Performance prediction method for non-linear closed-loop control system recessive degradation execution mechanism

Abstract

The invention provides a non-linear closed-loop control system recessive degradation execution mechanism performance prediction method and device, and relates to the technical field of residual service life. The method comprises the following steps: acquiring nonlinear system control data, constructing a recessive performance index of an execution structure recessive degradation characteristic, and establishing a degradation model by adopting a Wiener random process; designing a prediction algorithm architecture fusing an FNN algorithm and adaptive Kalman filtering, and obtaining an error result by analyzing an estimation error of an unmodeled dynamic state to a degradation state; a filter structure of an adaptive Kalman filter is improved, an online weight updating mechanism of an FNN algorithm is designed, unmodeled dynamic items are estimated in real time through the updated weight of the FNN algorithm, updated unmodeled dynamic items are obtained, covariance distribution of recessive performance indexes and degradation model parameters is updated, and the performance of the adaptive Kalman filter is improved. And calculating by adopting a total probability formula to obtain a residual service life prediction result. The method can improve the accuracy of the prediction result of the residual service life of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of remaining useful life prediction, and in particular to a method and a device for predicting the performance of an actuator of a nonlinear closed-loop control system with latent degradation. Background Art

[0002] The development of industrial modernization is the dominant force related to the lifeline of the national economy. In actual industrial production, industrial control systems will issue control instructions to controlled objects according to the needs of industrial products. As an important link for transmitting and converting signals between industrial controllers and controlled objects, the role of actuators represented by various motors is particularly important. Therefore, with the continuous improvement of safety and reliability requirements, if necessary performance monitoring and prediction are carried out on degraded actuators, the remaining service life of actuators is predicted in a timely and accurate manner, and effective maintenance strategies are implemented, major property losses can be avoided, which plays a significant role in improving the reliability and safety of control systems. In the current existing technology, research on performance degradation and remaining service life prediction of actuators is usually based on measurable performance indicators, that is, it focuses on the remaining service life prediction of components in open-loop control systems. At present, algorithms based on random processes are widely used in remaining service life prediction because they can explicitly describe degradation models and prediction uncertainties. Due to the influence of sensor deviations, changes in the external environment and internal uncertainties, the established degradation model will inevitably deviate from the actual production process. In order to achieve the remaining useful life for the measurable performance indicators of the open-loop system, a degradation model containing three uncertainties, namely, measurement-related differences, individual characteristic-related differences, and time-related differences, can be established by using the Wiener random process fused with the Kalman filter. However, for the prediction of actuator performance degradation in closed-loop control systems, a generalized degradation model that can characterize uncertainty needs to be established on the basis of implicit degradation performance indicators that cannot be measured in real time. It is difficult for general nonlinear filters to accurately describe the degradation model from highly nonlinear data, but it is necessary to consider the unmodeled dynamics introduced in the linearization process. In order to predict the degradation of actuators in more complex and common nonlinear control systems, how to compensate for the unmodeled dynamic terms in the evaluation of implicit performance indicators and the distribution of remaining useful life plays an important role in improving the prediction results.

[0003] At present, in order to improve the accuracy of the filter without changing the system structure, artificial neural networks provide an effective solution for system modeling and nonlinear fitting under the filter framework. Extensive research on the fusion of filters and neural networks mainly focuses on external cooperation and internal cooperation between filters and neural networks. In the widely studied external cooperation mode, in order to obtain an accurate system model, the independently running filter module and neural network module are generally connected in parallel. In this external cooperation mode, parameter identification and model fitting are usually performed before and after the filter, and the filter design is not affected by the output error of the neural network. In the internal cooperation mode with relatively few research results, the neural network will act on each iteration process of the filter, and the gain matrix and auxiliary matrix of the filter will change with the output of the neural network. At present, most of these algorithms are based on the fusion of neural network algorithms and classical Kalman filters to only realize the evaluation of unmodeled dynamic terms. How to design an adaptive Kalman filter that considers the output error of the neural network to simultaneously estimate the unmodeled dynamic terms and implicit performance indicators in the form of augmentation needs to be further explored. Summary of the invention

[0004] In order to solve the technical problem in the prior art that it is difficult to model and predict the implicit degradation performance of an actuator in a nonlinear closed-loop control system under the influence of unmodeled dynamic terms, an embodiment of the present invention provides a method and device for predicting the performance of an actuator with implicit degradation in a nonlinear closed-loop control system. The technical solution is as follows:

[0005] On the one hand, a method for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation is provided. The method is implemented by a device for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation. The method comprises:

[0006] S1. Acquire nonlinear system control data, construct implicit performance indicators capable of characterizing implicit degradation characteristics of the execution structure according to the system control data and the system state model, and establish a degradation model using a Wiener random process for the implicit performance indicators;

[0007] S2. According to the degradation model, a prediction algorithm architecture integrating the FNN algorithm and the adaptive Kalman filter is designed, and an error result is obtained by analyzing the estimation error of the degradation state of the unmodeled dynamics; according to the error result, the filter structure of the adaptive Kalman filter is improved to obtain an improved gain matrix of the adaptive Kalman filter and an updated adaptive Kalman filter structure;

[0008] S3. According to the gain matrix of the improved adaptive Kalman filter, an online weight update mechanism of the FNN algorithm is designed, and the estimation of the unmodeled dynamic term is updated in real time by the updated weight of the FNN algorithm to obtain an updated unmodeled dynamic term;

[0009] S4, updating the covariance distribution of the implicit performance index and the degradation model parameter according to the updated adaptive Kalman filter structure, the improved gain matrix and the updated unmodeled dynamic term, and obtaining independent distribution update and conditional distribution update of the implicit performance index and the degradation model parameter;

[0010] S5. Based on the independent distribution update and conditional distribution update of implicit performance indicators and degradation model parameters, the full probability formula is used for calculation to obtain the remaining useful life prediction result represented by a probability density function.

