Wind wave energy multi-degree-of-freedom broadband control method based on pilot frequency coupling

Abstract

The invention belongs to the technical field of comprehensive utilization of ocean renewable energy sources, and particularly relates to a pilot frequency coupling-based wind wave energy multi-degree-of-freedom broadband control method, which comprises the following steps of: performing frequency domain identification on wind wave environment characteristics, and constructing a wind wave pilot frequency energy coupling model; based on the motion response characteristics of the multi-degree-of-freedom floating platform, designing a pilot frequency energy regulation and conversion mechanism; a full-working-condition dynamic optimization control strategy is adopted, and cooperative operation and energy flow self-adaptive distribution of the wind energy conversion unit and the wave energy conversion unit are achieved. Aiming at the significant difference and relevance of wind energy and wave energy in frequency characteristics, time scale and spatial distribution, a pilot frequency wind wave coupling dynamic model is established, and broadband capture of low-frequency wind-induced and high-frequency wave-induced coupling energy is realized under complex sea conditions through multi-degree-of-freedom motion decoupling and state observation, so that the wind-induced and high-frequency wave-induced coupling energy is obtained. The energy utilization rate, the attitude stability and the structural safety of the system are effectively improved, and good robustness and engineering adaptability are achieved.

Description

Technical Field

[0001] This invention belongs to the field of marine renewable energy comprehensive utilization technology, and in particular, it is a multi-degree-of-freedom broadband control method for wind and wave energy based on heterogeneous frequency coupling. Background Technology

[0002] With the continuous growth in demand for marine renewable energy development, wind and wave energy have become important directions for the comprehensive utilization of marine energy due to their abundant resources and strong complementarity. Existing wind-wave combined systems typically couple floating wind power generation systems with wave energy conversion units to achieve simultaneous utilization of wind and wave energy. However, traditional wind-wave combined energy harvesting devices still have significant shortcomings in terms of structural layout, energy capture mechanisms, and control methods, making it difficult to meet the demand for efficient and stable power generation under complex sea conditions.

[0003] Traditional wind-wave combined systems often employ single-degree-of-freedom vibration energy capture mechanisms (such as heave or pitch) in their wave energy capture modules, failing to fully utilize the multi-degree-of-freedom coupled motion energy induced by waves, thus limiting the energy acquisition range and conversion efficiency. Furthermore, traditional wind-wave combined systems are typically located on or near the water surface, making them susceptible to wave impacts and extreme sea conditions. This results in complex structural stresses, high operational risks, and high maintenance costs. In harsh environments, they often require shutdown for safety reasons, affecting the continuity of power generation.

[0004] Existing wind and wave combined energy harvesting systems mostly adopt passive or independent control methods, failing to establish a broadband collaborative control system and a dynamic optimization mechanism for all operating conditions oriented towards inter-frequency energy coupling. This makes it difficult to simultaneously consider energy capture efficiency, attitude stability, and structural safety, resulting in poor adaptive and robust performance of the system under complex sea conditions.

[0005] The applicant's earlier Chinese invention patent application CN120351093A discloses a suspended floating wind-wave combined generator set and a coordinated control method. The combined generator set includes a semi-submersible floating platform. A wind power generation device is installed on the upper part of the semi-submersible floating platform. The semi-submersible floating platform is anchored by three sets of anchor chains. Three power generation sleeves are hinged inside the semi-submersible floating platform. The lower end of each power generation sleeve is respectively installed on the upper end of a variable-length damping diagonal strut. The lower end of the damping diagonal strut is hinged to the suspended hammer through a universal joint. The power generation sleeves, damping diagonal struts and suspended hammer together form an inverted triangular pyramid-shaped wave energy generation device. Both the wind power generation device and the wave energy generation device are controlled by the control system. This patent application employs a layout of "semi-submersible platform + three sets of variable-length damping braces connecting the suspension weights + power generation sleeves." Wave energy is primarily obtained through the axial expansion and contraction of the three braces. While wind and wave energy control is described as coordinated, it lacks a joint modeling of wind speed disturbances and wave spectra across different frequencies. Furthermore, it fails to provide a unified expression for the coupled dynamics and frequency-dependent damping and stiffness of the multi-degree-of-freedom (6DOF platform + suspension weights + PTO stroke) system. The control side relies mainly on local and independent adjustments, lacking a state machine for full-condition, constrained optimization control and protection. Under complex sea conditions, it is difficult to simultaneously address attitude and mooring load suppression while simultaneously capturing wide-bandwidth energy.

[0006] Therefore, there is an urgent need for a comprehensive collaborative control method that can be used in different frequency wind and wave environments and take into account multi-degree-of-freedom response, so as to achieve efficient, stable and intelligent energy harvesting of wind and wave energy combined devices. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-degree-of-freedom broadband control method for wind and wave energy based on heterogeneous frequency coupling. This method can achieve broadband capture of low-frequency wind-induced and high-frequency wave-induced coupled energy under complex sea conditions, effectively improving the system's energy utilization, attitude stability, and structural safety, and possessing good robustness and engineering adaptability.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A multi-degree-of-freedom broadband control method for wind and wave energy based on heterogeneous frequency coupling includes the following steps: S1. Establish a wind-wave heterogeneous frequency energy coupling model By constructing a wind-wave heterofrequency energy coupling model in the frequency domain, the energy correlation between wind speed disturbances and ocean waves at different frequency scales is uniformly described. This wind-wave heterofrequency energy coupling model uses the joint wind-wave heterofrequency spectral density function as its core mathematical representation to characterize the statistical properties and energy transfer mechanisms of wind and wave energy under heterofrequency conditions.

[0009] The joint spectral density function of wind and waves at different frequencies is defined as: ,in: For the combined frequency spectral density of wind and waves; ω w Let be the angular frequency of the wind speed disturbance. ω p Let be the angular frequency of the ocean waves; the two are independent frequency variables. S U ( ω w () represents the wind speed perturbation spectral density function based on the standard wind speed spectral model; S η ( ω p () represents the sea surface displacement / wave height spectral density function based on the standard wave spectral model; ψ ( ω w ,ω p ) is the wind-wave frequency energy coupling weighting function, which reflects the energy transfer relationship between wind disturbances of different frequencies and waves of the same / different frequencies.

[0010] By using the aforementioned joint wind and wave frequency spectra, the statistical characteristics of wind and wave energy and their inter-frequency coupling mechanism can be characterized within a unified frequency domain framework. Based on this, 0th–2nd order spectral moments are calculated for both wind and wave edge spectra. Utilizing the correspondence between spectral moments and time-domain statistics, frequency domain information is mapped to time-domain characteristic parameters such as equivalent mean, variance, and effective frequency. Furthermore, based on the obtained time-domain statistics, an equivalent excitation time history consistent with the target frequency band energy distribution is generated, serving as the energy input boundary condition for subsequent collaborative control strategy design and optimization, providing a foundation for stable and efficient energy acquisition across the entire operating range.

[0011] S2. Multi-degree-of-freedom heterogeneous frequency wind and wave coupled dynamics modeling Based on the joint wind-wave spectrum input obtained in step S1, a multi-degree-of-freedom wind-wave heterofrequency coupled dynamic model considering both wind-induced and wave-induced loads is established. This multi-degree-of-freedom wind-wave heterofrequency coupled dynamic model characterizes the unsteady response of the system under low-frequency wind-induced disturbances and high-frequency wave-induced excitations. Specifically, based on the joint spectrum input given by the wind-wave heterofrequency energy coupling model, heterofrequency coupled dynamic equations including wind-induced and wave-induced load terms are constructed, providing a structural dynamics foundation for subsequent PTO energy feedback modeling and collaborative control strategy design.

