Constant-voltage output anti-interference control method for wireless energy transmission system of offshore floating platform

Abstract

The invention provides a constant voltage output anti-interference control method for a wireless energy transmission system of an offshore floating platform, and the method comprises the steps: connecting a Buck converter in series at a receiving end of the wireless energy transmission system, and achieving the constant voltage output through adjusting the duty ratio of the Buck converter; the input voltage of the Buck converter serves as a scheduling variable, and a linear time-varying parameter model is adopted to fit the time-varying characteristic of the Buck converter under the large-signal working condition; a data-driven closed-loop identification framework is adopted to perform parameter identification on the linear time-varying parameter model, so that online updating of model parameters can be realized; and designing a model prediction controller based on the linear time-varying parameter model obtained through identification, and introducing a time-varying weight coefficient related to the scheduling variable into the model prediction controller to realize anti-interference control of the system. The method effectively inhibits output voltage fluctuation caused by mutual inductance change, and has the advantages of fast dynamic response, good robustness and strong adaptability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power electronics and wireless energy transmission, and particularly relates to a modeling and control method of a wireless energy transmission system suitable for constant voltage output anti-interference control applied to a wireless energy transmission system of a sea floating platform. BACKGROUND

[0002] Wireless energy transmission technology has been widely applied in the fields of electric vehicles, underwater equipment, unmanned aerial vehicles and sea platforms due to its safety, flexibility and reliability. However, when applied to sea floating platforms, the transmitting end and the receiving end platform will produce continuous relative motion due to wave action, resulting in the mutual inductance between the coils changing with time, and then causing significant fluctuations in the system transmission power and output voltage, which seriously affects the stability and reliability of the power supply of the load equipment. In order to suppress the above power fluctuations, the existing technology mainly exists in the following several schemes, but they all have obvious limitations: I. Design a high anti-offset coupling mechanism: cannot completely avoid power fluctuations, and has problems such as large size, high cost, and inconvenient installation, which limits its engineering applicability; II. Adopt a hybrid compensation topology: the control logic is complex, and additional switching devices are needed, which increases the complexity and failure rate of the system.

[0003] III. System-level control strategy based on traditional modeling: a DC-DC converter is introduced at the receiving end and the duty cycle is adjusted to stabilize the output. However, the model of the converter relied on by this strategy is usually based on the state space averaging method or the small signal modeling method. These mechanism modeling methods have two inherent defects: (1) they rely on accurate circuit parameters, but in practice, the parameters of components have tolerances, aging drift and non-ideal characteristics, which are difficult to obtain accurately; (2) they cannot represent nonlinear dynamics: the small signal model is only linearized effective near a certain static operating point. When the mutual inductance fluctuates, the input voltage of the DC-DC converter changes in a large range, and the system operating point moves accordingly. The traditional linear model cannot accurately describe the nonlinear dynamic characteristics at this time, resulting in the deterioration of the performance of the controller in the global operating condition, and even instability. SUMMARY

[0004] The application mainly solves the problem of output voltage fluctuation caused by the application of a wireless energy transmission system with time-varying mutual inductance (such as a sea floating platform), and realizes constant voltage output.

[0005] Another object of the application is to provide a large signal linear time-varying parameter model of a DC-DC converter based on closed-loop parameter identification, to reduce the system modeling complexity and implementation threshold, accurately describe the nonlinear characteristics of the DC-DC converter under the condition of large range dynamic change of the input voltage, and realize online updating of the model parameters through closed-loop identification, which has high adaptability and practicality.

[0006] Another objective of this invention is to design a model predictive controller that processes control constraints in real time and introduces time-varying weight coefficients related to scheduling variables to enhance the controller's adaptability to the time-varying characteristics of the model, thereby achieving optimal control of linear time-varying parameter models.

[0007] The above-mentioned technical problems of the present invention are mainly solved by the following technical solutions: A constant voltage output disturbance rejection control method for a wireless power transfer system on a floating offshore platform includes: A Buck converter is connected in series at the receiving end of the wireless power transmission system, and constant voltage output is achieved by adjusting the duty cycle of the Buck converter. Using the input voltage of the Buck converter as a scheduling variable, a linear time-varying parameter model is used to fit the time-varying characteristics of the Buck converter under large signal conditions. A data-driven closed-loop identification framework is used to identify the parameters of the linear time-varying parameter model. Based on the identified linear time-varying parameter model, a model predictive controller is designed, and time-varying weight coefficients related to the scheduling variable are introduced into the model predictive controller to achieve disturbance rejection control of the system.

