Model prediction control method for efficient power generation of floating eagle type wave power generation device

Abstract

The invention discloses a model prediction control method for efficient power generation of a floating eagle type wave power generation device, and belongs to the technical field of renewable energy utilization. The method comprises the following steps: firstly, establishing a kinetic model of the floating eagle-type wave power generation device and a motion model of an eagle-type wave-absorbing floater, and converting into a state space form; acquiring data through a wave test sensor to predict future wave excitation, determining an optimization scheme with maximization of average capture power as a target, applying PTO torque, motion range of the wave absorbing floater and speed constraint, and performing rolling solution to obtain optimal control torque; and finally, data are acquired through hardware such as an inertial measurement unit and a wave measurement buoy, and the PTO system is driven to apply control torque after the data are processed by the main controller.

Description

Technical Field

[0001] This invention relates to the field of renewable energy utilization technology, specifically to a model predictive control method for efficient power generation of a floating eagle-shaped wave energy power generation device. Background Technology

[0002] Against the backdrop of the continuous development of renewable energy utilization, the development of marine renewable energy has become an important direction. Floating eagle wave energy power generation devices have become a hot topic in the field of marine wave energy utilization research due to their advantages of simple structure and easy maintenance.

[0003] However, existing eagle-type wave energy generation devices have significant limitations in their energy capture methods, primarily relying on passive eagle-type wave-absorbing buoys and PTO damping to capture wave energy. Once the passive eagle-type wave-absorbing buoy is excited by waves and begins to move, its motion is entirely determined by the wave action, making it impossible to flexibly adjust the buoy's spatial position and attitude angle according to real-time changes in wave conditions. In actual sea conditions, the wave environment is complex and variable, with a wide wave spectrum and dynamic frequency changes over time. This means that passively damped eagle-type wave-absorbing buoys can only maintain good energy capture efficiency at specific optimal operating points; once deviating from this operating point, the energy capture efficiency drops significantly.

[0004] Meanwhile, eagle-type wave absorbers are typically installed on semi-submersible platforms. These platforms experience swaying and rolling motions under wave action, which are directly transmitted to the eagle-type wave absorbers, significantly altering their actual position and pitch angle. This, in turn, affects the interaction between the eagle-type wave absorber and the waves, ultimately reducing its energy capture efficiency. Furthermore, most existing floating eagle-type wave energy generation devices can only passively follow the waves at the same frequency, without implementing active control strategies for the eagle-type wave absorber's attitude. This means that outside of the optimal sea state for which the device operates, its energy capture efficiency remains low under other wave conditions, preventing the exploitation of additional energy gain potential. Summary of the Invention

[0005] The purpose of this invention is to provide a model predictive control method for efficient power generation of a floating eagle wave energy generator, which solves the problem that existing eagle wave energy generators rely on passive wave-absorbing floats and cannot adjust their position and attitude in real time according to wave conditions.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A model predictive control method for efficient power generation of a floating eagle-shaped wave energy power generation device includes the following steps:

[0008] Establish the dynamic model of the floating eagle-type wave energy generation device, the motion model of the wave-absorbing float, and the state-space equations;

[0009] Collect historical wave data and the status data of the Eagle Wave Energy Power Generation Device, and use the historical wave data to predict future wave excitation to obtain future wave excitation data;

[0010] Based on the state-space equation and the future wave excitation data, the optimization objective and physical constraints are determined, and the cost function corresponding to the optimization objective and the physical constraints are obtained.

[0011] Based on the cost function, the physical constraints, the current system state, and the future wave excitation data, a rolling optimization solution is performed to obtain the optimal control sequence;

[0012] The first control torque of the optimal control sequence is applied to the PTO system, and the current system state is updated according to the real-time state data of the Eagle Wave Energy Generator to achieve model predictive control of the device.

