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Related Concept Videos

Effects of feedback01:24

Effects of feedback

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Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
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The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
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Animal organs and organ systems constantly adjust to internal and external changes through a process called homeostasis ("steady state"). Examples of these changes include regulation of the level of glucose or calcium in the blood or internal responses to external temperatures. Homeostasis requires  maintaining an internal dynamic equilibrium:
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Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
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Spreading Control in Two-Layer Multiplex Networks.

Entropy (Basel, Switzerland)·2020
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Output-Feedback Control for Discrete-Time Spreading Models in Complex Networks.

Luis A Alarcón Ramos1, Roberto Bernal Jaquez2, Alexander Schaum3

  • 1Posgrado en Ciencias Naturales e Ingeniería, Universidad Autónoma Metropolitana, Cuajimalpa, Mexico-City 05348, Mexico.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study addresses stabilizing spreading processes on complex networks using Markov chains. It provides conditions for actuator/sensor placement and guarantees exponential stability for desired distributions.

Keywords:
complex networksdiscrete-time Markov-chain spreading modelsfeedback control

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Area of Science:

  • Complex network analysis
  • Control theory
  • Probability theory

Background:

  • Spreading processes on networks are crucial in various domains.
  • Controlling these processes to a target distribution is challenging.
  • Network node dynamics are often modeled using Markov chains.

Purpose of the Study:

  • To develop methods for stabilizing spreading processes on complex networks.
  • To identify conditions for optimal actuator and sensor placement.
  • To ensure exponential stability of the network's probability distribution.

Main Methods:

  • Modeling network dynamics with discrete-time Markov-chain processes.
  • Deriving conditions for actuator and sensor positioning.
  • Establishing sufficient conditions for exponential stability.

Main Results:

  • Theoretical conditions for stabilizing spreading processes were derived.
  • Specific criteria for actuator and sensor identification were provided.
  • Exponential stability of the target probability distribution was demonstrated.

Conclusions:

  • The proposed methods effectively stabilize spreading processes on complex networks.
  • Actuator and sensor placement significantly impacts process stability.
  • Simulation results validate the theoretical findings for large-scale networks.