[0008] Furthermore, the linear time-varying parameter model is in discrete-time form, and its model coefficients are polynomial functions of the input voltage, which are used to accurately describe the nonlinear dynamic characteristics of the Buck converter when the input voltage changes.

[0009] Moreover, the data-driven closed-loop identification framework can update the model parameters of the Buck converter online to adapt to different load conditions and maintain model accuracy.

[0010] Moreover, the model predictive controller solves the constrained optimization problem in each sampling period, outputs a duty cycle control signal, realizes rolling time-domain control of the linear time-varying parameter model, and adapts the time-varying characteristics of the system through the time-varying weight coefficient.

[0011] Furthermore, the parameter identification process includes injecting an excitation signal into the control signal of the Buck converter and simultaneously sampling the input voltage, output voltage, and duty cycle signal to form a dataset for identification.

[0012] Moreover, the parameter identification process uses a closed-loop least squares iterative algorithm for linear time-varying parameter models to estimate parameters, including using the input voltage as the scheduling variable of the linear time-varying parameter model, and using the closed-loop least squares iterative algorithm to identify parameters in the dataset to obtain the discrete-time linear time-varying parameter model.

[0013] Furthermore, the time-varying weighting coefficient is dynamically adjusted according to the instantaneous change in the input voltage to enhance the controller's adaptability to time-varying systems.

[0014] On the other hand, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements a constant voltage output anti-interference control method for a wireless power transmission system of a floating offshore platform as described above.

[0015] On the other hand, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements a constant voltage output anti-disturbance control method for a wireless power transmission system of a floating offshore platform as described above.

[0016] On the other hand, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements a constant voltage output anti-interference control method for a wireless power transmission system of a floating offshore platform as described above.

[0017] Through the above scheme, this invention proposes a data-driven closed-loop parameter identification and modeling method that eliminates the need to measure any circuit component parameters. The introduced linear time-varying parameter model uses the input voltage as the scheduling variable, enabling the model parameters to adaptively adjust with changes in the operating point, thus achieving accurate large-signal modeling of the DC-DC converter. The introduction of the closed-loop identification framework allows for online updating of model parameters without disconnecting the control loop, resulting in higher practicality. A model predictive control framework is designed for the identified large-signal model to handle constrained time-varying systems, and the controller's adaptability to time-varying models is further enhanced by introducing time-varying weight coefficients related to the scheduling variable. The modeling control scheme proposed in this invention avoids the tedious process of designing complex nonlinear robust controllers, features simple parameter tuning, and strong adaptive capability, making it highly suitable for unattended offshore platform applications.

[0018] Therefore, the present invention has the following advantages: 1. High practicality and easy implementation: It adopts a fully data-driven closed-loop parameter identification and modeling method, which can update the model parameters online under closed-loop operation without measuring circuit components, and has a high degree of operational convenience; 2. Good adaptability and robustness: The linear time-varying parameter model can adapt to changes in the operating point, and the model predictive control with the introduction of time-varying weight coefficients can effectively handle constraints and time-varying dynamics, so that the wireless power transmission system exhibits excellent robustness to time-varying mutual inductance fluctuations. Attached Figure Description

[0019] Figure 1 The present invention relates to a method and apparatus for constant voltage output anti-interference control of a wireless power transmission system.

[0020] Figure 2 A schematic diagram of the data sampling process for closed-loop identification in an embodiment of the present invention.

[0021] Figure 3 The embodiments of the present invention include a PRBS sequence and a sampled dataset of Buck closed-loop input and output voltages.

[0022] Figure 4 The parameter iteration process in the closed-loop identification process of this invention embodiment.

[0023] Figure 5 Verification of the model fit obtained by identification in the embodiments of the present invention. Detailed Implementation

[0024] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0025] This invention provides a system identification, modeling, and model predictive control method for a wireless power transmission system applied to offshore floating platforms. By performing large-signal modeling and optimal control on the DC-DC converter at the receiving end, output voltage fluctuations are suppressed.

[0026] The embodiment provides a constant voltage output disturbance rejection control method and device for a wireless power transmission system with time-varying mutual inductance applied to a floating offshore platform. The method is based on a data-driven closed-loop parameter identification method to model the large-signal linear time-varying parameter model of the DC-DC converter at the receiving end, and designs a model predictive controller with time-varying weight coefficients to achieve constant voltage output of the wireless power transmission system under time-varying mutual inductance.