[0013] In one possible implementation, the dynamic model of the floating eagle-type wave energy generator, the motion model of the wave-absorbing buoy, and the state-space equations include:

[0014] A 6-DOF dynamic model of the Eagle-type wave energy generator is established, and a motion model of the Eagle-type wave-absorbing float is established through rigid body kinematic equations. The dynamic model and the motion model of the wave-absorbing float are converted into the state-space equations to obtain the dynamic model, the motion model of the wave-absorbing float, and the state-space equations.

[0015] In one possible implementation, the dynamic model is:

[0016]

[0017] In the formula: η(t) represents the 6-DOF motion of the device, including displacement and angle; M is the mass matrix; A c0 For the additional mass matrix; K f (t-τ) is the radiation damping matrix; K s K is the still water stiffness matrix; m F is the stiffness matrix of the mooring system. exc (t) represents the wave excitation force; F pto (t) represents the PTO and active control force matrix.

[0018] In one possible implementation, the motion model of the eagle-type wave-absorbing float is as follows:

[0019]

[0020] In the formula: X h (t) represents the coordinates of the eagle-type microwave absorber buoy relative to the center of gravity of the device; θ h (t) represents the angular displacement of the eagle-type absorbing float; R(θ(t)) is the coordinate transformation matrix from the center of gravity of the device to the position of the eagle-type absorbing float; r0 is the coordinate offset of the eagle-type absorbing float at the initial moment; θ h The initial angular displacement of the eagle-type wave-absorbing buoy is given.

[0021] In one possible implementation, the state-space equation is:

[0022]

[0023] In the formula: The state vector includes angular displacement and angular velocity; u = M PTO is the control input; w is the wave excitation input; A, B, and E are the system matrices.

[0024] In one possible implementation, the optimization objective is determined, including:

[0025] With maximizing the average captured power within the prediction time domain as the optimization objective, a cost function is constructed with the total captured energy as the objective. This cost function is the negative of the sum of the products of the instantaneous power at each moment in the prediction time domain and the control step size.

[0026] The goal of the control is to maximize the average capture power over the prediction time domain Np. The cost function J is designed as the total capture energy:

[0027]

[0028] In the formula: P pto (k+i|k)=u(k+i)·θ h (t)(k+i|k) is the instantaneous power predicted at the current time k at the future time k+i; θ h (t) represents the angular displacement of the eagle-type wave-absorbing buoy; N c To control the time domain, N c ≤N p Δt is the control step size.

[0029] In one possible implementation, determining the physical constraints includes: setting a PTO torque constraint, a hawk-type buoy motion range constraint, and a buoy motion speed constraint. The PTO torque constraint defines the upper and lower limits of the PTO torque, the motion range constraint defines the upper and lower limits of the buoy pitch angle, and the motion speed constraint defines the upper and lower limits of the buoy motion speed.

[0030] The PTO torque constraint is as follows:

[0031] u min ≤u(k+i)≤u max

[0032] The range of motion constraint is as follows:

[0033] θ h,min ≤θ h (k+i|k)≤θ h,max

[0034] The motion speed constraint is:

[0035]

[0036] One possible implementation involves performing a rolling optimization solution, including:

[0037] An inertial measurement unit is installed on the eagle-type wave-absorbing buoy to collect the attitude angle, angular velocity and acceleration of the wave-absorbing buoy. A wave measurement buoy is deployed in front of the device to collect wave data. A torque sensor and an encoder are installed inside the PTO system to collect the actual output control torque and the relative rotational speed of the wave-absorbing buoy, respectively.

[0038] The Kalman filter algorithm is used to fuse the collected data to estimate the current system state and predict the future wave force sequence;

[0039] Based on the state-space equation, the cost function and the physical constraints are transformed into a quadratic programming optimization problem. Solving the quadratic programming optimization problem yields the optimal control sequence.

[0040] In one possible implementation, the first control torque of the optimal control sequence is applied to the PTO system, and the current system state is updated based on the real-time status data of the Eagle Wave Power Generation Device, including:

[0041] The main controller receives the optimal control sequence and sends the instruction corresponding to the first control torque to the underlying driver of the PTO system.

