Robust stability of event-triggered nonlinear moving horizon estimation
For control engineers, this work provides a theoretically grounded event-triggered estimation method that reduces sensor-to-estimator communication while ensuring stability, though the improvement over existing methods is incremental.
This paper proposes an event-triggered moving horizon estimation scheme for nonlinear systems, demonstrating robust global exponential stability under a novel event-triggering rule. The method reduces communication while maintaining estimation performance, validated through two examples.
In this work, we propose an event-triggered moving horizon estimation (ET-MHE) scheme for the remote state estimation of general nonlinear systems. In the presented method, whenever an event is triggered, a single measurement is transmitted and the nonlinear MHE optimization problem is subsequently solved. If no event is triggered, the current state estimate is updated using an open-loop prediction based on the system dynamics. Moreover, we introduce a novel event-triggering rule under which we demonstrate robust global exponential stability of the ET-MHE scheme, assuming a suitable detectability condition is met. In addition, we show that with the adoption of a varying horizon length, a tighter bound on the estimation error can be achieved. Finally, we validate the effectiveness of the proposed method through two illustrative examples.