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A Nonlinear Event-Triggered Filtering Method with Stochastic Modeling Errors

An event-triggered, stochastic modeling technology, applied in electrical components, adaptive networks, impedance networks, etc., can solve problems such as large filtering errors, and achieve the effect of reducing the relative error of filtering, easy to solve and implement

Active Publication Date: 2022-02-11
HARBIN UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a non-linear event-triggered filtering method with random modeling errors to solve the problem that the existing filtering technology cannot simultaneously process random modeling errors and filter gain disturbances under event-triggered conditions, resulting in large filtering errors

Method used

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  • A Nonlinear Event-Triggered Filtering Method with Stochastic Modeling Errors
  • A Nonlinear Event-Triggered Filtering Method with Stochastic Modeling Errors
  • A Nonlinear Event-Triggered Filtering Method with Stochastic Modeling Errors

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specific Embodiment approach 1

[0020] Specific implementation mode one: combine figure 1 The present embodiment is described. A non-linear event-triggered filtering method with random modeling error provided in this embodiment specifically includes the following steps:

[0021] Step 1. Establishing a dynamic model of a nonlinear stochastic system with random modeling errors and filter gain disturbances based on an event-triggered mechanism;

[0022] Step 2, performing filter design on the dynamic model established in step 1;

[0023] Step 3, calculating the upper bound of the one-step prediction error covariance matrix;

[0024] Step 4. According to the upper bound Σ of the one-step forecast error covariance matrix obtained in step 3 k+1|k , calculate the filter gain matrix K at time k+1 k+1 ;

[0025] Step five, the filter gain matrix K obtained in step four k+1 Substitute into the filter in step 2 to get the state estimation at time k+1

[0026] Judging whether k+1 reaches the total filtering dura...

specific Embodiment approach 2

[0028] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that the state space form of the dynamic model of the nonlinear stochastic system with random modeling error and filter gain disturbance based on the event-triggered mechanism described in step 1 is:

[0029]

[0030] the y k =C k x k +ξ k E. k ψ(x k )+ν k (2)

[0031] where x k is the state variable of the dynamic model of the nonlinear stochastic system at time k, x k+1 is the state variable of the dynamic model of the nonlinear stochastic system at time k+1; y k is the measurement output of the system at time k; A k is the system matrix of the nonlinear stochastic system at time k, B k is time k The coefficient matrix of C k is the measurement matrix of the nonlinear stochastic system at time k, D k is the noise distribution matrix of the nonlinear stochastic system at time k, E k ψ(x k ) coefficient matrix, A k ,B k ,C k ,D k ,E k are known matrices; ...

specific Embodiment approach 3

[0033] Specific embodiment three: the difference between this embodiment and specific embodiment two is that the specific process of filter design described in step two includes:

[0034] First, select the following event trigger function:

[0035]

[0036] In the formula, Indicates the measured value at the last trigger moment, k i is the previous triggering moment corresponding to the current k moment, the superscript T represents transposition, and δ is a triggering threshold, which is a known positive scalar; then the next triggering moment sequence is iteratively generated by the following formula:

[0037]

[0038] Among them, Z + is a positive integer, inf{} is the lower limit function;

[0039] The measurements passed to the filter after the event trigger mechanism are:

[0040]

[0041] Construct filter formula:

[0042]

[0043]

[0044] in, for x k The estimate at time k, is the state estimate at time k+1, for x k For a one-step forecast ...

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Abstract

The invention provides a nonlinear event-triggered filtering method with random modeling errors, which belongs to the technical field of state estimation. The present invention first establishes the dynamic model of the nonlinear stochastic system with random modeling error and filter gain disturbance based on the event trigger mechanism, and performs filter design on the dynamic model; then calculates the upper bound of the one-step prediction error covariance matrix; through one-step prediction error The upper bound of the covariance matrix is ​​calculated to obtain the filter gain matrix K at time k+1 k+1 ; Then K k+1 Substitute into the filter in step 2 to get the state estimation at time k+1 and according to the filter gain matrix K k+1 , calculate the upper bound Σ of the filter error covariance matrix k+1|k+1 ; Repeat the above steps until the total filter duration is satisfied. The invention solves the problem that the existing filtering technology cannot deal with the random modeling error and the disturbance of the filtering gain under the condition of event triggering at the same time, which further leads to the problem that the filtering error is large. The present invention can be used for filtering of stochastic nonlinear time-varying systems.

Description

technical field [0001] The invention relates to a nonlinear event trigger filtering method, which belongs to the technical field of state estimation. Background technique [0002] Filtering is the operation of filtering out the frequency of a specific band in the signal, and it is a basic and important measure for selecting signals and suppressing interference. Filtering is an important research problem in control systems, and it is widely used in signal estimation tasks in radar ranging, target tracking systems, image acquisition and other fields. In the network environment, due to factors such as limited bandwidth, network induced phenomena such as network congestion and data loss will be caused. It is very necessary to design a filtering method that adapts to these network induced phenomena. [0003] The current existing methods cannot deal with the filtering problem with event-triggered mechanism and random modeling error at the same time, especially ignoring the random...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03H21/00
CPCH03H21/0016H03H21/0043
Inventor 胡军贾朝清赵文杰张红旭陈东彦张昌露
Owner HARBIN UNIV OF SCI & TECH
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