[0042] The underlying driver applies the first control torque at the hinge of the wave-absorbing float according to the instruction. When the sea state characteristics change, it updates the dynamic model and the state space equation based on the real-time state data of the device, adjusts the optimization objective and the physical constraints, and performs rolling optimization again to update the optimal control sequence and the current system state.

[0043] Compared with the prior art, the advantages of this invention are as follows:

[0044] ① Regarding improving energy capture efficiency, this invention first collects current wave data in real time and predicts wave information for a future period based on this data. Then, relying on model predictive control methods and combining the dynamic characteristics of the device, it calculates the optimal PTO control torque. After transmitting this optimal PTO control torque to the device's control execution unit, it can drive the eagle-type wave-absorbing float to adjust its motion state in advance, enabling it to achieve optimal phase matching with the wave excitation force over a wider range of wave frequencies, thereby achieving an approximate resonance effect and ultimately significantly improving the average power generation of the device in irregular wave environments.

[0045] ② Regarding ensuring the safe operation of the device, this invention specifically adds physical constraints during the implementation of the MPC algorithm. Physical constraint parameters related to the safe operation of the device, such as the range of motion, speed, and PTO torque of the eagle-type radar absorber buoy, are directly incorporated into the solution process of the control optimization problem. When the device encounters extreme sea conditions, such as giant waves, the controller will automatically prioritize meeting safety constraints and initiate corresponding control strategy calculations. For example, by applying a large damping force to the PTO system, it can suppress the violent movement of the eagle-type radar absorber buoy, preventing it from colliding with the limit block or causing overload of the PTO system.

[0046] ③ In terms of adapting to complex sea conditions, this invention relies on a continuously updated system model during operation. This model constantly adjusts parameters based on real-time operating data to ensure accuracy. Simultaneously, it incorporates real-time prediction data of the marine environment to form an adaptive control mechanism. When sea state characteristics change, such as significant fluctuations in wave frequency or height, the control strategy automatically adjusts parameters and execution logic to ensure the device maintains the highest energy capture state under the current sea state, thus demonstrating strong environmental adaptability.

[0047] ④ Regarding the structure of the protection device, compared to the problem of traditional locking control methods easily generating huge impact loads, the control torque obtained by the optimization algorithm in this invention has continuous and smooth characteristics. The continuous and smooth control torque can avoid causing severe impacts to the mechanical structure of the device, thereby effectively reducing the accumulation of fatigue damage to key components such as hinges, supports, and hydraulic cylinders, further improving the operational reliability of the device, and enhancing the economic efficiency of the device from a long-term use perspective. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic flowchart of the model predictive control method for efficient power generation of the floating eagle wave energy power generation device in an embodiment of the present invention. Detailed Implementation

[0050] 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 this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0051] Example:

[0052] It should be noted that the terms "comprising" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0053] Figure 1 This is a flowchart illustrating the model predictive control method for high-efficiency power generation of the floating eagle-shaped wave energy power generation device in an embodiment of the present invention. See also... Figure 1 This invention provides a model predictive control method for high-efficiency power generation of a floating eagle-type wave energy power generation device, comprising the following steps:

[0054] Step 1: Establish the dynamic model of the floating eagle wave energy power generation device, the motion model of the wave-absorbing float, and the state-space equations.

[0055] In this step, the dynamic model of the floating eagle-type wave energy generator, the motion model of the wave-absorbing buoy, and the state-space equations are established, including:

[0056] A 6-DOF dynamic model of the Eagle-type wave energy generator is established, and a motion model of the Eagle-type wave-absorbing float is established through rigid body kinematic equations. The dynamic model and the motion model of the wave-absorbing float are converted into the state-space equations to obtain the dynamic model, the motion model of the wave-absorbing float, and the state-space equations.

