Switching Event-Triggered Adaptive Neural Network Control for Switched Nonlinear Systems Under Hybrid Attacks
针对传感器到控制器通道中混合欺骗和拒绝服务攻击，提出一种切换事件触发自适应神经网络输出反馈控制方案，通过引入有效DoS攻击概念和切换动态事件触发机制，解决了异步切换和稳定性问题，并用单连杆机器人系统验证了有效性。
This article proposes a switching event-triggered (ET) adaptive neural network (NN) output-feedback control scheme for a family of networked switched nonlinear systems under hybrid deception and denial-of-service (DoS) attacks in sensor-to-controller channel. The concept of “effective” DoS attacks is introduced for removing the assumption of the time sequences of DoS off/on and on/off transitions being known. In the active and inactive intervals of effective DoS attacks, an ET dual-switched NN observer, a dual-switched update law, common coordinate transformations in backstepping and a switching adaptive NN controller of each subsystem are constructed. Then, hybrid attacks are coped with and the difficulty in stability analysis caused by different coordinate transformations is overcome. Moreover, by designing a new switching dynamic event-triggering mechanism and a new Lyapunov function dependent on the switching signal of controller and DoS attacks, asynchronous switching between candidate subsystems and candidate observers and controllers is handled, and the convergence of tracking error to a small neighborhood around the origin is proved under a new class of switching signals with average dwell time. The effectiveness and applicability of the scheme proposed are illustrated by a switched one-link robotic manipulator system.