[0012] S3. PTO Energy Feedback and Nonlinear Coupling Modeling Based on the multi-degree-of-freedom hetero-frequency wind-wave coupled dynamics model established in step S2, a PTO energy feedback model is constructed, forming an energy feedback system and a nonlinear damping coupled system under the joint excitation of wind and waves. The stiffness matrix and damping matrix of the multi-degree-of-freedom hetero-frequency wind-wave coupled dynamics model simultaneously consider the frequency distribution of the joint excitation of wind and waves and the PTO feedback effect, forming a total stiffness matrix with nonlinear coupling characteristics. K total ( ω ) and total damping matrix C total ( ω ): K total ( ω )= K ( ω )+ K PTO ( ω ) C total ( ω )= C ( ω )+ C PTO ( ω ) in, K ( ω )and C ( ω These are the frequency-dependent stiffness matrix and damping matrix without PTO feedback, respectively. K PTO ( ω ) and C PTO ( ω ) represents the equivalent stiffness and equivalent damping terms generated during the PTO energy feedback process, used to characterize the impact of energy extraction on the structural dynamics.

[0013] S4. Construction of Dynamic Optimization Control Objective Function Based on the response of the multi-degree-of-freedom heterogeneous frequency wind-wave coupled dynamics model in step S2 and the PTO energy feedback output in step S3, a multi-objective optimization function is established with wind energy output power, wave energy output power, and system motion stability as variables. By setting different weighting coefficients, a synergistic balance between maximizing energy gain and stability constraints is achieved, providing a unified objective for solving subsequent control parameters.

[0014] S5. Full-condition adaptive cooperative control strategy To achieve full-band energy capture and stable system response of the wind and wave energy combined device under complex sea conditions, an adaptive and cooperative control strategy based on model predictive control (MPC) is constructed based on the multi-degree-of-freedom heterogeneous frequency wind and wave coupled dynamic model established in step S2 and the PTO energy feedback model in step S3.

[0015] The control strategy employs a time-domain rolling optimization algorithm based on model predictive control (MPC), using the effective wave spectrum at each sampling time. Effective wind speed spectrum The linear transfer function (LTF) is used to predict external excitations in the future. Frequency domain weights are introduced into the optimization objective function to increase the penalty weights on the attitude response and structural load corresponding to the main wave frequency band, so as to suppress the response of this frequency band and improve the overall energy gain capability of the system.

[0016] In the process of the time-domain rolling optimization algorithm, the total stiffness matrix is ​​combined. K total ( ω ) and the total damping matrix C total ( ω The system solves for control variables online and explicitly sets operational constraints, including: PTO current and rate of change, strut stroke and velocity, platform attitude angle, tower base bending moment, and upper limit of mooring tension. Real-time solutions are obtained using quadratic programming or equivalent convex optimization algorithms; reinforcement learning algorithms are introduced when necessary to achieve adaptive parameter adjustment.

[0017] By introducing a constraint feedback and protection mechanism within the model-based predictive control framework, the system automatically switches to a damping enhancement mode when any constraint exceeds the limit. This mode suppresses platform amplitude and energy peak by increasing virtual damping, reducing equivalent stiffness, and limiting energy feedback intensity, thereby ensuring the structural safety of the system.

[0018] To ensure that the control optimization process is consistent with the inherent dynamic characteristics of the system, the unit's natural frequency constraint is introduced during the control parameter scheduling process. The unit's natural frequency is obtained by solving the following characteristic equation: ω | K tot det 2 M |=0, Where: ω is the angular frequency of the natural vibration (rad / s); K totThe total stiffness matrix includes hydrostatic, mooring, and PTO equivalent stiffness terms; M is the overall mass-inertia matrix of the unit. Through the aforementioned natural frequency constraints, adverse resonances with key structural modes can be avoided during control parameter adjustments, ensuring structural safety.

[0019] Building upon the aforementioned model predictive control optimization and inherent frequency constraints of the unit, this invention further constructs a hierarchical collaborative control system for low-frequency, mid-frequency, and high-frequency bands. Specifically, in the low-frequency band, control of platform attitude and overall drift response is achieved primarily through adjusting equivalent stiffness and mooring system parameters; in the mid-frequency band, optimization of PTO energy feedback and damping matching is emphasized to improve wind and wave power output; and in the high-frequency band, additional damping and local adjustments are used to suppress wave-induced vibration and local structural response. The control actions in each band achieve global collaborative adjustment of energy flow and structural response through the coordinated allocation of virtual stiffness and virtual damping parameters.

[0020] The corresponding optimization constraints include: relative displacement constraints. s i = J i -ω ; i Indicates PTO number index ( i =1,2,3, as in the third set i =3); s i Let be the axial relative displacement of the i-th PTO; be the Jacobian matrix determined by the installation point vector and the axial unit vector. ξ For the platform's six-DOF small configuration vector; Attitude angle constraints | H x |、| H y |、| H z |≤ H max ; H x Indicates platform bypass x The attitude angle of the axis; H y Indicates platform bypass y The attitude angle of the axis; H z Indicates platform bypass z The attitude angle of the axis; H max This indicates the maximum allowable attitude angle limit of the platform; And energy saturation constraints: ξ p ≤ Φ max , Φp The equivalent energy index of PTO; Φ max This indicates the maximum allowable energy output limit for PTO.

[0021] Through the aforementioned hierarchical adaptive collaborative control strategy, the system can form a closed-loop control mechanism of "self-sensing-self-adjustment-self-optimization", thereby significantly improving the energy capture efficiency and structural safety of the wind and wave energy combined device.

[0022] The system adopts a hierarchical control architecture. The execution layer generates a reference current for PTO based on the virtual stiffness and virtual damping allocated by the upper layer, thereby realizing energy feedback and virtual damping / stiffness injection, and thus completing the coordinated regulation of energy flow and structural response.

[0023] The execution layer generates a reference current based on the virtual stiffness and damping allocated by the upper layer: , Energy feedback and virtual damping / stiffness injection are achieved through a current closed loop.

[0024] in: i i * For execution layer reference current; k i v , c i v These represent virtual stiffness and virtual damping coefficients, respectively. i 0,i This is the bias current; s i , This represents relative displacement / velocity.

[0025] S6. Establishment of constraint feedback and protection mechanisms: Based on the multi-degree-of-freedom heterogeneous frequency wind and wave coupled dynamic model response in step S2, the PTO energy feedback characteristics in step S3, and the control output in step S5, a multi-level constraint feedback and protection mechanism is established to form a safety protection for the full-condition adaptive cooperative control strategy.

[0026] In the aforementioned constraint feedback and protection mechanism, relative displacement is introduced as a key constraint variable, which satisfies the following relationship: s i = J i Φ in: s i Let be the axial relative displacement of the i-th PTO. Ji The Jacobian matrix is ​​determined by the mounting point vector and the axial unit vector; ξ For the platform's six-DOF small configuration vector; Based on this, a protection state machine with hysteresis is set up: when the effective wave height... H s Exceeding the entry threshold H in Or the platform attitude angle amplitude |θ| exceeds the entry threshold. ξ in Or PTO current amplitude | i | Exceeding the entry threshold i in When any of the conditions in the above conditions are met, the system enters protection mode; when the system state... T safe Continuous time satisfies H s <H ou 、 |θ |< θ out And | i |< i out When this happens, the system smoothly exits protection mode; where: H s Significant wave height; θ This represents the amplitude of the platform's attitude angle. i The PTO current amplitude; H in , θ in , i in ) is the entry threshold; H out , θ out , i out () represents the exit threshold; T safe The shortest duration to meet continuous safety conditions.

