates real-time position deviation; Preferably, in, Represents the first fundamental shape factor. This represents the first disturbance sensitivity coefficient. Indicates the maximum thrust of the propulsion unit. This represents the total disturbance estimate output by the extended state observer in the inner-loop Active Disturbance Rejection Controller (ADRC).

[0012] Preferably, the continuous-time update law of the extended state observer is as follows: in, This indicates the low-frequency speed output in step two. This represents the observer's estimate of the low-frequency velocity. This represents the total disturbance estimate output by the observer. Indicates the bandwidth gain of the first observer. Indicates the bandwidth gain of the second observer. Indicates thrust control gain. This indicates the current control thrust.

[0013] The beneficial effects of this invention are as follows: Traditional electronic anchor control systems typically feed raw sensor data directly to the controller, causing the thruster to frequently follow the high-frequency swaying of the waves and perform ineffective thrust reversal, resulting in severe mechanical wear and extremely high energy consumption. The EKF hybrid state observer in step two of this invention achieves mathematical decoupling of high and low frequency motion states, extracting only the low-frequency drift component to participate in the control, making the thruster commands smooth and stable, significantly extending the motor life and significantly reducing the total system energy consumption. This invention achieves feedforward active disturbance rejection under complex and unknown sea conditions, greatly improving dwell accuracy. Traditional PID control is a lag-type error feedback control, which only exerts force to resist when the wind and waves blow the ship off course. Step three of this invention introduces an active disturbance rejection controller with ESO in the inner loop. It can estimate the equivalent disturbance force in advance through disturbance observation inside the velocity loop before the environmental disturbance causes a significant deviation in the ship's position, and instantly superimpose a reverse thrust to cancel it out. This active defense mechanism allows the engine to remain firmly anchored at the target point even under sudden gusts of wind or undercurrents, exponentially improving the disturbance rejection capability. It also improves the dynamic response and stability of the system, decoupling position control and velocity thrust control into inner and outer double loops. The outer loop focuses on reducing position overshoot, while the inner loop focuses on overcoming hydrodynamic environmental and... Motor delay; this time-scale separation architecture avoids system oscillations that are prone to occur when a single-loop controller faces large errors, allowing the top-mounted engine to respond quickly during long-distance displacement and converge smoothly when approaching the anchor point; it forms a thrust physical closed loop, enhancing the system robustness in the event of temporary sensor failure. This invention feeds back the actual control thrust output in step three to the EKF model in step two as a known input; this means that the filter prediction not only relies on mathematical inertia but also incorporates real physical forces; even if the GNSS signal is briefly lost or jumps under severe weather conditions, the EKF can still accurately deduce the current position and speed of the ship based on the thrust data and physical model from the previous moment, ensuring the ultimate reliability of the electronic anchor system under extreme conditions. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of an electronic anchor control method based on dual-loop active disturbance rejection control as described in this invention. Detailed Implementation

[0015] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0016] Numerous specific details are set forth in the following description to provide a thorough understanding of the invention. The described embodiments are only a part of, and not all, of the embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0018] One embodiment of the present invention provides an electronic anchor control method based on dual-loop active disturbance rejection control, the method comprising: Step 1: Deploy a sensor group on the top flow machine. The sensor group includes a GNSS receiver, a geomagnetic sensor, and an inertial sensor. Collect data from the sensor group on the top flow machine, perform coordinate system transformation and alignment on the sensor group data, and construct a system observation vector for state estimation. Step 2: Establish a hybrid state-space model, obtain the actual control thrust of the previous control cycle as a known input, process the system observation vector through the extended Kalman filter algorithm, perform recursive state estimation on the hybrid state-space model, separate the high-frequency motion state, and output the low-frequency position and low-frequency speed of the top flow machine at the current moment. Step 3: Using the current low-frequency position and low-frequency velocity as input, the system enters a dual-loop control architecture, which includes an outer loop and an inner loop. The outer loop, as the position loop, calculates the position deviation between the low-frequency position and the preset target anchor point, and generates target velocity commands in the north and east directions based on the position deviation. The inner loop, as the velocity loop, uses an active disturbance rejection controller to track the target velocity commands and compensate for environmental disturbances, calculates the control thrust in the north and east directions at the current moment, sends the control thrust to the actuator, and simultaneously feeds it back to Step 2 for state estimation in the next control cycle. The active disturbance rejection controller in the inner loop includes an extended state observer.

