Data Driven Distributed Bipartite Consensus Tracking for nonlinear Multiagent Systems Via Iterative Learning Control

This paper explores a data-driven distributed bipartite consensus tracking (DBCT) problem for discrete-time multi-agent systems (MASs) with coopetition networks under repeatable operations. To solve this problem, a time-varying linearization model along the iteration axis is first established by usi...

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Veröffentlicht in:	IEEE access 2020-01, Vol.8, p.1-1
Hauptverfasser:	Zhao, Huarong, Peng, Li, Yu, Hongnian
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Bipartite consensus Data-driven control (DDC) Discrete time systems Iterative control (ILC) Iterative learning control Iterative methods Machine learning Mathematical model Mathematical models Multi-agent systems Multi-agent systems (MASs) Multiagent systems Network topologies Network topology Nonlinear discrete-time systems Nonlinear systems Switches Switching Topology Tracking errors
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