Online identification and input design method and device for wireless power transmission system

Abstract

The invention provides an online identification and input design method and device for a wireless power transmission system, and the method comprises the steps: carrying out the online estimation of a transfer function parameter of a target system, and building an initial small signal model; constructing a full-order model corresponding to the target system, and performing order adaptive order reduction processing; determining a loss function of the target system by taking minimization of disturbance of the excitation signal to the target system as a target; on the basis of defining the loss function, constructing an optimization problem of the target system under disturbance constraint, and performing optimal excitation input design; and constructing a robust controller of the target system by adopting a robust control method in combination with the initial small signal model. According to the method, the system modal change can be tracked in real time under the closed-loop working condition, the modeling precision and the real-time performance are improved, and the problem that the identification precision is poor in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric energy transmission, and in particular to an online identification and input design method and device for a wireless electric energy transmission system. BACKGROUND

[0002] Wireless Power Transfer (WPT) systems are widely used in scenarios such as ship shore power, underwater equipment, mobile terminals, etc. due to their non-contact and high-efficiency power supply advantages. However, in actual operation, the parameters of the receiving coil position and load power of such systems are in a state of continuous change, making the dynamic model of the system have significant time-varying characteristics. Existing controllers are mostly designed based on static parameters in the starting stage of the system, and lack real-time response capability to changes in parameters during operation, resulting in a decline in closed-loop control performance and affecting the stability of the output voltage and the robustness of the system.

[0003] Currently, the mainstream system identification method mostly uses an open-loop identification strategy, that is, an excitation signal is applied by disconnecting the feedback path to fit the dynamic model of the system. However, this method is difficult to apply to actual wireless electric energy transmission scenarios: on the one hand, disconnecting the closed-loop structure will destroy the stability of the system; on the other hand, open-loop identification is difficult to continuously track changes in the system state, and cannot meet the dual requirements of dynamic identification and control update. In addition, existing identification methods for wireless electric energy transmission systems often lack a modeling mechanism for balancing the identification accuracy and system disturbance, and strong excitation in the closed-loop state may cause disturbance to the load, affecting the reliability of the system during identification.

[0004] There is currently no effective solution to the problem of poor identification accuracy of existing related technologies. SUMMARY

[0005] The present application provides an online identification and input design method and device for a wireless electric energy transmission system to solve the defect of poor identification accuracy of existing related technologies, realize real-time modeling and controller switching of dynamic changes of the wireless electric energy transmission system, and improve the adaptive control capability and operation reliability of the system.

[0006] In a first aspect, the present application provides an online identification and input design method for a wireless electric energy transmission system, comprising: online estimation of the transfer function parameters of the target system, and establishment of an initial small-signal model; construction of a full-order model corresponding to the target system, and adaptive order reduction processing; determination of a loss function of the target system with the goal of minimizing the disturbance of the excitation signal to the target system; On the basis of defining the loss function, an optimization problem of the target system under disturbance constraint is constructed, and optimal excitation input design is performed. In combination with the initial small signal model, the A robust control method is used to construct a robust controller of the target system.

[0007] According to the online identification and input design method of the wireless power transmission system provided by the application, the transfer function parameters of the target system are estimated online, and an initial small signal model is established, which comprises: The input-output relationship of the target system is modeled as an output error structure; The parameters to be identified of the target system are recursively updated by the least square method; A confidence ellipsoid of parameter uncertainty is constructed based on the identification covariance of the target system; the confidence ellipsoid is used to represent the uncertainty range of the estimated parameters to be identified.

[0008] According to the online identification and input design method of the wireless power transmission system provided by the application, the corresponding full-order model of the target system is constructed, and order adaptive reduction processing is performed, which comprises: Based on the topological structure of the target system, the order of the corresponding full-order model is determined, and the identification processing of the full-order model is performed; A balanced realization model of the full-order model is constructed, so that the controllability and observability Gramian matrix of the target system are simultaneously diagonalized; In combination with the contribution degree of each order state of the target model to the output, the main modal of the target system is determined.

[0009] According to the online identification and input design method of the wireless power transmission system provided by the application, in combination with the contribution degree of each order state of the target model to the output, the main modal of the target system is determined, which comprises: Based on the contribution degree of each order state of the target model to the output, a reduced-order model is constructed; The Gramian matrix is normalized to determine the main modal of the target system and the total contribution of the remaining states.

