A method and system for monitoring and adaptive control of the navigation state of an electric hydrofoil

Abstract

The present application belongs to the technical field of automatic control of ships, and in particular to a kind of electric hydrofoil ship navigation state monitoring and adaptive control method and system, comprising the following steps: multi-source data acquisition, real-time acquisition of inertial navigation data of hydrofoil ship, hydrofoil fluid dynamics data, water surface relative distance data and real-time working condition data of main power system;Multi-source data fusion and attitude solution, based on the inertial navigation data, and using the hydrofoil fluid dynamics data and the water surface relative distance data for compensation correction, the high-precision real-time attitude information of the hydrofoil ship is solved;Through the fusion of inertial measurement unit, current meter, water surface sensor, propeller working parameter and other multi-source sensing information, combined with adaptive data fusion algorithm and multi-modal state evaluation model, the overall monitoring and hierarchical control of hydrofoil ship navigation state are realized, and the stability, safety and energy efficiency of hydrofoil ship under full speed domain and multiple sea conditions are improved.

Description

Technical Field

[0001] This invention relates to the field of ship automatic control technology, specifically to a method and system for monitoring and adaptively controlling the navigation status of an electric hydrofoil. Background Technology

[0002] A hydrofoil is a high-speed vessel that uses underwater fins to generate lift, lifting the hull off the water's surface for propulsion. Electric hydrofoils, in particular, are partially or completely submerged underwater. Navigating in waves requires an active control system to adjust the hydrofoil's angle of attack or center of gravity in real time to maintain flight. This places extremely high demands on the control system's response speed, stability, and robustness.

[0003] The current hydrofoil boat control system mainly suffers from the following technical problems: First, the sensor information sources are relatively limited, resulting in incomplete state perception. Existing control schemes mainly rely on gyroscopes and accelerometers to collect attitude data, and do not make sufficient use of key information such as thruster operating parameters and changes in water level, leading to an incomplete perception of the navigation environment by the system.

[0004] Second, the attitude adjustment methods are limited, and the control robustness needs to be improved. Existing technologies mostly rely on adjusting the hydrofoil's angle of attack as the sole means of attitude adjustment. When the hydrofoil adjustment mechanism experiences response delay or control saturation, the system's stability margin decreases significantly.

[0005] Third, the navigation state classification assessment is insufficient, and the control strategy lacks adaptability. Existing control algorithms lack a fine-grained classification mechanism between state assessment and control output, making it difficult to adopt differentiated control strategies according to different navigation stages and risk levels, resulting in less than ideal control performance under certain operating conditions.

[0006] Therefore, there is an urgent need to provide a hydrofoil boat navigation status monitoring and control method that can integrate multi-source sensor information, adopt multi-actuator collaborative control, and has hierarchical adaptive adjustment capabilities to solve the above problems. Summary of the Invention

[0007] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a method and system for monitoring and adaptively controlling the navigation status of an electric hydrofoil. By integrating multi-source sensor information such as inertial measurement unit (IMU), current meter, water surface sensor, and propeller operating parameters, and combining adaptive data fusion algorithms and multimodal state evaluation models, it achieves comprehensive monitoring and hierarchical control of the hydrofoil's navigation status, improving the stability, safety, and energy efficiency of the hydrofoil under full speed range and multiple sea state conditions, and solving the problems mentioned in the background technology.

[0008] (II) Technical Solution To achieve the above objectives, the present invention specifically adopts the following technical solution: A method for monitoring and adaptively controlling the navigation status of an electric hydrofoil includes the following steps: S1: Multi-source data acquisition, real-time acquisition of inertial navigation data, hydrofoil hydrodynamic data, water surface relative distance data, and real-time operating condition data of the main propulsion system of the hydrofoil; S2: Multi-source data fusion and attitude calculation: Based on the inertial navigation data, and using the hydrofoil hydrodynamic data and the relative distance data to the water surface for compensation and correction, the high-precision real-time attitude information of the hydrofoil is calculated. S3: Navigation status classification assessment, compares the relative distance data on the water surface with preset multi-level height thresholds, and combines the high-precision real-time attitude information with the real-time operating condition data to assess the current navigation status of the hydrofoil as one of multiple levels, including drainage navigation status, takeoff transition status, wing navigation stability status and abnormal status. S4: Multi-actuator adaptive cooperative control, generating and executing cooperative control commands for at least two different types of actuators, including a main propulsion unit and a ballast tank system, based on the assessed navigation status level.

[0009] Furthermore, in step S1: The inertial navigation data includes at least the roll angular velocity, pitch angular velocity, and yaw angular velocity collected by a gyroscope, and the triaxial acceleration collected by an accelerometer. The hydrofoil hydrodynamic data includes at least the water flow velocity near the hydrofoil, collected by a flow meter; The relative distance data to the water surface includes the hydrofoil's flight altitude above the water surface detected by water surface sensors deployed at different locations on the hull, and the water surface waveform is obtained by inverting multiple flight altitudes. The real-time operating data includes at least the thruster speed, thrust output value, and power consumption data.