[0019] The working principle and effect of the above technical solution are as follows: First, the absolute position is obtained through a GNSS receiver, and the attitude and high-frequency motion trend are obtained through geomagnetic and inertial sensors. Since the installation positions of each sensor are different and the data types are different, the system performs coordinate system transformation and center of gravity alignment at the bottom layer, transforming the dispersed physical quantities into system observation vectors unified in the local navigation coordinate system, providing a standardized and unbiased reference data source for subsequent state estimation. In real sea conditions, the observation data collected by sensors inevitably contains a mixture of high-frequency oscillations caused by first-order waves and low-frequency drift caused by wind, waves, and currents. This invention introduces a hybrid state-space model that incorporates physical dynamics and high-frequency motion characteristics. Using the actual thrust of the previous cycle as a known derivation condition, an Extended Kalman Filter (EKF) is used for recursive "prediction-update" calculations. Essentially, it is a wave filter that, through comparison with internal mathematical models, accurately isolates high-frequency noise with no control value, reconstructs and outputs clean low-frequency position and velocity, preventing the control system from blindly responding to the high-frequency pounding of waves. After acquiring the clean low-frequency state, the system employs a spatiotemporal separation strategy: an outer loop captures position, while an inner loop captures force. The outer loop, as a slower position loop, only focuses on the geometric deviation between the current low-frequency position and the target anchor point, calculating the required target velocity command to achieve gentle trajectory planning. The inner loop, as the highest-speed velocity loop, receives the velocity command from the outer loop. The Active Disturbance Rejection Controller (ADRC) at the core of the inner loop utilizes an Extended State Observer (ESO) to treat unmodeled dynamics caused by external environmental forces such as sea winds and ocean currents, as well as changes in the ship's own mass, as a unified total disturbance for real-time online estimation, and performs feedforward compensation before converting it into thrust. The final calculated control thrust is sent to the actuators and simultaneously fed back to step two to form a complete closed-loop physical simulation link.

[0020] In one embodiment of the present invention, the process of acquiring sensor data from the top-flow machine, performing coordinate system transformation and alignment on the sensor data, and constructing a system observation vector for state estimation includes: Data from the GNSS receiver, geomagnetic sensor, and inertial sensor are collected synchronously at a preset sampling frequency. Error compensation is performed on the data from the geomagnetic sensor and inertial sensor using pre-stored calibration parameters, and interpolation and rejection processing is performed on the data from the GNSS receiver. The data from the GNSS receiver, after interpolation and elimination processing, is converted into planar position coordinates in a local navigation coordinate system with a preset anchor point as the origin. The angular velocity data from the geomagnetic sensor after error compensation and the inertial sensor are fused and calculated to obtain the real-time heading angle and turning angular velocity of the top flow machine in the local navigation coordinate system; Based on the pre-measured installation position deviation of the GNSS receiver relative to the center of gravity of the top-flow aircraft, the planar position coordinates are transformed to the center of gravity of the top-flow aircraft based on the rotation matrix and the real-time heading angle to obtain the corrected center of gravity position coordinates. The corrected center of gravity coordinates, real-time heading angle, and the ship's longitudinal velocity, lateral velocity, and yaw rate derived from the sensor array are combined in a preset order to construct the system observation vector.