[0010] According to the online identification and input design method of the wireless power transmission system provided by the application, the loss function of the target system is determined by minimizing the disturbance of the excitation signal to the target system, which comprises: The excitation signal of the target system is obtained by shaping through a Mc-order finite impulse response filter, and the power spectrum of the excitation signal is determined; In combination with the disturbance effect of the excitation signal on the target system, the loss function of the target system is determined.

[0011] According to the present invention, an online identification and input design method for a wireless power transmission system is provided. Based on the defined loss function, an optimization problem of the target system under perturbation constraints is constructed, and the optimal excitation input is designed, including: The excitation signal spectrum of the target system is determined as the optimization variable; the excitation signal spectrum is composed of the coefficients of the finite impulse response filter; Based on the KYP lemma, the spectrum optimization is transformed into a linear matrix inequality form, and the optimization problem is defined. The optimization problem is solved to determine the optimal excitation sequence for the target system.

[0012] According to the online identification and input design method for a wireless power transfer system provided by the present invention, the optimization problem is solved to determine the optimal excitation sequence of the target system, including: The optimization problem is solved by setting the order of the finite impulse response filter and optimizing to obtain the excitation signal spectral coefficients expressed in the form of the finite impulse response filter. The optimal excitation sequence is obtained by filtering and shaping a standard white noise sequence.

[0013] According to the present invention, an online identification and input design method for a wireless power transmission system is provided, which, in conjunction with the initial small-signal model, employs... The robust control method constructs a robust controller for the target system, including: Construct all small-signal models of the target system in the confidence ellipsoid as an uncertain model set of the current small-signal model, and determine the frequency domain weighting function; Based on the frequency domain weighting function, a new robust controller for the target system is obtained through the robust control toolbox.

[0014] Secondly, the present invention also provides an online identification and input design apparatus for a wireless power transmission system, comprising: The estimation module is used to estimate the transfer function parameters of the target system online and establish an initial small-signal model; The order reduction module is used to construct the full-order model corresponding to the target system and perform order adaptive reduction processing. The determination module is used to determine the loss function of the target system with the objective of minimizing the disturbance of the excitation signal to the target system; The processing module is used to construct the optimization problem of the target system under perturbation constraints based on the defined loss function, and to design the optimal excitation input; The control module is used to combine the initial small-signal model and employ... The robust control method constructs a robust controller of the target system.

[0015] In a third aspect, the application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the online identification and input design method of the wireless power transmission system according to the first aspect when executing the program.

[0016] In a fourth aspect, the application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the online identification and input design method of the wireless power transmission system according to the first aspect.

[0017] In a fifth aspect, the application further provides a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the online identification and input design method of the wireless power transmission system according to the first aspect.

[0018] Compared with the prior art, the application has the following beneficial effects: The online identification and input design method of the wireless power transmission system provided by the application can solve the problem of inaccurate modeling caused by time-varying parameters in the operation process of the wireless power transmission system. The method proposes an online closed-loop identification method, collects input and output data in real time, realizes dynamic order self-adaption of the model structure, can track the system modal change in real time under closed-loop working conditions, improves the modeling accuracy and real-time performance, and solves the problem of poor identification accuracy in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 is a flowchart of the online identification and input design method of the wireless power transmission system provided by the application; Figure 2 is a schematic diagram of the closed-loop identification hardware framework of the wireless power transmission system in the embodiment of the application; Figure 3 is a schematic diagram of the closed-loop identification and control flow framework of the wireless power transmission system in the embodiment of the application; Figure 4 is a schematic diagram of the frequency domain response of the full-order model and the reduced-order model in the embodiment of the application; Figure 5is a schematic diagram of the frequency domain and time domain characteristics of the spectrum density function of the optimal excitation in the embodiment of the application; Figure 6 is a schematic diagram of the ellipsoid uncertainty region of the system identification parameters in the embodiment of the application; Figure 7 is a structural block diagram of the online identification and input design device of the wireless power transmission system provided by the application; Figure 8 is a structural schematic diagram of the electronic device provided by the application. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described below in conjunction with the accompanying drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative work fall within the protection scope of the present application.