[0011] On the other hand, a device for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation is provided. The device is applied to a method for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation. The device comprises:

[0012] An acquisition and construction unit, used for acquiring nonlinear system control data, constructing implicit performance indicators capable of characterizing implicit degradation characteristics of the execution structure according to the system control data and the system state model, and establishing a degradation model using a Wiener random process for the implicit performance indicators;

[0013] A design unit is used to design a prediction algorithm architecture integrating the FNN algorithm and the adaptive Kalman filter according to the degradation model, and obtain an error result by analyzing the estimation error of the degradation state of the unmodeled dynamics; according to the error result, improve the filter structure of the adaptive Kalman filter to obtain an improved gain matrix of the adaptive Kalman filter and an updated adaptive Kalman filter structure;

[0014] The first updating unit is used to design an online weight updating mechanism of the FNN algorithm according to the gain matrix of the improved adaptive Kalman filter, and to update the estimation of the unmodeled dynamic term in real time through the updated weight of the FNN algorithm to obtain an updated unmodeled dynamic term.

[0015] A second updating unit updates the covariance distribution of the implicit performance indicator and the degradation model parameter according to the updated adaptive Kalman filter structure, the improved gain matrix and the updated unmodeled dynamic term to obtain independent distribution update and conditional distribution update of the implicit performance indicator and the degradation model parameter;

[0016] The calculation unit uses the full probability formula to calculate according to the independent distribution update and conditional distribution update of the implicit performance indicators and degradation model parameters to obtain the remaining service life prediction result represented by the probability density function.

[0017] On the other hand, a device for predicting the performance of an actuator of an implicitly degraded nonlinear closed-loop control system is provided, the device comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the methods for predicting the performance of an actuator of an implicitly degraded nonlinear closed-loop control system is implemented.

[0018] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned methods for predicting the performance of an actuator of a latently degraded nonlinear closed-loop control system.

[0019] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0020] The embodiment of the present invention first obtains nonlinear system control data, and constructs implicit performance indicators that can characterize implicit degradation characteristics of the execution structure according to the system control data and the system state model. For the implicit performance indicators, a degradation model is established using the Wiener random process; secondly, according to the degradation model, a prediction algorithm architecture that integrates the FNN algorithm and the adaptive Kalman filter is designed, and the error result is obtained by analyzing the estimation error of the degradation state of the unmodeled dynamics; according to the error result, the filter structure of the adaptive Kalman filter is improved to obtain the gain matrix of the improved adaptive Kalman filter and the updated adaptive Kalman filter structure; according to the improved adaptive Kalman filter The gain matrix of the FNN algorithm is used to design an online weight update mechanism of the FNN algorithm. The estimation of the unmodeled dynamic terms is updated in real time through the updated weights of the FNN algorithm to obtain updated unmodeled dynamic terms. According to the updated adaptive Kalman filter structure, the improved gain matrix and the updated unmodeled dynamic terms, the covariance distribution of the implicit performance indicators and the degradation model parameters is updated to obtain independent distribution updates and conditional distribution updates of the implicit performance indicators and the degradation model parameters. Finally, according to the independent distribution updates and conditional distribution updates of the implicit performance indicators and the degradation model parameters, the full probability formula is used for calculation to obtain the remaining service life prediction result represented by the probability density function.

[0021] The embodiment of the present invention introduces a feedforward neural network into a prediction framework based on an adaptive Kalman filter, establishes an online update mechanism for the neural network weights, and proves the convergence of the prediction error by establishing a Lyapunov function, thereby realizing compensation for unmodeled dynamic terms; due to the introduction of the unmodeled dynamic compensation term, the gain term of the adaptive Kalman filter is improved, so that the degradation index and the degradation model parameters are estimated more accurately; the distribution of the adaptive Kalman filter using the improved gain matrix is ​​re-derived, and the probability density function of the remaining service life is also synchronously updated.

[0022] The embodiment of the present invention can accurately describe the degradation path of the actuator in the closed-loop control system by integrating a neural network with nonlinear fitting and a Kalman filter with robustness to external uncertainties, while avoiding the influence of the unmodeled dynamic terms in the degradation model on the remaining service life prediction results. The present invention can improve the accuracy of the performance prediction of the actuator with implicit degradation in the nonlinear closed-loop control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a structural schematic diagram of performance prediction of an actuator of a nonlinear closed-loop control system with implicit degradation provided by an embodiment of the present invention;

[0025] Figure 2 It is a flow chart of a method for predicting performance of an actuator of a nonlinear closed-loop control system with implicit degradation provided by an embodiment of the present invention;

[0026] Figure 3 It is a block diagram of a performance prediction device for an actuator of a nonlinear closed-loop control system with implicit degradation provided by an embodiment of the present invention;

[0027] Figure 4 It is a structural schematic diagram of a device for predicting performance of an actuator of a nonlinear closed-loop control system with implicit degradation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0029] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0030] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0031] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be written in non-subscript form such as W1. When the difference is not emphasized, the meaning is the same.