[0010] Furthermore, in step S2, the multi-source data fusion and attitude calculation specifically employ an extended Kalman filter algorithm or a complementary filter algorithm; wherein, the accelerometer data is used to correct the gyroscope's angle integral drift under static or low dynamic conditions, and the process noise covariance matrix or fusion coefficient of the filtering algorithm is dynamically adjusted using the water flow velocity in the hydrofoil hydrodynamic data and the flight altitude change rate in the water surface relative distance data, in order to suppress attitude calculation errors under high dynamic maneuvers.

[0011] Furthermore, in step S3, the navigation status classification assessment specifically includes: When the flight altitude is less than the minimum safe altitude threshold and the thruster speed is less than the first speed threshold, it is determined to be in a drainage navigation state. When the flight altitude is between the minimum safe altitude threshold and the cruise wing altitude threshold, and the thruster speed is in the rising range, it is determined to be a takeoff transition state; When the flight altitude is between the cruise wing altitude threshold and the maximum safe altitude threshold, and the absolute values ​​of the pitch angle and roll angle are less than the corresponding stable attitude thresholds, it is determined to be a stable wing state. An abnormal state is determined when the flight altitude is continuously lower than the minimum safe altitude threshold or higher than the maximum safe altitude threshold, or when the absolute value of the pitch angle / roll angle exceeds its corresponding safe attitude threshold.

[0012] Furthermore, in step S3, a fuzzy logic reasoning system or a hysteresis comparison mechanism is introduced for state evaluation; the fuzzy logic reasoning system takes the flight altitude, flight altitude change rate, pitch angle and airspeed as input variables and outputs the membership degree of each state level; the hysteresis comparison mechanism is used to introduce time delay or threshold hysteresis when switching states to avoid frequent state level jumps caused by signal noise.

[0013] Further, in step S4, the multi-actuator adaptive cooperative control specifically includes: When in the aforementioned drainage navigation state, a thrust increase command is generated with the aim of increasing the speed. When in the takeoff transition state, a command is generated simultaneously to increase the thrust of the propeller according to the preset acceleration curve, and a command is generated to drive the ballast tank system to distribute water according to the preset takeoff ballast scheme, so as to assist the hull to leave the water smoothly. When in the stable wing state, it enters the steady-state fine-tuning mode and generates a joint fine-tuning command with the ballast water tank system as the main component and the main propulsion thruster as the auxiliary component. By adjusting the water distribution of each ballast water tank, the position of the ship's center of gravity and / or buoyancy are changed, and the thruster thrust is adjusted according to the pitch angle deviation and flight altitude deviation. When the abnormal state is in effect, a safe landing command is generated. The command first controls the main propulsion unit to reduce thrust according to a preset deceleration curve. Once the safety conditions are met, the command then controls the ballast tank system to return to its initial balanced configuration.

[0014] Furthermore, in the steady-state fine-tuning mode under the stable wing-like condition, the control law of the ballast water tank system is an incremental PID control law: ; in, —Water volume adjustment amount of the i-th ballast tank; —The deviation between the real-time attitude angle and the target attitude angle; —Rate of change of deviation; —Time integral of the deviation; —Proportional, derivative, and integral control parameters; Furthermore, when the roll angle is detected to exceed the preset range, the water volume of the ballast tanks on both sides of the lateral side is adjusted first to compensate for the roll; when the pitch angle is detected to exceed the preset range, the water volume of the longitudinal ballast tanks at the front and rear is adjusted first to compensate for the pitch.

[0015] Furthermore, in step S4, under the stable wing-mounted state, a joint optimization step of the power system and ballast tank system is also included: Construct a comprehensive objective function that includes attitude error terms, thruster power increment terms, and absolute values ​​of ballast tank regulation water volume terms: ;in: —The squared term of attitude error; —Thruster power increment; —Absolute value of ballast water tank regulating water volume ; —Weighting coefficients, satisfying ; Under constraints including attitude safety boundary, flight altitude boundary, thruster physical limit and ballast tank capacity boundary, solve for the optimal control allocation that minimizes the comprehensive objective function J, so as to determine the optimal thrust output of the main propulsion thruster and the optimal water volume adjustment of each ballast tank.

[0016] An electric hydrofoil's navigation status monitoring and adaptive control system, used to execute the method, the system comprising: The multi-source sensing unit includes an inertial measurement unit for acquiring inertial navigation data, a flow meter for acquiring hydrofoil hydrodynamic data, and a water surface sensor for acquiring relative distance data between the water surface and the surface. The data acquisition interface is connected to the controller of the main propulsion thruster to acquire real-time operating data of the thruster. The data fusion and attitude calculation module receives data from the multi-source sensing unit and the data acquisition interface, and outputs high-precision real-time attitude information. The navigation status classification and assessment module is connected to the data fusion and attitude calculation module and the data acquisition interface. Based on preset multi-level thresholds and logical rules, it assesses the current state of the hydrofoil as one of multiple preset navigation status levels. The multi-actuator collaborative control module generates collaborative control commands for the main propulsion propulsion system and the ballast tank system based on the status level output by the navigation status classification and evaluation module. The main propulsion thruster receives and executes thrust control commands from the multi-actuator cooperative control module; The ballast water tank system includes multiple distributed ballast water tanks, water pumps and valve groups, and receives and executes water distribution instructions from the multi-actuator collaborative control module.

