Event-Triggered Tracking Control of Nonlinear Systems Under Sparse Attacks and its Application to Rigid Aircraft

This study is aimed at addressing the event-based state estimation and tracking control problems for a class of nonlinear systems, the output of which is measured by multiple sensors, but the adversary can manipulate nearly half of the measurements simultaneously. First, a sampled-data-based event-triggered strategy is developed to reduce unnecessary data transmissions under sparse sensor attacks, and the transmitted data are filtered by a data selector to obtain reliable data. Subsequently, an output-prediction-based continuous-discrete observer is improved so that it can estimate continuous-time system states from the event-triggered output, rather than being limited to time-triggered sampled output. Further, to design a tracking controller with the segmentally differentiable estimated states, a backstepping method incorporating tracking differentiators is proposed. Finally, the effectiveness of the proposed method is demonstrated by applying it in the simulation of a rigid aircraft.