Abstract
The second-order consensus problem depends on not only the topology condition but also the coupling strength of the relative positions and velocities between neighboring agents. This paper seeks to solve the finite-time consensus problem of second-order multi-agent systems by games with special structures. Potential game and weakly acyclic game were applied for modeling the second-order consensus problem with different topologies. Furthermore, this paper introduces the event-triggered asynchronous cellular learning automata algorithm for optimizing the decision making process of the agents, which facilitates a convergence with the Nash equilibrium. Finally, numerical examples illustrate the effectiveness of the models.
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