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.

中文翻译：

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基于事件的自适应安全渐近跟踪非线性信息物理系统对抗未知欺骗攻击

本文解决了未知欺骗攻击下一类非线性信息物理系统的事件触发自适应神经网络渐近跟踪控制问题。在递归设计过程中，基于边界估计方法、反步技术和一些平滑函数，提出了一种新型自适应渐近跟踪控制策略。所设计的渐近跟踪控制器可以保证系统的输出渐近跟踪期望信号，同时保证闭环系统中的所有信号都有界。特别是可以保证底层系统具有更快的收敛响应和更高的控制精度。此外，芝诺行为也被排除。最后以两级化学反应器为例验证了所设计控制算法的可行性和可行性。