[0057] Furthermore, the dynamic model is as follows:

[0058]

[0059] In the formula: η(t) represents the 6-DOF motion of the device, including displacement and angle; M is the mass matrix; A c0 For the additional mass matrix; Kf (t-τ) is the radiation damping matrix; K s K is the still water stiffness matrix; m F is the stiffness matrix of the mooring system. exc (t) represents the wave excitation force; F pto (t) represents the PTO and active control force matrix.

[0060] Furthermore, the motion model of the eagle-type wave-absorbing float is as follows:

[0061]

[0062] In the formula: X h (t) represents the coordinates of the eagle-type microwave absorber buoy relative to the center of gravity of the device; θ h (t) represents the angular displacement of the eagle-type absorbing float; R(θ(t)) is the coordinate transformation matrix from the center of gravity of the device to the position of the eagle-type absorbing float; r0 is the coordinate offset of the eagle-type absorbing float at the initial moment; θ h The initial angular displacement of the eagle-type wave-absorbing buoy is given.

[0063] Furthermore, the state-space equation is:

[0064]

[0065] In the formula: The state vector includes angular displacement and angular velocity; u = M PTO is the control input; w is the wave excitation input; A, B, and E are the system matrices.

[0066] Step 2: Collect historical wave data and the status data of the Eagle Wave Energy Power Generation Device, and use the historical wave data to predict future wave excitation to obtain future wave excitation data.

[0067] In this step, historical wave data is collected by sensors, including using an upstream wave height gauge or pressure sensor to collect the historical wave data, and using neural networks or machine learning methods to process the historical wave data to obtain the future wave excitation data.

[0068] Step 3: Based on the state-space equation and the future wave excitation data, determine the optimization objective and physical constraints, and obtain the cost function corresponding to the optimization objective and the physical constraints.

[0069] In this step, the optimization objective is determined, including:

[0070] With maximizing the average captured power within the prediction time domain as the optimization objective, a cost function is constructed with the total captured energy as the objective. This cost function is the negative of the sum of the products of the instantaneous power at each moment in the prediction time domain and the control step size.

[0071] The goal of the control is to maximize the average capture power over the prediction time domain Np. The cost function J is designed as the total capture energy:

[0072]

[0073] In the formula: P pto (k+i|k)=u(k+i)·θ h (t)(k+i|k) is the instantaneous power predicted at the current time k at the future time k+i; θ h (t) represents the angular displacement of the eagle-type wave-absorbing buoy; N c To control the time domain, N c ≤N p Δt is the control step size.

[0074] Furthermore, determining the physical constraints includes:

[0075] The following constraints are set: PTO torque constraint, eagle-type wave-absorbing float motion range constraint, and wave-absorbing float motion speed constraint. The PTO torque constraint limits the upper and lower limits of the PTO torque, the motion range constraint limits the upper and lower limits of the wave-absorbing float pitch angle, and the motion speed constraint limits the upper and lower limits of the wave-absorbing float motion speed.

[0076] The PTO torque constraint is as follows:

[0077] u min ≤u(k+i)≤u max

[0078] The range of motion constraint is as follows:

[0079] θ h,min ≤θ h (k+i|k)≤θ h,max

[0080] The motion speed constraint is:

[0081]

[0082] Step 4: Based on the cost function, the physical constraints, the current system state, and the future wave excitation data, perform rolling optimization to obtain the optimal control sequence.

[0083] In this step, a rolling optimization solution is performed, including:

[0084] An inertial measurement unit is installed on the eagle-type wave-absorbing buoy to collect the attitude angle, angular velocity and acceleration of the wave-absorbing buoy. A wave measurement buoy is deployed in front of the device to collect wave data. A torque sensor and an encoder are installed inside the PTO system to collect the actual output control torque and the relative rotational speed of the wave-absorbing buoy, respectively.

[0085] The Kalman filter algorithm is used to fuse the collected data to estimate the current system state and predict the future wave force sequence;

[0086] Based on the state-space equation, the cost function and the physical constraints are transformed into a quadratic programming optimization problem. Solving the quadratic programming optimization problem yields the optimal control sequence.

