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Sliding mode based-variable selective control for robust tracking of uncertain Internet-based systems.

Behrooz Rahmani1

  • 1Control Research Lab., Department of Mechanical Engineering, Yasouj University, 75914-353 Yasouj, Iran.

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|September 8, 2019
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Summary
This summary is machine-generated.

This study introduces an event-triggered robust control for uncertain linear Internet-based systems. The method ensures stability and tracking despite network delays and uncertainties, reducing communication load.

Keywords:
Mismatched uncertaintyNetworked control systemRobust controlSliding mode controlTracking

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Area of Science:

  • Control Systems Engineering
  • Networked Control Systems
  • Robust Control Theory

Background:

  • Internet-based systems face challenges from model uncertainties and network impairments.
  • Event-triggered control strategies aim to optimize communication in networked systems.
  • Ensuring stability and performance in the presence of delays and packet loss is critical.

Purpose of the Study:

  • To develop a robust control strategy for uncertain linear Internet-based systems.
  • To reduce communication load using an event-triggered approach.
  • To guarantee system stability, robustness, and output tracking.

Main Methods:

  • Design of a novel sliding surface for linear Internet-based systems via linear matrix inequalities.
  • Application of Lyapunov's stability theorem for stability analysis.
  • Development of a discrete-time sliding mode control method to handle time delays and packet dropouts.

Main Results:

  • The proposed control strategy effectively manages model uncertainties and network effects.
  • The method demonstrates resilience to significant network delays and packet dropouts.
  • Asymptotic stability and robustness of the closed-loop system are validated.

Conclusions:

  • The event-triggered robust control method is effective for uncertain linear Internet-based systems.
  • The approach enhances stability and tracking performance under network constraints.
  • Simulation results confirm the method's efficacy against model uncertainty and band-limited networks.