Intelligent Resilient Security Control for Fractional-Order Multiagent Networked Systems Using Reinforcement Learning and Event-Triggered Communication Mechanism

The main objective of this study is to develop an intelligent, resilient event-triggered control method for fractional-order multiagent networked systems (FOMANSs) using reinforcement learning (RL) to address challenges resulting from unknown dynamics, actuator faults, and denial-of-service (DoS) attacks. First, the challenge of unknown system dynamics within their environment must be addressed to achieve desired system stability in the face of unknown dynamics or to optimize consensus in FOMANSs. To address this problem, an adaptive learning law is implemented to handle unknown nonlinear dynamics, parameterized by a neural network, which establishes weights for a fuzzy logic system utilized in cooperative tracking protocols. A novel distributed control policy facilitates signal sharing through RL among agents, reducing error variables through learning. Moreover, this study combines an RL algorithm with the sliding mode control strategy to optimize the parameterization of the distributed control protocol, thereby eliminating its constraints on initial conditions. Second, realizing that DoS attacks typically make the actuator signal inaccessible for distributed control protocols, an innovative intelligent dual-event-triggered control strategy is formulated to reduce the effects of DoS attacks. By coordinating nested event triggers across various channels, the distributed control input is protected from incorrect signals from DoS attacks, thus ensuring its resilience. To address this problem, an intelligent security dual-event-triggered control protocol guarantees Mittag-Leffler stability of the closed-loop system and ensures effective sliding motion conditions. This distributed control protocol ensures robust tracking of control tasks and mitigates "Zeno behavior" during event triggering. The proposed control strategy is validated using a single-link flexible-joint robotic manipulator system.