College of Control Science and Engineering, Bohai University, Jinzhou 121013, China.
School of Automation, Qingdao University, Qingdao, 266071, China.
Neural Netw. 2024 Jun;174:106249. doi: 10.1016/j.neunet.2024.106249. Epub 2024 Mar 19.
This paper addresses the resilient event-triggering adaptive neural network (NN) control problem for networked control systems under mixed cyber attacks. Compared with the conventional event-triggered mechanism (ETM) with constant threshold, a novel resilient ETM is designed to withstand the affect of denial-of-service attacks and conserve communication resources. Different from the energy-bounded deception attacks, an unknown state-dependent nonlinear attack signal is considered in this work. To identify the deception attack, the NN technique is utilized to approximate the unknown attack signal. Subsequently, an adaptive controller is established to compensate for the malicious affects of deception attacks on the system. Furthermore, sufficient conditions for the boundedness of the system are derived via applying the Lyapunov functional, and a co-design strategy for control gain and event-triggering parameter is provided. Finally, the feasibility of the proposed approach is validated through a robot manipulator system.
本文针对网络控制系统中存在的混合网络攻击问题，研究了弹性事件触发自适应神经网络（NN）控制问题。与传统的具有固定阈值的事件触发机制（ETM）相比，设计了一种新的弹性 ETM 来抵抗拒绝服务攻击并节省通信资源。与能量受限的欺骗攻击不同，本文考虑了一种未知的状态相关非线性攻击信号。为了识别欺骗攻击，利用 NN 技术来逼近未知的攻击信号。随后，建立了一个自适应控制器来补偿欺骗攻击对系统的恶意影响。此外，通过应用李雅普诺夫函数推导出了系统有界性的充分条件，并提供了控制增益和事件触发参数的协同设计策略。最后，通过机器人操纵器系统验证了所提方法的可行性。
