Integrated Modeling and State Prediction Method of Floating Platform with Wave Energy Conversion Device, Computer Equipment and Computer Readable Storage Medium

Abstract

The embodiment of the present application discloses an integrated modeling and state prediction method for a floating platform containing a wave energy conversion device, a computer device, and a computer-readable storage medium. The method includes: obtaining the first motion state information of the subsystem that reflects the wave energy conversion device, and the second motion state information of the subsystem that reflects the floating platform; constructing a first model according to the first motion state information, and constructing a second model according to the second motion state information; constructing a third model according to the first output vector of the first model to obtain an integrated model by combining; designing a parameter estimation adaptive law according to the integrated model; substituting the system input and output into the parameter estimation adaptive law to estimate the key parameters to predict the motion state of the floating platform containing the wave energy conversion device. Therefore, the present application can jointly establish a unified integrated model by combining various subsystems, and design a parameter estimation adaptive law to estimate the key parameters to predict the motion state of the platform.

Description

Technical Field

[0001] The present application belongs to the technical field of new energy equipment, and in particular, relates to an integrated modeling and state prediction method of a floating platform including a wave energy conversion device, a computer device, and a computer-readable storage medium. Background Art

[0002] In the design and development of floating foundations, the key point is to ensure its dynamic stability, so as to ensure that the floating foundation and the equipment carried by it can meet the required stability in various operating and survival conditions throughout its life cycle. However, there are multiple sources of excitation such as wind, waves and currents in the marine environment, and the floating foundation is bound to undergo complex vibration motions with multiple frequencies and degrees of freedom, which poses a huge challenge to the safe service of the floating platform, and also brings higher costs and difficulties to the development and maintenance of the floating platform and its equipment.

[0003] The existing modeling methods are only established for a single floating platform or a single wave energy device, and do not consider combining the models to form a unified integrated model, which results in the need to configure two independent controllers for the system, increasing the control implementation cost and the difficulty of designing the vibration reduction control strategy. In addition, the separate modeling method cannot fully reflect the coupling relationship between the wave energy conversion device and the floating platform, making it difficult to accurately predict the motion state of the system. How to predict and control the motion state of the platform based on the design model of the entire platform is a technical problem that needs to be solved urgently by technicians in this field.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the invention

[0005] Based on this, it is necessary to propose an integrated modeling and state prediction method for a floating platform containing a wave energy conversion device, a computer device and a computer-readable storage medium to address the above problems, which can effectively predict the motion state of the floating platform.

[0006] The present application solves the technical problem by adopting the following technical solutions:

[0007] The present application provides an integrated modeling and state prediction method for a floating platform including a wave energy conversion device, which is applied to a floating platform including a wave energy conversion device. The floating platform including the wave energy conversion device includes a wave energy conversion device subsystem and a floating platform subsystem, and the wave energy conversion device subsystem and the floating platform subsystem are mechanically connected. The method includes the following steps: respectively obtaining first motion state information of the wave energy conversion device subsystem and second motion state information of the floating platform subsystem; establishing a non-smooth dynamic parameterized expression function caused by the mechanical connection; constructing a first model according to the first motion state information, the first model being a mathematical model of the wave energy conversion device subsystem; constructing a second model according to the second motion state information, the second model being a mathematical model of the floating platform subsystem; obtaining a first output vector of the first model, and constructing a non-smooth dynamic parameterized expression function according to the first output vector and the non-smooth dynamic parameterized expression function. The expression function is used to construct a third model, which characterizes the unmeasurable non-smooth dynamics of the wave energy conversion device subsystem and the floating platform subsystem caused by mechanical connection; the first model, the second model and the third model are combined to obtain an integrated model, which includes a parameter matrix to be identified; an adaptive observer is designed according to the integrated model; an auxiliary matrix is ​​constructed after filtering the integrated model, and a parameter estimation adaptive law is designed according to the auxiliary matrix, and the adaptive observer and the parameter estimation adaptive law are used to estimate the parameter matrix to be identified without using intermediate unmeasurable signals in the floating platform containing the wave energy conversion device; the system input and output of the floating platform containing the wave energy conversion device are obtained, and the system input and output are substituted into the parameter estimation adaptive law to estimate key parameters; the key parameters and the system input and output are substituted into the integrated model to predict the motion state of the floating platform containing the wave energy conversion device.

[0008] In an optional embodiment of the present application, constructing a first model according to the first motion state information includes: obtaining motion state information of each wave energy conversion device in the wave energy conversion device subsystem, including the angle and angular velocity between the wave energy conversion device and the floating platform subsystem, and the first model is expressed as:

[0009]

[0010] in, is the motion state information of the ith wave energy conversion device, and is the relative rotation angle θ between the ith wave energy conversion device and the floating platform subsystem. i and angular velocity u i is the input data of the ith wave energy conversion device, including environmental excitation and active vibration reduction control input; A 1i is the system matrix of the ith wave energy conversion device, B 1i is the input matrix of the ith wave energy conversion device, C 1iis the output matrix of the i-th wave energy conversion device; y 1i is the output of each wave energy conversion device within the wave energy conversion device subsystem, and is an intermediate unmeasurable signal.