[0027] Specifically, addressing the problem of time-varying mutual inductance in coils caused by waves, leading to fluctuations in system output voltage, a Buck converter is connected in series at the receiving end, and an integrated modeling and control framework is proposed. First, a data-driven closed-loop parameter identification and modeling method is employed, using the Buck converter input voltage as the scheduling variable to establish a linear time-varying parameter model that accurately describes its large-signal dynamic characteristics. This model requires no circuit parameter measurement, is highly practical, and allows for online parameter updates. Then, a model predictive controller is designed to control the linear time-varying parameter model. The duty cycle control quantity is calculated through online rolling optimization, and a time-varying weight coefficient is introduced to adapt to the time-varying characteristics. This invention effectively suppresses output voltage fluctuations caused by changes in mutual inductance, exhibiting advantages such as fast dynamic response, good robustness, and strong adaptability.

[0028] The embodiments present a constant voltage output disturbance rejection control method and device for a wireless power transfer system for offshore floating platforms, the system structure of which is as follows: Figure 1As shown, the system includes a digital signal processor (DSP) based controller, a wireless power transfer main circuit (including a transmitting coil and a receiving coil), and a Buck converter connected in series at the receiving end of the main circuit. In the transmitting end of the wireless power transfer main circuit, a DC source is connected to an inverter, followed by a transmitting resonant network and a transmitting coil. At the receiving end, a rectifier is connected after the receiving coil and its resonant network, followed by a Buck converter and a load.

[0029] The Buck converter is a DC-DC step-down converter composed of switching devices, capacitors, inductors, and diodes. It performs step-down conversion by adjusting the duty cycle (D) of the pulse width modulation (PWM) signal of its internal switching devices. The DSP is a digital signal processor; a high-frequency embedded chip (such as the TMS320F28335) is preferred. The control loop samples the input and output voltages of the Buck converter through a voltage sampling circuit, runs a model predictive control algorithm in the DSP, and outputs a duty cycle control signal to the PWM generator and drive module. This ultimately generates a PWM signal with varying duty cycle, which is then sent to the Buck converter to control the output voltage. Figure 2 As shown.

[0030] In practical implementation, the circuit parameters of each device in the embodiment can be set according to the situation. The meaning of the parameters and examples of settings are shown in Table 1 below: Table 1 Parameters of Wireless Power Transfer System

[0031] Based on the above-mentioned device, the control includes the following steps: Step 1, System construction and data acquisition: The system constructed in this invention has a Buck converter connected in series at the receiving end, and constant voltage output is achieved by adjusting its duty cycle.

[0032] A schematic diagram of the data sampling process in the example is shown below. Figure 2 As shown, the dashed line represents the data sampling process for closed-loop identification, and the solid line represents the control signal transmission process. In the closed-loop operation of the Buck converter, a pseudo-random binary (PRBS) sequence is injected into the output signal of the controller (which can be of any type) to excite the system (i.e.,... r 2) Synchronously sample its input voltage V in (i.e., scheduling variables) p ), output voltage V out (Right now y ) and duty cycle signals containing PRBS sequences u ,set up k Representing the current time, forming a dataset. D S ={ Vout ( k ), u ( k ), V in ( k )}, length is N At the same time, a reference voltage is also required for closed-loop operation. V ref (Right now r 1).

[0033] Step 2, Buck converter large-signal model identification: Using the input voltage as the scheduling variable, the large-signal time-varying characteristics of the Buck converter are fitted with a linear time-varying parameter model; a data-driven closed-loop identification framework is used to identify the parameters of the linear time-varying parameter model.

[0034] The present invention further proposes: The linear time-varying parameter model is in discrete-time form, and its coefficients are polynomial functions of the input voltage. It can accurately express the nonlinear time-varying characteristics of the Buck converter under a wide range of input voltage variations, and is suitable for wireless power transmission systems with time-varying mutual inductance.

[0035] A data-driven closed-loop identification framework is used to identify parameters of the linear time-varying parameter model, thereby enabling online updates of Buck converter model parameters and ensuring that the model accuracy is maintained under different load conditions.

[0036] Unlike small-signal models based on local linearization, this invention addresses the dynamic characteristics of Buck converters under conditions of large-scale input voltage variations (i.e., large-signal operating conditions). Under these conditions, the converter exhibits significant nonlinearity, which traditional small-signal linear models cannot accurately describe due to the failure of their modeling premises. To address this issue, this invention employs a linear time-varying parameter model to fit the nonlinear dynamics of the Buck converter under large-signal operating conditions. The core advantage of this model lies in its time-varying nature; that is, the model parameters can adaptively adjust to changes in the input voltage, a scheduling variable, thereby accurately characterizing the nonlinear behavior of the system under large-scale operating point variations using a linear model with variable parameters.