[0022] The present application provides an online identification and input design method for a wireless power transmission system, Figure 1 is a flowchart of the online identification and input design method for a wireless power transmission system provided by the application, as Figure 1 shown, the method comprises the following steps: Step S101, online estimation of the transfer function parameters of the target system is performed, and an initial small signal model is established; Step S102, a full-order model corresponding to the target system is constructed, and order adaptive reduction processing is performed; Step S103, a loss function of the target system is determined with the objective of minimizing the disturbance of the excitation signal to the target system; Step S104, on the basis of defining the loss function, an optimization problem of the target system under the disturbance constraint is constructed, and optimal excitation input design is performed; Step S105, in combination with the initial small signal model, a robust controller of the target system is constructed by using a robust control method.

[0023] In the method, firstly, the transfer function parameters of the wireless power transmission system to be identified and designed, i.e., the target system, are estimated online to establish an initial small signal model. Then, through the input and output information of the target system in the identification process, order analysis is periodically performed in the closed-loop identification process to realize the model expression of the lowest order to reduce the operation complexity of the target system. In order to minimize the disturbance of the excitation signal to the closed-loop system in the identification process, the loss function of the target system is designed by taking the output disturbance power as an index. Then, on the basis of defining the loss function, a spectral optimization problem under constraints is further constructed to balance the model identification accuracy of the target system and the output disturbance with the minimum power excitation. Finally, combined with the initial small signal model, a robust control method is adopted to construct the robust controller of the target system. In the above process, in view of the problem of inaccurate modeling caused by parameter time-varying in the running process of the wireless power transmission system, the online closed-loop identification method is proposed to realize the dynamic order self-adaptation of the model structure, track the system modal change in real time under the closed-loop working condition, improve the modeling accuracy and real-time performance, and solve the problem of poor identification accuracy existing in the related art.

[0024] In some embodiments, the step S101 of estimating the transfer function parameters of the target system online and establishing an initial small signal model comprises: modeling the input-output relationship of the target system as an output error structure; recursively propagating the parameters to be identified of the target system by the least square method; constructing a confidence ellipsoid of parameter uncertainty based on the identification covariance of the target system; and the confidence ellipsoid is used to represent the uncertainty range of the estimated parameters to be identified.

[0025] In this embodiment, first, a hardware framework for online identification structure and data acquisition communication of the target system needs to be built, as shown in Figure 2 Figure 2 FIG. 1 is a schematic diagram of the hardware framework of the closed-loop identification of the wireless power transmission system in the embodiment of the present application. Taking a series resonant type wireless power transmission system as an example, a system architecture including a transmitting end system control module, a receiving end sampling module and an online identification and controller design module of an upper computer is constructed. Among them, the receiving end uses an STM32F407 chip to collect the system output, and uses an nRF24L01 wireless communication module to send to the receiving end; the transmitting end controller uses a combination of an STM32 and a Field Programmable Gate Array (FPGA) chip to realize inverter driving, communication, identification data reception and preprocessing, and the transmitting end periodically uploads the packaged input and output data to the online identification module of the PC end through the RS485 serial port for parameter estimation and controller update, to constitute a real-time identification and control path of the closed-loop operation of the system. Figure 3 ​This is a schematic diagram of the closed-loop identification and control process framework of the wireless power transmission system in an embodiment of the present invention, as shown below. Figure 3 As shown, the closed-loop identification and robust control of a wireless power transfer system are divided into input design, closed-loop identification, and... The robust controller consists of three parts.

[0026] For example, the closed-loop recursive prediction error method is used to estimate the transfer function parameters of the target system online, establishing an initial small-signal model. The input-output relationship of the target system is modeled as an output error structure:

[0027] in, Indicates the output voltage of the wireless power transfer system. This represents the regression vector composed of input and output. Indicates the parameter to be identified. Indicates zero-mean noise. k Indicates the first k A discrete time series. Parameter recursion uses a least squares algorithm with a forgetting factor, the specific formula of which is as follows:

[0028] in, Indicates the estimated model parameters. This represents the gain matrix. The initial covariance is set to... P Forgetting factor Control the weighting of historical data. Based on identified covariance. The confidence ellipsoid is constructed using the following formula:

[0029] in, U Indicates the confidence ellipsoid. Represents the actual parameters of the system. Indicates the currently estimated parameters. express transpose, Indicates accuracy constraints. The confidence ellipsoid represents the confidence level for the current estimated parameters. The range of uncertainty, within a certain confidence level (by... Under control, the actual parameters of the system It may fall within the confidence ellipsoid region. After multiple closed-loop identifications, the parameters at the center of the ellipsoid, i.e., the "most likely" parameter values ​​of the target system, can be obtained through experimental data, providing a basis for further input design and control strategy construction.