[0027] To address the PTO (Push-to-Touch) travel limit problem, constraints are applied to the travel end region within the constraint feedback and protection mechanism, and a potential barrier function for the travel end region is introduced into the MPC (Multi-Process Control) optimization objective function. , in: θ The potential barrier function for the end region of the stroke; s i Let be the axial relative displacement of the i-th PTO set; s min , smax These represent the lower and upper limits of the safe operating stroke, respectively. This barrier function suppresses impacts at the stroke end and prevents the mechanism from overtraveling. When a sensor or PTO malfunction is detected, the system control strategy automatically degrades to passive power generation or passive damping mode to ensure the safe operation of the device under abnormal conditions.

[0028] In step S1 Φ w ψ(ω p ) =1 ,ω [ +λexp w -κ(ω p ) 2 ] in: -ω This is the coupling gain coefficient; λ This is the frequency adjustment factor.

[0029] The wind and wave frequency-coupled weighting function ψ( κ w ω p Adaptive correction based on sea state observation data, correction factor ,ω and λ The system automatically adjusts based on the measured wind and wave spectrum matching degree to achieve dynamic energy input balance.

[0030] The effective wave spectrum is obtained by integrating the wind frequency variable. : , in: Indicates a valid wave spectrum; the superscript eff means valid; the subscript... κ This indicates that the physical quantity corresponding to this spectrum is sea surface displacement; η p Indicates the circular frequency of the wave; This indicates a full-frequency domain integration over the wind frequency variable; Represents the joint spectral density function of wind and waves at different frequencies; ω w The angular frequency representing the wind speed disturbance.

[0031] For the joint spectral density function By performing double integration across the entire frequency domain, an effective combined wind and wave energy input can be obtained. : , By analyzing the effective wave spectrum By dividing the frequency band, we can obtain the first... i Effective wind and wave combined energy components within each frequency band Furthermore, the representative effective frequency components corresponding to this frequency band are defined. It is used to characterize the dominant frequency features of energy within this frequency band.

[0032] In step S2, the multi-degree-of-freedom heterogeneous frequency coupling dynamic model is as follows: , Where: the superscript P indicates the platform subsystem, the superscript WEC indicates the wave energy conversion subsystem, and the subscript total indicates the total quantity including hydrodynamics, structure, aerodynamics, and PTO feedback; j= ω is the angular frequency variable; M p This represents the platform's mass-inertia matrix. Indicates the platform at circular frequency ω The total damping matrix under; Indicates the platform at circular frequency ω The overall stiffness matrix is ​​shown below; X P ( ω ) indicates the platform at angular frequency ω The corresponding frequency domain shift vector; M WEC This represents the mass-inertia matrix of a wave energy conversion device unit; X WEC ( ω ) indicates that the wave energy conversion device subsystem operates at an angular frequency of ω The frequency domain displacement response vector; This indicates that the wave energy conversion device unit operates at an angular frequency. ω The total damping matrix under; This indicates that the wave energy conversion device unit operates at an angular frequency. ω The overall stiffness matrix is ​​shown below; Indicates the platform at angular frequency ω The total external generalized load is obtained by superimposing wind-induced load, wave-induced load, and joint excitation increment term; Indicated by wave spectrum S η ( ω p The generated wave-induced load; Represents the wind speed disturbance spectrum S U ( ωw The generated wind-induced load; This represents the excitation increment term caused by the wind-wave coupling effect, which can be expressed by the wind-wave heterogeneous frequency joint spectrum. It is obtained by constructing the corresponding load transfer function.

[0033] The overall stiffness matrix in step S3 is: , in: ω It is the platform's 6-DOF generalized displacement vector; ξ It is the generalized displacement vector of the 6-DOF suspension weight; s It is the axial relative displacement vector of PTO; f It is the generalized coordinate vector of the structural flexible modes; K ff It is a flexible modal equivalent stiffness sub-block; K ξξ It is a 6DOF stiffness sub-block of the platform, made of hydrostatics. K hyd mooring K moor It is composed of three parts: the PTO projected stiffness, the PTO projection stiffness, and the PTO projection stiffness. K χχ It is a 6DOF stiffness sub-block for the suspended weight, including its own gravity / geometric stiffness. K g,h PTO writeback stiffness; K ξχ =- K T χξ It is a platform-suspension-weighted coupling stiffness sub-block; the negative sign comes from the definition of relative displacement. K ss It is a diagonal matrix of the axial equivalent stiffness of PTO; K ξs , K sξ The platform-PTO travel coupling stiffness is determined by the geometric mapping of 'a' and the axial equivalent stiffness. K sξ = K T ξs ; K χs , K sχ It is the suspension-PTO stroke coupling stiffness. Ksχ = K T χs ; k i It is the first i PTO axial equivalent stiffness; K hyd This is the platform's hydrostatic stiffness matrix, reflecting the restoring effect of buoyancy on displacement disturbances. K moor This is the stiffness matrix of the mooring system, generated by the geometric stiffness and initial tension of the anchor chain or mooring cable; K g,h For the gravity and geometric stiffness sub-items of the suspension structure, representing the contribution of the suspension weight itself and the geometric nonlinearity of the structure to the stiffness; k bi,f It represents the equivalent stiffness coefficient of the foundation support corresponding to the i-th PTO under the combined excitation of wind and waves, where i represents the PTO number index, i=1,2,3; k t,fa This represents the equivalent stiffness coefficient in the PTO drivetrain introduced by the mechanism geometry and mounting angles; k t,ss This represents the equivalent stiffness coefficient introduced by structural flexibility or connecting components in the PTO transmission system. k i eq This represents the axial equivalent stiffness coefficient of the i-th PTO, where i represents the PTO number index, i=1,2,3; J i Let be the Jacobian matrix of the i-th PTO installation point. T This indicates transpose, used to map the six-degree-of-freedom configuration changes of the platform to the PTO axial direction; it is determined by the mounting point coordinates and the axial unit vector. H i Let be the geometric mapping matrix of the suspended hammer element (i). T This indicates transpose and is used to describe the transformation relationship between the local coordinates of the pendulum and the global platform coordinates. J i T Let represent the transpose of the Jacobian matrix row vector corresponding to the i-th PTO installation point, where i represents the PTO index, i=1,2,3; H iT denoted as the transpose of the geometric mapping matrix between the i-th PTO and the pendulum unit, where i represents the PTO number index, i=1,2,3; The total damping matrix is: , in, C ff This represents the equivalent damping sub-block corresponding to the flexible mode of the structure; C ξξ The equivalent damping sub-block representing the 6 degrees of freedom of the platform is composed of the projection contributions of wave radiation damping, aerodynamic damping, and PTO equivalent damping on the platform side. C rad ( χ () is the wave radiation damping matrix, which reflects the energy dissipation caused by wave radiation due to platform motion; C aero This is the aerodynamic damping matrix, reflecting the damping effect of wind-induced aerodynamic forces on the platform's motion; C χχ It is the equivalent damping sub-block of the 6-DOF suspension weight, which is damped by radiation. C rad ( ω It consists of PTO write-back damping; C ξχ , C χξ These represent the damping coupling sub-blocks between the platform and the helical weight, respectively, which satisfy a symmetrical pairing relationship. C ξχ = C T χξ ; C ss It consists of three sets of PTO axial equivalent damping diagonal arrays; C i eq It is the first i The axial equivalent damping of the linear motor is used in... C ξξ, C χχ Composed of equal blocks; C ξs, C sξ These represent the damping coupling sub-blocks between the 6 DOFPTO axial degrees of freedom of the platform, satisfying... C ξs= C T sξ ; C χs , C sχ These represent the damping coupling sub-blocks between the 6DOF and PTO axial degrees of freedom of the suspension weight, respectively, satisfying... C χs = C T sχ ; c b1,f , c b2,f , c b3,f This represents the equivalent damping coefficient of the flexible mode of the foundation support; c t,fa , c t,ss This represents the equivalent damping coefficient introduced by the flexible links in the transmission chain.