[0017] Furthermore, the water surface sensor includes multiple ultrasonic sensors or capacitive water level sensors deployed at the front and rear of the hydrofoil, and the detection data is used to calculate the flight altitude and retrieve the real-time waveform of the water surface. The ballast water tank system adopts a nine-grid or matrix layout, and each ballast water tank is independently equipped with a water volume sensor and a liquid level controller to achieve precise water volume adjustment and center of gravity control. The navigation status classification assessment module has a built-in fuzzy logic inferencer or hysteresis comparator to achieve smooth transition of status levels or noise-resistant stability judgment. The multi-actuator cooperative control module further includes a joint optimization solver, which is used to solve online for a control command allocation scheme that optimizes the overall energy consumption and attitude error of the system under wing-stability conditions, based on a preset comprehensive objective function and constraints.

[0018] (III) Beneficial Effects Compared with the prior art, the present invention provides a method and system for monitoring and adaptively controlling the navigation status of an electric hydrofoil, which has the following advantages: 1. Comprehensive information fusion and accurate state perception: This invention comprehensively collects information from multiple sources, including gyroscopes, accelerometers, current meters, and surface sensors. For the first time, it incorporates propeller operating data (speed, thrust, power) into the navigation state assessment system, achieving comprehensive perception of the hydrofoil's navigation state. Data fusion via extended Kalman filtering / complementary filtering algorithms effectively eliminates measurement errors and drift problems inherent in single sensors, providing a high-quality data foundation for precise control.

[0019] 2. Multimodal state hierarchical evaluation and adaptive control strategy: This invention subdivides the navigation state into four levels: drainage navigation, takeoff transition, wing stability, and anomaly. Differentiated control strategies are adopted for different levels, achieving adaptive control throughout the entire process from takeoff to wing stability and landing. The introduction of fuzzy logic evaluation method makes the determination of state boundaries smoother, avoiding control jumps caused by hard threshold switching.

[0020] 3. Multi-actuator collaborative control enhances system robustness. This invention uses the ballast water tank system and the propulsion system as collaborative actuators, forming a redundant control architecture. In the stable wing-wing state, a dual-channel control mode is employed, with ballast water tank fine-tuning as the primary method and propulsion fine-tuning as a secondary method. During takeoff transition, the two systems work together to effectively reduce the impact of single actuator failure or saturation on system stability, significantly enhancing the system's fault tolerance and robustness.

[0021] 4. High control precision and good energy efficiency: This invention uses an industrial control computer as a unified control platform, with strong real-time control algorithms. PID control parameters can be pre-calibrated based on the dynamic model, resulting in high control precision. The joint optimization steps of the power system and ballast tank system enable the system to maintain attitude stability while also controlling energy consumption, thus extending the range of the electric hydrofoil. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the process system of the present invention; Figure 2 This is an overall flowchart of the electric hydrofoil boat navigation status monitoring and control method of the present invention; Figure 3 This is a flowchart of the multi-source sensor data fusion and attitude calculation process of the present invention; Figure 4 This is a schematic diagram of the navigation status classification assessment decision tree of the present invention. Figure 5 This is a schematic diagram of the multi-actuator cooperative control strategy of the present invention; Figure 6 This is a block diagram of the steady-state fine-tuning mode PID control of the present invention; Figure 7 This is a flowchart illustrating the joint optimization of the power system and ballast water tank system of the present invention. Detailed Implementation

[0023] The technical solutions of 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.

[0024] Example like Figure 1 As shown in the figure, an embodiment of the present invention proposes a method for monitoring and adaptively controlling the navigation status of an electric hydrofoil, which includes the following steps: S1: Multi-source data acquisition, real-time acquisition of inertial navigation data, hydrofoil hydrodynamic data, relative distance data to the water surface, and real-time operating data of the main propulsion system of the hydrofoil; in step S1: The inertial navigation data includes at least the roll angular velocity, pitch angular velocity, and yaw angular velocity collected by a gyroscope, and the triaxial acceleration collected by an accelerometer. The hydrofoil hydrodynamic data includes at least the water flow velocity near the hydrofoil, collected by a flow meter; The relative distance data to the water surface includes the hydrofoil's flight altitude above the water surface detected by water surface sensors deployed at different locations on the hull, and the water surface waveform is obtained by inverting multiple flight altitudes. The real-time operating data includes at least the thruster speed, thrust output value, and power consumption data.

[0025] S2: Multi-source data fusion and attitude calculation. Based on the inertial navigation data, and using the hydrofoil hydrodynamic data and the relative distance data to the water surface for compensation and correction, high-precision real-time attitude information of the hydrofoil is calculated. In step S2, the multi-source data fusion and attitude calculation specifically adopts an extended Kalman filter algorithm or a complementary filter algorithm. Specifically, the accelerometer data is used to correct the angle integral drift of the gyroscope under static or low dynamic conditions, and the process noise covariance matrix or fusion coefficient of the filtering algorithm is dynamically adjusted using the water flow velocity in the hydrofoil hydrodynamic data and the flight altitude change rate in the relative distance data to the water surface, so as to suppress the attitude calculation error under high dynamic maneuvering.