[0030] In some embodiments, step S102, the full-order model corresponding to the target system is constructed, and order self-adaptive reduction processing is performed, including: determining the order of the full-order model corresponding to the target system based on the topological structure of the target system, and performing identification processing of the full-order model; constructing a balanced realization model of the full-order model, so that the controllability and observability Gramian matrix of the target system are simultaneously diagonalized; and determining the main modal of the target system in combination with the contribution of each order state of the target model to the output.

[0031] Specifically, in combination with the contribution of each order state of the target model to the output, the main modal of the target system is determined, including: constructing a reduced-order model based on the contribution of each order state of the target model to the output; normalizing the Gramian matrix to determine the main modal of the target system and the total contribution of the remaining states.

[0032] In the embodiment, first, based on the topological structure of the current target system, the order of the full-order model is determined, where n is the number of all energy storage elements in the target system, and the identification experiment of the full-order model is performed. Secondly, the balanced realization model of the full-order model is constructed, and the controllability and the observability of the system are simultaneously diagonalized, and the specific formula is as follows:

[0033] Among them, represents the controllability Gramian matrix, represents the observability Gramian matrix, represents the Gramian matrix of the system, and reflects the importance of each state in the input-output relationship, and a larger singular value indicates that the contribution of the order state to the output is greater. Therefore, by ignoring smaller states, a reduced-order model can be constructed. In order to dynamically quantify the contribution of each state variable during online operation, the Gramian matrix is normalized. Assuming that the k-order state can represent the main modal of the system, then the total contribution of the remaining states is:

[0034] Among them, represents the total contribution of the remaining states, is a preset accuracy threshold, which is selected according to actual needs, represents the normalized Gramian matrix coefficient.

[0035] Exemplarily, taking a discrete transfer function identified in the form of a difference equation as an example, assuming that the i-th closed-loop identification is performed, first, the system parameters shown in Table 1 are identified using the full-order model structure.

[0036] Table 1 Wireless power transmission system parameter table

[0037] The system parameters of the target system corresponding to Table 1 are identified, and the fifth-order discrete transfer function is obtained as follows:

[0038] in, G ( z Let represent the discrete transfer function, and z represent the discrete-time complex frequency domain variables. Calculate its Gramm matrix as follows:

[0039] in, Let represent the Grammy matrix of the target system. Normalizing it, we obtain the normalized Grammy matrix:

[0040] in, Represents the normalized Gramm-Mass matrix. Sets the precision threshold. Therefore, the main dynamic characteristics of the target system are expressed by a first-order model. Then, the system is identified again using the first-order model structure, yielding the following first-order discrete transfer function:

[0041] in, G ( z () represents the discrete transfer function. The Bode plots of fifth-order and first-order transfer function models are shown below. Figure 4 As shown, Figure 4 This is a schematic diagram of the frequency domain response of the full-order model and the reduced-order model in the embodiment of the present invention. It can be seen that the first-order transfer function can well express the dynamics of the full-order model at low frequencies.

[0042] In some embodiments, step S103, with the goal of minimizing the perturbation of the excitation signal on the target system, determines the loss function of the target system, including: obtaining the excitation signal of the target system by shaping it through a Mc-order finite impulse response (FIR) filter and determining the power spectrum of the excitation signal; and determining the loss function of the target system in combination with the perturbation effect of the excitation signal on the target system.