[0087] Step 5: Apply the first control torque of the optimal control sequence to the PTO system, and update the current system state according to the real-time state data of the Eagle Wave Energy Generator to achieve model predictive control of the device.

[0088] In this step, the first control torque of the optimal control sequence is applied to the PTO system, and the current system state is updated based on the real-time status data of the Eagle Wave Energy Generator, including:

[0089] The main controller receives the optimal control sequence and sends the instruction corresponding to the first control torque to the underlying driver of the PTO system.

[0090] The underlying driver applies the first control torque at the hinge of the wave-absorbing float according to the instruction. When the sea state characteristics change, it updates the dynamic model and the state space equation based on the real-time state data of the device, adjusts the optimization objective and the physical constraints, and performs rolling optimization again to update the optimal control sequence and the current system state.

[0091] As a preferred example, this embodiment of the invention provides a model predictive control method for high-efficiency power generation of a floating eagle-type wave energy power generation device, comprising the following steps:

[0092] 1. Establish a dynamic model of the floating eagle wave energy generation device to accurately describe the motion of the eagle wave-absorbing float under the action of waves and its interaction with the PTO system.

[0093] (1) Dynamic model of floating eagle wave energy generation device

[0094]

[0095] Where: η(t): the 6-DOF motion of the device, including displacement (sway, pitch, heave) and angle (roll, pitch, yaw); M: mass matrix; A c0 Additional mass matrix K f (t-τ): Radiation damping matrix; K s : Static stiffness matrix; K m : Stiffness matrix of the mooring system; F exc (t): Wave excitation force; F pto (t): PTO and active control force matrix.

[0096] (2) Motion modeling of the eagle-type wave-absorbing buoy

[0097] From the kinematic equations of rigid bodies, the motion of the eagle-type wave-absorbing float can be obtained from the motion of the device:

[0098]

[0099] Where: X h (t): Coordinates of the eagle-type radar absorber buoy relative to the center of gravity of the device; θ h (t): Angular displacement of the eagle-type absorbing buoy; R(θ(t)): Coordinate transformation matrix from the center of gravity of the device to the position of the eagle-type absorbing buoy; r0: Coordinate offset of the eagle-type absorbing buoy at the initial moment; θ h : Angular displacement of the eagle-type wave absorber float at the initial moment. (3) State-space equation of floating eagle-type wave energy generator

[0100] To facilitate control design, the dynamic model is transformed into a state-space form:

[0101]

[0102] in: State vector, including angular displacement and angular velocity; u = M PTO : Control input; w: Wave excitation input; A, B, E: System matrix, derived from the dynamic equations of the linearized dynamic model of the floating eagle wave energy generator.

[0103] The model incorporates nonlinear factors (such as viscous damping) to improve accuracy while maintaining computational efficiency to meet real-time control requirements.

[0104] 2. Implementation of Model Predictive Control Method

[0105] Model predictive control (MPC) optimizes the PTO torque to maximize energy capture by predicting the future behavior of the device and wave excitation. The implementation of MPC includes the following steps:

[0106] (1) Wave prediction

[0107] Historical wave data is collected using upstream wave testing sensors (such as wave height meters or pressure sensors), and future wave excitation is predicted using neural networks or other machine learning methods.

[0108] (2) Determine the optimization objective

[0109] The goal of the control is to maximize the average capture power over the prediction time domain Np. The cost function J is designed as the total capture energy:

[0110]

[0111] P pto (k+i|k)=u(k+i)·θ h (t)(k+i|k) is the instantaneous power predicted at the current time k at the future time k+i; θ h (t): Angular displacement of the eagle-type wave-absorbing buoy; N c It is the control time domain (N) c ≤N p ); Δt is the control step size.