[0034] The axial force of the i-th PTO satisfies the following relationship: , in: F i For the i-th PTO axial force; K t The motor force constant, K e R is the back electromotive force constant, and R is the stator equivalent resistance; s i , Relative displacement / velocity; c s,i 、k s,i Mechanical damping / stiffness; i i For current; The contributions of the three PTOs to the platform's equivalent stiffness and damping are as follows: , in: K PTO and C PTO These are the equivalent stiffness / damping matrices of the PTO relative to the platform. k i eq , c i eq Let be the axial equivalent stiffness / damping coefficient of the i-th PTO set; Ji For rows of the Jacobian matrix; T Indicates transpose, where, J i It is determined by the location of the installation point and the axial direction.

[0035] In step S4, the objective function J for: ω wave +βE wind J=αE motion， in: E wave It is the energy output of wave energy; E wind It refers to the energy output of wind power; A motion It is the amplitude of the floating foundation's motion response; coefficient -γA Energy input in conjunction with wind and waves under operating conditions Dynamic adjustment.

[0036] The beneficial effects of this invention are: This invention achieves the following through a collaborative framework: wind-wave heterogeneous frequency joint spectrum modeling + multi-degree-of-freedom coupled dynamics + spectrum-weighted MPC + adaptive protection mechanism. (1) Wideband energy capture: In sea conditions where low-frequency wind-induced and mid-to-high-frequency wave-induced energy coexist, the joint spectrum and edge spectrum moments make the equivalent excitation consistent with the actual frequency band energy, thereby improving power generation efficiency; (2) Attitude and load suppression: Total stiffness and total damping explicitly include PTO write-back terms, and frequency band weighting suppresses the peak values ​​of the main frequency attitude and mooring load; (3) Robust Adaptive: The coupling weight parameters are corrected online with the observation data, and the MPC weights and constraints are dynamically scheduled according to the working conditions; (4) Engineering safety: The hysteresis protection state machine and the end region barrier function trigger damping enhancement when the limit is exceeded, avoiding end region impact and large attitude; (5) Ease of implementation: The model and control are seamlessly integrated in industrial chains such as AQWA-wecSim-Simulink, facilitating engineering implementation and hardware-in-the-loop verification. The above effects have been verified in regular wave conditions (H=2.5m, T=6.5s), demonstrating a comprehensive benefit of attitude stability, reduced mooring cable tension, and increased average PTO power. Attached Figure Description

[0037] α,β,γ This is the time history diagram of the total PTO power generation of the system at H=2.5m and T=6.5s; Figure 1The time history diagram of the platform oscillation of the system at H=2.5m and T=6.5s is shown. Figure 2 The time history diagram of the platform heave of the system at H=2.5m and T=6.5s is shown. Figure 3 It is the platform pitch time history diagram of the system at H=2.5m and T=6.5s; Figure 4 This is the time history diagram of the mooring cable tension under the conditions of H=2.5m and T=6.5s. Detailed Implementation

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0039] The structures, proportions, and sizes illustrated in the accompanying drawings are merely for illustrative purposes and to aid those skilled in the art in understanding and reading the invention. They are not intended to limit the scope of the invention and therefore have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, provided they do not affect the effectiveness or purpose of the invention, should still fall within the scope of the technical content disclosed herein. Furthermore, the terms "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity and not intended to limit the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention's implementation.

[0040] A multi-degree-of-freedom broadband control method for wind and wave energy based on heterogeneous frequency coupling includes the following steps: S1. Establish a wind-wave heterogeneous frequency energy coupling model By constructing a wind-wave heterofrequency energy coupling model in the frequency domain, the energy correlation between wind speed disturbances and ocean waves at different frequency scales is uniformly described. This wind-wave heterofrequency energy coupling model uses the joint wind-wave heterofrequency spectral density function as its core mathematical representation to characterize the statistical properties and energy transfer mechanisms of wind and wave energy under heterofrequency conditions.

[0041] The joint spectral density function of wind and waves at different frequencies is defined as: ,in: For the combined frequency spectral density of wind and waves; Figure 5 w Let be the angular frequency of the wind speed disturbance. ω p Let be the angular frequency of the ocean waves; the two are independent frequency variables. S U ( ω w() represents the wind speed perturbation spectral density function based on the standard wind speed spectral model; S η ( ω p () represents the sea surface displacement / wave height spectral density function based on the standard wave spectral model; ω ( ψ w ω p ) is the wind-wave frequency energy coupling weighting function, which reflects the energy transfer relationship between wind disturbances of different frequencies and waves of the same / different frequencies.

[0042] ,ω ( ψ w ω p )=1+ ,ω [ λexp w -κ(ω p ) 2 ] in: -ω This is the coupling gain coefficient; λ This is the frequency adjustment factor.

[0043] Wind and Wave Frequency Coupling Weighting Function κ ( ψ w ω p Adaptive correction is achieved through sea state observation data, with correction factors... ,ω and λ The system automatically adjusts based on the measured wind and wave spectrum matching degree to achieve dynamic energy input balance.

[0044] The effective wave spectrum is obtained by integrating the wind frequency variable. : ,

[0045] For the joint spectral density function By performing double integration across the entire frequency domain, an effective combined wind and wave energy input can be obtained. : , By analyzing the effective wave spectrum By dividing the frequency band, we can obtain the first... i Effective wind and wave combined energy components within each frequency band Furthermore, the representative effective frequency components corresponding to this frequency band are defined. It is used to characterize the dominant frequency features of energy within this frequency band.

[0046] By using the aforementioned joint wind and wave frequency spectra, the statistical characteristics of wind and wave energy and their inter-frequency coupling mechanism can be characterized within a unified frequency domain framework. Based on this, 0th–2nd order spectral moments are calculated for both wind and wave edge spectra. Utilizing the correspondence between spectral moments and time-domain statistics, frequency domain information is mapped to time-domain characteristic parameters such as equivalent mean, variance, and effective frequency. Furthermore, based on the obtained time-domain statistics, an equivalent excitation time history consistent with the target frequency band energy distribution is generated, serving as the energy input boundary condition for subsequent collaborative control strategy design and optimization, providing a foundation for stable and efficient energy acquisition across the entire operating range.

[0047] S2. Multi-degree-of-freedom heterogeneous frequency wind and wave coupled dynamics modeling Based on the wind-wave heterofrequency joint spectrum input obtained in step S1, a multi-degree-of-freedom wind-wave heterofrequency coupled dynamic model considering both wind-induced and wave-induced loads is established. This multi-degree-of-freedom wind-wave heterofrequency coupled dynamic model characterizes the unsteady response characteristics of the system under low-frequency wind-induced disturbances and high-frequency wave-induced excitations. Specifically, based on the wind-wave heterofrequency joint spectrum input given by the wind-wave heterofrequency energy coupling model, heterofrequency coupled dynamic equations including wind-induced and wave-induced load terms are constructed, providing a structural dynamics foundation for subsequent PTO energy feedback modeling and collaborative control strategy design.

[0048] The multi-degree-of-freedom heterogeneous frequency coupling dynamic model is as follows: , Where: the superscript P indicates the platform subsystem, the superscript WEC indicates the wave energy conversion subsystem, and the subscript total indicates the total quantity including hydrodynamics, structure, aerodynamics, and PTO feedback; j= ω is the angular frequency variable; M p This represents the platform's mass-inertia matrix. Indicates the platform at circular frequency κ The total damping matrix under; Indicates the platform at circular frequency ω The overall stiffness matrix is ​​shown below; X P ( ω ) indicates the platform at angular frequency ω The corresponding frequency domain shift vector; M WEC This represents the mass-inertia matrix of a wave energy conversion device unit; X WEC ( ω ) indicates that the wave energy conversion device subsystem operates at an angular frequency of ωThe frequency domain displacement response vector; This indicates that the wave energy conversion device unit operates at an angular frequency. ω The total damping matrix under; This indicates that the wave energy conversion device unit operates at an angular frequency. ω The overall stiffness matrix is ​​shown below; Indicates the platform at angular frequency ω The total external generalized load is obtained by superimposing wind-induced load, wave-induced load, and joint excitation increment term; Indicated by wave spectrum S η ( ω p The generated wave-induced load; Represents the wind speed disturbance spectrum S U ( ω w The generated wind-induced load; This represents the excitation increment term caused by the wind-wave coupling effect, which can be expressed by the wind-wave heterogeneous frequency joint spectrum. It is obtained by constructing the corresponding load transfer function.