[0026] S3: Navigation status classification assessment, comparing the relative distance data to the water surface with preset multi-level height thresholds, and combining the high-precision real-time attitude information with the real-time operating condition data, assessing the current navigation status of the hydrofoil as one of several levels, including displacement navigation status, takeoff transition status, wing-borne stable status, and abnormal status; Step S3, the navigation status classification assessment specifically includes: When the flight altitude is less than the minimum safe altitude threshold and the thruster speed is less than the first speed threshold, it is determined to be in a drainage navigation state. When the flight altitude is between the minimum safe altitude threshold and the cruise wing altitude threshold, and the thruster speed is in the rising range, it is determined to be a takeoff transition state; When the flight altitude is between the cruise wing altitude threshold and the maximum safe altitude threshold, and the absolute values ​​of the pitch angle and roll angle are less than the corresponding stable attitude thresholds, it is determined to be a stable wing state. An abnormal state is determined when the flight altitude is continuously lower than the minimum safe altitude threshold or higher than the maximum safe altitude threshold, or when the absolute value of the pitch angle / roll angle exceeds its corresponding safe attitude threshold.

[0027] In step S3, a fuzzy logic reasoning system or a hysteresis comparison mechanism is further introduced for state evaluation. The fuzzy logic reasoning system takes the flight altitude, flight altitude change rate, pitch angle and airspeed as input variables and outputs the membership degree of each state level. The hysteresis comparison mechanism is used to introduce time delay or threshold hysteresis when switching states to avoid frequent state level jumps caused by signal noise.

[0028] S4: Multi-actuator adaptive cooperative control, generating and executing cooperative control commands for at least two different types of actuators, including a main propulsion unit and a ballast tank system, based on the evaluated navigation state level. Specifically, step S4 includes: When in the aforementioned drainage navigation state, a thrust increase command is generated with the aim of increasing the speed. When in the takeoff transition state, a command is generated simultaneously to increase the thrust of the propeller according to the preset acceleration curve, and a command is generated to drive the ballast tank system to distribute water according to the preset takeoff ballast scheme, so as to assist the hull to leave the water smoothly. When in the stable wing state, it enters the steady-state fine-tuning mode and generates a joint fine-tuning command with the ballast water tank system as the main component and the main propulsion thruster as the auxiliary component. By adjusting the water distribution of each ballast water tank, the position of the ship's center of gravity and / or buoyancy are changed, and the thruster thrust is adjusted according to the pitch angle deviation and flight altitude deviation. When the abnormal state is in effect, a safe landing command is generated. The command first controls the main propulsion unit to reduce thrust according to a preset deceleration curve. Once the safety conditions are met, the command then controls the ballast tank system to return to its initial balanced configuration.

[0029] In the steady-state fine-tuning mode under the stable wing-like condition, the control law of the ballast water tank system is an incremental PID control law: ; in, —Water volume adjustment amount of the i-th ballast tank; —The deviation between the real-time attitude angle and the target attitude angle; —Rate of change of deviation; —Time integral of the deviation; —Proportional, derivative, and integral control parameters; Furthermore, when the roll angle is detected to exceed the preset range, the water volume of the ballast tanks on both sides of the lateral side is adjusted first to compensate for the roll; when the pitch angle is detected to exceed the preset range, the water volume of the longitudinal ballast tanks at the front and rear is adjusted first to compensate for the pitch.

[0030] In step S4, under the stable wing condition, a joint optimization step of the power system and ballast tank system is also included: Construct a comprehensive objective function that includes attitude error terms, thruster power increment terms, and absolute values ​​of ballast tank regulation water volume terms: ;in: —The squared term of attitude error; —Thruster power increment; —Absolute value of ballast water tank regulating water volume ; —Weighting coefficients, satisfying ; Under constraints including attitude safety boundary, flight altitude boundary, thruster physical limit and ballast tank capacity boundary, solve for the optimal control allocation that minimizes the comprehensive objective function J, so as to determine the optimal thrust output of the main propulsion thruster and the optimal water volume adjustment of each ballast tank.

[0031] An electric hydrofoil's navigation status monitoring and adaptive control system, used to execute the method, the system comprising: The multi-source sensing unit includes an inertial measurement unit for acquiring inertial navigation data, a flow meter for acquiring hydrofoil hydrodynamic data, and a water surface sensor for acquiring relative distance data between the water surface and the surface. The data acquisition interface is connected to the controller of the main propulsion thruster to acquire real-time operating data of the thruster. The data fusion and attitude calculation module receives data from the multi-source sensing unit and the data acquisition interface, and outputs high-precision real-time attitude information. The navigation status classification and assessment module is connected to the data fusion and attitude calculation module and the data acquisition interface. Based on preset multi-level thresholds and logical rules, it assesses the current state of the hydrofoil as one of multiple preset navigation status levels. The multi-actuator collaborative control module generates collaborative control commands for the main propulsion propulsion system and the ballast tank system based on the status level output by the navigation status classification and evaluation module. The main propulsion thruster receives and executes thrust control commands from the multi-actuator cooperative control module; The ballast water tank system includes multiple distributed ballast water tanks, water pumps and valve groups, and receives and executes water distribution instructions from the multi-actuator collaborative control module.

[0032] like Figure 1As shown, in some embodiments, the water surface sensor includes multiple ultrasonic sensors or capacitive water level sensors deployed at the front and rear of the hydrofoil, whose detection data is used to calculate the flight altitude and invert the real-time waveform of the water surface.

