Distributed Saturation-Tolerant Fuzzy Control for Constrained Stochastic Multiagent Systems With Resilient Quantitative Behaviors

This article presents a distributed saturation-tolerant fuzzy control scheme for stochastic multiagent systems (MASs) with unknown measurement sensitivity, where the states, consensus errors, and control inputs all are constrained. A concise nonlinear mapping is tactfully devised to impose appropriate constraints on full states without reliance on feasibility conditions. Besides, a novel resilient quantitative prescribed performance control (RQPPC) is developed, which incorporates finite-time performance boundaries with input-relevant dynamic boundaries generated by an auxiliary system, being expected to flexibly adapt to input saturation without violating performance constraints. Building upon the RQPPC, the transient and steady-state behaviors of consensus errors can be quantitatively predesigned free from repeated parameter tuning processes. Uncertain nonlinear terms in the converted system are successfully addressed by fuzzy approximation. The superiority and effectiveness of the proposed algorithms are confirmed by simulations with comparisons.