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Distributed reconfigurable control strategies for switching topology networked multi-agent systems.

Z Gallehdari1, N Meskin2, K Khorasani3

  • 1Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada.

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|June 21, 2017
PubMed
Summary

This study presents distributed control reconfiguration for multi-agent systems facing actuator faults and unreliable fault detection. The strategies ensure consensus and bounded team performance despite uncertainties.

Keywords:
Active fault recoveryControl reconfigurationDistributed controlMulti-agent systemsNetwork of unmanned underwater vehiclesSwitching topology networks

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Networked Systems

Background:

  • Multi-agent systems (MAS) are susceptible to actuator faults.
  • Fault detection and isolation (FDI) modules can provide unreliable fault severity estimations.
  • Maintaining system performance and consensus under fault conditions is challenging.

Purpose of the Study:

  • To develop distributed control reconfiguration strategies for MAS with directed switching topologies.
  • To address actuator faults (loss of effectiveness, outage, stuck) under uncertain FDI information.
  • To ensure consensus and bounded team performance during fault recovery.

Main Methods:

  • Utilized quadratic and convex hull (composite) Lyapunov functions.
  • Designed two cooperative and distributed recovery strategies for control gain selection.
  • Investigated directed switching topology networked multi-agent systems.

Main Results:

  • Successfully developed and validated distributed control reconfiguration strategies.
  • Demonstrated compensation for simultaneous actuator faults in autonomous underwater vehicles (AUVs).
  • Showcased effectiveness despite uncertainties and unreliabilities in fault diagnosis modules.

Conclusions:

  • The proposed distributed reconfiguration control laws effectively manage actuator faults in MAS.
  • The strategies ensure system consensus and performance bounds under challenging fault conditions.
  • The methods are applicable to complex systems like networked AUVs with switching topologies.