[0033] like Figure 1 As shown, in some embodiments, the ballast water tank system adopts a nine-square grid or matrix layout, and each ballast water tank is independently equipped with a water volume sensor and a liquid level controller to achieve precise water volume adjustment and center of gravity control.

[0034] like Figure 1 As shown, in some embodiments, the navigation state classification assessment module has a built-in fuzzy logic inferencer or hysteresis comparator to achieve smooth transition of state levels or noise-resistant stability judgment.

[0035] like Figure 1 As shown, in some embodiments, the multi-actuator cooperative control module further includes a joint optimization solver, which is used to solve online for a control command allocation scheme that optimizes the overall energy consumption and attitude error of the system under wing-stability conditions, based on a preset comprehensive objective function and constraints.

[0036] The electric hydrofoil includes a hull, a main propulsion unit, a ballast tank system, and an onboard industrial control computer, which integrates the electric hydrofoil navigation status monitoring and adaptive control system described above.

[0037] Example 1: Please refer to Figure 2-7 As shown, this embodiment provides a method for monitoring and adaptively controlling the navigation status of an electric hydrofoil, applicable to semi-submersible or fully submersible electric hydrofoil boats. The hydrofoil boat is equipped with: at least one electric propulsion unit (such as a propeller or waterjet propulsion unit), a ballast water tank system with a nine-grid layout (including nine independently controlled ballast water tanks, water pumps, valve groups, and level sensors), and an industrial control computer platform serving as the core control unit. The specific steps are as follows: Step S101: Multi-source sensor data acquisition and preprocessing: This step acquires the following navigation status parameters in real time at a sampling frequency of 50 Hz (which can be adjusted between 20 Hz and 100 Hz according to actual needs): Gyroscope: Roll angular velocity Pitch angular velocity Bow roll rate ; Accelerometer: Longitudinal acceleration lateral acceleration Vertical acceleration ; Flow meter: Water flow velocity in front of the hydrofoil Unit: m / s Water surface sensor: Employs ultrasonic or capacitive water level sensors to detect the flight altitude of the hydrofoil above the water surface. (Unit: m). Simultaneously, the real-time waveform of the water surface (including wave height, wavelength, and waveform slope) is reconstructed by using the difference in flight altitude detected by two surface sensors deployed at the front and rear of the hull.

[0038] The raw data from the above sensors are denoised by median filtering or low-pass filtering and normalized to remove outliers that are significantly outside the physical range (e.g., data with a flight altitude greater than 5 m or less than -0.5 m).

[0039] Step S102: Thruster Data Acquisition: Read the operating data from the thruster controller in real time via CAN bus or other industrial fieldbuses, including: thruster speed. (Unit: rpm); Thruster thrust (Unit: N; if a thrust sensor is configured, the value can be obtained directly; otherwise, it can be estimated using a speed-thrust model); Thruster power consumption (Unit: W). The above data is sent to the industrial control computer at a refresh rate of no less than 20 Hz.

[0040] Step S103: Attitude calculation based on extended Kalman filter: The extended Kalman filter (EKF) algorithm is used to fuse the data from the gyroscope and accelerometer in step S101 to eliminate the integral drift of the gyroscope and the dynamic noise interference of the accelerometer, and calculate high-precision real-time attitude information.

[0041] The system state vector is established as follows: ; in: These are the roll angle, pitch angle, and bow angle, respectively. These represent the corresponding angular velocities. The system observation vector is: ; The prediction and update steps of the extended Kalman filter are as follows: Prediction Phase: Update the predicted state value based on the integral of the angular velocity output by the gyroscope.

[0042] in The system state transition function, The sampling time interval is 0.02s in this embodiment.

[0043] Update phase: using accelerometer observations The predicted values ​​are corrected, and the Kalman gain is calculated. And update the state estimate: ; in The observation function maps the state vector to the acceleration observation space.

[0044] At low speeds or when stationary, accelerometer measurements primarily reflect the gravitational acceleration component and are used to correct for gyroscope angular drift. During high-speed wing-wing navigation, due to the significant acceleration caused by the hull motion, this embodiment utilizes the adaptive characteristics of the extended Kalman filter, adjusting the readings based on the water flow velocity. Dynamically adjust the observation noise covariance matrix based on the rate of change of flight altitude This suppresses the interference of dynamic acceleration on tilt angle calculation.

[0045] As an alternative, a complementary filtering algorithm can be used for attitude calculation, and its formula is as follows:

[0046] in This is the integral value of the gyroscope angle. To calculate the angle using the accelerometer, These are complementary filter coefficients, with a range of values. By adjusting It can balance the long-term accuracy of the gyroscope with the dynamic response of the accelerometer.

[0047] Step S104: Navigation Status Classification Assessment: The attitude angles calculated in step S103 are... Flight altitude collected in step S101 and water flow velocity The thruster rotation speed obtained in step S102 They are input into the navigation status assessment module.

[0048] This embodiment classifies the hydrofoil's navigation status into four levels: displacement navigation status, takeoff transition status, wing-based stable status, and abnormal status. The evaluation rules are shown in the table below:

[0049] Typical parameter values ​​are: h_min = 0.2 m (minimum safe flight altitude), h_cruise = 0.5 m (cruise wing altitude), h_max = 0.8 m (maximum safe flight altitude), and N_takeoff is the minimum thruster speed required for takeoff (calibrated by actual ship tests). ,

[0050] To avoid frequent changes in state level due to transient noise, a hysteresis comparison mechanism is introduced: when the state switches from stable to abnormal, the flight altitude must be continuously lower than h_min for more than 0.5 seconds.

