Adaptive Event-Triggered Saturation-Tolerant Control for Multiagent Systems Based on Finite-Time Fuzzy Learning
Xiaohui Yue, Huaguang Zhang, Jiayue Sun, Lei Wan
Abstract
In this article, the event-triggered saturation-tolerant control problem of nonlinear multiagent systems (MASs) is investigated based on the finite-time fuzzy composite learning approach. Specifically, a novel concept, named as deferred saturation-tolerant prescribed performance control, is proposed, which guarantees the flexible prescribed performance in the face of input saturation, while there are no needs of initial restrictions on distributed errors. Moreover, by extracting weight errors from filtering operations and auxiliary variables, a finite-time fuzzy composite learning rule driven by weight and distributed errors is developed for improving the learning performance and ensuring that unknown nonlinearities are precisely estimated. Then, resorting to event-triggered communication mechanism, signal transmissions among connected agents only occur when triggering conditions are satisfied, contributing to a reduced communication burden. Finally, simulations with comparative studies are provided to confirm the effectiveness and superiority of the proposed method.