Adaptive tracking control for stochastic nonlinear multiagent systems via an improved synchronous self-triggered mechanism

With the increased need for information exchanges, the communication part of stochastic nonlinear multiagent systems (MASs) suffers from the influence of limited communication resources, which brings challenges to the efficiency of the information exchange between agents. This paper investigates the synchronous self-triggered adaptive tracking control problem for stochastic nonlinear MASs. To begin with, a novel synchronous self-triggered mechanism is constructed based on nonlinear impulsive dynamics, which ensures simultaneous updates of agent states and adaptive laws without continuous monitoring. Besides, this mechanism ensures the consistency of triggering instants for the same-order states of multiple agents, thereby effectively avoiding the communication congestion problem caused by asynchronous triggering. Next, a new class of adaptive triggering update law is designed, which expands the feasible parameter domain and enhances system flexibility. In addition, a first-order filter is introduced into the controller design to address the discontinuity of virtual control signal caused by the triggering mechanism. Based on the Lyapunov stability theory, it is shown that the designed controller can ensure that all signals in the closed-loop system stay bounded in probability. Finally, a simulation example is conducted to further demonstrate the validity of the presented control method.