[0051] Step S105: Generation of Multi-Actuator Cooperative Control Commands: Based on the navigation status level assessed in step S104, corresponding cooperative control commands are generated and sent to the main propulsion unit and the ballast tank system, respectively. The specific strategy is as follows.

[0052] (1) Control strategy for displacement navigation state, objective: to increase speed so that the hull meets the takeoff conditions.

[0053] The control output module generates a thrust increase command for the thruster, with a preset acceleration curve (e.g., linear increase, acceleration 0.1 m / s²). 2 Gradually increase the thruster speed to In this state, the ballast water tank system maintains its initial balanced configuration (the water volume in each of the nine compartments is evenly distributed, with each compartment containing approximately 50% of the total capacity) and does not actively adjust.

[0054] (2) Control strategy for takeoff transition state, objective: to help the hull leave the water smoothly and suppress attitude fluctuations during takeoff.

[0055] The control output module generates two control commands simultaneously: Thruster control command: Gradually increase thrust according to the preset takeoff thrust curve, the formula is:

[0056] in Initial thrust , The thrust rate of climb (e.g., 200 N / s). For time.

[0057] Ballast tank adjustment command: According to the pre-set takeoff ballast scheme, a certain amount of ballast liquid (such as 30% of the total adjustment capacity) is transferred from the center tank to the aft tank, so that the ship's center of gravity moves aft by about 0.05 m-0.10 m, increases the bow pitch angle by about 2°-3°, and assists in lifting the hull.

[0058] (3) Control strategy for stable wing flight state, objective: maintain stable wing flight and compensate for attitude deviation in real time.

[0059] The control output module enters steady-state fine-tuning mode, and the control law adopts incremental PID (the actual formula is positional PID, but the expression in the briefing material is used in this embodiment):

[0060] in The water volume adjustment amount for the i-th ballast tank (unit: kg or L). The deviation between the real-time attitude angle and the target attitude angle For a pitch angle of , The PID control parameters were simulated and tuned using a six-degree-of-freedom dynamic model of the ship's hull, and further optimized in actual ship tests. The specific adjustment strategy is as follows: When the absolute value of the roll angle exceeds the allowable tolerance, At that time, calculate the required roll compensation moment: ;in This is the tilt ratio factor (unit: ). Then, the difference in regulating water volume between the two transverse water tanks is determined based on the compensating torque:

[0061] in The density of water (1000 kg / m³) 3 ), The acceleration due to gravity (9.8 m / s²) 2 ), The center-to-center distance between the two side tanks is given in meters. The water pumps are controlled to drain water from the higher tank and fill the lower tank, ensuring the water volume difference between the two sides is [value missing]. .

[0062] When the absolute value of the pitch angle exceeds the allowable tolerance, At that time, calculate the required pitch compensation moment: ,in The inclination ratio factor Then determine the difference in regulating water volume between the front and rear water tanks.

[0063] in The center-to-center distance between the longitudinal fore and aft ballast tanks (unit: m). Water is transferred to the bow or stern ballast tanks as needed. When attitude deviations are within permissible limits, the ballast tank system maintains the current water level configuration, with only minor thrust adjustments made by the propulsion system (e.g., by fine-tuning the propulsion speed to compensate for remaining attitude deviations).

[0064] (4) Abnormal state control strategy, objective: to ensure navigation safety and guide the ship to land safely.

[0065] When the status assessment indicates an anomaly, the control output module immediately issues an audible and visual alarm signal and initiates a safe landing procedure, which consists of three stages: Phase 1: The thruster gradually reduces thrust output according to a preset deceleration curve, with a thrust reduction rate of... Set to no greater than the takeoff thrust rate of climb This is 1.5 times the thrust, to avoid a sudden drop in thrust causing severe pitching of the hull. The deceleration curve can be linear: until the thrust drops to the initial thrust. Phase Two: When the thruster speed drops to... At this point, the ballast tank system gradually returns to its initial balanced configuration, that is, the ballast scheme of the takeoff phase is executed in reverse, and the liquid transferred to the aft tanks is pumped back to the center tank, so that the ship's center of gravity returns to neutral.

[0066] Phase Three: When the flight altitude Furthermore, when the thruster speed is below the low-speed threshold (e.g., 300 rpm), it is confirmed that the hull has safely landed on the water surface, completing the landing procedure.

[0067] 6.2 Joint Optimization of Power System and Ballast Tank System (Preferred Implementation) In wing flight mode, in order to balance attitude stability and energy consumption control, the present invention further provides a joint optimization method for the power system and the ballast tank system.

[0068] Define the joint optimization objective function: ; in: , where is the square of the attitude composite error; , which is the deviation between the current power of the thruster and the reference cruise power (unit: W). , which is the sum of the absolute values ​​of the regulating water volume of each ballast tank (unit: kg or L). For the weighting coefficients, satisfying Typical value ; Rated power of the thruster (unit: W). Total regulating capacity of the ballast water tank system (unit: kg or L).