[0021] In specific application scenarios, time-domain synchronization and data cleaning are crucial. Different sensors naturally have varying sampling frequencies and signal quality; for example, GNSS frequencies are low and easily obstructed, while IMU frequencies are high but suffer from integral drift. The system first performs forced time alignment using a preset reference frequency. Then, it uses pre-stored calibration parameters to eliminate IMU zero bias and hard / soft magnetic interference from the Earth's magnetic field. Interpolation algorithms fill in lost GNSS frames, and a culling algorithm removes outliers caused by multipath effects, ensuring the absolute purity of the input source. For mapping absolute coordinates to relative space, GNSS output is typically WGS-84 spherical latitude and longitude coordinates, which cannot be directly used for linear control. The system establishes a local navigation Cartesian coordinate system (NED coordinate system, NED) with a preset target anchor point as the origin, projecting the spherical coordinates into planar coordinates (x, y) in meters, greatly simplifying subsequent controller position deviation calculations. Geomagnetic sensors can provide drift-free absolute heading, but are susceptible to electromagnetic interference from ship hull metal and motors; inertial sensors (gyroscopes) offer extremely high short-term accuracy but suffer from long-term cumulative errors. The system uses fusion calculations (such as complementary filtering or attitude Kalman filtering) to output real-time heading angle ψ and bow roll rate r, achieving both extremely high short-term smoothness and long-term absolute accuracy. Since GNSS antennas are typically mounted on the mast high up on the ship, and the physical origin of the ship's motion is its center of gravity (CG), when the ship rolls or pitches, even if the CG remains stationary, the antenna at the high position will experience significant linear displacement. The system utilizes the known installation offset and combines it with the real-time heading angle to construct a rotation matrix R(ψ), which translates and rotates the antenna's measured position in the opposite direction, precisely projecting it onto the ship's CG, thus completely eliminating spurious displacements introduced by attitude changes. Finally, all corrected physical quantities are packaged into a standardized system observation vector.

[0022] One embodiment of the present invention establishes a hybrid state-space model, including: The hybrid space state model includes a 15-dimensional state vector and a set of state differential equations characterizing the dynamic properties of the state vector.

[0023] In one embodiment of the present invention, the 15-dimensional state vector is composed of a six-dimensional high-frequency motion sub-vector, a three-dimensional low-frequency position sub-vector, a three-dimensional low-frequency velocity sub-vector, and a three-dimensional environment sub-vector; the six-dimensional high-frequency motion sub-vector includes high-frequency displacements and their high-frequency velocities in the longitudinal, lateral, and heading directions; the three-dimensional low-frequency position sub-vector includes north coordinates, east coordinates, and heading angle; the three-dimensional low-frequency velocity sub-vector includes longitudinal low-frequency velocity, lateral low-frequency velocity, and heading low-frequency velocity; and the three-dimensional environment sub-vector includes longitudinal environmental force, lateral environmental force, and environmental torque.

[0024] In one embodiment of the present invention, the state differential equation is: in, This represents the trend of change of the variables in the 15-dimensional state variables at the current moment. This represents the state matrix of a high-frequency oscillating system. Represents a high-frequency motion state vector. This represents the high-frequency noise input matrix, which determines how random wave energy is converted into specific high-frequency oscillations of the ship's hull. Gaussian white noise representing high-frequency oscillations This represents the low-frequency velocity sub-vector of the ship's hull. This represents the rotation matrix that varies with the heading angle. Represents the environmental force state vector. The matrix representing the inverse of the time constant matrix of the time delay characteristic. This represents low-frequency environmental force variation noise. The matrix representing the inverse of the ship's dynamic mass matrix. This represents the control thrust input vector. denoted as low-frequency motion noise, and D represents the hull linear damping matrix.

