scholarly journals A Data-Driven Distributed Adaptive Control Approach for Nonlinear Multi-Agent Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 207884-207893
Author(s):  
Xian Yu ◽  
Shangtai Jin ◽  
Genfeng Liu ◽  
Ting Lei ◽  
Ye Ren
Keyword(s):  
Adaptive Control ◽  
Data Driven ◽  
Multi Agent Systems ◽  
Control Approach ◽  
Agent Systems ◽  
Distributed Adaptive Control ◽  
Multi Agent

Mitigating the Effects of Sensor Uncertainties in Networked Multi-Agent Systems

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Ehsan Arabi ◽  
Tansel Yucelen ◽  
Wassim M. Haddad
Keyword(s):  
Adaptive Control ◽  
System Performance ◽  
Closed Loop ◽  
Control Architecture ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Distributed Adaptive Control ◽  
Exogenous Disturbances ◽  
Multi Agent ◽  
System Uncertainties

Networked multi-agent systems consist of interacting agents that locally exchange information, energy, or matter. Since these systems do not in general have a centralized architecture to monitor the activity of each agent, resilient distributed control system design for networked multi-agent systems is essential in providing high system performance, reliability, and operation in the presence of system uncertainties. An important class of such system uncertainties that can significantly deteriorate the achievable closed-loop system performance is sensor uncertainties, which can arise due to low sensor quality, sensor failure, sensor bias, or detrimental environmental conditions. This paper presents a novel distributed adaptive control architecture for networked multi-agent systems with undirected communication graph topologies to mitigate the effect of sensor uncertainties. Specifically, we consider agents having identical high-order, linear dynamics with agent interactions corrupted by unknown exogenous disturbances. We show that the proposed adaptive control architecture guarantees asymptotic stability of the closed-loop dynamical system when the exogenous disturbances are time-invariant and uniform ultimate boundedness when the exogenous disturbances are time-varying. Two numerical examples are provided to illustrate the efficacy of the proposed distributed adaptive control architecture.

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