[0049] S3. PTO Energy Feedback and Nonlinear Coupling Modeling Based on the multi-degree-of-freedom hetero-frequency wind-wave coupled dynamics model established in step S2, a PTO energy feedback model is constructed, forming an energy feedback system and a nonlinear damping coupled system under the joint excitation of wind and waves. The stiffness matrix and damping matrix of the multi-degree-of-freedom hetero-frequency wind-wave coupled dynamics model simultaneously consider the frequency distribution of the joint excitation of wind and waves and the PTO feedback effect, forming a total stiffness matrix with nonlinear coupling characteristics. K total ( ω ) and total damping matrix C total ( ω ): K total ( ω )= K ( ω )+ K PTO ( ω ) C total ( ω )= C ( ω )+ C PTO( ω ) in, K ( ω )and C ( ω These are the frequency-dependent stiffness matrix and damping matrix without PTO feedback, respectively. K PTO ( ω ) and C PTO ( ω ) represents the equivalent stiffness and equivalent damping terms generated during the PTO energy feedback process, used to characterize the impact of energy extraction on the structural dynamics.

[0050] The overall stiffness matrix is: , in: ξ It is the platform's 6-DOF generalized displacement vector; χ It is the generalized displacement vector of the 6-DOF suspension weight; s It is the axial relative displacement vector of PTO; f It is the generalized coordinate vector of the structural flexible modes; K ff It is a flexible modal equivalent stiffness sub-block; K ξξ It is a 6DOF stiffness sub-block of the platform, made of hydrostatics. K hyd mooring K moor It is composed of three parts: the PTO projected stiffness, the PTO projection stiffness, and the PTO projection stiffness. K χχ It is a 6DOF stiffness sub-block for the suspended weight, including its own gravity / geometric stiffness. K g,h PTO writeback stiffness; K ξχ =- K T χξ It is a platform-suspension-weighted coupling stiffness sub-block; the negative sign comes from the definition of relative displacement. K ss It is a diagonal matrix of the axial equivalent stiffness of PTO; K ξs , K sξ The platform-PTO travel coupling stiffness is determined by the geometric mapping of 'a' and the axial equivalent stiffness. K sξ = K T ξs ; K χs , K sχ It is the suspension-PTO stroke coupling stiffness. K sχ = K T χs ; k i It is the first i PTO axial equivalent stiffness; K hyd This is the platform's hydrostatic stiffness matrix, reflecting the restoring effect of buoyancy on displacement disturbances. K moor This is the stiffness matrix of the mooring system, generated by the geometric stiffness and initial tension of the anchor chain or mooring cable; K g,h For the gravity and geometric stiffness sub-items of the suspension structure, representing the contribution of the suspension weight itself and the geometric nonlinearity of the structure to the stiffness; k bi,f It represents the equivalent stiffness coefficient of the foundation support corresponding to the i-th PTO under the combined excitation of wind and waves, where i represents the PTO number index, i=1,2,3; k t,fa This represents the equivalent stiffness coefficient in the PTO drivetrain introduced by the mechanism geometry and mounting angles; k t,ss This represents the equivalent stiffness coefficient introduced by structural flexibility or connecting components in the PTO transmission system. k i eq This represents the axial equivalent stiffness coefficient of the i-th PTO, where i represents the PTO number index, i=1,2,3; J i Let be the Jacobian matrix of the i-th PTO installation point. T This indicates transpose, used to map the six-degree-of-freedom configuration changes of the platform to the PTO axial direction; it is determined by the mounting point coordinates and the axial unit vector. H i Let be the geometric mapping matrix of the suspended hammer element (i). TThis indicates transpose and is used to describe the transformation relationship between the local coordinates of the pendulum and the global platform coordinates. J i T Let represent the transpose of the Jacobian matrix row vector corresponding to the i-th PTO installation point, where i represents the PTO index, i=1,2,3; H i T denoted as the transpose of the geometric mapping matrix between the i-th PTO and the pendulum unit, where i represents the PTO number index, i=1,2,3; The total damping matrix is: , in, C ff This represents the equivalent damping sub-block corresponding to the flexible mode of the structure; C ξξ The equivalent damping sub-block representing the 6 degrees of freedom of the platform is composed of the projection contributions of wave radiation damping, aerodynamic damping, and PTO equivalent damping on the platform side. C rad ( ω () is the wave radiation damping matrix, which reflects the energy dissipation caused by wave radiation due to platform motion; C aero This is the aerodynamic damping matrix, reflecting the damping effect of wind-induced aerodynamic forces on the platform's motion; C χχ It is the equivalent damping sub-block of the 6-DOF suspension weight, which is damped by radiation. C rad ( ω It consists of PTO write-back damping; C ξχ , C χξ These represent the damping coupling sub-blocks between the platform and the helical weight, respectively, which satisfy a symmetrical pairing relationship. C ξχ = C T χξ ; C ss It consists of three sets of PTO axial equivalent damping diagonal arrays; C i eq It is the first i The axial equivalent damping of the linear motor is used in... Cξξ, C χχ Composed of equal blocks; C ξs, C sξ These represent the damping coupling sub-blocks between the 6 DOFPTO axial degrees of freedom of the platform, satisfying... C ξs = C T sξ ; C χs , C sχ These represent the damping coupling sub-blocks between the 6DOF and PTO axial degrees of freedom of the suspension weight, respectively, satisfying... C χs = C T sχ ; c b1,f , c b2,f , c b3,f This represents the equivalent damping coefficient of the flexible mode of the foundation support; c t,fa , c t,ss This represents the equivalent damping coefficient introduced by the flexible links in the transmission chain.

[0051] The axial force of the i-th PTO satisfies the following relationship: , in: F i For the i-th PTO axial force; K t The motor force constant, K e R is the back electromotive force constant, and R is the stator equivalent resistance; s i , Relative displacement / velocity; c s,i 、k s,i Mechanical damping / stiffness; i i For current; The contributions of the three PTOs to the platform's equivalent stiffness and damping are as follows: , in: K PTOand C PTO These are the equivalent stiffness / damping matrices of the PTO relative to the platform. k i eq , c i eq Let be the axial equivalent stiffness / damping coefficient of the i-th PTO set; J i For rows of the Jacobian matrix; T Indicates transpose, where, J i It is determined by the location of the installation point and the axial direction.

[0052] In step S4, the objective function J for: J=αE wave +βE wind -γA motion， in: E wave It is the energy output of wave energy; E wind It refers to the energy output of wind power; A motion It is the amplitude of the floating foundation's motion response; coefficient α,β,γ Energy input in conjunction with wind and waves under operating conditions Dynamic adjustment.

[0053] S4. Construction of Dynamic Optimization Control Objective Function Based on the response of the multi-degree-of-freedom heterogeneous frequency wind-wave coupled dynamics model in step S2 and the PTO energy feedback output in step S3, a multi-objective optimization function is established with wind energy output power, wave energy output power, and system motion stability as variables. By setting different weighting coefficients, a synergistic balance between maximizing energy gain and stability constraints is achieved, providing a unified objective for solving subsequent control parameters.