[0011] In an alternative embodiment of the present application, constructing a second model based on the second motion state information includes: The second motion state information includes the pitch angle, roll angle, heave displacement, pitch angular velocity, roll angular velocity, and heave velocity of the floating platform subsystem; obtaining a control input distribution matrix, where the input distribution matrix is determined by the topological structure and arrangement of the wave energy conversion device subsystem; obtaining the input signal of the floating platform subsystem, where the input signal of the floating platform subsystem is the output vector of the third model; constructing a second model based on the pitch angle, roll angle, heave displacement, pitch angular velocity, roll angular velocity, heave velocity, control input distribution matrix, and input signal of the floating platform subsystem. The second model is expressed as:

[0012]

[0013] where, is the second motion state information, which are respectively the pitch angle, roll angle, heave displacement, pitch angular velocity, roll angular velocity, and heave velocity of the floating platform subsystem; A2 is the system matrix of the floating platform subsystem, B2 is the input matrix of the floating platform subsystem, and C2 is the output matrix of the floating platform subsystem; γ = B2Δ is the control input matrix of the wave energy conversion device subsystem to the floating platform subsystem after control distribution, and Δ is the control input distribution matrix; is the input signal of the floating platform subsystem and is an intermediate unmeasurable signal.

[0014] In an alternative embodiment of the present application, constructing a third model based on the first output vector includes: obtaining a non-smooth dynamic parameterization function to process the first output vector to obtain the third model, where the non-smooth dynamic parameterization function is represented by a continuous piecewise linear neural network. The third model is expressed as:

[0015]

[0016] where, F() is the non-smooth dynamic parameterization function, [y 11 , y 12 ,..., y 1n is the first output vector; q in (x) and p in (x) respectively represent the upper and lower bounds of the variable x within n piecewise subintervals; ζ n (y 1n , p i , q i ) is the basis function and can be expressed as: ζ n (y 1n, p i ,q i )=max(y 1n ,min(p i ,q i )), p i ,q i is the segment boundary; W T is the parameter matrix to be identified, and Ψ(Y1) is the regression vector.

[0017] In an optional embodiment of the present application, the first model, the second model and the third model are combined to obtain an integrated model, including: obtaining a control input matrix, the control input matrix represents a mathematical expression of the control input of the wave energy conversion device subsystem to the floating platform subsystem after control distribution; combining the first model, the second model and the third model, and substituting the system input and output and the control input matrix to obtain an integrated model, and the integrated model can be expressed as:

[0018]

[0019] Where X is the augmented system state, expressed as X = [x 11 , x 12 ,...x 1i , x2] T , which includes the first motion state information and the second motion state information; U is the augmented control input vector, Y is the augmented output matrix, which can be directly obtained; A is the augmented system matrix, B is the augmented input matrix, and C is the augmented output matrix; Λ is the extended input matrix, which is determined by the control input matrix; Ψ(Y1) is the regression vector; W T is the parameter matrix to be identified.

[0020] In an optional embodiment of the present application, an adaptive observer is designed according to the integrated model, including: obtaining a system state observation value and a system output observation value of the integrated model, and designing an adaptive observer based on the first motion state information and the second motion state information, wherein the adaptive observer is expressed as:

[0021]

[0022] in, is the system state observation value, is the system output observation value; K is the feedback gain matrix, which is used to ensure that there is a symmetric positive definite matrix P, so that for any positive definite symmetric matrix Q, A0 is satisfied. T P+PA0=-Q; is the parameter matrix to be identified W T An estimated value of To use the estimated output state Replace the regression vector after the unmeasurable output Y in the wave energy conversion device subsystem.

[0023] In an alternative embodiment of the present application, after performing a filtering operation on the integrated model, an auxiliary matrix is constructed, including: obtaining a reconstructed model according to the integrated model; introducing a first-order low-pass filter into the reconstructed model, and the following filtering model can be obtained after arrangement:

[0024]

[0025] where γ + =(γ T γ) -1 γ T is the generalized inverse matrix of γ, γ is a directly obtainable control input matrix; x2 is the second motion state information; κ f is a preset filtering coefficient; A2 is the system matrix of the floating platform subsystem; W T is the parameter matrix to be identified, Ψ(Y1) is the regression vector; ε is the residual, expressed as Design the auxiliary matrix according to the filtering model Expressed as: where μ is the bounded residual; and are the auxiliary matrices designed according to the filtering model, and Expressed as:

[0026]

[0027] l is a preset positive constant, used to ensure the boundedness of and .

[0028] In an alternative embodiment of the present application, according to the auxiliary matrix, a parameter estimation adaptive law is designed, including: designing a parameter estimation adaptive law for estimating the parameter matrix to be identified according to the auxiliary matrix, and the parameter estimation adaptive law is expressed as: The parameter estimation adaptive law can make the parameter estimation error and the adaptive observer error corresponding to the parameter matrix to be identified converge simultaneously; where Γ is the gain matrix of the preset adaptive law; η is the preset leakage gain; F is the preset matching matrix; is the output error of the adaptive observer.

[0029] The present application also provides a computer device, including a processor and a memory: the processor is used to execute the computer program stored in the memory to implement the method as described above.

[0030] The present application also provides a computer-readable storage medium, storing a computer program, which implements the method as described above when the computer program is executed by a processor.

