Welikala, ShiranthaCassandras, Christos G.2021-08-132021-08-132020-12-14Shirantha Welikala, Christos G Cassandras. 2020. "Event-Driven Receding Horizon Control For Distributed Persistent Monitoring on Graphs." 2020 59th IEEE Conference on Decision and Control (CDC). 2020 59th IEEE Conference on Decision and Control (CDC). 2020-12-14 - 2020-12-18. https://doi.org/10.1109/cdc42340.2020.9303882https://hdl.handle.net/2144/42882We consider the optimal multi-agent persistent monitoring problem defined on a set of nodes (targets) inter-connected through a fixed graph topology. The objective is to minimize a measure of mean overall node state uncertainty evaluated over a finite time interval by controlling the motion of a team of agents. Prior work has addressed this problem through on-line parametric controllers and gradient-based methods, often leading to low-performing local optima or through off-line computationally intensive centralized approaches. This paper proposes a computationally efficient event-driven receding horizon control approach providing a distributed on-line gradient-free solution to the persistent monitoring problem. A novel element in the controller, which also makes it parameter-free, is that it self-optimizes the planning horizon over which control actions are sequentially taken in event-driven fashion. Numerical results show significant improvements compared to state of the art distributed on-line parametric control solutions.en-USConference materials10.1109/cdc42340.2020.93038820000-0002-1625-7658 (Cassandras, Christos G)553956