[0043] In this embodiment, to minimize the disturbance of the excitation signal to the closed-loop system during the identification process, a frequency domain loss function with output disturbance power as the index is designed, and a finite impulse response filter is introduced in the spectral design for spectral parameterization. The excitation signal R consists of zero-mean white noise. The power spectrum is obtained by shaping with an Mc-order FIR filter and is expressed as follows:

[0044] wherein, denotes the power spectrum, denotes the exponential form of the frequency variable, denotes the coefficient to be optimized, denotes the basis function of the FIR filter. Based on the first i closed-loop identification experiment, the current model of the target system G 0 and the controller C are known at this time, in order to evaluate the disturbance effect of the excitation signal on the system, the following loss function is introduced:

[0045] wherein, denotes the loss function, G 0 denotes the current model of the system, S i denotes the sensitivity function of the closed-loop system, and , denotes the spectrum of the excitation signal, denotes the angular frequency, j denotes the imaginary unit. The loss function reflects the degree of influence of the excitation signal on the output at different frequencies, and essentially measures the output disturbance power of the target system. By minimizing , a high signal-to-noise ratio input can be obtained without affecting normal operation, thereby improving identification accuracy and reducing system burden. The introduction of the FIR filter ensures the physical realizability of the designed spectrum, and facilitates the introduction of frequency domain constraints to form a solvable optimization problem.

[0046] In some embodiments, step S104, based on the definition of the loss function, an optimization problem of the target system under disturbance constraint is constructed, and optimal excitation input design is performed, including: determining the excitation signal spectrum of the target system as an optimization variable; the excitation signal spectrum consists of the coefficient of the finite impulse response filter; based on the KYP (Kalman-Yakubovich-Popov) lemma, the frequency spectrum optimization is converted into a linear matrix inequality (LMI) form, and the optimization problem is determined; the optimization problem is solved to determine the optimal excitation sequence of the target system.

[0047] Specifically, the optimization problem is solved to determine the optimal excitation sequence of the target system, including: solving the optimization problem, setting the order of the finite impulse response filter, and optimizing to obtain the excitation signal spectrum coefficient expressed in the form of the finite impulse response filter; a standard white noise sequence is used for filtering shaping to obtain the optimal excitation sequence.

[0048] In this embodiment, based on the defined loss function, a constrained spectral optimization problem is further constructed, aiming to achieve a balance between model identification accuracy and system output perturbation with minimum power excitation. The optimization variable is the excitation signal spectrum. It consists of FIR filter coefficients. Using the KYP lemma, the spectrum optimization is transformed into LMI form, and the complete optimization problem is as follows:

[0049] in, Indicates optimized input u Spectrum To obtain the minimum upper bound of output power , express transpose, Indicates precision constraints. Indicates the identification parameters, Represents the actual parameters. Indicates the input power spectrum Covariance under the following conditions This represents the lower limit of the disturbance power, and is usually set to a constant slightly greater than 0. Indicates the upper limit of output power. It is a small positive real number.

[0050] The constraints mentioned above correspond to: model frequency response error control, parameter uncertainty ellipsoid constraint, upper and lower bound control of output perturbation power, and nonnegative spectrum constraint. This optimization problem has a good convex structure and can be efficiently solved using existing solvers (such as the MOOSE2 toolbox in MATLAB), ultimately generating an optimal excitation spectrum with small perturbations and high identification accuracy. To prevent excessive output perturbations from damaging the system, an upper limit is set for the system's output perturbation power, i.e. Upper limit of output power According to the system reference voltage Adaptive adjustment, the specific formula is as follows:

[0051] in, Indicates the upper limit of output power. k a Indicates the voltage reference value gain. Indicates the system reference voltage. This represents the true frequency domain model.

[0052] For example, the target system is expressed as a second-order system, thus the accuracy constraint in the optimization problem... Determined by a chi-square distribution with 2 degrees of freedom and a confidence level of 0.95, i.e. The parameter identification covariance is expressed as a linear form of the input spectrum:

[0053] wherein, represents the parameter identification covariance, represents the standard deviation of the noise, which is about 0.001; , represents the estimated parameter of the transfer function model, R 0 represents a number irrelevant to , and is set as 0.01. The above optimization problem is solved by using the MOOSE2 toolbox of matlab, the order of the FIR filter is set as 10, and the excitation signal spectrum expressed in the form of the FIR filter is obtained by optimization The coefficients of the FIR filter are as follows:

[0054] At this time, the 10-order FIR filter is as follows:

[0055] wherein, represents the FIR filter. The optimal excitation sequence is obtained by shaping the standard white noise sequence with the mean of 0 and the variance of 1 through the FIR filter. The frequency domain characteristics of the FIR filter, and the time domain images of the white noise and the optimal sequence are given by Figure 5 , Figure 5 is the frequency domain and time domain characteristic diagram of the spectrum density function of the optimal excitation in the embodiment of the application.