[0031] The embodiments of the present application have the following beneficial effects:

[0032] The present application can combine the input and output of each subsystem of a floating platform containing a wave energy conversion device, and consider the relationship between the subsystems to establish a unified integrated model; it can identify the key parameters of the system based on the established continuous integrated model under the condition of only using the input and output data of the system, avoiding the use of a large number of sensors and unmeasurable intermediate variables; through the constructed integrated model, a parameter estimation adaptive law is designed, and the designed parameter estimation adaptive law can simultaneously ensure the simultaneous convergence of the observation error of the adaptive observer and the parameters to be identified, so as to quickly estimate the key parameters; finally, the constructed integrated model and the estimated key parameters can be used to predict the motion state of the platform, providing a reliable reference for controlling the platform to resist the influence of wind and waves.

[0033] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with the accompanying drawings. It should be understood that the above general description and the detailed description below are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] in:

[0036] Figure 1 A schematic flow chart of an integrated modeling and state prediction method for a floating platform including a wave energy conversion device provided by an embodiment;

[0037] Figure 2 An assembly diagram of a floating foundation including a wave energy conversion device provided in an embodiment;

[0038] Figure 3 A schematic diagram of an integrated model architecture provided by an embodiment;

[0039] Figure 4 A schematic diagram of a key parameter identification algorithm framework provided by an embodiment;

[0040] Figure 5 A schematic block diagram of the structure of a computer device provided by an embodiment. Detailed implementation mode

[0041] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0042] With the increasing emphasis on clean energy, the ocean has gradually become the focus of new energy development. Using a floating platform to carry power generation equipment is one of the products. However, there are multi-source excitations such as wind, wave and current in the ocean environment, which bring higher costs and difficulties to the development and maintenance of the floating platform and its installed equipment. In order to improve the stability of the floating platform and reduce the vibration of the floating platform in the multi-source ocean environment, the relevant structure of the floating platform (such as adding heave plates, etc.) can be optimized to change the inherent dynamic response of the system to achieve stability, or the stability can be achieved by adjusting the water volume in the ballast tank installed on the floating platform. However, the regulation range of such methods is limited and the real-time regulation is poor, so that the platform can only adapt to a certain or certain specific sea conditions and is difficult to adapt to the complex and changeable sea conditions under the combined excitation of wind, wave and current. In addition, the wave energy conversion device can be connected to the floating platform by introducing springs, dampers and active actuators as vibration damping elements, and then passive / active vibration damping control is introduced to effectively enhance the stability of the floating platform in general sea conditions and extreme sea conditions. And the wave energy conversion device can store the wave energy through a kinetic energy recovery device to make full use of the surrounding ocean resources. In addition, since the wave energy conversion device can share the mooring system with the floating foundation, the manufacturing cost of the equipment can be reduced and the overall economic benefit can be improved. Therefore, combining the wave energy conversion device with the floating platform can improve the overall dynamic response of the system, and realize the active vibration damping of the floating platform by introducing actuators, so as to achieve the safe, reliable and efficient operation goals of the floating platform.

[0043] However, how to construct an integrated mathematical model of a floating foundation containing a wave energy conversion device is the key to analyzing the overall dynamic response of the system and designing passive / active vibration reduction control strategies. The existing modeling methods are only established for a single floating platform or a single wave energy device, and do not consider combining the models to form a unified integrated model. As a result, the system needs to be configured with two independent controllers, which increases the control implementation cost and the difficulty of designing the vibration reduction control strategy. In addition, the separate modeling method cannot fully reflect the coupling relationship between the wave energy conversion device and the floating platform, making it difficult to accurately predict the motion state of the system. In addition, the integrated model of the floating foundation containing a wave energy conversion device is composed of three subsystems in series: the wave energy conversion device subsystem, the non-smooth dynamics caused by mechanical connection (such as friction, dead zone and saturation, etc.), and the floating platform subsystem. The non-smooth dynamics exist between the wave energy conversion device subsystem and the floating platform subsystem, which makes the entire system have two unmeasurable intermediate variables of the input and output of the non-smooth dynamic link, making it difficult to achieve integrated modeling and parameter identification. The usual method for parameter identification for this type of system is to divide the overall system into two modules and obtain the parameters of the two modules respectively through a step-by-step iterative method. The existing technology is based on a discrete model and cannot be directly extended to a continuous state space expression. How to perform parameter identification based on an integrated model established in a continuous state space to predict the motion state of a floating platform? This application proposes an integrated modeling and state prediction method for a floating platform containing a wave energy conversion device. In order to clearly describe the integrated modeling and state prediction method for a floating platform containing a wave energy conversion device provided in this embodiment, please refer to Figure 1 ~Figure, including steps S110~S140.