[0032] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0033] The embodiment of the present invention provides a method for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation. The method can be implemented by a device for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation. The device for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation can be a terminal or a server. Figure 1The figure shows a structural schematic diagram of performance prediction of an actuator of a nonlinear closed-loop control system with implicit degradation provided by an embodiment of the present invention; in a feasible implementation manner, for the remaining service life prediction process of the actuator of the control system under the action of a feedback controller, due to the existence of estimation errors of implicit performance degradation indicators, the unmodeled dynamic terms in the degradation model established based on the traditional nonlinear filter have a great influence on the prediction results; therefore, a performance prediction framework based on the unmodeled dynamic and implicit performance indicators of the degradation model with synchronous update is designed, by combining the FNN neural network with the adaptive Kalman filter; the gain matrix of the adaptive Kalman filter based on the least squares filtering and classical Kalman filtering principles is recalculated, and the convergence of the degradation information estimation value containing the implicit degradation indicator is also proved; in order to compensate for the lack of unmodeled dynamic terms in the linearized model, the degradation state estimation residual is used to guide the weight update of the hidden layer of the neural network; based on the real-time updated degradation state estimation value, degradation state distribution value and degradation model parameters, the probability density function of the remaining service life of the actuator evolving in the form of implicit degradation is updated in real time.

[0034] like Figure 2 The flowchart of the method for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation is shown in FIG. The processing flow of the method may include the following steps:

[0035] S1. Obtain nonlinear system control data, and construct implicit performance indicators that can characterize the implicit degradation characteristics of the execution structure based on the system control data and the system state model. For the implicit performance indicators, a degradation model is established using the Wiener random process.

[0036] The nonlinear system control data includes: state data of the nonlinear system, control output data and system output data.

[0037] Optionally, the specific implementation process of S1 may include S11-S13:

[0038] S11, obtaining state data, control output data and system output data of the nonlinear system;

[0039] The state model of the nonlinear control system can be expressed by formula (1):

[0040]

[0041] Among them, x k represents the state of the system at time k; u k represents the control output of the system at time k; y k represents the output of the system at time k; f(·) represents the nonlinear mapping function; C represents the output equation coefficient matrix; ω k ~N(0,V1 ) represents the first vector of uncorrelated white noise; ω k ~N(0,V 2 ) represents the second vector of uncorrelated white noise; V 1 V represents the variance term of the first vector of white noise; 2 represents the variance term of the second vector of white noise; N represents the normal distribution function; k represents the sampling time.

[0042] S12, using a classical nonlinear Kalman filter to linearize the state model of the nonlinear control system, and obtain a priori system model with implicit degradation performance indicators of the execution structure;

[0043] Among them, the equation of the implicit performance index with the implicit degradation characteristics of the execution structure can be expressed by formula (2):

[0044]

[0045] in, Indicates that the first state equation parameter matrix is ​​a Jacobian matrix; Indicates that the parameter matrix of the second state equation is the Jacobian matrix; λ k Indicates an implicit degradation performance indicator with a value between 0 and 1.

[0046] Among them, subject to the unmeasurable λ k Due to the influence of , the state equation parameter matrix of the linearized process deviates from the actual model; considering the deviation as unmodeled dynamics, an accurate model that characterizes the system state including unmodeled dynamics can be obtained according to formula (2), where the model can be expressed by the following formula (3):

[0047] x k =A k x k-1 +B k u k +E k λ k +Φ k (3)

[0048] Among them, Φ k represents unmodeled dynamics; A k Represents the state parameter matrix of the system state equation; B k Represents the input parameter matrix of the system state equation; E k Represents the implicit performance index parameter of the system state equation; x k-1 Represents the system state vector at the last sampling moment.

[0049] S13. Based on the equation of the implicit performance index with the implicit degradation characteristics of the actuator structure, a degradation model of the implicitly degraded actuator is established using the Wiener random process.

[0050] When the performance index is not implicit, the degradation model of the implicitly degraded actuator is established by using the Wiener random process. The process of using the Wiener process to establish the degradation model for the measurable performance index can be expressed by the following formula (4):

[0051]

[0052] Among them, λ k-1 Represents the implicit performance index value at the previous moment; θ k represents the drift parameter of the degradation model; θ k-1 represents the drift parameter of the degradation model at the last moment; r k represents the first vector of white noise in the degradation model; κ k represents the second vector of the white noise of the degradation model; T Time,k represents the running time of sampling k points; T Time,k-1 represents the running time of sampling k-1 points.

[0053] In a feasible implementation, due to the implicit performance indicator λ k It is unmeasurable. According to formula (3) and formula (4), the degradation model of the implicitly degraded actuator in the system can be established. The degradation model of the implicitly degraded actuator in the system can be expressed by the following formula (5):

[0054]

[0055] S2. According to the degradation model, a prediction algorithm architecture integrating the FNN algorithm and the adaptive Kalman filter is designed, and the error result is obtained by analyzing the estimation error of the unmodeled dynamics on the degradation state; according to the error result, the filter structure of the adaptive Kalman filter is improved to obtain the gain matrix of the improved adaptive Kalman filter and the updated adaptive Kalman filter structure.

[0056] Optionally, the specific implementation process of S2 may include S21-S23:

[0057] S21, using an adaptive Kalman filter to assess degradation status;

[0058] In a feasible implementation, formula (4) is rearranged to obtain a generalized model of the state, wherein the generalized model of the state can be expressed by the following formula (6):

[0059] η k =Dη k-1 +ζ k (6)

[0060] in, represents the parameter matrix of the generalized model; ΔTTime =T Time,k -T Time,k-1 Represents the difference between two sampling moments; represents the white noise vector of the generalized model; Represents x k and η k Augmentation vector; η k Represents λ k and θ k The augmented vector.