[0025] In specific application scenarios, a 15-dimensional hybrid state-space model is constructed. This model rigorously decomposes the chaotic motion of the top-flow machine under complex sea conditions into four interconnected sub-physical systems. The specific operational logic is as follows: the first-order wave high-frequency oscillation subsystem should be based on the first line of the differential equation. In real sea conditions, wave forces mainly manifest as high-frequency reciprocating oscillations caused by first-order wave forces. This scheme utilizes a second-order linear damped oscillator model to transform the irregular high-frequency wave energy (Gaussian white noise) into a more stable and dynamic state. and input matrix The driving force is transformed into a specific six-dimensional high-frequency motion state vector. This allows the model to accurately simulate the high-frequency pounding characteristics of waves at the mathematical level, providing a theoretical basis for subsequent Kalman filtering to remove high-frequency noise. The low-frequency kinematic mapping subsystem (corresponding to the second line of the differential equation R(ψ)v) constitutes the spatial geometric link for ship attitude transformation. Through a rotation matrix R(ψ) updated in real time with the bow angle, the three-dimensional low-frequency velocity of the jacking turbine in the ship's coordinate system is represented. Seamless integration (longitudinal, lateral, and bow) yields a three-dimensional low-frequency absolute position in the geodetic coordinate system (local navigation coordinate system). Environmental slow-drift disturbance subsystem (corresponding to the third line of the differential equation). To address external environmental disturbances such as wind, ocean currents, and second-order wave drift forces, which are difficult to measure directly, this scheme breaks through the limitation of traditional models treating them as constants, and innovatively models them as first-order Gauss-Markov stochastic processes. This is achieved through the inverse of the time-delay characteristic constant matrix. With noise Driven by this, the sub-equation possesses the adaptive capability to dynamically track the force evolution trend in the real environment online. (Rigid body low-frequency dynamics subsystem (corresponding to the fourth line of the differential equation)) This term is the core of the entire structure's force distribution (following the Newton-Euler equations). It controls the thrust τ and the environmental forces transformed into the ship's coordinate system. The total external force on the ship is obtained by vector superposition of the hydrodynamic linear damping Dv; then multiplied by the inverse of the mass matrix. The low-frequency acceleration is calculated, and the low-frequency velocity v is obtained by integration, so as to realize the closed-loop physical deduction of thrust input and ship motion output.

[0026] In one embodiment of the present invention, the system observation vector is processed by an extended Kalman filter algorithm to perform recursive state estimation on the hybrid state-space model, separate the high-frequency motion state, and output the low-frequency position and low-frequency velocity of the top-flow machine at the current moment, including: The state differential equations of the hybrid state-space model are linearized by calculating the Jacobian matrix. The linearized continuous state equations are discretized using the forward Euler method to obtain the state transition matrix; The prior estimate of the state transition matrix is ​​calculated through the prediction step of the extended Kalman filter. Based on the system observation vector, the prior estimate is corrected through the update step of the extended Kalman filter, and the low-frequency position and low-frequency velocity of the top flow machine are output after filtering out the high-frequency motion state.

[0027] In a specific application scenario, a nonlinear optimal state observer based on a prediction-correction architecture was constructed, realizing accurate mapping and state reconstruction from continuous physical space to discrete digital space. The specific operating mechanism is as follows: Analytical linearization of multidimensional nonlinear manifolds (Jacobi matrix calculation): Due to the inclusion of a rotation matrix R(ψ) that varies drastically with the heading angle and complex hydrodynamic coupling terms in the aforementioned 15-dimensional mixed state differential equations, the system exhibits extremely strong nonlinearity. In this scheme, within each control cycle, the optimal state estimate at the current moment is used as the operating point. A first-order Taylor series expansion is performed on the high-dimensional nonlinear function f(x,τ,w). By solving the partial derivative matrix (i.e., the Jacobian matrix), the originally complex nonlinear surface is locally approximated as a linear tangent plane, thus satisfying the linear assumption of the Kalman filter; Digital domain mapping for microprocessor execution (forward Euler discretization): The real physical time is continuous, while the digital control system relies on discrete sampling clocks. This scheme uses a forward Euler numerical integration algorithm to slice the continuous-time domain Jacobian state-space equations along the time axis with a very small control step size (i.e., sampling period Δt). This process transforms differential equations into difference equations, generating a discrete state transition matrix that directly determines the state evolution at the next moment, thus establishing an execution channel between physical dynamics and the underlying computing power of the computer. The system first uses the discrete state transition matrix and the currently issued thrust command to blindly deduce a priori estimate of the ship's 15-dimensional state at the next moment based on pure physical laws, while simultaneously transmitting the error covariance. After obtaining the observation vector from the actual sensor system after centroid projection correction, the system calculates the deviation between the predicted and actual measured values. Using a dynamically calculated Kalman gain matrix, the prior estimate is optimally weighted and corrected to obtain the posterior estimate. In this closed-loop iteration process, since the 15-dimensional model itself already contains the mathematical characteristics of high-frequency waves, the EKF algorithm can adaptively absorb the high-frequency oscillation components in the sensor input into the high-frequency state sub-vector, thereby extracting an extremely smooth and delay-free low-frequency position and velocity of the top-flow turbine.