[0044] Based on the above description, the object of this application is a floating platform with a wave energy conversion device, so the method is also applied to a floating platform with a wave energy conversion device. The floating platform with a wave energy conversion device includes a wave energy conversion device subsystem and a floating platform subsystem, and the wave energy conversion device subsystem and the floating platform subsystem are mechanically connected, for example, by means of springs, damping, etc. For the structure of the floating platform with a wave energy conversion device, please refer to Figure 2 .like Figure 2 As shown, the wave energy conversion device subsystem consists of n wave energy conversion devices ( Figure 2Taking three wave energy conversion devices as examples, namely 101, 102, and 103). The wave energy conversion devices form a mechanical connection with the floating platform subsystem through spring damping and active actuators (such as hydraulic actuators or actuator motors) to adjust the dynamic response of the overall system, and achieve passive / active vibration reduction adjustment of the overall system under multi-source excitations of external wind, waves, and currents. n wave energy conversion devices are connected to the floating platform subsystem according to the corresponding topological structure and layout method. The wave energy conversion device subsystem will ensure that the floating platform subsystem remains stable and does not capsize under the condition of multi-source excitation through passive / active vibration reduction methods.

[0045] The main body of the floating platform subsystem consists of n pontoons ( Figure 2 Taking three pontoons as examples, namely 201, 202, and 203), a central column 204, and connecting rod members. The connecting flanges on the floating platform subsystem can carry new energy devices such as wind turbines and photovoltaics to provide energy support for various sensors and actuators and other devices, and grid-connect the excess energy to achieve power supply.

[0046] Furthermore, the floating platform containing wave energy conversion devices may further include a data acquisition module and a data storage device 302. The data acquisition module and the data storage device 302 can be installed on the floating platform subsystem. The data acquisition module may include various sensors, including but not limited to a wave gauge 301, an inertial navigation 303, an anemometer 304, and a voltage acquisition instrument, etc., for acquiring various data required for predicting the motion state; the data storage device 302 is used to store the data of the data acquisition module in real time. Among them, the anemometer 304 can be connected to the pontoon 201 through bolts; the inertial navigation 303 and the voltage acquisition instrument can be installed in the equipment cabin of the central column 204.

[0047] Step S110: Obtain the first motion state information reflecting the wave energy conversion device subsystem and the second motion state information reflecting the floating platform subsystem respectively; establish a non-smooth dynamic parameterization expression function reflecting the mechanical connection.

[0048] In an embodiment, the first motion state information and the second motion state information can be obtained according to each sensor in the data acquisition module. The specific types included, as well as the constructed non-smooth dynamic parameterization expression function, will be described when constructing the third model and will not be elaborated here for the time being.

[0049] Step S120: Construct a first model based on the first motion state information, where the first model is a mathematical model of the wave energy conversion device subsystem; construct a second model based on the second motion state information, where the second model is a mathematical model of the floating platform subsystem; obtain a first output vector of the first model, and construct a third model based on the first output vector and the non-smooth dynamic parameterization expression function, where the third model characterizes the unmeasurable non-smooth dynamics caused by the mechanical connection between the wave energy conversion device subsystem and the floating platform subsystem.

[0050] In one embodiment, step S120: Construct a first model based on the first motion state information, including: obtaining the motion state information of each wave energy conversion device in the wave energy conversion device subsystem to construct the first model.

[0051] In one embodiment, as described above, the wave energy conversion device subsystem is composed of multiple wave energy conversion devices. To establish the first model reflecting the mathematical model of the wave energy conversion device subsystem, it can be constructed according to the motion state information of the wave energy conversion devices, specifically including the angle and angular velocity between the wave energy conversion device and the floating platform subsystem.

[0052] The linearized first model of the i-th wave energy conversion device can be expressed as:

[0053]

[0054] In Equation (1), is the motion state information of the i-th wave energy conversion device, which are the relative rotation angle θ i and angular velocity between the i-th wave energy conversion device and the floating platform subsystem. The above information is all included in the first motion information. u i is the input data of the i-th wave energy conversion device, including environmental excitation and active vibration damping control input. Among them, the environmental excitation can specifically include, but is not limited to, wave height and wind speed data, etc.; for the active vibration damping control input, it is the input signal to the wave energy conversion device when the controller in the wave energy conversion device performs active control for vibration damping. A 1i is the system matrix of the i-th wave energy conversion device, B 1i is the input matrix of the i-th wave energy conversion device, C 1i is the output matrix of the i-th wave energy conversion device, A 1i 、B 1i and C 1i are known parameters. Further, the inputs in each wave energy conversion device are combined to form a control input vector U = [u1, u2,..., u n T ​, the outputs of each wave energy conversion device are combined to obtain the first output vector Y1 = [y 11 , y 12 , ..., y 1n T , where y 1i is the output of each wave energy conversion device within the wave energy conversion device subsystem, which is an intermediate unmeasurable signal and will be replaced in the subsequent estimation process.

[0055] In one embodiment, step S120: constructing a second model according to the second motion state information, including: the second motion state information includes the pitch angle, roll angle, heave displacement, pitch angular velocity, roll angular velocity, and heave velocity of the floating platform subsystem; obtaining a control input distribution matrix, which is determined by the topological structure and arrangement of the wave energy conversion device subsystem; obtaining the input signal of the floating platform subsystem; constructing a second model according to the pitch angle, roll angle, heave displacement, pitch angular velocity, roll angular velocity, heave velocity, control input distribution matrix, and input signal of the floating platform subsystem.