[0056] In some embodiments, step S105, in combination with the initial small signal model, adopts a robust control method to construct a robust controller of the target system, including: constructing all small signal models of the target system in a confidence ellipsoid as an uncertain model set of the current small signal model, and determining a frequency domain weighting function; based on the frequency domain weighting function, obtaining a new robust controller of the target system through a robust control toolbox.

[0057] In the embodiment, after the parameter confidence ellipsoid is obtained, the application adopts an H∞ robust control method to construct a robust controller for the uncertain system. The robust controller needs to ensure that the closed loop performance of all possible models in satisfies the small gain condition. The model set is defined as:

[0058] wherein, D represents the model set, represents a system transfer function model, Z 2 represents the polynomial coefficient of the transfer function molecule, Z1 represents the transfer function denominator polynomial coefficient. The head frequency domain weighting function, representing the modeling error boundary:

[0059] wherein, represents the frequency domain weighting function, G s represents the true model, represents the nominal model.

[0060] Using MATLAB robust control toolbox, the nominal model and the weighting function set , , construct the generalized system , and solve the optimal H∞ controller . Wherein, and are set according to experience. When the new identified model corresponds to the confidence ellipsoid deviating from the current controller applicable area, the controller switching mechanism is executed, the controller structure matching the new parameters is called, and the system still has robust performance and stability under model drift or load disturbance.

[0061] Exemplarily, by input optimization, the desired dynamics of the target system are excited, and then online closed-loop identification is performed to obtain the estimated parameters of the current model and the covariance of the reconstructed parameters An uncertainty set U of closed-loop parameters is obtained:

[0062] wherein, U represents the confidence ellipsoid, represents the true parameter of the system, represents the current estimated parameter, represents the transpose of , and represents the precision constraint.

[0063] The confidence ellipsoid of the uncertain parameter set is shown in Figure 6 , and Figure 6 is the schematic diagram of the ellipsoid uncertainty region of the system identification parameter in the embodiment of the application. All the models in the ellipsoid are constructed as the uncertain model set Figure 6 of the current model The center of the ellipsoid confidence region is taken as the nominal model G n The model set The model set :

[0064] Based on this, the new robust controller of the system is obtained by the matlab robust control toolbox as follows:

[0065] wherein, C represents the robust controller. Finally, the parameters of the new robust controller are transmitted to the transmitting end STM32 control board through the host computer on the PC side, and the robust controller is updated to maintain the closed-loop performance of the system.

[0066] In summary, the online closed-loop identification method based on the exponentially weighted recursive least squares method is proposed to solve the problem of inaccurate modeling caused by time-varying parameters in the operation process of the wireless power transmission system. The dynamic order of the model structure is self-adaptive by combining the Gram matrix analysis, which can track the system modal changes in real time under closed-loop conditions, construct a parameter uncertainty set, and improve the modeling accuracy and real-time performance. The frequency spectrum parameterization method based on the FIR filter is proposed to solve the problem of balancing between identification disturbance and control performance. The KYP lemma is used to convert the frequency domain disturbance constraint into LMI form, and a convex optimization problem is constructed to generate the optimal excitation spectrum. The H∞ robust control strategy is combined to realize the online switching and updating of the controller, and the stability and identification-control collaborative performance of the system under dynamic conditions are improved.

[0067] In order to realize the steps described in the above method, a device is designed, the structure of which is shown in Figure 2 , which includes a wireless power transmission system, a data acquisition and communication device, and a host computer. The wireless power transmission system works in a closed-loop state, and the receiving end acquires the system output voltage in real time and sends it to the host computer through wireless communication. The host computer performs system model identification and controller parameter updating based on the received input and output data, and designs the next period excitation signal that meets the power constraint and identification performance requirement, and sends it to the system controller, thereby realizing real-time updating of the system model and adaptive switching of the controller.

