A Dynamic Event-Triggered Saturation Method for Nonlinear Estimation with Application to Drag-free Control Systems

This study investigates a dynamic event-triggered saturation nonlinear observer dedicated to addressing the challenge of event-based nonlinear observation in the presence of measurement noise. We propose a architecture incorporating dynamic event-triggered saturation injection to enhance system robustness against measurement noise while optimizing communication resource utilization by event-based sampling. As a prerequisite for this injection study, we rigorously prove the exponential convergence of system observation error under ideal conditions without measurement noise. Building upon this foundation, we explore the upper bound of convergence for system observation error in the presence of measurement errors. The proposed design is applied to in-orbit drag-free systems, serving as a key technology for significant scientific missions such as gravity field exploration, verification experiments on general relativity in space, and space gravitational wave detection. Drag-free systems face constraints on space transmission resources and require high mission performance with minimal sensor measurement noise, and our proposed method effectively addresses these challenges encountered in studying system dynamics. Additionally, we provide a numerical example to illustrate the feasibility and performance of our design by comparing it with a standard observer