[0061] Among them, by rearranging formula (5), we can obtain x k and η k Augmentation vector The generalized model can be expressed by the following formula (7):

[0062]

[0063] in, F k =[E k 0], in, represents the state parameter matrix of the generalized model; represents the input parameter matrix of the generalized model; Φ k represents the unmodeled dynamics of the generalized model; F k Representation Matrix The internal matrix of represents the white noise vector of the generalized model;

[0064] Among them, if there is no unmodeled dynamics in formula (7), the degradation model is only established for the time-varying system with implicit performance indicators.

[0065] Among them, the adaptive Kalman filter can be used to estimate the degradation state

[0066] S22. According to the degradation assessment state and the degradation model, a fusion FNN adaptive Kalman filter algorithm is designed, and the error result is obtained by analyzing the estimation error of the degradation state by the unmodeled dynamics;

[0067] The process of designing the fusion FNN adaptive Kalman filter algorithm can be expressed by the following formula (8):

[0068]

[0069] in, represents the prior estimate of the system state at time k at time k-1; represents the a posteriori estimate of the system state at time k; represents the posterior estimate at time k-1; represents the k-1 time for η k-1 An estimated value of represents the k-time dependence of η k-1 An estimated value of represents the k-time relationship between Φ k estimates; represents time k Φ k The estimation error of represents the system output error at time k; k K represents the first auxiliary matrix of the adaptive Kalman filter at time k; k represents the second auxiliary matrix of the adaptive Kalman filter at time k; S k Represents the third auxiliary matrix of the adaptive Kalman filter at time k.

[0070] The system output error at time k can be expressed by the following formula (9):

[0071]

[0072] In a feasible implementation, according to formula (5) and formula (8), the system state estimation error at time k can be obtained, which can be expressed by the following formula (10):

[0073]

[0074] S23. According to the error result, an auxiliary matrix is ​​established to improve the structure of the adaptive Kalman filter, obtain the gain matrix of the improved adaptive Kalman filter, and update the structure of the adaptive Kalman filter.

[0075] In a feasible implementation, in order to clearly represent the relationship between various states, an auxiliary matrix is ​​established, wherein the auxiliary matrix can be expressed by the following formula (11):

[0076]

[0077] Among them, ε k represents the first auxiliary vector of the adaptive Kalman filter at time k; S k-1 represents the third auxiliary matrix of the adaptive Kalman filter at time k; R k Represents the fourth auxiliary matrix of the adaptive Kalman filter at time k.

[0078] According to the auxiliary matrix, the state model of the nonlinear control system can be modified into the following formula (12):

[0079]

[0080] in, represents the system state estimation error at time k-1; ε k-1 represents the first auxiliary vector of the adaptive Kalman filter at time k-1; S k-1 represents the third auxiliary matrix of the adaptive Kalman filter at time k-1; R k-1 represents the fourth auxiliary matrix of the adaptive Kalman filter at time k-1; represents the k-1 time for η k-1 An estimated value of represents k-1 time Φ k The estimation error.

[0081] Wherein, by substituting formula (10) and formula (2) into formula (11), the expression of the first auxiliary vector of the adaptive Kalman filter at time k can be obtained, which is expressed by the following formula (13):

[0082]

[0083] Among them, according to the properties of the classic Kalman filter, we can get E(ω k )=0 and E(υ k )=0, wherein the second auxiliary matrix and the fourth auxiliary matrix of the adaptive Kalman filter can be expressed by the following formula (14):

[0084]

[0085] Among them, P k|k-1 represents the prior covariance of the system state at time k-1 to time k; P k-1|k-1 represents the posterior covariance of the system state at time k-1; P k|k represents the posterior covariance of the system state at time k; Σ k represents the fifth auxiliary matrix of the adaptive Kalman filter at time k; G k represents the sixth auxiliary matrix of the adaptive Kalman filter at time k; J k H represents the seventh auxiliary matrix of the adaptive Kalman filter at time k; k H represents the eighth auxiliary matrix of the adaptive Kalman filter at time k; k-1 represents the eighth auxiliary matrix of the adaptive Kalman filter at time k-1; when the representation of the third auxiliary matrix is ​​designed as the following formula (15), the error term can be proved to gradually tend to zero.

[0086] The representation of the third auxiliary matrix can be expressed by the following formula (15):

[0087]

[0088] S3. According to the gain matrix of the improved adaptive Kalman filter, an online weight update mechanism of the FNN algorithm is designed. The estimation of the unmodeled dynamic term is updated in real time by the updated weight of the FNN algorithm to obtain an updated unmodeled dynamic term.

[0089] Optionally, after the estimation of the unmodeled dynamic term is updated in real time by the updated weight of the FNN algorithm in S3, the method further includes:

[0090] The unmodeled dynamic terms are updated by updating the FNN weights, and the Lyapunov function is designed to verify the stability and convergence of the filter.

