Narayanan G, Karthikeyan Rajagopal, Lee Sangmoon, Ahn Sangtae
IEEE Trans Cybern. 2025 Mar 7;PP. doi: 10.1109/TCYB.2025.3542838.
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.
本研究的主要目标是为分数阶多智能体网络系统（FOMANSs）开发一种智能、弹性的事件触发控制方法，利用强化学习（RL）来应对未知动态、执行器故障和拒绝服务（DoS）攻击带来的挑战。首先，必须应对其环境中未知系统动态的挑战，以便在面对未知动态时实现所需的系统稳定性，或在FOMANSs中优化一致性。为了解决这个问题，实施了一种自适应学习律来处理由神经网络参数化的未知非线性动态，该神经网络为协作跟踪协议中使用的模糊逻辑系统建立权重。一种新颖的分布式控制策略促进了智能体之间通过RL进行信号共享，通过学习减少误差变量。此外，本研究将RL算法与滑模控制策略相结合，以优化分布式控制协议的参数化，从而消除其对初始条件的限制。其次，认识到DoS攻击通常会使执行器信号无法用于分布式控制协议，制定了一种创新的智能双事件触发控制策略来减少DoS攻击的影响。通过协调跨各种通道的嵌套事件触发，保护分布式控制输入免受DoS攻击的错误信号影响，从而确保其弹性。为了解决这个问题，一种智能安全双事件触发控制协议保证了闭环系统的Mittag-Leffler稳定性，并确保了有效的滑动运动条件。这种分布式控制协议确保了对控制任务的鲁棒跟踪，并减轻了事件触发期间的“芝诺行为”。所提出的控制策略通过单链路柔性关节机器人操纵器系统进行了验证。