[0037] The example uses input voltage V i Scheduling variables as linear time-varying parameter models p The LPV-CLSRIV closed-loop identification algorithm (linear variable parameter closed-loop auxiliary variable least squares iterative algorithm) is used to identify parameters in the dataset obtained in step 1, thereby obtaining a discrete-time linear time-varying parameter model that can accurately describe the dynamic characteristics of the Buck converter under a wide range of input voltages. Let the second-order model to be identified be... The structure is shown below, where For difference operators, V in ( k ( ) represents the time-varying input voltage of the Buck converter. a 1,0 , a 2,0 , b 0,1 The model parameters to be identified are: (1) in and The transfer functions to be identified are respectively The numerator and denominator polynomials have parameter vectors. .

[0038] The specific steps of the LPV-CLSRIV closed loop identification algorithm are as follows: (1) Initialization: Given the convergence accuracy ɛ Model order and maximum number of iterations N max Estimating the initial parameter vector using the least squares method ; (2) Constructing the regression vector as follows: (2) (3) Filter update: based on the first τ The parameter vector of the next iteration elements , Update filter ; (4) Auxiliary vector estimation: Constructing auxiliary vectors as follows: (3) The superscript * indicates that the noiseless signal is calculated by the closed-loop auxiliary model shown in equations (6) and (7), and This is the input signal in the closed-loop auxiliary model. For the reason and the identification model of the τth iteration The calculated noiseless output is: (4) (5) in C It is a closed-loop controller. r 1( k () is the reference voltage. r 2( k() is the PRBS excitation signal. and The identification model for the τth iteration is respectively The numerator and denominator polynomials.

[0039] (5) Signal filtering: using filters right , and Filtering is performed to obtain the filtered regression vector estimate. Auxiliary variables and output voltage As follows, superscript or subscript Indicates after the first τ Sub-iteration filtering process: (6) (7) (6) Update the model parameter vector estimate using the following formula : (8) Where the superscript T denotes matrix transpose, N Represents the sampled dataset used for identification. D S The length.

[0040] (7) Convergence check: If the model parameter vector estimation Convergence or reaching the maximum number of iterations N max Then output the recognition result. Otherwise, return to step (3) and enter the iteration again.

[0041] Step 3, Design and implement the model predictive controller: Design a model predictive controller based on the model and introduce time-varying weight coefficients to control the linear time-varying parameter model.

[0042] The present invention further proposes that the model predictive controller solves a constrained optimization problem in each sampling period to output a duty cycle control signal, thereby realizing rolling time-domain optimization control of the linear time-varying parameter model, and adopts time-varying weight coefficients related to the scheduling variables to adapt to the time-varying characteristics of the system.

[0043] Based on the linear time-varying parameter model established in step 2, the embodiment designs a model predictive controller and determines controller parameters such as prediction step size and control step size. Within each sampling period, using the current input voltage as the scheduling variable, it solves a constrained optimization problem, calculates and outputs the optimal duty cycle control quantity in real time, and achieves constant voltage control. If the identified Buck converter large-signal model is as shown in equation (1), and the prediction step size and control step size are both... N m Then the prediction equation is: (9) in, To predict the output vector, The state matrix, For state vectors, For the input matrix, Given the input increment vector, we have: (10) (11) (12) in , Representing the state matrix respectively and input matrix The i Line number j Column elements ( i , j =1, 2,…, N m (The current time is omitted) k The expression .

[0044] According to the prediction equation (9), we can obtain that k The optimal control problem to be solved in the time-matter predictive control is shown in equation (13), and its optimization objective is to calculate the optimal control under the constraints. k Optimal input increment vector at time step This makes in k objective function at time 1 To reach the minimum value, where the objective function is... Penalty term for output error and penalty term for input increment The former suppresses output errors, while the latter prevents excessive input increment changes.

[0045] (13) in The reference voltage constant vector for the desired output. u ( k)for k Input at any time for k The input increment at time +1 N m 3D diagonal matrix Q and R These are the error weights and the control weights, respectively.