[0091] In a feasible implementation, according to the adaptive Kalman filter, the augmentation vector can be obtained: The estimated value at time k, where the estimated value of the augmented vector at time k can be expressed by the following formula (16):

[0092]

[0093] in, Represents the augmented vector at time k An estimated value of Indicates the augmented vector at time k-1 Estimated value of k Represents the parameter matrix of the output error at time k; Γ k represents the parameter matrix of the unmodeled dynamic estimation error at time k;

[0094] in,

[0095] Among them, e k-1 Indicates the augmented vector at time k-1 The estimation error of

[0096] in,

[0097] The state parameter can be expressed by the following formula (17):

[0098]

[0099] In one implementation, when the weights are updated accurately, the actual unmodeled dynamic term can be expressed by the FNN algorithm as:

[0100]

[0101] Among them, Φ k represents the actual unmodeled dynamic term; W kRepresents the actual weight from the hidden layer to the output layer of the FNN at time k; It means that at time k is a fixed Gaussian distribution function from the input FNN output layer to the hidden layer;

[0102] The estimated error can be expressed by the following formula (19):

[0103]

[0104] in, Indicates the response of W at time k k An estimated value of Indicates the response of W at time k k The estimation error of The influence of the first constant term is negligible;

[0105] The error of the weight can be expressed by the following formula (20):

[0106]

[0107] in,

[0108] in, It means that at time k is a fixed Gaussian distribution function from the input FNN output layer to the hidden layer; It means that at time k is a fixed Gaussian distribution function from the FNN output layer to the hidden layer as input; wherein, according to the above formula, the state parameter can be further rewritten and expressed by the following formula (21):

[0109]

[0110] Among them, e k Represents the augmented vector at time k The estimation error of k represents the ninth auxiliary matrix of the adaptive Kalman filter at time k;

[0111] In a feasible implementation, the Lyapunov function can be expressed by the following formula (22):

[0112]

[0113] Where P represents the tuning constant matrix; V k represents the Lyapunov function at time k;

[0114] According to the Lyapunov function, the Lyapunov function at time k-1 can be obtained, which can be expressed by the following formula (23):

[0115]

[0116] Among them, V k-1 represents the Lyapunov function at time k-1; tr represents the trace function; Indicates the K-1 time for W k-1 The estimation error of k-1 represents the parameter matrix of the unmodeled dynamic estimation error at time k-1;

[0117] In one possible implementation, to prove stability, the discrete Lyapunov function needs to prove V k With V k-1 The difference ΔV k , meet certain conditions, the specific implementation process may include:

[0118] make The following formula (24) can be obtained:

[0119]

[0120] Among them, ε′ is The second constant term has negligible influence; where ΔV k Indicates V k With V k-1 The difference,

[0121] Among them, if the augmented vector at time k-1 The second norm of the estimation error ||e k-1 || and W at time k-1 k-1 The second norm of the estimation error Exceeding the maximum interval range, using the designed W for k+1 time k+1 The update rate of prediction weights The unmodeled dynamic term error can be limited. Among them, the improved filter gain and FNN online weight update mechanism can ensure the accurate estimation of the degradation state expectation and distribution.

[0122] S4. According to the updated adaptive Kalman filter structure, the improved gain matrix and the updated unmodeled dynamic terms, the covariance distribution of the implicit performance indicators and the degradation model parameters is updated to obtain independent distribution updates and conditional distribution updates of the implicit performance indicators and the degradation model parameters.

[0123] In a feasible implementation, according to the above formula (8), the k-time η k The predicted value can be expressed by formula (25):

[0124]

[0125] In a feasible implementation, Substituting into formula (25), we can obtain the following formula (26) for η at time k: k The prediction error

[0126]

[0127] in, Represents the change of η at time k-1 k-1 The prediction error of Represents the linear combination vector of the white noise sequence containing independent distributions, at time k η k The estimated error covariance of The calculation process of can be expressed by the following formula (27):

[0128]

[0129] in, Indicates that at time k-1 η k The estimated error covariance of Indicates that at time k-1 x k The estimated error covariance of It can be obtained through the covariance property of the classic Kalman filter;

[0130] make λ k Relative θ k The conditional distribution p(λ k |θ k ), λ k Relative θ k The distribution of p(θ k ) and λ k Relative λ k The distribution of p(λ k ), can be expressed by the following formula (28):

[0131]

[0132] S5. Based on the independent distribution update and conditional distribution update of implicit performance indicators and degradation model parameters, the full probability formula is used for calculation to obtain the remaining useful life prediction result represented by a probability density function.

[0133] Optionally, the specific implementation process of S5 includes:

[0134] According to the independent distribution update and conditional distribution update of implicit performance indicators and degradation model parameters, the inverse Gaussian distribution algorithm and the total probability formula are used to calculate and obtain the calculation result of the probability density function; wherein, the calculation result of the probability density function is the remaining useful life prediction result.

[0135] In a feasible implementation, the process of calculating the probability density function of the remaining useful life according to the total probability formula can be expressed by the following formula (29):

[0136]

[0137] Among them, l k Indicates the remaining service life; k For λ k and θ k The conditional distribution of satisfies the inverse Gaussian distribution operation and can be expressed by the following formula (30):

[0138]

[0139] Among them, ω represents the threshold of RUL; σ 2 represents the variance of the diffusion parameter of the Wiener process; Formula (29) can be rewritten as the following formula (31):

[0140]

[0141] The auxiliary vector can be expressed by the following formula (32):

[0142]

[0143] Among them, Z 1 is the first auxiliary matrix of PDF; Z 2 is the second auxiliary matrix of PDF; Z 3 is the third auxiliary matrix of PDF; Z 4 is the fourth auxiliary matrix of PDF; Z 5 is the fifth auxiliary matrix of PDF.