[0028] In one embodiment of the present invention, the position loop controller employs an improved PID algorithm, which includes an integral inverse calculation anti-saturation strategy: When the output of the PID controller is not saturated, the integral term accumulates normally based on the position deviation. When the output of the PID controller reaches the physical limit amplitude and enters the saturation state, the difference between the saturated output and the unsaturated output is calculated through the negative feedback channel. This difference is multiplied by the back-calculated gain as a dynamic compensation term and introduced to the input of the integrator, thereby proportionally reducing the integral term in reverse to suppress the integral overcharging phenomenon in real time. Specifically, the integral back-calculation anti-saturation update equation is as follows: in, This represents the output value of the effective integral term at the current moment, where t represents the current moment and s represents the historical time integration variable from 0 to t. This represents the difference between the target position of the ship and the actual position measured by the sensors. This represents the thrust value of the theoretical unsaturated thrust command. This represents the thrust value of the actual saturation thrust command. Indicates the conventional integral coefficient. This indicates the inverse gain coefficient.

[0029] In specific application scenarios, traditional PID controllers suffer from excessive accumulation of integral terms under thrust saturation conditions. That is, when the controller output thrust has not reached its physical limit (i.e., not saturated), the integrator normally accumulates the error signal weighted by the integral coefficient (K_i) of the difference between the target position and the actual position, continuously eliminating position residuals and correcting steady-state errors. When the PID output thrust reaches its physical limit and enters saturation, the continuous accumulation of errors by the conventional integrator may cause the thrust to be locked at the boundary for a long time, preventing the system from recovering quickly. To address this, this solution uses a negative feedback channel to dynamically calculate the difference between the actual saturated thrust and the theoretical unsaturated thrust. This difference is multiplied by the back-calculated gain and used as a compensation term, introduced into the integrator input. This compensation term proportionally reduces the integral term, thereby offsetting the excessive accumulation caused by saturation in real time, achieving adaptive dynamic reduction and rapid reset of the integral term.

[0030] Traditional PID controllers, when their output is physically limited, suffer from severe overcharging due to the continuous accumulation of the integral term, leading to slow system recovery and susceptibility to overshoot and oscillation. Through an integral inverse calculation mechanism, excess integral quantity can be dynamically reduced in the saturation state, enabling rapid integrator reset and effectively avoiding difficulty in reversing or control lock-up. Ensuring response speed and accuracy under extreme conditions, the integral inverse calculation channel provides adaptive error correction capability within the thrust saturation range. Even under extreme conditions (such as strong winds and waves, sudden disturbances), the controller can ensure rapid desaturation, shorten recovery time, and improve dynamic response speed and steady-state accuracy.