[0056] In one embodiment, similar to constructing the first model, for the mathematical model of the floating platform subsystem, it is also necessary to rely on the second motion information reflecting the floating platform subsystem. The second motion state information includes the pitch angle, roll angle, heave displacement, pitch angular velocity, roll angular velocity, and heave velocity of the floating platform subsystem, etc. It is also necessary to obtain the input signal of the floating platform subsystem, where the input signal of the floating platform subsystem is the output vector of the third model, and the construction of the third model will be described later. The constructed second model can be specifically expressed as:

[0057]

[0058] Equation (2), is the second motion state information, which are respectively the pitch angle, roll angle, heave displacement, pitch angular velocity, roll angular velocity, and heave velocity of the floating platform subsystem. A2 is the system matrix of the floating platform subsystem, B2 is the input matrix of the floating platform subsystem, C2 is the output matrix of the floating platform subsystem, and the same A2, B2, and C2 are known parameters. γ = B2Δ is the control input matrix of the wave energy conversion device subsystem to the floating platform subsystem after control distribution. Δ is the control input distribution matrix, which is related to the topological structure and arrangement of the wave energy conversion device subsystem and is used to transfer the forces of each wave energy conversion device to the center of gravity of the floating platform subsystem according to the geometric configuration and can be directly obtained. is the input signal of the floating platform subsystem, which is an intermediate unmeasurable signal and will be replaced in the subsequent estimation process.

[0059] ​In one embodiment, step S120: constructing a third model according to the first output vector, including: obtaining a non-smooth dynamic parameterization function to process the first output vector to obtain a third model, and the non-smooth dynamic parameterization function is represented by a continuous piecewise linear neural network.

[0060] In one embodiment, the third model is used to represent the non-smooth dynamic parameterization between the wave energy conversion device subsystem and the floating platform subsystem. The non-smooth dynamics are mainly caused by the mechanical connection between the wave energy conversion device subsystem and the floating platform subsystem, including non-linear links such as dead zones and friction. Therefore, for the construction of the third model, the first output vector Y1 of the first model can be obtained, and the first output vector Y1 of the first model can be processed by using the non-smooth dynamic parameterization function constructed in step S110 to achieve. Among them, the non-smooth dynamic parameterization function is a function F() obtained by parameterizing the non-smooth dynamic representation according to a continuous piecewise linear neural network (CPLNN). The construction process of the third model can be expressed as:

[0061]

[0062] In Equation (3), F() is the non-smooth dynamic parameterization function, [y 11 , y 12 ,..., y 1n is the first output vector; q in (x) and p in (x) respectively represent the upper and lower bounds of the variable x in n piecewise sub-intervals.

[0063] ζ n (y 1n , p i , q i ) is the basis function and can be expressed as:

[0064] ζ n (y 1n , p i , q i ) = max(y 1n , min(p i , q i )) (4)

[0065] In Equation (4), p i , q i are the piecewise boundaries used to distinguish piecewise sub-intervals.

[0066] In Equation (3), W T is the parameter matrix to be identified, and Ψ(Y1) is the regression vector, which can be respectively expressed as:

[0067]

[0068] Step S130: Combine the first model, the second model, and the third model to obtain an integrated model, where the integrated model includes a parameter matrix to be identified; design an adaptive observer according to the integrated model; construct an auxiliary matrix after performing a filtering operation on the integrated model, and design a parameter estimation adaptive law based on the auxiliary matrix. The adaptive observer and the parameter estimation adaptive law are used to estimate the parameter matrix to be identified without using the intermediate unmeasurable signals inside the floating platform with wave energy conversion devices.

[0069] In one embodiment, Step S130: Combining the first model, the second model, and the third model to obtain an integrated model includes: obtaining a control input matrix, where the control input matrix represents the mathematical expression of the control input of the wave energy conversion device subsystem to the floating platform subsystem after control allocation; combining the first model, the second model, and the third model, and substituting the system input-output and the control input matrix to obtain the integrated model.

[0070] In one embodiment, the control input matrix is also Δ. This allocation matrix is related to the topological structure and arrangement mode of the wave energy conversion device subsystem, and is used to transfer the acting forces of each wave energy conversion device to the center of gravity of the floating platform subsystem according to the geometric configuration, and can be directly obtained. This will be reflected in the integrated model later, and will not be elaborated here for the time being.

[0071] Combining Equation (2) and Equation (5) gives the reconstructed model:

[0072]

[0073] Since the output vector of the wave energy conversion device subsystem is difficult to directly measure, parameter estimation cannot be directly performed based on Equation (6). Therefore, Equation (6) is combined with Equation (1) to establish the integrated model as follows:

[0074]

[0075] In Equation (7), X is the augmented system state, expressed as X = [x 11 , x 12 ,... x 1i , x2] T , which includes the first motion state information and the second motion state information. U is the augmented control input vector, and Y is the augmented output matrix, both of which can be directly obtained. Λ is the extended input matrix, determined by the control input matrix, specifically expressed as A = [O, γ] T . Ψ(Y1) is the regression vector; W T is the parameter matrix to be identified. A is the augmented system matrix, which can be expressed as:

[0076]

[0077] B is the augmented input matrix, which can be expressed as B = [B 11 , B 12 , ..., B 1n , O] T ; C is the augmented output matrix, which can be expressed as C = [O, C2] T . Similar to the first model and the second model mentioned above, A, B and C are all known parameters and can be directly obtained. For ease of understanding, the mathematical model structure of the integrated model obtained by combining the first model, the second model and the third model can be referred to Figure 3 shown. Figure 3 The input-output relationship between the models within the integrated model is demonstrated, making it easier to understand the model construction and internal data processing flow.

