Event-Triggered State Estimation and Control for Networked Nonlinear Systems Under Dynamic Sparse Attacks

In this article, we investigate the problem of event-triggered state estimation and tracking control for a class of nonlinear networked control systems subject to measurement disturbances and dynamic sparse attacks, where an attacker can select and manipulate some of the measurements in a time-varying manner. First, a voting-based secure event-triggered mechanism is constructed to reduce the transmission of measurement data under attack. Second, considering the effect of the measurement disturbances, two output estimation algorithms are proposed to estimate the actual system output from the transmitted data. Subsequently, according to the event-triggered estimated output, a filtered observer with low sensitivity to disturbances is constructed to estimate the continuous-time system states. Finally, a novel finite-time compensation system that can compensate for both filtering errors and input saturation is constructed, and a tracking controller is designed to ensure that the system output converges to a neighborhood of the desired output. Several comparative simulations based on a manipulator system with motor dynamics are performed to verify the effectiveness of the proposed method.