[0144] The embodiment of the present invention first obtains nonlinear system control data, and constructs implicit performance indicators that can characterize implicit degradation characteristics of the execution structure according to the system control data and the system state model. For the implicit performance indicators, a degradation model is established using the Wiener random process; secondly, according to the degradation model, a prediction algorithm architecture that integrates the FNN algorithm and the adaptive Kalman filter is designed, and the error result is obtained by analyzing the estimation error of the degradation state of the unmodeled dynamics; according to the error result, the filter structure of the adaptive Kalman filter is improved to obtain the gain matrix of the improved adaptive Kalman filter and the updated adaptive Kalman filter structure; according to the improved adaptive Kalman filter The gain matrix of the FNN algorithm is used to design an online weight update mechanism of the FNN algorithm. The estimation of the unmodeled dynamic terms is updated in real time through the updated weights of the FNN algorithm to obtain updated unmodeled dynamic terms. According to the updated adaptive Kalman filter structure, the improved gain matrix and the updated unmodeled dynamic terms, the covariance distribution of the implicit performance indicators and the degradation model parameters is updated to obtain independent distribution updates and conditional distribution updates of the implicit performance indicators and the degradation model parameters. Finally, according to the independent distribution updates and conditional distribution updates of the implicit performance indicators and the degradation model parameters, the full probability formula is used for calculation to obtain the remaining service life prediction result represented by the probability density function.

[0145] The embodiment of the present invention introduces a feedforward neural network into a prediction framework based on an adaptive Kalman filter, establishes an online update mechanism for the neural network weights, and proves the convergence of the prediction error by establishing a Lyapunov function, thereby realizing compensation for unmodeled dynamic terms; due to the introduction of the unmodeled dynamic compensation term, the gain term of the adaptive Kalman filter is improved, so that the degradation index and the degradation model parameters are estimated more accurately; the distribution of the adaptive Kalman filter using the improved gain matrix is ​​re-derived, and the probability density function of the remaining service life is also synchronously updated.

[0146] The embodiment of the present invention can accurately describe the degradation path of the actuator in the closed-loop control system by integrating a neural network with nonlinear fitting and a Kalman filter with robustness to external uncertainties, while avoiding the influence of the unmodeled dynamic terms in the degradation model on the remaining service life prediction results. The present invention can improve the accuracy of the performance prediction of the actuator with implicit degradation in the nonlinear closed-loop control system.

[0147] Figure 3 The block diagram of a device for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation according to an exemplary embodiment is shown. The device is used in a method for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation. Figure 3The device includes an acquisition and construction unit 310, a design unit 320, a first updating unit 330, a second updating unit 340 and a calculation unit 350. Among them:

[0148] The acquisition and construction unit 310 is used to acquire nonlinear system control data, and construct an implicit performance indicator capable of characterizing the implicit degradation characteristics of the execution structure according to the system control data and the system state model, and establish a degradation model using a Wiener random process for the implicit performance indicator;

[0149] The design unit 320 is used to design a prediction algorithm architecture integrating the FNN algorithm and the adaptive Kalman filter according to the degradation model, and obtain an error result by analyzing the estimation error of the unmodeled dynamic state to the degradation state; according to the error result, improve the filter structure of the adaptive Kalman filter to obtain a gain matrix of the improved adaptive Kalman filter and an updated adaptive Kalman filter structure;

[0150] A first updating unit 330 is used to design an online weight updating mechanism of the FNN algorithm according to the gain matrix of the improved adaptive Kalman filter, and to update the estimation of the unmodeled dynamic term in real time by updating the weight of the FNN algorithm to obtain an updated unmodeled dynamic term;

[0151] A first updating unit 340 is used to update the covariance distribution of the implicit performance indicator and the degradation model parameter according to the updated adaptive Kalman filter structure, the improved gain matrix and the updated unmodeled dynamic term to obtain independent distribution update and conditional distribution update of the implicit performance indicator and the degradation model parameter;

[0152] The calculation unit 350 is used to calculate based on the independent distribution update and conditional distribution update of the implicit performance index and the degradation model parameters using the full probability formula to obtain the remaining service life prediction result represented by the probability density function.

[0153] Optionally, the acquisition and construction unit 310 is used to:

[0154] Obtain state data, control output data and system output data of nonlinear systems;

[0155] The state model of the nonlinear control system is linearized by using the classic nonlinear Kalman filter to obtain a priori system model with implicit degradation performance indicators of the execution structure.

[0156] According to the equation of implicit performance index with implicit degradation characteristics of actuator structure, the degradation model of implicitly degraded actuator is established by using Wiener stochastic process.

[0157] Optionally, the design unit 320 is used to:

[0158] Adaptive Kalman filter is used to evaluate degradation status;

[0159] According to the degradation assessment state and the degradation model, a fusion FNN adaptive Kalman filter algorithm is designed, and the error result is obtained by analyzing the estimation error of the degradation state by the unmodeled dynamics;

[0160] According to the error result, an auxiliary matrix is ​​established, the structure of the adaptive Kalman filter is improved, the gain matrix of the improved adaptive Kalman filter is obtained, and the structure of the adaptive Kalman filter is updated.

[0161] Optionally, after the estimation of the unmodeled dynamic term is updated in real time by the updated weight of the FNN algorithm in S3, the method further includes:

[0162] The unmodeled dynamic terms are updated by updating the FNN weights, and the Lyapunov function is designed to verify the stability and convergence of the filter.

[0163] Optionally, the computing unit 350 is configured to:

[0164] According to the independent distribution update and conditional distribution update of implicit performance indicators and degradation model parameters, the inverse Gaussian distribution algorithm and the total probability formula are used to calculate and obtain the calculation result of the probability density function; wherein, the calculation result of the probability density function is the remaining useful life prediction result.

