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20182024
most citedControl Design for Risk-Based Signal Temporal Logic Specifications

21 citations · 25 across the 14 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

eess.SY2020

Revisiting the Water Quality Sensor Placement Problem: Optimizing Network Observability and State Estimation Metrics

Ahmad F. Taha, Shen Wang, Yi Guo +4

Real-time water quality (WQ) sensors in water distribution networks (WDN) have the potential to enable network-wide observability of water quality indicators, contamination event d…

eess.SY2020★ 21 cited

Control Design for Risk-Based Signal Temporal Logic Specifications

Sleiman Safaoui, Lars Lindemann, Dimos V Dimarogonas +2

We present a general framework for risk semantics on Signal Temporal Logic (STL) specifications for stochastic dynamical systems using axiomatic risk theory. We show that under our…

math.DS2020

Robust Control Design for Linear Systems via Multiplicative Noise

Benjamin Gravell, Peyman Mohajerin Esfahani, Tyler Summers

Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for di…

eess.SY2020★ 2 cited

Trust-based user-interface design for human-automation systems

Abraham P. Vinod, Adam J. Thorpe, Philip A. Olaniyi +2

We present a method for dynamics-driven, user-interface design for a human-automation system via sensor selection. We define the user-interface to be the output of a MIMO LTI syste…

eess.SY2020

Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints

Venkatraman Renganathan, Iman Shames, Tyler H. Summers

Safely deploying robots in uncertain and dynamic environments requires a systematic accounting of various risks, both within and across layers in an autonomy stack from perception…

eess.SY2020

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…