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Related Concept Videos

State Space to Transfer Function01:21

State Space to Transfer Function

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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
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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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Transfer Function to State Space01:23

Transfer Function to State Space

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

239
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.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Three State Estimation Fusion Methods Based on the Characteristic Function Filtering.

Yiran Yuan1, Chenglin Wen1,2, Yiting Qiu1

  • 1Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

Three state estimation fusion filters were developed for nonlinear systems. The sequential filter offers robust performance, even with communication delays and packet loss, outperforming the Extended Kalman Filter.

Keywords:
characteristic functionfusion methodmulti-sensorparallel filteringsequence filtering

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Nonlinear Dynamics

Background:

  • State estimation is crucial for nonlinear measurement systems.
  • Existing fusion methods face challenges with non-ideal communication.

Purpose of the Study:

  • To develop and compare three novel state estimation fusion filters for strong nonlinear measurement systems.
  • To evaluate filter performance under ideal and non-ideal communication conditions.

Main Methods:

  • Characteristic function filter-based centralized, parallel, and sequential fusion filters were designed.
  • Performance was analyzed under ideal conditions (accuracy, complexity) and non-ideal conditions (delay, packet loss).
  • Comparison with the Extended Kalman Filter (EKF) was conducted.

Main Results:

  • Centralized filter provides best accuracy under ideal conditions.
  • Parallel filter simplifies computation but offers lower accuracy.
  • Sequential filter achieves accuracy close to centralized, with superior robustness to delay and packet loss.
  • All designed filters outperformed the Extended Kalman Filter.

Conclusions:

  • The sequential filter demonstrates superior resilience to communication impairments.
  • The developed filters offer viable alternatives to the Extended Kalman Filter for nonlinear systems.
  • Filter selection depends on the trade-off between accuracy, computational load, and communication reliability.