[0046] Furthermore, to accommodate the time-varying characteristics of linear time-varying parameter models, this invention introduces time-varying weighting coefficients. Q ( k With input voltage (i.e., scheduling variable) V in exist k Time and k The change is determined by the amount of change between -1 and the time interval, and its expression is as follows. K It is an adjustable parameter. R To control the weight matrix R Diagonal elements: (14) The unconstrained optimal solution of the optimization problem in equation (13) It can be obtained as follows: (15) When the duty cycle range constraint in equation (13) is taken into account, the model predictive controller obtains k The optimal control quantity at time t is: (16) in i =1, 2, …, N m , The unconstrained optimal solution The i Each element.

[0047] The main advantages of this invention are as follows: First, it uses a linear time-varying parameter model to fit the nonlinear characteristics of the Buck converter when the input voltage changes over a wide range, and uses a data-driven closed-loop parameter identification method for modeling, without the need for complex derivation and circuit component measurement; Second, it introduces a model predictive controller with time-varying weight coefficients (i.e., Equation (14)) to perform online optimal control of the linear time-varying parameter model (i.e., Equation (1)), as shown in Equations (13) and (16), which has good robustness; Finally, the online update capability of the model parameters brought about by the closed-loop parameter identification is applicable to any load condition, and the model predictive controller can directly substitute the updated parameter vector into Equation (13) to solve for the optimal control quantity, without the need to repeatedly design the controller parameters, which has strong practicality and convenience.

[0048] To facilitate understanding of the technical effects of this invention, an actual operational experiment was conducted using a constant voltage output disturbance rejection control method and device for a wireless power transmission system with time-varying mutual inductance provided in the embodiments, including the following steps: Step 1: System Construction and Data Acquisition: With the Buck converter in closed-loop operation, a PRBS excitation sequence (20ms shift period, 4-bit shift register) is injected into its duty cycle signal. The control chip DSP TMS320F28335 sends corresponding duty cycle commands to the PWM generator to drive the Buck converter's switching devices (i.e., IGBTs), and samples the input voltage. V in ( k Output voltage V out ( k ) and control signals containing PRBS sequences u ( k The dataset is then formed. D S ={ V out ( k ), u ( k ), V in ( k In the wireless power transmission system shown in Table 1 (its circuit diagram is as follows) Figure 1 The dataset obtained is shown below. Figure 3 As shown, the data length is 3000 and the sampling period is 0.5ms.

[0049] Step 2: Identify the large-signal linear time-varying parameter model of the Buck converter under closed-loop operation. First, determine the model order. Since the Buck converter contains two energy storage elements, the model order is set to 2, and its model structure is set as shown in Equation (1).

[0050] Based on LPV-CLSRIV algorithm and Figure 3 The dataset shown yields the following model with a sampling period of 0.5ms.

[0051] (17) The iterative process of parameter estimation is as follows: Figure 4 As shown, the parameter vector is initialized to The goodness of fit is calculated using the following formula. FIT The values ​​are used to verify the accuracy of the model, where It is the output of the identification model. It is the measurement output. yes Average value: (18) The model fit in equation (18) is as follows: Figure 5 As shown, FIT The value is 97.13%, indicating that the identified large-signal linear time-varying parameter model can accurately reflect the nonlinear time-varying characteristics of the Buck converter under input voltage changes.

[0052] Step 3: Model Predictive Control. The model predictive controller is designed based on the identified model equation (17), with both the prediction step size and the control step size set to 5, and the time-varying weighting coefficient parameters are adjusted online. K The optimization problem shown in equation (13) is solved in real time in the DSP TMS320F28335, and the optimal duty cycle signal (16) is output. The results of the embodiment show that the output voltage can be stabilized near the reference voltage with a control period of 0.5ms and the fluctuation range is no more than 2.5%.

[0053] In practice, the above process can be automated using computer software technology.

[0054] To verify the effectiveness of the method of this invention, several typical modeling and control methods were selected for comparative evaluation under the operating scenario of a wireless power transmission system with time-varying mutual inductance. The evaluation indicators included: Modeling methods and model parameter update capabilities: mainly divided into cumbersome mechanism modeling and simple and fast data-driven identification modeling. Among them, closed-loop identification modeling has online parameter update capability compared with open-loop identification, which is suitable for wireless power transmission systems with load changes. Control strategy: This reflects the ease of controller design and debugging. The fewer parameters that need to be debugged, the simpler the controller debugging is. Output voltage fluctuation rate: quantifies the effect of constant voltage output control target and reflects the quality of the overall framework of modeling and control strategy; Power rating: Reflects the power transmission capability of a wireless power transmission system.

