T
Tyler H. Summers
Researcher at University of Texas at Dallas
Publications - 166
Citations - 3261
Tyler H. Summers is an academic researcher from University of Texas at Dallas. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 24, co-authored 153 publications receiving 2596 citations. Previous affiliations of Tyler H. Summers include École Polytechnique Fédérale de Lausanne & Australian National University.
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On Submodularity and Controllability in Complex Dynamical Networks
TL;DR: In this article, the authors show that several important classes of metrics based on the controllability and observability Gramians have a strong structural property that allows for either efficient global optimization or an approximation guarantee by using a simple greedy heuristic for their maximization.
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Coordinated Standoff Tracking of Moving Targets: Control Laws and Information Architectures
TL;DR: A proof of heading convergence using feedback is complete, and a novel approach for heading convergence that does not require continuous feedback in the ideal case (no wind, stationary target), taking advantage of an analytical solution to the guidance field is proposed.
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Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables
TL;DR: In this article, an AC optimal power flow (OPF) approach is proposed to optimize system-level performance objectives while coping with uncertainty in both RES generation and loads, where probabilistic constraints are utilized to enforce voltage regulation with prescribed probability.
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Control of Minimally Persistent Leader-Remote-Follower and Coleader Formations in the Plane
TL;DR: This paper proposes a decentralized control law where each agent executes its control using only the relative position measurements of agents to which it must maintain its distance, and applies center manifold theory to show local exponential stability of the desired formation shape.
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Optimal Sensor and Actuator Placement in Complex Dynamical Networks
Tyler H. Summers,John Lygeros +1 more
TL;DR: It is shown that an important class of metrics based on the controllability and observability Gramians has a strong structural property that allows efficient global optimization: the mapping from possible placements to the trace of the associated Gramian is a modular set function.