Resilient-triggered fault-tolerant fuzzy consensus for DPS-based MASs against multiple attacks and data compression

• A novel dual-scale reduction methodology is established for nonlinear parabolic DPS-based MASs by synergistically integrating Galerkin spectral decomposition with T-S fuzzy system theory. • A holistic defense framework is developed to simultaneously mitigate disclosure, replay, and disruption attacks in DPS-MASs with switching topologies. • An enhanced resilient hybrid dynamic event-triggered scheme is proposed, improving threshold adaptability to reduce communication load. • A unified framework integrating three data compression techniques is explored to coun-teract disruption-induced congestion. This paper develops a novel event-triggered (ET) leader-follower fault-tolerant fuzzy consensus framework for nonlinear parabolic distributed parameter system (DPS)-based multi-agent systems (MASs) under multiple cyber attacks. First, a dual-scale modeling framework is proposed, synergizing Galerkin spectral decomposition with Takagi-Sugeno fuzzy techniques to derive a finite-dimensional MASs that accurately captures the dominant dynamics of the original DPS. Second, a resilient hybrid dynamic ET mechanism is devised to intelligently schedule transmissions, significantly alleviating network bandwidth burden while enhancing dynamic performance beyond conventional ET schemes. Subsequently, an event-based fault-tolerant consensus protocol incorporating three data compression mechanisms is designed to counteract multiple cyber attacks. Sufficient conditions for achieving attack-resilient cooperative consensus are then established using a tailored Lyapunov functional approach, with controller gains derived via linear matrix inequality formulations. Finally, simulations on a thermal management system for hypersonic vehicle cooling fins validate the theoretical advances and demonstrate significant engineering applicability.