Observer-Based Adaptive NN Tracking Control for Nonlinear NCSs Under Intermittent DoS Attacks: A Finite-Time Prescribed Performance Method
研究了间歇性拒绝服务攻击下非线性网络控制系统的自适应神经网络跟踪控制问题，构建了切换状态观测器和有限时间预设性能函数，确保跟踪误差在有限时间内落入预定边界。
This article investigates the adaptive neural network (NN) tracking control problem for nonlinear networked control systems (NCSs) with finite-time prescribed performance (FTPP) subject to intermittent denial-of-service (DoS) attacks. It is noticeable that when the DoS attacker is active, the controller does not receive any information, which makes the controller fail to work. To tackle the challenge, an adaptive NN switching state observer is first built to estimate the unmeasurable states. Second, an FTPP function is constructed to boost the transient and steady-state performances of NCSs. Third, under the framework of the backstepping technique, an adaptive command filter is established by combining the dynamic adaptive technique with the switching state observer, which handles the “complexity explosion” problem and improves the robustness of NCSs. Besides, it is rigorously proved mathematically that the boundedness of all signals in the closed-loop system and the designed controller compels the tracking error to fall into the predefined boundary within a finite time. Finally, an application-oriented example of the single-link robotic arm system is utilized to demonstrate the viability of the proposed control method.