[0112] (3) Physical constraints

[0113] To protect equipment security and considering hardware physical limitations, the following constraints must be imposed:

[0114] PTO torque constraint: The total torque that a PTO can provide is limited.

[0115] u min ≤u(k+i)≤u max

[0116] Hawk-type wave-absorbing buoy range of motion constraint: The pitch angle of the Hawk-type wave-absorbing buoy cannot exceed the limit allowed by its mechanical structure.

[0117] θ h,min ≤θ h (k+i|k)≤θ h,max

[0118] Eagle-type wave-absorbing buoy speed constraint: Limit the speed of the eagle-type wave-absorbing buoy to reduce impact.

[0119]

[0120] Rolling optimization solution: At the beginning of each control cycle (time k): measure or estimate the current system state z(k); predict the future wave force sequence d(k),…,d(k+N) p-1); Based on the state-space model, solve the constrained quadratic programming optimization problem to obtain the optimal control sequence U = [u^(k), u^(k+1), ..., u^(k+N_c-1)]. Only the first element u^(k) of the optimal control sequence is applied to the PTO system as the actual control torque at the current time. The above steps are repeated at the next time k+1. This rolling execution process allows the control to continuously adapt to changes in the system and environment.

[0121] 3. Hardware implementation of MPC control for floating eagle-type wave energy generator.

[0122] An inertial measurement unit (IMU) is installed on the eagle-type wave-absorbing buoy to measure the eagle-type wave-absorbing buoy's attitude angle, angular velocity, and acceleration in real time; a wave measurement buoy is deployed at a certain distance in front of the device to provide input for the wave excitation force prediction model; a torque sensor and encoder are installed inside the PTO system to measure the actual output control torque and the relative rotational speed of the eagle-type wave-absorbing buoy.

[0123] The main controller receives real-time data from the control program above, including: a. a state estimation algorithm (such as Kalman filtering) that fuses multi-sensor data to obtain the most accurate system state z(k); b. a wave excitation force prediction module that predicts future wave force d(k) based on wave measurement data; and c. an MPC optimization solver that calculates the optimal control torque at the current moment.

[0124] The main controller sends the calculated optimal torque command to the underlying drive of the PTO system (e.g., the servo valve controller of the hydraulic system or the frequency converter of the motor) to accurately apply the target control torque at the hinge of the eagle-type absorbing float.

[0125] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0126] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A model predictive control method for efficient power generation of a floating eagle wave energy converter, characterized by, The method comprises the following steps: A dynamic model of the floating eagle wave energy device, a motion model of the wave-absorbing float and a state space equation are established; Historical wave data and state data of the eagle wave energy device are collected, and future wave excitation data is obtained by predicting future wave excitation using the historical wave data; An optimization objective and physical constraint conditions are determined according to the state space equation and the future wave excitation data, and a cost function corresponding to the optimization objective and the physical constraint conditions are obtained; Rolling optimization is performed based on the cost function, the physical constraint conditions, a current system state and the future wave excitation data to obtain an optimal control sequence; A first control torque of the optimal control sequence is applied to a PTO system, and the current system state is updated according to real-time state data of the eagle wave energy device to realize model predictive control of the device.

2. The model predictive control method for efficient power generation of the floating eagle wave energy power plant according to claim 1, characterized in that, The establishment of the dynamic model of the floating eagle wave energy device, the motion model of the wave-absorbing float and the state space equation comprises: A 6-DOF dynamic model of the eagle wave energy device is established, a motion model of the eagle wave-absorbing float is established through rigid body kinematics equations, the dynamic model and the motion model of the wave-absorbing float are converted into the state space equation, and the dynamic model, the motion model of the wave-absorbing float and the state space equation are obtained.

3. The model predictive control method for efficient power generation of the floating eagle wave energy power plant according to claim 2, characterized in that, The dynamic model is: where: η(t) is the 6-DOF motion of the device, including displacement and angle; M is the mass matrix; A c0 is the added mass matrix; K f (t - τ) is the radiation damping matrix; K s is the hydrostatic stiffness matrix; K m is the mooring system stiffness matrix; F exc (t) is the wave excitation force; F pto (t) is the PTO and active control force matrix.