[0054] S5. Full-condition adaptive cooperative control strategy To achieve full-band energy capture and stable system response of the wind and wave energy combined device under complex sea conditions, based on the multi-degree-of-freedom heterogeneous frequency wind and wave coupled dynamic model established in step S2 and the PTO energy feedback model in step S3, a full-condition adaptive cooperative control strategy based on model predictive control (MPC) is constructed.

[0055] The control strategy employs a time-domain rolling optimization algorithm based on model predictive control (MPC), acquiring the effective wave spectrum at each sampling time. Effective wind speed spectrum The linear transfer function (LTF) is used to predict external excitations in the future. Frequency domain weights are introduced into the optimization objective function to increase the penalty weights on the attitude response and structural load corresponding to the main wave frequency band, so as to suppress the response of this frequency band and improve the overall energy gain capability of the system.

[0056] In the process of the time-domain rolling optimization algorithm, the total stiffness matrix is ​​combined. K total ( ω ) and the total damping matrix C total ( ω The system solves for control variables online and explicitly sets operational constraints, including: PTO current and rate of change, strut stroke and velocity, platform attitude angle, tower base bending moment, and upper limit of mooring tension. Real-time solutions are obtained using quadratic programming or equivalent convex optimization algorithms; reinforcement learning algorithms are introduced when necessary to achieve adaptive parameter adjustment.

[0057] By introducing a constraint feedback and protection mechanism within the model-based predictive control framework, the system automatically switches to a damping enhancement mode when any constraint exceeds the limit. This mode suppresses platform amplitude and energy peak by increasing virtual damping, reducing equivalent stiffness, and limiting energy feedback intensity, thereby ensuring the structural safety of the system.

[0058] To ensure that the control optimization process is consistent with the inherent dynamic characteristics of the system, the unit's natural frequency constraint is introduced during the control parameter scheduling process. The unit's natural frequency is obtained by solving the following characteristic equation: det | K tot - ω 2 M |=0, Where: ω is the angular frequency of the natural vibration (rad / s); K tot The total stiffness matrix includes hydrostatic, mooring, and PTO equivalent stiffness terms; M is the overall mass-inertia matrix of the unit. Through the aforementioned natural frequency constraints, adverse resonances with key structural modes can be avoided during control parameter adjustments, ensuring structural safety.

[0059] Building upon the aforementioned model predictive control optimization and inherent frequency constraints of the unit, this invention further constructs a hierarchical collaborative control system for low-frequency, mid-frequency, and high-frequency bands. Specifically, in the low-frequency band, control of platform attitude and overall drift response is achieved primarily through adjusting equivalent stiffness and mooring system parameters; in the mid-frequency band, optimization of PTO energy feedback and damping matching is emphasized to improve wind and wave power output; and in the high-frequency band, additional damping and local adjustments are used to suppress wave-induced vibration and local structural response. The control actions in each band achieve global collaborative adjustment of energy flow and structural response through the coordinated allocation of virtual stiffness and virtual damping parameters.

[0060] The corresponding optimization constraints include: relative displacement constraints. s i = J i ξ ; i Indicates PTO number index ( i =1,2,3, as in the third set i =3); s i Let be the axial relative displacement of the i-th PTO set; J i The Jacobian matrix is ​​determined by the mounting point vector and the axial unit vector; ξ For the platform's six-DOF small configuration vector; The corresponding optimization constraints include: relative displacement constraints. s i = J i ξ ; i Indicates PTO number index ( i =1,2,3, as in the third set i =3); s i Let be the axial relative displacement of the i-th PTO; be the Jacobian matrix determined by the installation point vector and the axial unit vector. ξ For the platform's six-DOF small configuration vector; Attitude angle constraints | H x |、| H y |、| H z |≤ H max ; H x Indicates platform bypass x The attitude angle of the axis; H y Indicates platform bypassy The attitude angle of the axis; H z Indicates platform bypass z The attitude angle of the axis; H max This indicates the maximum allowable attitude angle limit of the platform; And energy saturation constraints: Φ p ≤ Φ max , Φ p The equivalent energy index of PTO; Φ max This indicates the maximum allowable energy output limit for PTO.

[0061] Through the aforementioned hierarchical adaptive collaborative control strategy, the system can form a closed-loop control mechanism of "self-sensing-self-adjustment-self-optimization", thereby significantly improving the energy capture efficiency and structural safety of the wind and wave energy combined device.

[0062] The system adopts a hierarchical control architecture. The execution layer generates a reference current for PTO based on the virtual stiffness and virtual damping allocated by the upper layer, thereby realizing energy feedback and virtual damping / stiffness injection, and thus completing the coordinated regulation of energy flow and structural response.

[0063] The execution layer generates a reference current based on the virtual stiffness and damping allocated by the upper layer: , Energy feedback and virtual damping / stiffness injection are achieved through a current closed loop.

[0064] in: i i * For execution layer reference current; k i v , c i v These represent virtual stiffness and virtual damping coefficients, respectively. i 0,i This is the bias current; s i , This represents relative displacement / velocity.

[0065] S6. Establishment of constraint feedback and protection mechanisms: Based on the multi-degree-of-freedom heterogeneous frequency wind and wave coupled dynamic model response in step S2, the PTO energy feedback characteristics in step S3, and the control output in step S5, a multi-level constraint feedback and protection mechanism is established to form a safety protection for the full-condition adaptive cooperative control strategy.

[0066] In the aforementioned constraint feedback and protection mechanism, relative displacement is introduced as a key constraint variable, which satisfies the following relationship: s i = J i ξ in: s i Let be the axial relative displacement of the i-th PTO. J i The Jacobian matrix is ​​determined by the mounting point vector and the axial unit vector; ξ For the platform's six-DOF small configuration vector; Based on this, a protection state machine with hysteresis is set up: when the effective wave height... H s Exceeding the entry threshold H in Or platform attitude angle amplitude | θ | Exceeding the entry threshold θ in Or PTO current amplitude | i | Exceeding the entry threshold i in When any of the conditions in the above conditions are met, the system enters protection mode; when the system state... T safe Continuous time satisfies H s <H ou 、 |θ |< θ out And | i |< i out When this happens, the system smoothly exits protection mode; where: H s Significant wave height; θ This represents the amplitude of the platform's attitude angle. i The PTO current amplitude; H in , θ in , i in ) is the entry threshold; H out , θ out , i out () represents the exit threshold; T safe The shortest duration to meet continuous safety conditions.

[0067] To address the PTO (Push-to-Touch) travel limit problem, constraints are applied to the travel end region within the constraint feedback and protection mechanism, and a potential barrier function for the travel end region is introduced into the MPC (Multi-Process Control) optimization objective function. , in: Φ The potential barrier function for the end region of the stroke; s i Let be the axial relative displacement of the i-th PTO set; s min , s max These represent the lower and upper limits of the safe operating stroke, respectively. This barrier function suppresses impacts at the stroke end and prevents the mechanism from overtraveling. When a sensor or PTO malfunction is detected, the system control strategy automatically degrades to passive power generation or passive damping mode to ensure the safe operation of the device under abnormal conditions.

[0068] Example 1: Verification of regular waves (Beihai H=2.5 m, T=6.5 s); as Figures 1-5 As shown.

[0069] The typical regular wave condition in the North Sea was selected: H = 2.5 m, period T = 6.5 s, and angular frequency. ω =2π / T.

[0070] The energy input is defined using the joint wind and wave spectral density to ensure consistency with random sea states. This is achieved through the joint spectrum... Calculate the effective wave spectrum Then by spectral moments Obtain meaningful wave height Furthermore, this effective wave spectrum is used as the frequency domain energy input boundary of the control and dynamics model.

[0071] Frequency domain hydrodynamic models of the platform and the suspension weight were established in AQWA, and the additional mass matrix was obtained. A ( ω Radiation damping C rad ( ω Wave excitation F exc ( ω hydrostatic stiffness K hyd With linearized mooring stiffness K moor .