[0068] The application also provides an online identification and input design device for a wireless power transmission system. The online identification and input design device for a wireless power transmission system provided by the application is described as follows, and the online identification and input design device for a wireless power transmission system described below can be correspondingly referred to the online identification and input design method for a wireless power transmission system described above. Figure 7 is a structural block diagram of the online identification and input design device for a wireless power transmission system provided by the application, as shown in Figure 7 , the device comprises: An estimation module 701 is configured to perform online estimation on the transfer function parameters of the target system and establish an initial small signal model. The order reduction module 702 is configured to construct a full-order model corresponding to the target system, and perform order adaptive reduction; The determination module 703 is configured to determine a loss function of the target system, aiming to minimize the disturbance of the excitation signal to the target system; The processing module 704 is configured to construct an optimization problem of the target system under the disturbance constraint on the basis of defining the loss function, and perform optimal excitation input design; The control module 705 is configured to combine the initial small-signal model, and construct a robust controller of the target system by using a robust control method. The control module 705 is configured to combine the initial small-signal model, and construct a robust controller of the target system by using a robust control method.

[0069] In use, first, the estimation module 701 performs online estimation on the transfer function parameters of the wireless power transmission system to be identified and designed, that is, the target system, and establishes an initial small-signal model. Then, the order reduction module 702 periodically performs order analysis in the closed-loop identification process through the input and output information of the target system in the identification process, realizes the model expression with the lowest order, and reduces the operation complexity of the target system. In order to minimize the disturbance of the excitation signal to the closed-loop system in the identification process, the order reduction module 702 further designs a loss function of the target system by taking the output disturbance power as an index. Then, the processing module 704 further constructs a spectral optimization problem under the constraint on the basis of defining the loss function, aiming to balance the model identification accuracy of the target system and the output disturbance by using the minimum power excitation. Finally, the control module 705 combines the initial small-signal model, and constructs a robust controller of the target system by using a robust control method. In the above process, in view of the problem of inaccurate modeling caused by parameter time-varying in the running process of the wireless power transmission system, the method proposes an online closed-loop identification method, realizes dynamic order adaptation of the model structure in real time by collecting input and output data in real time, can track the system modal change in real time under the closed-loop working condition, improves the modeling accuracy and real-time performance, and solves the problem of poor identification accuracy in the related art.

[0070] Figure 8 An example of an entity structure diagram of an electronic device is shown in Figure 8 As shown, the electronic device can include a processor 801, a communications interface 802, a memory 803, and a communications bus 804, wherein the processor 801, the communications interface 802, and the memory 803 complete mutual communication through the communications bus 804. The processor 801 can invoke a logical instruction in the memory 803 to execute an online identification and input design method of a wireless power transmission system, and the method includes: online estimation on the transfer function parameters of the target system, and establishment of an initial small-signal model; constructing a full-order model corresponding to the target system and performing order adaptive reduction processing; determining a loss function of the target system aiming at minimizing disturbance of the excitation signal to the target system; on the basis of defining the loss function, constructing an optimization problem of the target system under disturbance constraint and performing optimal excitation input design; combining the initial small signal model, adopting constructing a robust controller of the target system by a robust control method.

[0071] In addition, the logical instructions in the memory 803 described above can be realized in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0072] On the other hand, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, and the computer can execute the online identification and input design method of the wireless power transmission system provided by the above-mentioned method, the method comprises: online estimation of the transfer function parameters of the target system, and establishment of an initial small signal model; constructing a full-order model corresponding to the target system and performing order adaptive reduction processing; determining a loss function of the target system aiming at minimizing disturbance of the excitation signal to the target system; on the basis of defining the loss function, constructing an optimization problem of the target system under disturbance constraint and performing optimal excitation input design; combining the initial small signal model, adopting constructing a robust controller of the target system by a robust control method.

[0073] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the online identification and input design method of the wireless power transmission system provided by the above method, the method comprising: online estimation of the transfer function parameters of the target system, and establishment of an initial small-signal model; construction of a full-order model corresponding to the target system, and adaptive order reduction processing; determination of a loss function of the target system, with the goal of minimizing disturbance of the excitation signal to the target system; on the basis of the definition of the loss function, construction of an optimization problem of the target system under disturbance constraint, and optimal excitation input design; combined with the initial small-signal model, construction of a robust controller of the target system using a robust control method.