4. The model predictive control method for efficient power generation of the floating eagle wave energy power plant according to claim 2, characterized in that, The motion model of the eagle wave-absorbing float is: wherein: X h (t) is the coordinate of the hawk wave-absorbing float relative to the center of gravity of the device; θ h (t) is the angular displacement of the hawk wave-absorbing float; R(θ(t)) is the coordinate conversion matrix from the center of gravity of the device to the position of the hawk wave-absorbing float; r0 is the coordinate offset of the hawk wave-absorbing float at the initial moment; θ h is the angular displacement of the hawk wave-absorbing float at the initial moment.

5. The model predictive control method for efficient power generation of the floating eagle wave energy power plant according to claim 3, characterized in that, The state space equation is: where: is the state vector, including angular displacement, angular velocity; u = M PTO is the control input; w is the wave excitation input; A, B, E are system matrices.

6. The model predictive control method for efficient power generation of the floating eagle wave energy device according to claim 1, characterized in that, The optimization objective is determined, comprising: The optimization objective is to maximize the average captured power in the prediction time domain, a cost function with the total captured energy as the objective is constructed, the cost function is the negative value of the sum of the instantaneous power at each time in the prediction time domain multiplied by the control step, and wherein The objective of control is to maximize the average captured power in the prediction time domain Np. The cost function J is designed as the total captured energy: where: P pto (k+i|k) = u(k+i) · θ h (t) (k+i|k) is the predicted instantaneous power at future time k+i at current time k; θ h (t) is the angular displacement of the swallow-shaped wave-absorbing float; N c is the control time domain, N c ≤ N p ; Δt is the control step.

7. The model predictive control method for efficient power generation of the floating eagle wave energy power plant according to claim 1, characterized in that, The physical constraint conditions are determined, comprising: PTO torque constraints, eagle wave-absorbing float motion range constraints and wave-absorbing float motion speed constraints are set, the PTO torque constraints define the upper and lower limits of the PTO torque, the motion range constraints define the upper and lower limits of the pitch angle of the wave-absorbing float, and the motion speed constraints define the upper and lower limits of the motion speed of the wave-absorbing float, wherein The PTO torque constraints are: u min ≤ u(k + i) ≤ u max The motion range constraints are: θ h,min ≤θ h (k+i|k)≤θ h,max The motion speed constraints are:

8. The model predictive control method for efficient power generation of the floating eagle wave energy power plant according to claim 1, characterized in that, The rolling optimization is solved, comprising: An inertial measurement unit is installed on the eagle wave-absorbing float to collect the attitude angle, angular velocity and acceleration of the wave-absorbing float, wave measuring buoys are arranged in front of the device to collect wave data, and a torque sensor and an encoder are installed inside the PTO system to collect the actual output control torque and the relative rotation speed of the wave-absorbing float, respectively; Kalman filtering algorithm is used to fuse the collected data to estimate the current system state and predict the future wave force sequence; The cost function and the physical constraint conditions are converted into a quadratic programming optimization problem based on the state space equation, the quadratic programming optimization problem is solved, and the optimal control sequence is obtained.

9. The model predictive control method for efficient power generation of the floating eagle wave energy power plant according to claim 1, characterized in that, applying the first control torque of the optimal control sequence to the PTO system, and updating the current system state according to real-time state data of the Eagle wave energy device, including: the main controller receives the optimal control sequence, sends an instruction corresponding to the first control torque to the bottom driver of the PTO system; the bottom driver applies the first control torque at the wave absorber hinge according to the instruction, and when the sea state characteristics change, updates the dynamic model and the state space equation based on the real-time state data of the device, adjusts the optimization objective and the physical constraint condition, and re-performs rolling optimization solution to update the optimal control sequence and the current system state.

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