Near-Optimal Sparse Sensor and Actuator Selection

Ali Jadbabaie

Massachusetts Institute of Technology

In this talk, I present our recent efforts in developing rigorous approaches to sparse sensor and actuator selection in large-scale linear dynamical systems. While sparse sensor and actuator selection is known to be NP-Hard, using tools from optimal experiment design and submodular optimization, we develop a framework for near- optimal sensor and actuator selection with provable approximation guarantees using greedy algorithms. We then extend these results to develop a robust variant of the approximations themes, where the optimization of sensor selection is performed in presence of an adversary who can cause a subset of sensors to fail. Next, using recent developments in graph sparsification and column selection  literature, we show how to select a sparse subset of sensors or actuators while guaranteeing performance with respect to the fully sensed or actuated system (and not the optimal sparse one). As a corollary we show  that by utilizing a time varying sense or actuator selection schedule, one can guarantee near-optimal sensing/control performance by selecting a dimension-independent (constant) number of sensors or actuators. Joint work with Vassilis Tzoumas (Penn), Milad Siami(MIT), and Alex Olshevsky (BU).

Published on March 19th, 2018

Last updated on March 19th, 2018