Event-triggered distributed consensus in multi-agent cooperative control
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Cooperative control in multi-agent systems involves managing a group of agents that collaborate to accomplish a shared task. This approach has significant potential across various applications, including environmental monitoring, distributed aperture observing, traffic management, and more. A key technology employed in cooperative control is the consensus algorithm, which guides all agents to reach a common decision via communication. This dissertation aims to develop event-triggered consensus algorithms for linear multiagent systems (MASs) under general directed network. Under proposed algorithms, continuous information transmission and control input update are avoided so that unnecessary consumption of communication and computation resources can be reduced. In practical scenarios, agents may lack access to their complete state information as well as the overall network data. Building on the developed event-triggered consensus algorithm, an observer is designed to estimate each agent's state information, while an adaptive control law is utilized to infer the unknown aspects of the network. This allows the consensus algorithm to be entirely dependent on local state information, making it fully distributed. Using a developed extension method, these algorithms are first formulated for strongly connected networks and then adapted for general directed networks that contain a directed spanning tree.
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Ph. D.