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Related Concept Videos

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. 
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Sampling Plans01:23

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Upsampling01:22

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Reducing Line Loss01:18

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Sampling Methods: Sample Types01:18

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Sampling materials are classified into three main types: solid, liquid, and gas.
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Multirate control strategies for avoiding sample losses. Application to UGV path tracking.

Julián Salt1, José Alcaina1, Ángel Cuenca1

  • 1Systems Eng. and Control Dept., Instituto de Automatica e Informatica Industrial, Universitat Politecnica de Valencia, Cno. Vera, s/n, E-46022 Valencia, Spain.

ISA Transactions
|January 23, 2020
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Summary
This summary is machine-generated.

Model-based dual-rate control (MBDR) outperforms inferential control (IC) when digital control systems experience sample losses and process uncertainties. This study compares their performance using lifted modeling and frequency response analysis.

Keywords:
Dual-rate systemsFrequency responseInferential controlModel-based controlQuantitative feedback theoryStabilityUGV

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Area of Science:

  • Control Engineering
  • Systems Theory

Background:

  • Digital control systems can suffer sample losses due to hardware or software limitations.
  • Multirate (MR) control strategies, such as inferential control (IC) and model-based dual-rate control (MBDR), offer solutions for these limitations.

Purpose of the Study:

  • To analyze and compare the behavior of dual-rate inferential control (IC) and model-based dual-rate control (MBDR) under sample loss conditions.
  • To assess the robust performance and disturbance rejection capabilities of both control strategies.

Main Methods:

  • Utilized lifted modeling to represent the periodically time-varying discrete-time systems resulting from MR control.
  • Employed an efficient algorithm for computing the frequency response of MR systems.
  • Developed a new Quantitative Feedback Theory (QFT) procedure for dual-rate system analysis.

Main Results:

  • Model-based dual-rate control (MBDR) demonstrated superior performance compared to inferential control (IC) when significant process uncertainties were present.
  • Robust performance and disturbance effects were analyzed under various sample loss scenarios and process uncertainties.

Conclusions:

  • MBDR is a more robust and effective control strategy than IC in the presence of model uncertainties and sample losses in digital control systems.
  • The study provides valuable insights and a new QFT procedure for the analysis and design of dual-rate control systems.