[0072] The BEMIO tool converts the AQWA output to a WEC-Sim readable *.h5 format, maintaining consistency between the frequency domain and time domain models.

[0073] A coupled model of a 6DOF platform and three sets of PTO axial degrees of freedom was built in WEC-Sim / Simulink. The geometric mapping relationship between the PTO stroke and the platform and suspension configuration was realized through the Jacobian matrix.

[0074] The equivalent stiffness and damping of the platform for the three sets of PTOs were calculated using formulas:

[0075] The system motion is described using generalized dynamic equations:

[0076] After linearization and discretization, the state equations are obtained: , Calculate the axial force of PTO: , And calculate the reference current: , Then, spectrum-weighted multi-objective MPC control is performed. MPC objective function:

[0077] Constraints include current amplitude, rate of change, stroke, speed, attitude angle, tower base bending moment, and mooring tension.

[0078] Constraint implementation includes barrier functions and hysteresis protection state mechanisms: , when H s >H in or | θ |> θ in or | i |> i in It will enter protection mode at that time.

[0079] (U)RANS calculations were performed using STAR-CCM+, and after calibration of the viscosity correction, the generalized force was written back to correct the system response.

[0080] Then, the PTO power is calculated:

[0081] Then, the time history, mean, and peak values ​​of the platform's sway, heave, pitch, and mooring tension are statistically analyzed and output.

[0082] The following results were obtained at H=2.5 m and T=6.5 s: The average pitch decreased by approximately 20.53%, the average heave decreased by approximately 8.3%, the average sway decreased by approximately 1.1%, the average mooring tension decreased by approximately 10.2%, the extreme value of PTO power was approximately 233111.18W, and the average value was approximately 115197.39W. Figures 1-5 As shown in the figure. This result demonstrates that the cooperative control of the present invention can effectively improve system stability and energy harvesting efficiency.

[0083] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A wind and wave energy multi-degree-of-freedom wide-band control method based on heterodyne coupling, characterized in that, Comprise the following steps: S1. Establish a wind wave frequency coupling model, the wind wave frequency coupling model takes the wind wave frequency joint spectrum density function as the core mathematical representation form, used to describe the statistical characteristics and energy transfer mechanism of wind energy and wave energy under the condition of frequency difference; S2, multi degree of freedom frequency coupling dynamics modeling, based on the wind wave joint energy spectrum input obtained in step S1, a multi degree of freedom frequency coupling dynamics model considering wind induced load and wave induced load is established; S3. PTO energy feedback and nonlinear coupling modeling, based on the multi degree of freedom frequency coupling dynamics model established in step S2, a PTO energy feedback model is constructed, forming an energy feedback system and a nonlinear damping coupling system under the joint excitation of wind and wave; The stiffness matrix and the damping matrix of the multi degree of freedom dynamics model consider the frequency distribution of wind and wave joint excitation and the PTO feedback effect, forming the total stiffness matrix and the total damping matrix with nonlinear coupling characteristics; S4. Dynamic optimization control objective function construction, based on the multi degree of freedom frequency coupling dynamics model response of step S2 and the PTO energy feedback output of step S3, a multi objective optimization function is established with wind power output, wave power output and system motion stability as variables, different weighting coefficients are set to realize the collaborative balance between maximum energy gain and stability constraint; S5. Full working condition adaptive collaborative control strategy, based on the multi degree of freedom frequency coupling dynamics model of step S2 and the PTO model of step S3, a full working condition adaptive collaborative control strategy based on model predictive control is constructed, a hierarchical collaborative control system is established; Realize the closed loop control of "self sensing - self adjusting - self optimization"; S6. Establishment of constraint feedback and protection mechanism: based on the dynamic model response of step S2, the PTO feedback characteristics of step S3 and the control output of step S5, a multi level constraint feedback and protection mechanism is established.

2. The wind wave energy multi-degree-of-freedom wide-band control method based on frequency coupling according to claim 1, characterized in that, The definition of wind wave frequency joint spectrum density function in step S1 is: ， wherein: is the wind-wave co-frequency coupled spectral density; ω w is the circular frequency of wind speed disturbance, ω p is the circular frequency of sea wave, both are independent frequency variables; S U ω w is the wind speed disturbance spectral density function based on the standard wind speed spectrum model; S η ω p is the sea surface displacement / wave height spectral density function based on the standard sea wave spectrum model; ψ ω w ,ω p is the wind-wave co-frequency energy coupling weight function, reflecting the energy transfer relationship between different frequency wind disturbance and same / different frequency wave.​​ ψ( ω w ,ω p )=1+ λexp [-κ( ω w -ω p ) 2 ], wherein: λ is the coupling gain coefficient; and K is a frequency adjustment factor.

3. The wind wave energy multi-degree-of-freedom wide-band control method based on frequency difference coupling according to claim 2, characterized in that, The wind and wave frequency-coupled weighting function ψ( ω w ,ω p Adaptive correction is achieved through sea state observation data, with correction factors... λ The system automatically adjusts to match the measured wind and wave spectrum to achieve dynamic energy input balance.

4. The wind wave energy multi-degree-of-freedom wide-band control method based on frequency difference coupling according to claim 3, characterized in that, The effective wave spectrum is obtained by integrating the wind frequency variable: ， Joint spectral density function Double integration over the whole frequency domain gives the effective wave energy input : ， By dividing the effective sea spectrum into frequency bands, the effective wind wave combined energy component i in the first frequency band is obtained, and the representative effective frequency component corresponding to the frequency band is further defined, which is used to represent the dominant frequency characteristics of the energy in the frequency band.

5. The method of claim 1, wherein the wind wave energy is controlled by the multi-degree-of-freedom wide-bandwidth control method based on the hetero-frequency coupling, characterized in that, The multi degree of freedom frequency coupling frequency domain dynamics model in step S2 is: ， Where: the superscript P indicates the platform subsystem, the superscript WEC indicates the wave energy conversion subsystem, and the subscript total indicates the total quantity including hydrodynamics, structure, aerodynamics, and PTO feedback; j= ω is the angular frequency variable; M p denotes the mass-inertia matrix of the platform; represents the total damping matrix of the platform at the circular frequency ω under consideration. represents the total stiffness matrix of the platform at the circular frequency ω under consideration. X P ( ω ) represents the frequency domain displacement corresponding vector of the platform at the angular frequency ω under consideration. M WEC a mass-inertia matrix representing the wave energy conversion device unit; X WEC ( ω ) represents the frequency domain displacement response vector of the wave energy conversion device subsystem at the angular frequency ω under consideration; represents the total damping matrix of the wave energy conversion device unit at the circular frequency ω under consideration. represents the total stiffness matrix of the wave energy conversion device unit at the circular frequency ω under consideration. represents the total external generalized loads on the platform at the angular frequency ω ω, which is the superposition of the wind-induced loads, the wave-induced loads, and the joint excitation increment term; represents wave-induced loads generated by a wave spectrum S η ( ω p ) representative of the wind speed disturbance spectrum S U ( ω w ) generated wind-induced loads; represents the excitation increment term due to the wind wave coupling effect, which can be obtained from the wind wave heterodyne spectrum is obtained by constructing the corresponding load transfer function.

