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Predictive cloud control for multiagent systems with stochastic event-triggered schedule.

Li Li1, Xijuan Wang1, Yuanqing Xia2

  • 1School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.

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|May 13, 2019
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Summary
This summary is machine-generated.

This study introduces a predictive cloud control method for linear multiagent systems, enhancing stability and consensus despite network delays and noise. It optimizes communication via event-triggered schedules and state estimation for robust control.

Keywords:
Multiagent systemsNoisesPredictive cloud controlRandom network delaysStochastic event-triggered schedule

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Area of Science:

  • Control Systems Engineering
  • Networked Systems
  • Distributed Computing

Background:

  • Linear multiagent systems face challenges from random network delays and noise.
  • Communication costs in networked control systems require optimization.
  • Achieving both stability and consensus in distributed systems is complex.

Purpose of the Study:

  • To develop a predictive cloud control scheme for linear multiagent systems.
  • To address random network delays and communication costs.
  • To ensure system stability and consensus.

Main Methods:

  • A stochastic event-triggered schedule was designed to minimize data transmission.
  • An optimal state estimation algorithm was developed to counteract feedback channel delays.
  • A predictive cloud control scheme was proposed, actively compensating for forward channel delays.

Main Results:

  • Sufficient and necessary conditions for stability and consensus were derived.
  • The proposed methods were validated through a numerical example.
  • The control scheme effectively achieved stability and consensus in the presence of network uncertainties.

Conclusions:

  • The predictive cloud control scheme offers an effective solution for networked multiagent systems.
  • Stochastic event-triggering and optimal state estimation enhance system performance and reduce communication overhead.
  • The derived conditions provide a theoretical foundation for system analysis and design.