Resilient Synchronization Control for Multi-Agent Systems under Stochastic Sampling-Based Triggering Algorithm

This study investigates the resilient synchronization problem for multi-agent systems (MASs) under the stochastic sampling-based triggering algorithm. First, to explicitly characterize the vulnerability of communication links, random link failures are modeled by Bernoulli-type link-availability variables and fault-induced perturbations. Second, to reduce redundant inter-agent communication, the stochastic sampling-based triggering algorithm is introduced by embedding random sampling intervals into the triggering mechanism. Then, based on the random link failures and the stochastic sampling-based triggering algorithm, a distributed leader-state observer and a Riccati-equation-based PID controller are developed to realize resilient adaptive synchronization. Furthermore, sufficient conditions are derived by using algebraic Riccati equations and a Lyapunov functional to guarantee the mean-square stability of the closed-loop system and the convergence of the synchronization error. Finally, simulation studies based on helicopter flight data demonstrate the effectiveness and robustness of the proposed strategy under the stochastic sampling-based triggering algorithm and random link failures.