17 citations · 28 across the 11 of their papers we have counts for
6 papers · 1 filter
Robust Data-Driven Output Feedback Control via Bootstrapped Multiplicative Noise
Benjamin Gravell, Iman Shames, Tyler Summers
We propose a robust data-driven output feedback control algorithm that explicitly incorporates inherent finite-sample model estimate uncertainties into the control design. The algo…
Policy Iteration for Multiplicative Noise Output Feedback Control
Benjamin Gravell, Matilde Gargiani, John Lygeros +1
We propose a policy iteration algorithm for solving the multiplicative noise linear quadratic output feedback design problem. The algorithm solves a set of coupled Riccati equation…
Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control
Venkatraman Renganathan, Sleiman Safaoui, Aadi Kothari +3
Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and predict…
Identification of Linear Systems with Multiplicative Noise from Multiple Trajectory Data
Yu Xing, Benjamin Gravell, Xingkang He +2
The paper studies identification of linear systems with multiplicative noise from multiple-trajectory data. An algorithm based on the least-squares method and multiple-trajectory d…
Anomaly Detection Under Multiplicative Noise Model Uncertainty
Venkatraman Renganathan, Benjamin J. Gravell, Justin Ruths +1
State estimators are crucial components of anomaly detectors that are used to monitor cyber-physical systems. Many frequently-used state estimators are susceptible to model risk as…
Linear System Identification Under Multiplicative Noise from Multiple Trajectory Data
Yu Xing, Ben Gravell, Xingkang He +2
The study of multiplicative noise models has a long history in control theory but is re-emerging in the context of complex networked systems and systems with learning-based control…