[0031] In one embodiment of the present invention, the improved PID algorithm further includes: in, The current time scale coefficient represents the continuous dynamic change of position error. This represents the integral coefficient at the current moment, which changes continuously and dynamically with the position error. The differential coefficients at the current moment represent the continuous dynamic changes with the position error. This represents the minimum value of a ship's restoring force. This indicates the maximum value of the ship's restoring force. This represents the maximum value of the ship's damping force (braking force). This represents the minimum value of the ship's damping force (braking force). This represents the maximum integral gain. This represents the attenuation factor of the first Gaussian curve. This represents the attenuation factor of the second Gaussian curve. This represents the decay rate of the third Gaussian curve. Indicates real-time position deviation; In one embodiment of the present invention, specifically, in, Represents the first fundamental shape factor. This represents the first disturbance sensitivity coefficient. Indicates the maximum thrust of the propulsion unit. This represents the total disturbance estimate output by the extended state observer in the inner-loop Active Disturbance Rejection Controller (ADRC).

[0032] In specific application scenarios, the proportional coefficient Integral coefficient Differential coefficients All are dynamically adjusted using a nonlinear function that varies with error: and The values ​​increase in an incremental manner as "the difference between the lower and upper bounds multiplied by 1 - Gaussian decay". That is, when the error is small, it approaches the lower bound, and when the error is large, it rises to the maximum value. This ensures that the system is sensitive and not prone to oscillation under small errors, and that the recovery and braking capabilities are rapidly enhanced under large errors, preventing the accumulation of deviations and the loss of system control. The integral gain is decreased using a Gaussian decay method. When the error is small, a large integral gain is maintained to eliminate steady-state error; when the error is large, the integral gain is significantly reduced, effectively suppressing system overshoot and oscillations that may be caused by the integral term during large-amplitude responses. and Further, the total perturbation estimate output by the extended state observer is used. For adaptive correction of independent variables; when the intensity of external disturbance increases ( (increase) The exponent decreases as the disturbance increases, making The function responds more quickly to errors, and the proportional gain is increased to improve the disturbance rejection and recovery capability. The effect increases linearly with increasing disturbance, making As the error decays faster, it further suppresses potential integral overcharge under large disturbances, preventing overshoot or even divergence in the control system under disturbed environments. Driven by both error and disturbance observations, the PID parameters achieve adaptive adjustment across all operating conditions. Regardless of whether the system is under minor disturbances or extreme disturbance conditions, the controller automatically switches to the optimal gain combination, balancing adjustment speed and system stability. This significantly enhances disturbance rejection capability and robustness. The parameter adjustment mechanism is highly sensitive to strong disturbance conditions; the proportional / derivative coefficients increase with increasing external disturbances, suppressing error propagation. The integral coefficient actively decays, preventing excessive accumulation of the integral term during large disturbances, significantly improving the ship's disturbance rejection control performance under extreme sea conditions such as wind, waves, and currents. It effectively eliminates overshoot and oscillations, ensuring high-precision positioning. The adaptive decay of the integral term, combined with the dynamic constraints of disturbance observations, greatly reduces system overshoot, maintains smooth response, and suppresses oscillations caused by sudden error changes or strong external disturbances, ensuring high accuracy and reliability of the ship positioning system under various extreme conditions.

[0033] In one embodiment of the present invention, the continuous-time update law of the extended state observer is specifically as follows: in, This indicates the low-frequency speed output in step two. This represents the observer's estimate of the low-frequency velocity. This represents the total disturbance estimate output by the observer. Indicates the bandwidth gain of the first observer. Indicates the bandwidth gain of the second observer. Indicates thrust control gain. This indicates the current control thrust.

[0034] In specific application scenarios, the Extended State Observer (ESO) adopts a state dimension expansion and prediction-correction closed-loop reconstruction mechanism. Its core logic is to unify and equate the hydrodynamic resistance, internal parameter perturbations, and external unknown environmental disturbances such as wind, waves, and currents that are difficult to model accurately in actual ship operation into a single lumped disturbance, and use it as an extended state variable of the system for real-time online estimation.