[0074] The device embodiments described above are merely illustrative, wherein the units illustrated as separate components can or can not be physically separated, and the components illustrated as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0075] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0076] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An online identification and input design method for wireless power transfer systems, characterized in that, The method comprises the following steps: online estimation of transfer function parameters of a target system and establishment of an initial small signal model; construction of a full-order model corresponding to the target system and adaptive order reduction processing; determination of a loss function of the target system with the aim of minimizing disturbance of an excitation signal to the target system; on the basis of definition of the loss function, construction of an optimization problem of the target system under disturbance constraint and optimal excitation input design; In combination with the initial small-signal model, a robust control method is used to construct a robust controller for the target system. A robust control method constructs a robust controller for the target system.

2. The online identification and input design method of wireless power transfer system according to claim 1, wherein, online estimation of transfer function parameters of a target system and establishment of an initial small signal model, comprising: modeling of an input-output relationship of the target system as an output error structure; recursive parameter estimation of to-be-identified parameters of the target system by least squares method; construction of a confidence ellipsoid of parameter uncertainty based on identification covariance of the target system; the confidence ellipsoid is used to represent an uncertainty range of estimation of the to-be-identified parameters.

3. The online identification and input design method of wireless power transfer system of claim 1, wherein, construction of a full-order model corresponding to the target system and adaptive order reduction processing, comprising: determination of an order of the full-order model corresponding to the target system based on a topological structure of the target system and identification processing of the full-order model; construction of a balanced realization model of the full-order model, so that controllability and observability Gramian matrices of the target system are simultaneously diagonalized; determination of main modes representing the target system in combination with contribution degrees of respective states of the target model to an output.

4. The online identification and input design method of wireless power transfer system of claim 3, wherein, determination of main modes representing the target system in combination with contribution degrees of respective states of the target model, comprising: construction of a reduced-order model based on the contribution degrees of respective states of the target model to the output; normalization of the Gramian matrices, determination of the main modes representing the target system and determination of total contribution of residual states.

5. The online identification and input design method of wireless power transfer system of claim 1, wherein, determination of a loss function of the target system with the aim of minimizing disturbance of an excitation signal to the target system, comprising: acquisition of the excitation signal of the target system by shaping through an Mc-order finite impulse response filter and determination of a power spectrum of the excitation signal; determination of the loss function of the target system in combination with disturbance effects of the excitation signal on the target system.

6. The online identification and input design method of wireless power transfer system of claim 1, wherein, on the basis of definition of the loss function, construction of an optimization problem of the target system under disturbance constraint and optimal excitation input design, comprising: determination of an excitation signal spectrum of the target system as an optimization variable; the excitation signal spectrum is composed of coefficients of the finite impulse response filter; conversion of spectrum optimization into a linear matrix inequality form based on KYP lemma and determination of an optimization problem; solving of the optimization problem and determination of an optimal excitation sequence of the target system.

7. The online identification and input design method of wireless power transfer system of claim 6, wherein, solving of the optimization problem and determination of an optimal excitation sequence of the target system, comprising: solving of the optimization problem, setting of an order of the finite impulse response filter and optimization of excitation signal spectrum coefficients expressed in the form of the finite impulse response filter; filter shaping through a standard white noise sequence to obtain an optimal excitation sequence.

8. The online identification and input design method of wireless power transfer system of claim 1, wherein, In combination with the initial small-signal model, a robust control method is used to construct a robust controller for the target system, including: The robust control method constructs a robust controller for the target system, including: construction of all small signal models of the target system in the confidence ellipsoid as an uncertainty model set of a current small signal model and determination of a frequency domain weighting function; Based on the frequency domain weighting function, a new robust controller of the target system is obtained through a robust control toolbox.

9. An apparatus for online identification and input design of a wireless power transfer system, characterized in that, The method comprises the following steps: An estimation module is configured to perform online estimation on a transfer function parameter of a target system and establish an initial small signal model; A reduction module is configured to construct a full-order model corresponding to the target system and perform adaptive order reduction; A determination module is configured to determine a loss function of the target system with the aim of minimizing disturbance of an excitation signal to the target system; A processing module is configured to construct an optimization problem of the target system under disturbance constraint on the basis of defining the loss function and perform optimal excitation input design. a control module for constructing a robust controller of the target system in combination with the initial small-signal model a robust control method constructs a robust controller of the target system.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the online identification and input design method of the wireless power transmission system according to any one of claims 1 to 8 when executing the program.

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