6. The method of claim 1, wherein the wind wave energy is controlled by the multi-degree-of-freedom wide-band controller based on the hetero-frequency coupling. The total stiffness matrix in step S3 is: , where: ξ is the platform 6 degree of freedom generalized displacement vector; χ is the pendant 6 degree of freedom generalized displacement vector; s is the axial relative displacement vector of the PTO; f is the generalized coordinate vector of the structural flexibility mode; K ff is a flexible modal equivalent stiffness sub-block; K ξξ is the platform 6DOF stiffness sub-block, which is the hydrostatic K hyd , mooring K moor and PTO projected stiffness are superimposed; K χχ is a suspended mass 6DOF stiffness sub-block, including self-gravity / geometry stiffness K g,h PTO back-write stiffness; K ξχ =- K T χξ is the platform—pendulum coupling stiffness sub-block, the negative sign comes from the definition of relative displacement; K ss is a diagonal matrix of PTO axial equivalent stiffnesses; K ξs , K sξ is the platform - PTO stroke coupling stiffness, determined by a geometric mapping and an axial equivalent stiffness, K sξ = K T ξs ; K χs , K sχ is the suspension hammer - PTO travel coupling stiffness, K sχ K T χs ;​ k i is the first i PTO shaft axial equivalent stiffness; K hyd Kp is the platform hydrostatic stiffness matrix, which reflects the restoring effect of the buoyancy of the floating body on the displacement disturbance; K moor The mooring system stiffness matrix is generated from the geometric stiffness and initial tension of the mooring lines or chains. K g,h G is the gravity and geometric stiffness sub-item for the suspended mass structure, representing the contribution of the suspended mass's own gravity and the geometric nonlinearity of the structure to the stiffness; k bi,f Kb,i represents the corresponding base support equivalent stiffness coefficient of the ith PTO under the combined excitation of wind and wave, i represents the PTO number index, i = 1, 2, 3. k t,fa Krepresents the equivalent stiffness coefficient introduced by the geometry of the mechanism and the installation angle in the PTO drive train. k t,ss Ks represents the equivalent stiffness coefficient introduced by structural flexibility or connecting members in the PTO driveline system; k i eq Kxi represents the axial equivalent stiffness coefficient of the i-th PTO, i represents the PTO number index, i = 1, 2, 3; J i Ji = Jacobian matrix for the ith set of PTO mounting points, T denotes the transpose, maps the six degrees of freedom platform pose change to the PTO axial direction; determined by the mounting point coordinates and the axial unit vector; H i a geometry mapping matrix for the pendulum unit (i), T denotes the transpose, for describing the conversion relationship between the pendulum local coordinates and the global platform coordinates; J i T represents the transpose of the Jacobian matrix row vector corresponding to the i-th set of PTO mounting points, i represents the PTO number index, i = 1, 2, 3; H i T denotes the transpose of the geometric mapping matrix between the i-th PTO and the pendulum unit, i denotes the PTO number index, i = 1, 2, 3; The total damping matrix is: , wherein, C ff represents an equivalent damping sub-block corresponding to the structural flexibility mode. C ξξ Equivalent damping sub-block representing the 6 degrees of freedom of the platform 6, composed of the contributions of the wave radiation damping, the aerodynamic damping and the projection on the platform side of the PTO equivalent damping; C rad ( ω ) is the wave radiation damping matrix, reflecting the wave radiation energy dissipation caused by platform motion; C aero is the aerodynamic damping matrix, reflecting the damping effect of wind-induced aerodynamic forces on the platform motion; C χχ is the equivalent damping sub-block of the 6-DOF pendulum 6, composed of radiation damping C rad ( ω ) and PTO back-damping C ξχ , C χξ represent the damping coupling sub-blocks between the platform and the suspended mass, which satisfy the symmetric and reciprocal relationship C ξχ = C T χξ ; C ss is a diagonal matrix of three sets of PTO axial equivalent dampings; C i eq is the first i set of axial equivalent dampings of the linear motor, participating C ξξ, C χχ equally divided blocks C ξs, C sξ represent the damping coupling sub-blocks between the platform 6-DOF PT0 axial degrees of freedom, respectively, satisfying C ξs = C T sξ ; C χs , C sχ respectively represent the damping coupling sub-blocks between the pendulum 6DOF and the PTO axial degree of freedom, satisfying C χs = C T sχ ; c b1,f , c b2,f , c b3,f represents the base support flexibility modal equivalent damping coefficient; c t,fa , c t,ss represents the equivalent damping coefficient introduced by the flexible link of the drive train.

7. The wind wave energy multi-degree-of-freedom wide-band control method based on frequency difference coupling according to claim 6, characterized in that, The axial force of the i th set of PTO satisfies the following relationship: , wherein: F i is the i-th PTO axial force; K t is the motor force constant, K e is the back EMF constant, R is the stator equivalent resistance; s i , is the relative displacement / speed; c s,i 、k s,i is the mechanical damping / stiffness; i i is the current; The equivalent stiffness and damping contribution of the three sets of PTO to the platform are: , wherein: K PTO and C PTO are the equivalent stiffness / damping matrices of the PTO to the platform, respectively; k i eq , c i eq are the equivalent axial stiffness / damping coefficients of the i-th PTO shaft; J i are the Jacobian matrix rows; T denotes the transpose, wherein, J i are determined by the mounting point position and the axial direction.

8. The method of claim 1, wherein the wind wave energy is controlled by the hetero-frequency coupling method, and the method further comprises: In the step S4, the objective function J is: J=αE wave +βE wind -γA motion， wherein: E wave is the wave energy output; E wind is the wind energy output; A motion is the floating foundation motion response amplitude; coefficient α, β, γ combined input energy with working conditions dynamic adjustment.

9. The multi-degree-of-freedom broadband control method for wind and wave energy based on heterogeneous frequency coupling as described in claim 1, characterized in that, In step S5, the full working condition adaptive control strategy adopts a time domain rolling optimization algorithm based on model predictive control, which realizes the feedforward-feedback collaborative control of the multi degree of freedom system by predicting the wind wave excitation sequence in the future; The time domain rolling optimization algorithm based on model predictive control contains frequency domain weight, which increases the attitude / load penalty weight in the wave main frequency band to suppress the response in this frequency band and improve the comprehensive energy gain; In the time domain rolling optimization process, the control variables are solved online by combining the total stiffness matrix and the total damping matrix, and the operating constraints are displayed and set, including PTO current and rate of change, diagonal brace stroke and speed, platform attitude angle, tower base bending moment and mooring tension upper limit; quadratic programming or equivalent convex optimization is used to solve online; The hierarchical collaborative control system includes low, medium and high frequency bands, and the control effect of each frequency band is achieved through the coordinated allocation of virtual stiffness and virtual damping parameters to achieve global collaborative regulation of energy flow and structural response; To ensure that the control optimization process is coordinated with the inherent dynamic characteristics of the system, the inherent frequency constraint of the unit is introduced in the control parameter scheduling process; A hierarchical control architecture is used for multi-degree-of-freedom systems, and the execution layer generates a reference current for the PTO according to the virtual stiffness and virtual damping allocated by the upper layer to achieve energy feedback and virtual damping / stiffness injection, thereby achieving collaborative regulation of energy flow and structural response.

10. The method of claim 1, wherein the method is characterized by, In the step S6, the relative displacement is introduced as a key constraint variable in the constraint feedback and protection mechanism, wherein the relative displacement satisfies: s i = J i ξ , wherein: s i is the axial relative displacement of the i-th PTO; J i is the Jacobian matrix determined by the installation point vector and the axial unit vector; ξ is the platform six-DOF small configuration vector; On this basis, a protection state machine mechanism with hysteresis is set: when H s >H in or ∣ θ ∣> θ in or ∣ i ∣> i in any of the above conditions is true, the protection mode is entered, and the system is continuously T safe falling back to H s <H out , ∣θ∣< θ out and ∣ i ∣< i out smoothly exit the protection. wherein: H s is the effective wave height; θ is the platform attitude angle amplitude; i is the PTO current amplitude; H in , θ in , i in is the entry threshold; H out , θ out , i out is the exit threshold; T safe is the minimum duration to meet the consecutive safety condition; For the PTO stroke limit problem, the stroke end region is constrained in the constraint feedback and protection mechanism, and a potential barrier function of the stroke end region is introduced in the MPC optimization objective function.

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