[0035] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An electronic anchor control method based on dual-loop active disturbance rejection control, characterized in that, The method includes: Step 1: Deploy a sensor group on the top flow machine. The sensor group includes a GNSS receiver, a geomagnetic sensor, and an inertial sensor. Collect data from the sensor group on the top flow machine, perform coordinate system transformation and alignment on the sensor group data, and construct a system observation vector for state estimation. Step 2: Establish a hybrid state-space model, obtain the actual control thrust of the previous control cycle as a known input, process the system observation vector through the extended Kalman filter algorithm, perform recursive state estimation on the hybrid state-space model, separate the high-frequency motion state, and output the low-frequency position and low-frequency speed of the top flow machine at the current moment. Step 3: Using the current low-frequency position and low-frequency velocity as input, the system enters a dual-loop control architecture, which includes an outer loop and an inner loop. The outer loop, as the position loop, calculates the position deviation between the low-frequency position and the preset target anchor point, and generates target velocity commands in the north and east directions based on the position deviation. The inner loop, as the velocity loop, uses an active disturbance rejection controller to track the target velocity commands and compensate for environmental disturbances, and calculates the control thrust in the north and east directions at the current moment. The active disturbance rejection controller in the inner loop includes an extended state observer. Step 4: Send the control thrust to the actuator and simultaneously feed it back to Step 2 for state estimation in the next control cycle.

2. The electronic anchor control method based on dual-loop active disturbance rejection control according to claim 1, characterized in that, The process involves collecting data from the sensor array on the top-flow machine, performing coordinate system transformation and alignment on the sensor data, and constructing a system observation vector for state estimation, including: Data from the GNSS receiver, geomagnetic sensor, and inertial sensor are collected synchronously at a preset sampling frequency. Error compensation is performed on the data from the geomagnetic sensor and inertial sensor using pre-stored calibration parameters, and interpolation and rejection processing is performed on the data from the GNSS receiver. The data from the GNSS receiver, after interpolation and elimination processing, is converted into planar position coordinates in a local navigation coordinate system with a preset anchor point as the origin. The angular velocity data from the geomagnetic sensor after error compensation and the inertial sensor are fused and calculated to obtain the real-time heading angle and turning angular velocity of the top flow machine in the local navigation coordinate system; Based on the pre-measured installation position deviation of the GNSS receiver relative to the center of gravity of the top-flow aircraft, the planar position coordinates are transformed to the center of gravity of the top-flow aircraft based on the rotation matrix and the real-time heading angle to obtain the corrected center of gravity position coordinates. The corrected center of gravity coordinates, real-time heading angle, and the ship's longitudinal velocity, lateral velocity, and yaw rate derived from the sensor array are combined in a preset order to construct the system observation vector.

3. The electronic anchor control method based on dual-loop active disturbance rejection control according to claim 1, characterized in that, The establishment of the hybrid state-space model includes: The hybrid space state model includes a 15-dimensional state vector and a set of state differential equations characterizing the dynamic properties of the state vector.

4. The electronic anchor control method based on dual-loop active disturbance rejection control according to claim 3, characterized in that, The 15-dimensional state vector consists of a six-dimensional high-frequency motion sub-vector, a three-dimensional low-frequency position sub-vector, a three-dimensional low-frequency velocity sub-vector, and a three-dimensional environment sub-vector. The six-dimensional high-frequency motion sub-vector includes high-frequency displacements and velocities in the longitudinal, lateral, and heading directions. The three-dimensional low-frequency position sub-vector includes north coordinates, east coordinates, and heading angle. The three-dimensional low-frequency velocity sub-vector includes longitudinal low-frequency velocity, lateral low-frequency velocity, and heading low-frequency velocity. The three-dimensional environment sub-vector includes longitudinal environmental force, lateral environmental force, and environmental torque.