[0055] The results are shown in Table 2. As can be seen from the table, the modeling and control strategy of this invention achieves leading performance in all four indicators, especially in terms of ease of modeling and model parameter update capability, fully demonstrating the convenience and practicality of this invention in accurately modeling nonlinear Buck converters. Meanwhile, the output voltage fluctuation rate also shows that this method has advantages in achieving constant voltage output control of wireless power transmission systems with time-varying mutual inductance, and the control strategy debugging is relatively simple (only two controller parameters need to be adjusted), making it valuable for engineering deployment.

[0056] Table 2 Comparative Evaluation

[0057] In specific implementation, the method proposed in the technical solution of this invention can be automatically executed by those skilled in the art using computer software technology. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of this invention and computer equipment including the computer program running the corresponding computer program, should also be within the protection scope of this invention.

[0058] The following embodiments describe the electronic device provided by the present invention. The electronic device described below can be referred to in correspondence with the constant voltage output disturbance rejection control method of the wireless power transmission system for offshore floating platforms described above.

[0059] The electronic device may include a processor, a communications interface, a memory, and a communication bus. The processor, communications interface, and memory communicate with each other via the communication bus. The processor can call logical instructions from the memory to execute the constant voltage output disturbance rejection control method for the wireless power transfer system of the offshore floating platform, which mainly includes the software processing part described above.

[0060] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0061] On the other hand, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, the computer program being stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is able to execute the software processing part of the constant voltage output anti-disturbance control method for the wireless power transmission system of the offshore floating platform provided by the above methods.

[0062] In another aspect, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the software processing portion of the constant voltage output disturbance rejection control method for a wireless power transmission system of a floating offshore platform provided by the methods described above.

[0063] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0064] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A constant voltage output disturbance rejection control method for a wireless power transfer system on a floating offshore platform, characterized in that, include: A Buck converter is connected in series at the receiving end of the wireless power transmission system, and constant voltage output is achieved by adjusting the duty cycle of the Buck converter. Using the input voltage of the Buck converter as a scheduling variable, a linear time-varying parameter model is used to fit the time-varying characteristics of the Buck converter under large signal conditions. A data-driven closed-loop identification framework is used to identify the parameters of the linear time-varying parameter model. Based on the identified linear time-varying parameter model, a model predictive controller is designed, and time-varying weight coefficients related to the scheduling variable are introduced into the model predictive controller to achieve disturbance rejection control of the system.

2. The constant voltage output disturbance rejection control method for a wireless power transmission system of a floating offshore platform according to claim 1, characterized in that: The linear time-varying parameter model is in discrete-time form, and its model coefficients are polynomial functions of the input voltage, used to accurately describe the nonlinear dynamic characteristics of the Buck converter when the input voltage changes.

3. The constant voltage output disturbance rejection control method for a wireless power transmission system of a floating offshore platform according to claim 1, characterized in that: The data-driven closed-loop identification framework can update the model parameters of the Buck converter online to adapt to different load conditions and maintain model accuracy.

4. The constant voltage output disturbance rejection control method for a wireless power transmission system of a floating offshore platform according to claim 1, characterized in that: The model predictive controller solves a constrained optimization problem in each sampling period, outputs a duty cycle control signal, realizes rolling time-domain control of the linear time-varying parameter model, and adapts the time-varying characteristics of the system through the time-varying weight coefficient.

5. The constant voltage output disturbance rejection control method for a wireless power transmission system of a floating offshore platform according to claim 1, characterized in that: The parameter identification process includes injecting an excitation signal into the control signal of the Buck converter and simultaneously sampling the input voltage, output voltage, and duty cycle signal to form a dataset for identification.

6. The constant voltage output disturbance rejection control method for a wireless power transmission system of a floating offshore platform according to claim 1, characterized in that: The parameter identification process uses a closed-loop least squares iterative algorithm for linear time-varying parameter models to estimate parameters. This includes using the input voltage as the scheduling variable of the linear time-varying parameter model and using the closed-loop least squares iterative algorithm to identify parameters in the dataset to obtain the discrete-time linear time-varying parameter model.

7. The constant voltage output disturbance rejection control method for a wireless power transmission system of a floating offshore platform according to claim 1, characterized in that: The time-varying weighting coefficient is dynamically adjusted according to the instantaneous change in the input voltage to enhance the controller's adaptability to time-varying systems.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the constant voltage output anti-interference control method for a wireless power transmission system of a floating marine platform as described in any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the constant voltage output anti-interference control method for a wireless power transmission system of a floating offshore platform as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by the processor, it implements the constant voltage output anti-interference control method for a wireless power transmission system of a floating offshore platform as described in any one of claims 1 to 7.

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