[0078] In one implementation, step S130: designing an adaptive observer according to the integrated model includes: obtaining system state observation values ​​and system output observation values, and designing an adaptive observer.

[0079] In one embodiment, an adaptive observer is designed to observe the system state. The purpose of designing the adaptive observer is that the output Y1 of the subsystem of the wave energy conversion device is difficult to directly obtain due to the limited cost and installation space of the sensor. Therefore, in order to avoid using the internal variable Y1 of the system in the parameter identification link, an adaptive observer based on the first motion state information and the second motion state information is designed. The adaptive observer can be expressed as:

[0080]

[0081] In formula (9), is the system state observation value, is the system output observation value. K is the feedback gain matrix, which is used to ensure that there is a symmetric positive definite matrix P, so that for any positive definite symmetric matrix Q, A0 is satisfied. T P+PA0=-Q. is the parameter matrix to be identified W T An estimated value of To use the estimated output state Regression vector after replacing the unmeasurable output Y in the wave energy conversion device subsystem.

[0082] In one embodiment, step S130: constructing an auxiliary matrix after filtering the integrated model, including: obtaining a reconstruction model according to the integrated model; introducing a first-order low-pass filter into the reconstruction model to obtain a filtering model; designing an auxiliary matrix according to the filtering model It is expressed as: Among them, μ is the bounded residual; and is an auxiliary matrix designed according to the filtering model.

[0083] In one embodiment, the reconstruction model is as shown in Equation (6). The filtering operation can be to introduce a first-order low-pass filter (·) on both sides of Equation (6) f =(·) / (κ f s + 1), and after processing, the filtered variable can be obtained:

[0084]

[0085] In Equation (10), κ f is a preset filtering coefficient to be adjusted. Then substituting Equation (10) into Equation (6), we can get:

[0086]

[0087] Next, by organizing Equation (11), the filtering model can be obtained:

[0088]

[0089] In Equation (12), γ + =(γ T γ) -1 γ T is the generalized inverse matrix of γ, and γ is a directly obtainable control input matrix. x2 is the second motion state information; A2 is the system matrix of the floating platform subsystem and belongs to known parameters. W T is the parameter matrix to be identified, and Ψ(Y1) is the regression vector. ε is the residual and is expressed as Since both Ψ and W are bounded functions, ε is also a bounded variable.

[0090] Design the auxiliary matrix and the auxiliary matrix for extracting the parameter estimation error through the measurable variable x2 and Ψ and can be expressed as:

[0091]

[0092] l is a preset positive constant for ensuring the boundedness of and According to Equation (13), design the auxiliary matrix as follows:

[0093]

[0094] where is the bounded residual, that is, there exists a constant Make the inequality hold. It can be seen from formula (14) that the auxiliary matrix contains parameter estimation error information Therefore, by using to drive parameter update will improve parameter estimation performance.

[0095] In one embodiment, step S130: Design a parameter estimation adaptive law according to the auxiliary matrix, including: The designed parameter estimation adaptive law is expressed as

[0096]

[0097] The design of the adaptive observer and the parameter estimation adaptive law is used to directly estimate the key parameters without using or avoiding using intermediate unmeasurable signals during the subsequent process of estimating the key parameters. And the parameter estimation adaptive law can also make the parameter estimation error corresponding to the parameter matrix to be identified and the adaptive observer error converge simultaneously. In formula (15), Γ is the gain matrix of the preset adaptive law, which is a positive definite diagonal matrix. η is the preset leakage gain, and the numerical setting is η > 0. F is the preset matching matrix, and the matching matrix needs to satisfy the formula PΛ = C T F T . is the output error of the adaptive observer, which can be expressed as

[0098] Step S140: Obtain the system input and output of the floating platform with a wave energy conversion device, substitute the system input and output into the parameter estimation adaptive law to estimate the key parameters; substitute the key parameters and the system input and output into the integrated model to predict the motion state of the floating platform with a wave energy conversion device.

[0099] In one embodiment, as described above, the present application uses the constructed integrated model and the designed parameter estimation adaptive law to avoid or not use the unmeasurable intermediate variables in the system, so as to quickly and accurately estimate the key parameters. In fact, it is an embodiment of an estimation algorithm. For ease of understanding how the present application implements this algorithm, reference can be made to Figure 3 the schematic diagram of the model architecture of the integrated model shown in Figure 4The algorithm framework diagram is shown. Based on the adaptive observer formula (9) and the adaptive law formula (15) and the system input signal U collected by the data acquisition module and the floating platform subsystem output signal y2, the parameter matrix W to be identified can be estimated online to determine the key parameters of the non-smooth dynamics in the integrated model. The key parameters are the specific values ​​estimated by the parameter matrix to be identified. It can be understood that the integrated model is an expression of the floating platform containing the wave energy conversion device, which includes not only the key parameters, but also the augmented system state matrix, augmented input vector and augmented output vector of the first model, the second model and the third model information. The above parameters can be obtained in the estimation process.

