Distributed adaptive consensus tracking control for stochastic nonlinear multi-agent systems with quantized input

This paper addresses an adaptive control problem for a class of stochastic nonlinear multi-agent systems with quantized input. By using the backstepping design method, the distributed controller is designed for each of the agents, which only uses the agent's local state and the states of its neighbors. According to the graph theory and the stochastic analysis, it is proved that the proposed method can drive the agents to achieve consensus while all of the closed-loop systems remain bounded in probability. Finally, a numerical example is provided to show the effectiveness of the proposed method.