Event-Based Fuzzy-Adaptive Consensus Tracking Control for Stochastic High-Order Nonlinear Multi-Agent Systems with Specified-Time Convergence

Abstract This paper proposes a novel specifified-time event-triggered fuzzy adaptive consensus control methodology for stochastic high-order nonlinear multi-agent networks, which is intrinsically challenging as the existence of stochasticity and high-order (positive odd integers greater than one) terms. In contrast with the state-of-the-art methodologies, the distinguishing contributions of this work lie in that: (a) a novel specifified-time performance function (STPF) is introduced and a new STPF-based high-order asymmetric time-varying barrier Lyapunov function (BLF) is designed to guarantee the local consensus tracking errors of entire network remain within a predefifined arbitrarily small residual set in specifified-time; (b) the communication effificiency of network is proved signifificantly owing to a new high-order switching threshold event-triggered mechanism, which is capable of resizing triggering threshold in real-time; (c) the fuzzy adaptive control algorithm and adaption laws are devised by combining adding one power integrator technique and fuzzy approximators. Comparative simulations are carried out to validate the effectiveness of the proposed scheme.