[0165] The embodiment of the present invention first obtains nonlinear system control data, and constructs implicit performance indicators that can characterize implicit degradation characteristics of the execution structure according to the system control data and the system state model. For the implicit performance indicators, a degradation model is established using the Wiener random process; secondly, according to the degradation model, a prediction algorithm architecture that integrates the FNN algorithm and the adaptive Kalman filter is designed, and the error result is obtained by analyzing the estimation error of the degradation state of the unmodeled dynamics; according to the error result, the filter structure of the adaptive Kalman filter is improved to obtain the gain matrix of the improved adaptive Kalman filter and the updated adaptive Kalman filter structure; according to the improved adaptive Kalman filter The gain matrix of the FNN algorithm is used to design an online weight update mechanism of the FNN algorithm. The estimation of the unmodeled dynamic terms is updated in real time through the updated weights of the FNN algorithm to obtain updated unmodeled dynamic terms. According to the updated adaptive Kalman filter structure, the improved gain matrix and the updated unmodeled dynamic terms, the covariance distribution of the implicit performance indicators and the degradation model parameters is updated to obtain independent distribution updates and conditional distribution updates of the implicit performance indicators and the degradation model parameters. Finally, according to the independent distribution updates and conditional distribution updates of the implicit performance indicators and the degradation model parameters, the full probability formula is used for calculation to obtain the remaining service life prediction result represented by the probability density function.

[0166] The embodiment of the present invention introduces a feedforward neural network into a prediction framework based on an adaptive Kalman filter, establishes an online update mechanism for the neural network weights, and proves the convergence of the prediction error by establishing a Lyapunov function, thereby realizing compensation for unmodeled dynamic terms; due to the introduction of the unmodeled dynamic compensation term, the gain term of the adaptive Kalman filter is improved, so that the degradation index and the degradation model parameters are estimated more accurately; the distribution of the adaptive Kalman filter using the improved gain matrix is ​​re-derived, and the probability density function of the remaining service life is also synchronously updated.

[0167] The embodiment of the present invention can accurately describe the degradation path of the actuator in the closed-loop control system by integrating a neural network with nonlinear fitting and a Kalman filter with robustness to external uncertainties, while avoiding the influence of the unmodeled dynamic terms in the degradation model on the remaining service life prediction results. The present invention can improve the accuracy of the performance prediction of the actuator with implicit degradation in the nonlinear closed-loop control system.

[0168] Figure 4 is a schematic diagram of the structure of a device for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation provided by an embodiment of the present invention. Figure 4 As shown, the actuator performance prediction device of the nonlinear closed-loop control system with implicit degradation may include the above Figure 3The actuator performance prediction device for implicitly degraded nonlinear closed-loop control system shown in FIG. Optionally, the actuator performance prediction device 410 for implicitly degraded nonlinear closed-loop control system may include a first processor 2001 .

[0169] Optionally, the actuator performance prediction device 410 for implicit degradation of a nonlinear closed-loop control system may further include a memory 2002 and a transceiver 2003 .

[0170] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0171] Combine the following Figure 4 The components of the actuator performance prediction device 410 for implicit degradation of a nonlinear closed-loop control system are specifically introduced as follows:

[0172] The first processor 2001 is the control center of the actuator performance prediction device 410 for implicit degradation of the nonlinear closed-loop control system, and can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0173] Optionally, the first processor 2001 can execute various functions of the actuator performance prediction device 410 for implicit degradation of the nonlinear closed-loop control system by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0174] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0175] In a specific implementation, as an embodiment, the actuator performance prediction device 410 of the nonlinear closed-loop control system with implicit degradation may also include multiple processors, such as Figure 4The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0176] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0177] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0178] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0179] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0180] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be used to predict the performance of the actuator 410 of the nonlinear closed-loop control system with implicit degradation through the interface circuit ( Figure 4(not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0181] It should be noted that Figure 4 The structure of the actuator performance prediction device 410 for implicitly degraded nonlinear closed-loop control systems shown in the figure does not constitute a limitation on the router, and the actual knowledge structure identification device may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0182] In addition, the technical effects of the performance prediction device 410 for an actuator of a nonlinear closed-loop control system with implicit degradation can refer to the technical effects of the performance prediction method for an actuator of a nonlinear closed-loop control system with implicit degradation described in the above method embodiment, which will not be repeated here.

[0183] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0184] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0185] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0186] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0187] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0188] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0189] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0190] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0191] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0192] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0193] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0194] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0195] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for predicting the performance of an actuator with implicit degradation in a nonlinear closed-loop control system, characterized in that: The method comprises: S1. Acquire nonlinear system control data, construct implicit performance indicators capable of characterizing implicit degradation characteristics of the execution structure according to the system control data and the system state model, and establish a degradation model using a Wiener random process for the implicit performance indicators; S2. According to the degradation model, a prediction algorithm architecture integrating the FNN algorithm and the adaptive Kalman filter is designed, and an error result is obtained by analyzing the estimation error of the degradation state of the unmodeled dynamics; according to the error result, the filter structure of the adaptive Kalman filter is improved to obtain an improved gain matrix of the adaptive Kalman filter and an updated adaptive Kalman filter structure; S3. According to the gain matrix of the improved adaptive Kalman filter, an online weight update mechanism of the FNN algorithm is designed, and the estimation of the unmodeled dynamic term is updated in real time by the updated weight of the FNN algorithm to obtain an updated unmodeled dynamic term; S4, updating the covariance distribution of the implicit performance index and the degradation model parameter according to the updated adaptive Kalman filter structure, the improved gain matrix and the updated unmodeled dynamic term, and obtaining independent distribution update and conditional distribution update of the implicit performance index and the degradation model parameter; S5. Based on the independent distribution update and conditional distribution update of implicit performance indicators and degradation model parameters, the full probability formula is used for calculation to obtain the remaining useful life prediction result represented by a probability density function.