5. The electronic anchor control method based on dual-loop active disturbance rejection control according to claim 1, characterized in that, The state differential equation is: in, This represents the trend of change of the variables in the 15-dimensional state variables at the current moment. This represents the state matrix of a high-frequency oscillating system. Represents a high-frequency motion state vector. This represents the high-frequency noise input matrix, which determines how random wave energy is converted into specific high-frequency oscillations of the ship's hull. Gaussian white noise representing high-frequency oscillations This represents the low-frequency velocity sub-vector of the ship's hull. This represents the rotation matrix that varies with the heading angle. Represents the environmental force state vector. The matrix representing the inverse of the time constant matrix of the time delay characteristic. This represents low-frequency environmental force variation noise. The matrix representing the inverse of the ship's dynamic mass matrix. This represents the control thrust input vector. denoted as low-frequency motion noise, and D represents the hull linear damping matrix.

6. The electronic anchor control method based on dual-loop active disturbance rejection control according to claim 1, characterized in that, The process of processing the system observation vector using the extended Kalman filter algorithm to perform recursive state estimation on the hybrid state-space model, separating the high-frequency motion state, and outputting the low-frequency position and low-frequency velocity of the top-flow machine at the current moment includes: The state differential equations of the hybrid state-space model are linearized by calculating the Jacobian matrix. The linearized continuous state equations are discretized using the forward Euler method to obtain the state transition matrix; The prior estimate of the state transition matrix is ​​calculated through the prediction step of the extended Kalman filter. Based on the system observation vector, the prior estimate is corrected through the update step of the extended Kalman filter, and the low-frequency position and low-frequency velocity of the top flow machine are output after filtering out the high-frequency motion state.

7. The electronic anchor control method based on dual-loop active disturbance rejection control according to claim 1, characterized in that, The position loop controller employs an improved PID algorithm, which includes an integral inverse calculation anti-saturation strategy. When the output of the PID controller is not saturated, the integral term accumulates normally based on the position deviation. When the output of the PID controller reaches the physical limit amplitude and enters the saturation state, the difference between the saturated output and the unsaturated output is calculated through the negative feedback channel, and this difference is multiplied by the back-calculated gain as a dynamic compensation term, which is introduced to the input of the integrator, thereby proportionally reducing the integral term in the reverse direction. Specifically, the integral back-calculation anti-saturation update equation is as follows: in, This represents the output value of the effective integral term at the current moment, where t represents the current moment and s represents the historical time integration variable from 0 to t. This represents the difference between the target position of the ship and the actual position measured by the sensors. This represents the thrust value of the theoretical unsaturated thrust command. This represents the thrust value of the actual saturation thrust command. Indicates the conventional integral coefficient. This indicates the inverse gain coefficient.

8. The electronic anchor control method based on dual-loop active disturbance rejection control according to claim 7, characterized in that, The improved PID algorithm also includes: in, The current time scale coefficient represents the continuous dynamic change of position error. This represents the integral coefficient at the current moment, which changes continuously and dynamically with the position error. The differential coefficients at the current moment represent the continuous dynamic changes with the position error. This represents the minimum value of a ship's restoring force. This indicates the maximum value of the ship's restoring force. This indicates the maximum value of the ship's damping force. This represents the minimum value of the ship's damping force. This represents the maximum integral gain. This represents the attenuation factor of the first Gaussian curve. This represents the attenuation factor of the second Gaussian curve. This represents the decay rate of the third Gaussian curve. This indicates the real-time position deviation.

9. The electronic anchor control method based on dual-loop active disturbance rejection control according to claim 8, characterized in that, Specifically, in, Represents the first fundamental shape factor. This represents the first disturbance sensitivity coefficient. Indicates the maximum thrust of the propulsion unit. This represents the total disturbance estimate output by the extended state observer in the inner-loop active disturbance rejection controller.

10. The electronic anchor control method based on dual-loop active disturbance rejection control according to claim 1, characterized in that, The continuous-time update law of the extended state observer is specifically as follows: in, This indicates the low-frequency speed output in step two. This represents the observer's estimate of the low-frequency velocity. This represents the total disturbance estimate output by the observer. Indicates the bandwidth gain of the first observer. Indicates the bandwidth gain of the second observer. Indicates thrust control gain. This indicates the current control thrust.

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