College of Control Science and Engineering, Bohai University, Jinzhou 121013, Liaoning, China.
College of Control Science and Engineering, Bohai University, Jinzhou 121013, Liaoning, China.
Neural Netw. 2024 Nov;179:106586. doi: 10.1016/j.neunet.2024.106586. Epub 2024 Jul 27.
In this paper, the design of an adaptive neural event-triggered control scheme for a class of switched nonlinear systems affected by external disturbances and deception attacks is presented. In order to address the effects caused by unknown disturbances, a switched nonlinear disturbance observer is used, and the error between the estimated signals and actual disturbances is small. Meanwhile, a prescribed performance function is introduced, which aims to ensure system output reaches the performance bounds within a predefined finite time. In addition, a dynamic event-triggered mechanism is designed to reduce the communication load. Based on the theoretical analysis, all signals within the closed-loop system are bounded, while simultaneously ensuring the complete elimination of Zeno behavior. Finally, the validity and efficacy of the scheme are proven by an example of numerical simulation.
本文针对一类受外部干扰和欺骗攻击影响的切换非线性系统，设计了一种自适应神经网络事件触发控制方案。为了解决未知干扰引起的问题，采用了切换非线性干扰观测器，观测器估计信号与实际干扰之间的误差较小。同时，引入了一个规定性能函数，旨在确保系统输出在预设的有限时间内达到性能边界。此外，设计了一个动态事件触发机制，以降低通信负载。基于理论分析，闭环系统内的所有信号都是有界的，同时确保完全消除 Zeno 行为。最后，通过数值仿真示例验证了该方案的有效性和功效。