[0069] Under the following constraints, the optimal allocation of thruster thrust and water volume in each tank is determined by solving the J minimization problem: Attitude constraints: ; Flight altitude constraints: ; Thruster constraints: ; Hydrofoil angle of attack control: (If the angle of attack of the hydrofoil is adjustable); Water tank capacity constraints: ,in Let i be the current water volume of the i-th water tank. Set its maximum capacity.

[0070] The joint optimal control quantity obtained by solving As the final control command output, it achieves the comprehensive optimization of energy consumption and adjustment range while ensuring attitude stability.

[0071] 6.3 System Composition: Corresponding to the above method, this embodiment also provides an electric hydrofoil boat navigation status monitoring and adaptive control system, including: Multi-source sensing unit: includes a gyroscope, accelerometer, current meter, and water surface sensor (one at the front and one at the back); Data acquisition interface: connected to the thruster controller via CAN bus; Data fusion and attitude calculation module: Runs extended Kalman filter or complementary filter algorithm; Navigation status classification and assessment module: built-in four-state decision tree and optional fuzzy logic inferencer; Multi-actuator cooperative control module: includes PID controller, safe landing logic and joint optimization solver; Main propulsion unit: receives thrust commands; Ballast tank system: Nine-grid layout, including water pumps, valve groups and level sensors.

[0072] All of the above modules are integrated into the shipborne industrial control computer and run in a 50 Hz control cycle.

[0073] 6.4 Experimental Verification Results: In a real-world test on a lake, the electric hydrofoil using the method described in this embodiment successfully achieved a smooth transition from displacement mode to wing-mounted mode in sea state 3 (wave height 0.5 m to 1.0 m). Takeoff time was reduced by approximately 15%, the standard deviation of roll angle in wing-mounted mode was less than 0.8°, and the standard deviation of pitch angle was less than 1.2°. Propulsion energy consumption was reduced by approximately 12% compared to the system without joint optimization. In abnormal state simulation tests, the safe landing procedure could safely land the vessel from wing-mounted mode onto the water surface within 5 seconds without any attitude instability.

[0074] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring and adaptively controlling the navigation status of an electric hydrofoil, characterized in that, Includes the following steps: S1: Multi-source data acquisition, real-time acquisition of inertial navigation data, hydrofoil hydrodynamic data, water surface relative distance data, and real-time operating condition data of the main propulsion system of the hydrofoil; S2: Multi-source data fusion and attitude calculation: Based on the inertial navigation data, and using the hydrofoil hydrodynamic data and the relative distance data to the water surface for compensation and correction, the high-precision real-time attitude information of the hydrofoil is calculated. S3: Navigation status classification assessment, compares the relative distance data on the water surface with preset multi-level height thresholds, and combines the high-precision real-time attitude information with the real-time operating condition data to assess the current navigation status of the hydrofoil as one of multiple levels, including drainage navigation status, takeoff transition status, wing navigation stability status and abnormal status. S4: Multi-actuator adaptive cooperative control, generating and executing cooperative control commands for at least two different types of actuators, including a main propulsion unit and a ballast tank system, based on the assessed navigation status level.

2. The method for monitoring and adaptively controlling the navigation status of an electric hydrofoil boat according to claim 1, characterized in that: In step S1: The inertial navigation data includes at least the roll angular velocity, pitch angular velocity, and yaw angular velocity collected by a gyroscope, and the triaxial acceleration collected by an accelerometer. The hydrofoil hydrodynamic data includes at least the water flow velocity near the hydrofoil, collected by a flow meter; The relative distance data to the water surface includes the hydrofoil's flight altitude above the water surface detected by water surface sensors deployed at different locations on the hull, and the water surface waveform is obtained by inverting multiple flight altitudes. The real-time operating data includes at least the thruster speed, thrust output value, and power consumption data.

3. The method for monitoring and adaptively controlling the navigation status of an electric hydrofoil boat according to claim 1, characterized in that: In step S2, the multi-source data fusion and attitude calculation specifically employ an extended Kalman filter algorithm or a complementary filter algorithm. Specifically, the accelerometer data is used to correct the gyroscope's angle integral drift under static or low dynamic conditions, and the process noise covariance matrix or fusion coefficient of the filtering algorithm is dynamically adjusted using the water flow velocity in the hydrofoil hydrodynamic data and the flight altitude change rate in the water surface relative distance data, in order to suppress attitude calculation errors under high dynamic maneuvers.

4. The method for monitoring and adaptively controlling the navigation status of an electric hydrofoil boat according to claim 1, characterized in that: In step S3, the navigation status classification assessment specifically includes: When the flight altitude is less than the minimum safe altitude threshold and the thruster speed is less than the first speed threshold, it is determined to be in a drainage navigation state. When the flight altitude is between the minimum safe altitude threshold and the cruise wing altitude threshold, and the thruster speed is in the rising range, it is determined to be a takeoff transition state; When the flight altitude is between the cruise wing altitude threshold and the maximum safe altitude threshold, and the absolute values ​​of the pitch angle and roll angle are less than the corresponding stable attitude thresholds, it is determined to be a stable wing state. An abnormal state is determined when the flight altitude is continuously lower than the minimum safe altitude threshold or higher than the maximum safe altitude threshold, or when the absolute value of the pitch angle / roll angle exceeds its corresponding safe attitude threshold.

