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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Output feedback stabilization of networked switched nonlinear systems with continuous-time observers and inter-sample

Jiani Cheng1, Jingxin Huang1, Xiangze Lin1

  • 1College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, PR China.

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Summary

This study stabilizes networked nonlinear systems using event-triggered sampling and observers. It achieves system stability using static and dynamic sampling mechanisms for improved control.

Keywords:
Continuous-time observersEvent-triggered sampling mechanismInter-sample output predictorsNetworked switched nonlinear systemsOutput feedback

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Area of Science:

  • Control Systems Engineering
  • Nonlinear Dynamics
  • Networked Systems

Background:

  • Networked systems introduce challenges in state estimation and control due to sampling and delays.
  • Event-triggered sampling offers efficiency by reducing data transmission compared to periodic sampling.

Purpose of the Study:

  • To develop a control strategy for output stabilization of nonlinear planar systems under event-triggered sampling.
  • To design switched continuous-time observers with inter-sample output predictors for state estimation.

Main Methods:

  • Employs a hybrid framework combining continuous-time observers with discrete-time sampling events.
  • Utilizes static and dynamic event-triggered sampling mechanisms.
  • Designs switched inter-sample output predictors to estimate unmeasurable states.

Main Results:

  • Achieves global ultimate boundedness for switched systems under static event-triggered sampling.
  • Guarantees global asymptotic stability for switched systems under dynamic event-triggered sampling.
  • Demonstrates the effectiveness of the proposed emulation method through simulations.

Conclusions:

  • The proposed method effectively stabilizes networked nonlinear switched systems.
  • Switched continuous-time observers combined with inter-sample output predictors offer a robust approach.
  • The integration of continuous-time observer design with discrete sampling advantages is highlighted.