Literature DB >> 28629866

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

Z Gallehdari1, N Meskin2, K Khorasani3.   

Abstract

In this paper, distributed control reconfiguration strategies for directed switching topology networked multi-agent systems are developed and investigated. The proposed control strategies are invoked when the agents are subject to actuator faults and while the available fault detection and isolation (FDI) modules provide inaccurate and unreliable information on the estimation of faults severities. Our proposed strategies will ensure that the agents reach a consensus while an upper bound on the team performance index is ensured and satisfied. Three types of actuator faults are considered, namely: the loss of effectiveness fault, the outage fault, and the stuck fault. By utilizing quadratic and convex hull (composite) Lyapunov functions, two cooperative and distributed recovery strategies are designed and provided to select the gains of the proposed control laws such that the team objectives are guaranteed. Our proposed reconfigurable control laws are applied to a team of autonomous underwater vehicles (AUVs) under directed switching topologies and subject to simultaneous actuator faults. Simulation results demonstrate the effectiveness of our proposed distributed reconfiguration control laws in compensating for the effects of sudden actuator faults and subject to fault diagnosis module uncertainties and unreliabilities.
Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Keywords:  Active fault recovery; Control reconfiguration; Distributed control; Multi-agent systems; Network of unmanned underwater vehicles; Switching topology networks

Year:  2017        PMID: 28629866     DOI: 10.1016/j.isatra.2017.06.008

Source DB:  PubMed          Journal:  ISA Trans        ISSN: 0019-0578            Impact factor:   5.468


  1 in total

1.  ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish.

Authors:  Iván García-Magariño; Raquel Lacuesta; Jaime Lloret
Journal:  Sensors (Basel)       Date:  2017-11-13       Impact factor: 3.576

  1 in total

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