5. The method for monitoring and adaptively controlling the navigation status of an electric hydrofoil boat according to claim 4, characterized in that: In step S3, a fuzzy logic reasoning system or a hysteresis comparison mechanism is further introduced for state evaluation. The fuzzy logic reasoning system takes the flight altitude, flight altitude change rate, pitch angle and airspeed as input variables and outputs the membership degree of each state level. The hysteresis comparison mechanism is used to introduce time delay or threshold hysteresis when switching states to avoid frequent state level jumps caused by signal noise.

6. The method for monitoring and adaptively controlling the navigation status of an electric hydrofoil boat according to claim 1, characterized in that: In step S4, the multi-actuator adaptive cooperative control specifically includes: When in the aforementioned drainage navigation state, a thrust increase command is generated with the aim of increasing the speed. When in the takeoff transition state, a command is generated simultaneously to increase the thrust of the propeller according to the preset acceleration curve, and a command is generated to drive the ballast tank system to distribute water according to the preset takeoff ballast scheme, so as to assist the hull to leave the water smoothly. When in the stable wing state, it enters the steady-state fine-tuning mode and generates a joint fine-tuning command with the ballast water tank system as the main component and the main propulsion thruster as the auxiliary component. By adjusting the water distribution of each ballast water tank, the position of the ship's center of gravity and / or buoyancy are changed, and the thruster thrust is adjusted according to the pitch angle deviation and flight altitude deviation. When the abnormal state is in effect, a safe landing command is generated. The command first controls the main propulsion unit to reduce thrust according to a preset deceleration curve. Once the safety conditions are met, the command then controls the ballast tank system to return to its initial balanced configuration.

7. The method for monitoring and adaptively controlling the navigation status of an electric hydrofoil boat according to claim 6, characterized in that: In the steady-state fine-tuning mode under the stable wing-like condition, the control law of the ballast water tank system is an incremental PID control law: ; in, —Water volume adjustment amount of the i-th ballast tank; —The deviation between the real-time attitude angle and the target attitude angle; —Rate of change of deviation; —Time integral of the deviation; —Proportional, derivative, and integral control parameters; Furthermore, when the roll angle is detected to exceed the preset range, the water volume of the ballast tanks on both sides of the lateral side is adjusted first to compensate for the roll; when the pitch angle is detected to exceed the preset range, the water volume of the longitudinal ballast tanks at the front and rear is adjusted first to compensate for the pitch.

8. The method for monitoring and adaptively controlling the navigation status of an electric hydrofoil boat according to claim 1, characterized in that: In step S4, under the stable wing condition, a joint optimization step of the power system and ballast tank system is also included: Construct a comprehensive objective function that includes attitude error terms, thruster power increment terms, and absolute values ​​of ballast tank regulation water volume terms: ;in: —The squared term of attitude error; —Thruster power increment; —Absolute value of ballast water tank regulating water volume ; —Weighting coefficients, satisfying ; Under constraints including attitude safety boundary, flight altitude boundary, thruster physical limit and ballast tank capacity boundary, solve for the optimal control allocation that minimizes the comprehensive objective function J, so as to determine the optimal thrust output of the main propulsion thruster and the optimal water volume adjustment of each ballast tank.

9. A navigation status monitoring and adaptive control system for an electric hydrofoil, characterized in that, The system for performing the method according to any one of claims 1 to 8 comprises: The multi-source sensing unit includes an inertial measurement unit for acquiring inertial navigation data, a flow meter for acquiring hydrofoil hydrodynamic data, and a water surface sensor for acquiring relative distance data between the water surface and the surface. The data acquisition interface is connected to the controller of the main propulsion thruster to acquire real-time operating data of the thruster. The data fusion and attitude calculation module receives data from the multi-source sensing unit and the data acquisition interface, and outputs high-precision real-time attitude information. The navigation status classification and assessment module is connected to the data fusion and attitude calculation module and the data acquisition interface. Based on preset multi-level thresholds and logical rules, it assesses the current state of the hydrofoil as one of multiple preset navigation status levels. The multi-actuator collaborative control module generates collaborative control commands for the main propulsion propulsion system and the ballast tank system based on the status level output by the navigation status classification and evaluation module. The main propulsion thruster receives and executes thrust control commands from the multi-actuator cooperative control module; The ballast water tank system includes multiple distributed ballast water tanks, water pumps and valve groups, and receives and executes water distribution instructions from the multi-actuator collaborative control module.

10. The electric hydrofoil boat navigation status monitoring and adaptive control system according to claim 9, characterized in that: The water surface sensor includes multiple ultrasonic sensors or capacitive water level sensors deployed at the front and rear of the hydrofoil, and its detection data is used to calculate the flight altitude and invert the real-time waveform of the water surface. The ballast water tank system adopts a nine-grid or matrix layout, and each ballast water tank is independently equipped with a water volume sensor and a liquid level controller to achieve precise water volume adjustment and center of gravity control. The navigation status classification assessment module has a built-in fuzzy logic inferencer or hysteresis comparator to achieve smooth transition of status levels or noise-resistant stability judgment. The multi-actuator cooperative control module further includes a joint optimization solver, which is used to solve online for a control command allocation scheme that optimizes the overall energy consumption and attitude error of the system under wing-stability conditions, based on a preset comprehensive objective function and constraints.

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