Event-Triggered Task-Switching Control Based on Distributed Estimation

This paper studies how to control an agent in an uncertain environment over a connected sensor network, such that the agent is able to finish a sequence of tasks, namely, reaching certain sets in order. Based on multiple offline reference trajectories and constrained communication between the agent and the sensor network, an event-triggered task-switching control framework is proposed, so that the agent state remains in each task set for the desired time and then switches to the next task. Employing a local predicted control law and the messages from neighboring sensors, a two time-scale distributed filter is proposed for each sensor to estimate the agent state. Under mild system conditions (i.e., stabilization and collective detectability), the estimation error and trajectory tracking error are shown to be asymptotically upper bounded.