欺骗
信息物理系统
非线性系统
事件(粒子物理)
计算机科学
跟踪(教育)
控制(管理)
计算机安全
控制理论(社会学)
自适应控制
心理学
人工智能
社会心理学
物理
教育学
量子力学
操作系统
作者
Yongjie Tian,Ning Zhao
标识
DOI:10.1016/j.jfranklin.2024.106766
摘要
This article addresses the event-triggered adaptive neural network asymptotic tracking control problem for a class of nonlinear cyber–physical systems under unknown deception attacks. In the process of recursive design, a novel adaptive asymptotic tracking control strategy is proposed based on bound estimation method, backstepping technique and some smooth functions. The designed asymptotic tracking controller can ensure that the output of the system asymptotically tracks the desired signal, while ensuring that all signals in the closed-loop system are bounded. Particularly, the underlying system can be guaranteed to possess faster convergence response and higher control precision. Additionally, the Zeno behavior is ruled out. Finally, a two-stage chemical reactor is employed as an example to demonstrate the feasibility and viability of the designed control algorithm.
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