[0100] Furthermore, in order to verify the accuracy of the established integrated model and predict the motion state of the overall system based on the established integrated model, the integrated model given in the integrated modeling module and the parameters estimated in the key parameter identification module can be integrated to form a complete integrated mathematical model; then, the system input signal U is fed into the established mathematical model to obtain the system output X p , and calculate X p The statistical data and fitting indexes (such as R-squared, RMSE, etc.) between the real output signal X of the system are used to verify the accuracy of the established integrated model. The key parameters and the system input signal U are fed into the verified integrated model to predict the motion state of the platform. Once the key parameters are obtained, the process of realizing motion state prediction belongs to the prior art and will not be described in detail.

[0101] Therefore, the present application can combine the input and output of each subsystem of the floating platform containing the wave energy conversion device, and consider the relationship between the subsystems to establish a unified integrated model. The established integrated model can more comprehensively confirm the coupling relationship between the subsystems by combining the input and output relationship of each subsystem, making the model establishment more accurate and providing guidance for the design of the overall system parameters; in addition, based on the proposed integrated model, there is no need to design a separate controller for each subsystem, which facilitates the implementation and design of the active vibration suppression controller.

[0102] The proposed parameter estimation framework is different from the step-by-step iterative identification framework based on discrete models. The proposed parameter estimation framework can identify the key parameters of the system based on the established continuous integrated model using only the system input and output data, thus avoiding the use of a large number of sensors and unmeasurable intermediate variables.

[0103] An adaptive law for parameter estimation is designed through the constructed integrated model. The designed adaptive law for parameter estimation can simultaneously ensure the simultaneous convergence of the observation error of the adaptive observer and the parameters to be identified, so as to quickly estimate the key parameters. Finally, using the constructed integrated model and the estimated key parameters, the motion state of the platform can be predicted, providing a reliable reference for controlling the platform to resist the influence of wind and waves, etc.

[0104] Figure 5 The internal structure diagram of a computer device in an embodiment is shown. The computer device can specifically be a terminal or a server. As Figure 5 shown, the computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and can also store a computer program. When the computer program is executed by the processor, the processor can implement the integrated modeling and state prediction method of a floating platform with a wave energy conversion device. The internal memory can also store a computer program. When the computer program is executed by the processor, the processor can execute the integrated modeling and state prediction method of a floating platform with a wave energy conversion device. Those skilled in the art can understand that Figure 5 the structure shown in [figure reference] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0105] In an embodiment, the present application also proposes a computer-readable storage medium storing a computer program. When the computer program is executed by the processor, the processor is caused to execute the steps of the foregoing method,

[0106] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0107] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0108] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for integrated modeling and state prediction of a floating platform with a wave energy conversion device, applied to a floating platform with a wave energy conversion device, wherein the floating platform with a wave energy conversion device comprises a wave energy conversion device subsystem and a floating platform subsystem, wherein the wave energy conversion device subsystem and the floating platform subsystem are mechanically connected, and characterized in that: The method comprises the following steps: Respectively obtaining first motion state information reflecting the wave energy conversion device subsystem and second motion state information reflecting the floating platform subsystem; establishing a parameterized expression function reflecting the non-smooth dynamics caused by the mechanical connection; Constructing a first model according to the first motion state information, the first model being a mathematical model of the wave energy conversion device subsystem; constructing a second model according to the second motion state information, the second model being a mathematical model of the floating platform subsystem; acquiring a first output vector of the first model, and constructing a third model according to the first output vector and the non-smooth dynamic parameterized expression function, the third model representing the unmeasurable non-smooth dynamics of the wave energy conversion device subsystem and the floating platform subsystem caused by mechanical connection; Combining the first model, the second model and the third model to obtain an integrated model, wherein the integrated model includes a parameter matrix to be identified; designing an adaptive observer based on the integrated model; After filtering the integrated model, an auxiliary matrix is ​​constructed, and a parameter estimation adaptive law is designed according to the auxiliary matrix, wherein the adaptive observer and the parameter estimation adaptive law are used to estimate the parameter matrix to be identified without using intermediate unmeasurable signals in the floating platform containing the wave energy conversion device; The system input and output of the floating platform including the wave energy conversion device are obtained, and the system input and output are substituted into the parameter estimation adaptive law to estimate key parameters; the key parameters and the system input and output are substituted into the integrated model to predict the motion state of the floating platform including the wave energy conversion device.

2. The integrated modeling and state prediction method of a floating platform with a wave energy conversion device according to claim 1, characterized in that: The step of constructing a first model according to the first motion state information includes: The motion state information of each wave energy conversion device in the wave energy conversion device subsystem is obtained, including the angle and angular velocity between the wave energy conversion device and the floating platform subsystem. The first model is expressed as: in, is the motion state information of the i-th wave energy conversion device, and is the relative rotation angle θ between the i-th wave energy conversion device and the floating platform subsystem. i and angular velocity u i A is the input data of the ith wave energy conversion device, including environmental excitation and active vibration reduction control input; 1i is the system matrix of the i-th wave energy conversion device, B 1i is the input matrix of the ith wave energy conversion device, C 1i is the output matrix of the i-th wave energy conversion device; y 1i It is the output of each of the wave energy conversion devices in the wave energy conversion device subsystem, and is the intermediate unmeasurable signal.

