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Nonlinear event trigger filtering method with random modelling error

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

Active Publication Date: 2018-11-20
HARBIN UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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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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  • Nonlinear event trigger filtering method with random modelling error
  • Nonlinear event trigger filtering method with random modelling error
  • Nonlinear event trigger filtering method with random modelling error

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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 trigger filtering method with random modelling error, and belongs to the technical field of the state estimation. The method comprises the following steps: firstly establishing a dynamic model of a nonlinear random system with random modelling error and filtering gain disturbance based on an event trigger mechanism, and performing filter design on the dynamic model; computing the upper bound of a one-step prediction error covariance matrix; computing through the upper bound of the one-step prediction error covariance matrix so obtain the k+1 moment filtering gain matrix Kk+1; and then substituting the Kk+1 into the filter in the step two to obtain the state estimation as shown in description at the k+1 moment, and computing the upper bound as shownin description of the filtering error covariance matrix according to the filtering gain matrix Kk+1; repeating the above steps until the filtering total duration is satisfied. The problem that the filtering error is large since the random modelling error and filtering gain disturbance under the event trigger condition cannot be simultaneously processed by the existing filtering technology is solved; and the filtering of the random nonlinear time-varying system can be used.

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 Applications(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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