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Aperiodic linear networked control considering variable channel delays: application to robots coordination.

Carlos Santos1, Felipe Espinosa2, Enrique Santiso3

  • 1Electronics Department, Polytechnics School, University of Alcala, Campus Universitario, Ctra. Madrid-Barcelona, Km. 33,600, 28871. Alcalá de Henares, Madrid, Spain. carlos.santos@depeca.uah.es.

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Summary
This summary is machine-generated.

This study introduces adaptive, aperiodic control for wireless robotics, reducing communication load and improving performance. It optimizes wireless channel usage by considering real-time network delay, unlike conservative methods.

Keywords:
adaptive self-triggered controlaperiodic control implementationnetworked robotsvariable channel delayswireless cyber-physical systems

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Area of Science:

  • Robotics
  • Wireless Communication
  • Cyber-Physical Systems

Background:

  • Wireless cyber-physical systems face challenges in balancing communication load and control performance.
  • Efficient resource allocation is crucial for shared wireless channels.

Purpose of the Study:

  • To design a remote control solution for wireless robotics that minimizes communication load.
  • To improve control performance by adapting to network conditions.

Main Methods:

  • Developed an aperiodic and adaptive triggering mechanism for remote control.
  • Incorporated real-time network delay into the triggering condition, avoiding conservative maximum delay assumptions.
  • Utilized the TrueTime simulation tool and implemented control laws on P3-DX robots.

Main Results:

  • The adaptive self-triggered control effectively reduced channel delay and enhanced resource availability.
  • Demonstrated improved network access and control performance compared to periodic and non-adaptive methods.
  • Validated the solution using simulations for IEEE 802.11g and practical implementation on multiple robots.

Conclusions:

  • The proposed aperiodic and adaptive triggering mechanism offers a superior approach for wireless robotic control.
  • This method optimizes wireless channel usage, balancing performance and resource efficiency.
  • The findings are applicable to systems requiring robust control over uncertain wireless networks.