3. The integrated modeling and state prediction method of a floating platform with a wave energy conversion device according to claim 1, characterized in that: The step of constructing a second model according to the second motion state information includes: The second motion state information includes the pitch angle, roll angle, heave displacement, pitch angular velocity, roll angular velocity and heave velocity of the floating platform subsystem; Acquire a control input allocation matrix, wherein the input allocation matrix is ​​determined by the topological structure and arrangement of the wave energy conversion device subsystem; acquire a floating platform subsystem input signal, wherein the floating platform subsystem input signal is an output vector of the third model; The second model is constructed according to the pitch angle, the roll angle, the heave displacement, the pitch angular velocity, the roll angular velocity, the heave velocity, the control input allocation matrix and the floating platform subsystem input signal. The second model is expressed as: in, is the second motion state information, which are respectively the pitch angle, the roll angle, the heave displacement, the pitch angular velocity, the roll angular velocity and the heave velocity of the floating platform subsystem; A2 is the system matrix of the floating platform subsystem, B2 is the input matrix of the floating platform subsystem, and C2 is the output matrix of the floating platform subsystem; is the control input matrix of the wave energy conversion device subsystem to the floating platform subsystem after control allocation, and Δ is the control input allocation matrix; The floating platform subsystem input signal is the intermediate unmeasurable signal.

4. The integrated modeling and state prediction method for a floating platform with a wave energy conversion device according to claim 1, characterized in that: The step of constructing a third model according to the first output vector and the non-smooth dynamic parameterized expression function comprises: The non-smooth dynamic parameterized function is characterized by a continuous piecewise linear neural network, and the third model is expressed as: Wherein, F() is the non-smooth dynamic parameterized function, [y 11 ,y 12 , ..., y 1n ] is the first output vector; q in (x) and p in (x) represents the upper and lower bounds of the variable x in the n segmented subintervals respectively; ζ n (y 1n , p i ,q i ) is the basis function and can be expressed as: n (y 1n , p i ,q i )=max(y 1n ,min(p i ,q i )), p i ,q i is the segment boundary; W T is the parameter matrix to be identified, and Ψ(Y1) is the regression vector.

5. The integrated modeling and state prediction method for a floating platform with a wave energy conversion device according to claim 1, characterized in that: The combining the first model, the second model and the third model to obtain an integrated model comprises: Acquire a control input matrix, wherein the control input matrix represents a mathematical expression of the control input of the wave energy conversion device subsystem to the floating platform subsystem after control allocation; The first model, the second model and the third model are combined, and the system input and output and the control input matrix are substituted to obtain an integrated model, which can be expressed as: Where X is the augmented system state, expressed as X = [x 11 , x 12 ,...x 1i , x2] T , which includes the first motion state information and the second motion state information; U is the augmented control input vector, Y is the augmented output matrix, which can be directly obtained; A is the augmented system matrix, B is the augmented input matrix, and C is the augmented output matrix; Λ is the extended input matrix, which is determined by the control input matrix; Ψ(Y1) is the regression vector; W T is the parameter matrix to be identified.

6. The integrated modeling and state prediction method for a floating platform with a wave energy conversion device according to claim 5, characterized in that: The step of designing an adaptive observer according to the integrated model comprises: The system state observation value and the system output observation value of the integrated model are obtained, and the adaptive observer based on the first motion state information and the second motion state information is designed. The adaptive observer is expressed as: in, is the system state observation value, is the output observation value of the system; K is the feedback gain matrix, which is used to ensure that there is a symmetric positive definite matrix P, so that for any positive definite symmetric matrix Q, A0 is satisfied. T P+PA0=-Q; is the parameter matrix W to be identified T An estimated value of To use the estimated output state The regression vector after replacing the unmeasurable output Y in the subsystem of the wave energy conversion device.

7. The integrated modeling and state prediction method for a floating platform with a wave energy conversion device according to claim 1, characterized in that: The step of constructing an auxiliary matrix after filtering the integrated model comprises: The reconstruction model is obtained according to the integrated model; a first-order low-pass filter is introduced into the reconstruction model, and the filtering model can be obtained by sorting: in, is the generalized inverse matrix of γ, γ is the control input matrix that can be directly obtained; x2 is the second motion state information; κ f is the preset filter coefficient; A2 is the system matrix of the floating platform subsystem; W T is the parameter matrix to be identified, Ψ(Y1) is the regression vector; ε is the residual, expressed as Design the auxiliary matrix according to the filtering model It is expressed as: Among them, μ is the bounded residual; and is the auxiliary matrix designed according to the filtering model, and It is expressed as: l is a preset positive constant used to ensure and The boundedness of .

8. The integrated modeling and state prediction method for a floating platform with a wave energy conversion device according to claim 7, characterized in that: The step of designing a parameter estimation adaptive law according to the auxiliary matrix comprises: According to the auxiliary matrix, a parameter estimation adaptive law is designed to estimate the parameter matrix to be identified. The parameter estimation adaptive law is expressed as: The parameter estimation adaptive law can make the parameter estimation error and the adaptive observer error corresponding to the parameter matrix to be identified converge simultaneously; wherein Γ is a preset gain matrix; η is a preset leakage gain; F is a preset matching matrix; is the output error of the adaptive observer.

9. A computer device, characterized in that: including a processor and a memory; The processor is configured to execute the computer program stored in the memory to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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