2. The method for predicting the performance of an actuator with implicit degradation in a nonlinear closed-loop control system according to claim 1, characterized in that: The step S1 acquires nonlinear system control data, constructs implicit performance indicators capable of characterizing implicit degradation characteristics of the execution structure according to the system control data and the system state model, and establishes a degradation model using the Wiener random process for the implicit performance indicators, including: S11, obtaining state data, control output data and system output data of the nonlinear system; S12, using a classic nonlinear Kalman filter to linearize the state model of the nonlinear control system to obtain a linearized system state model with implicit degradation performance indicators of the actuator; S13. Based on the linearized system state model of the implicit performance index with the implicit degradation characteristics of the actuator structure, a degradation model of the implicitly degraded actuator is established using the Wiener random process.

3. The method for predicting the performance of an actuator with implicit degradation in a nonlinear closed-loop control system according to claim 1, characterized in that: The S2 designs a prediction algorithm architecture integrating the FNN algorithm and the adaptive Kalman filter according to the degradation model, and obtains an error result by analyzing the estimation error of the degradation state by the unmodeled dynamics; According to the error result, the filter structure of the adaptive Kalman filter is improved to obtain the gain matrix of the improved adaptive Kalman filter and the updated adaptive Kalman filter structure, including: S21, using an adaptive Kalman filter to assess degradation status; S22, designing a fusion FNN adaptive Kalman filter algorithm according to the degradation assessment state and the degradation model, and obtaining an error result by analyzing the estimation error of the degradation state by the unmodeled dynamics; S23. According to the error result, an auxiliary matrix is ​​established to improve the structure of the adaptive Kalman filter, obtain the gain matrix of the improved adaptive Kalman filter, and update the structure of the adaptive Kalman filter.

4. The method for predicting the performance of an actuator with implicit degradation in a nonlinear closed-loop control system according to claim 1, characterized in that: After the estimation of the unmodeled dynamic term is updated in real time by the updated weight of the FNN algorithm in S3, the method further includes: The unmodeled dynamic terms are updated by updating the FNN weights, and the Lyapunov function is designed to verify the stability and convergence of the filter.

5. The method for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation according to claim 1, characterized in that: The S5 uses the full probability formula to calculate based on the independent distribution update and conditional distribution update of the implicit performance index and the degradation model parameters to obtain the remaining service life prediction result represented by the probability density function, including: According to the independent distribution update and conditional distribution update of implicit performance indicators and degradation model parameters, the inverse Gaussian distribution algorithm and the total probability formula are used to calculate and obtain the calculation result of the probability density function; wherein, the calculation result of the probability density function is the remaining useful life prediction result.

6. A device for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation, wherein the device is used to implement the method for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation as claimed in any one of claims 1 to 5, characterized in that: The device comprises: An acquisition and construction unit, used for acquiring nonlinear system control data, constructing implicit performance indicators capable of characterizing implicit degradation characteristics of the execution structure according to the system control data and the system state model, and establishing a degradation model using a Wiener random process for the implicit performance indicators; A design unit is used to design a prediction algorithm architecture integrating the FNN algorithm and the adaptive Kalman filter according to the degradation model, and obtain an error result by analyzing the estimation error of the degradation state of the unmodeled dynamics; according to the error result, improve the filter structure of the adaptive Kalman filter to obtain an improved gain matrix of the adaptive Kalman filter and an updated adaptive Kalman filter structure; A first updating unit is used to design an online weight updating mechanism of the FNN algorithm according to the gain matrix of the improved adaptive Kalman filter, and to update the estimation of the unmodeled dynamic term in real time by updating the weight of the FNN algorithm to obtain an updated unmodeled dynamic term; A second updating unit updates the covariance distribution of the implicit performance indicator and the degradation model parameter according to the updated adaptive Kalman filter structure, the improved gain matrix and the updated unmodeled dynamic term to obtain independent distribution update and conditional distribution update of the implicit performance indicator and the degradation model parameter; The calculation unit uses the full probability formula to calculate according to the independent distribution update and conditional distribution update of the implicit performance indicators and degradation model parameters to obtain the remaining service life prediction result represented by the probability density function.

7. The method for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation according to claim 6, characterized in that: The acquisition and construction unit is used to: Obtain state data, control output data and system output data of nonlinear systems; The state model of the nonlinear control system is linearized by using the classic nonlinear Kalman filter to obtain a priori system model with implicit degradation performance indicators of the execution structure. According to the equation of implicit performance index with implicit degradation characteristics of actuator structure, the degradation model of implicitly degraded actuator is established by using Wiener stochastic process.

8. The method for predicting the performance of an actuator of a nonlinear closed-loop control system with implicit degradation according to claim 6, characterized in that: The design unit is used to: Adaptive Kalman filter is used to evaluate the degradation state; According to the degradation assessment state and the degradation model, a fusion FNN adaptive Kalman filter algorithm is designed, and the error result is obtained by analyzing the estimation error of the degradation state by the unmodeled dynamics; According to the error result, an auxiliary matrix is ​​established, the structure of the adaptive Kalman filter is improved, the gain matrix of the improved adaptive Kalman filter is obtained, and the structure of the adaptive Kalman filter is updated.

9. A device for predicting the performance of an actuator with implicit degradation in a nonlinear closed-loop control system, characterized in that: The performance prediction device for the actuator of the nonlinear closed-loop control system with implicit degradation comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 5.

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