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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
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Sufficient stabilizing bit rate conditions for an n-dimensional nonlinear system based on event triggering.

Rundong Dou1, Qiang Ling2, Yuan Liu2

  • 1Nanjing Research Institute of Electronics Technology, Nanjing 210039, China.

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This study introduces a model-based event-triggered control method to stabilize nonlinear systems over digital networks. This approach reduces required bit rates by utilizing feedback packet timing, outperforming periodic sampling.

Keywords:
Feedback dropoutsModel-based event-triggered controlNonlinear systemStabilizing bit rate conditions

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Area of Science:

  • Control Systems Engineering
  • Networked Systems

Background:

  • Stabilizing nonlinear systems over digital networks is challenging due to delays and packet dropouts.
  • Traditional periodic sampling methods require high bit rates and are sensitive to network imperfections.

Purpose of the Study:

  • To propose a model-based event-triggered control method for stabilizing nonlinear systems transmitted over digital networks.
  • To reduce network bandwidth usage while ensuring system stability.

Main Methods:

  • Developed a model-based event-triggered control strategy.
  • Leveraged feedback packet receiving time instants for enhanced state information utilization.
  • Analyzed system performance under bounded processing and network delays, feedback dropouts, and process noise.

Main Results:

  • The proposed event-triggered method effectively stabilizes the nonlinear system.
  • Achieved lower stabilizing bit rates compared to periodic sampling methods.
  • Ensured input-to-state stability.

Conclusions:

  • The event-triggered control method offers improved performance and bandwidth efficiency for networked nonlinear systems.
  • Stability conditions are independent of bounded process noise, depending only on system parameters and network characteristics.