Event-Triggered Control with Deferred State Constraints

Over the past few decades, extensive attention has been focused on the cooperative control of multiagent systems (MASs), attributed to their inherent flexibility, robustness, and versatility (Zhang et al. 2020; Yang et al. 2019; Liang et al. 2019; Dong et al. 2021; Wu et al. 2020; Ren et al. 2020; Yang and Liu 2020). As a critical research topic in cooperative control, consensus control requires agents to achieve consistency on specific variables through local information interaction and has undergone in-depth exploration (Liang et al. 2020; Zhang and Chen 2017; Xiao et al. 2020; Li et al. 2021; Fan et al. 2020). Furthermore, with the practical application of MASs, complex dynamics involving unmatched uncertainties have emerged, rendering the consensus control problem more intractable and thus demanding urgent solutions. Consequently, various intelligent control approaches, including neural control and fuzzy control, have been integrated into the consensus control problem to tackle the unknown nonlinearities of MASs (Li et al. 2019; Zhang et al. 2020; Zhou et al. 2019; Song et al. 2016; Pan et al. 2020; Zhang and Jing 2021; Liu et al. 2021; Song et al. 2017). Chen et al. (2016) proposed an adaptive fuzzy controller combined with a high-gain observer for MASs, aiming to address the problems of unknown heterogeneous nonlinear dynamics and unmeasurable states. To handle the unknown nonlinear functions and actuator faults existing in the human-in-the-loop consensus control of MASs, a neural fault-tolerant controller with dynamic coupling gains was presented in Lin et al. (2020). Compared with uniformly ultimately bounded stability and practical finite-time stability, the compound tracking control strategy proposed in Liu et al. (2021) exhibited greater superiority. This strategy addressed the cooperative platoon consensus problem by integrating backstepping, adaptive neural control, and a modified prescribed performance technique. Wang et al. (2016) developed an adaptive neural control scheme for stochastic nonlinear MASs in nonstrict feedback form, which can effectively handle unknown nonlinearities and stochastic disturbance terms.