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Related Concept Videos

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Second Order systems II01:18

Second Order systems II

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In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
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Linear Approximation in Frequency Domain01:26

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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  11. Event-triggered State Filter Estimation For Nonlinear Systems With Packet Dropout And Correlated Noise.
  1. Home
  3. Research Domains
  5. Engineering
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  9. Signal Processing
  11. Event-triggered State Filter Estimation For Nonlinear Systems With Packet Dropout And Correlated Noise.

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Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise.

Guorui Cheng1, Jingang Liu1, Shenmin Song1

  • 1Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)
|February 10, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an event-triggered state estimation method for nonlinear systems, balancing performance and data transmission by minimizing redundant communication. The approach ensures stability despite packet dropout and correlated noise.

Keywords:
correlated noisecubature Kalman filterevent-triggered mechanismnonlinear system

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Nonlinear System Analysis

Background:

  • State estimation in nonlinear systems is challenged by packet dropout and correlated noise.
  • Event-triggered communication strategies aim to reduce data transmission load.
  • Reliable state estimation requires robust filtering under network uncertainties.

Purpose of the Study:

  • To develop an event-triggered state estimation framework for nonlinear systems with packet dropout.
  • To integrate noise decorrelation and unreliable network transmission into the estimator design.
  • To analyze the trade-off between estimation performance and communication rate.

Main Methods:

  • An event-triggered communication mechanism based on condition violation.
packet dropout
performance analysis
state estimation
  • Noise decorrelation techniques applied prior to filter design.
  • Integration of the event-triggered mechanism and network transmission for state estimation.
  • Utilizing the three-degree spherical-radial cubature rule for numerical implementation.

    • Main Results:

    • Adjusting the event-triggered threshold balances estimation performance and transmission rate.
    • The covariance matrix of the state estimation error remains bounded with a lower bound on packet dropout rate.
    • Stochastic stability of the state estimation error is confirmed.

    Conclusions:

    • The proposed event-triggered state estimation algorithm is effective for nonlinear systems.
    • The method provides a balance between estimation accuracy and communication efficiency.
    • Validation through a target tracking system simulation confirms the algorithm's efficacy.