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Related Concept Videos

Feedback control systems01:26

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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Control of Eating Behavior Using a Novel Feedback System
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Signal-to-noise ratio constrained feedback control: Robust stability analysis.

Alejandro J Rojas1

  • 1Departamento de Ingeniería Eléctrica, Universidad de Concepción, Chile.

ISA Transactions
|June 3, 2019
PubMed
Summary

This study quantifies signal-to-noise ratio (SNR) limitations in feedback control systems with unstable plants and model uncertainty. It redefines SNR limitations as channel capacity limitations for stabilization in specific cases.

Keywords:
Fundamental constraintsModel uncertaintiesNetworked control systemsRobust analysisSignal-to-noise ratio

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Area of Science:

  • Control Systems Engineering
  • Information Theory
  • Signal Processing

Background:

  • Feedback control systems face fundamental signal-to-noise ratio (SNR) limitations for stabilization when dealing with unstable linear time-invariant (LTI) plants.
  • Modeling real-world processes with LTI models introduces model uncertainty due to compromises between accuracy and complexity.

Purpose of the Study:

  • To investigate SNR limitations in continuous-time feedback control systems considering additive coloured Gaussian noise (ACGN) channels with bandwidth limitations.
  • To quantify the infimal SNR under simultaneous plant, channel, and noise model uncertainties.

Main Methods:

  • Analysis of continuous-time feedback control loops incorporating communication channel models.
  • Quantification of SNR limitations under additive coloured Gaussian noise (ACGN) and bandwidth constraints.
  • Examination of the impact of plant, channel, and noise model uncertainties.

Main Results:

  • The study quantifies the fundamental SNR limitations for stabilizing unstable LTI plants over communication channels.
  • For memoryless additive white Gaussian noise (AWGN) channels, the derived SNR limitation under plant model uncertainty is shown to be equivalent to a channel capacity limitation for stabilization.

Conclusions:

  • Model uncertainty significantly impacts the achievable SNR for stabilization in feedback control systems.
  • The findings provide a theoretical framework for understanding and overcoming limitations in control over noisy communication channels, with implications for system design and performance.