P
Paulo Tabuada
Researcher at University of California, Los Angeles
Publications - 300
Citations - 25801
Paulo Tabuada is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Control system & Control theory. The author has an hindex of 60, co-authored 288 publications receiving 20444 citations. Previous affiliations of Paulo Tabuada include University of California, Berkeley & Instituto Superior Técnico.
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Automaton-based Implicit Controlled Invariant Set Computation for Discrete-Time Linear Systems.
TL;DR: In this paper, the robust positively invariant sets of the corresponding closed-loop systems can be expressed by a set of linear inequality constraints in the joint space of system states and controller parameters, leading to an implicit representation of the invariant set in a lifted space.
Journal ArticleDOI
Sharp Performance Bounds for PASTA
TL;DR: In this paper , the authors present an improved theoretical analysis for a low-cost alternative to these methods named PASTA (Provably Accurate Simple Transformation Alignment), originally introduced in (Marchi et al., 2022), and provide a formal worst-case guarantee on the localization error and show, experimentally, that it is tight.
Proceedings ArticleDOI
A coding approach to localization using landmarks
TL;DR: In this article, the authors connect self-localization using landmarks with coding theory, which enables to translate Hamming distance properties to probabilistic localization guarantees given a certain number of errors in landmark identification; it also enables to leverage existing polynomial time decoding algorithms for localization.
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Rapid Top-Down Synthesis of Large-Scale IoT Networks
Pradipta Ghosh,Jonathan Bunton,Dimitrios Pylorof,Marcos A. M. Vieira,Kevin S. Chan,Ramesh Govindan,Gaurav S. Sukhatme,Paulo Tabuada,Gunjan Verma +8 more
TL;DR: In this article, two qualitatively different representations of the synthesis problems satisfiability modulo convex optimization (SMC) and mixed-integer linear programming (MILP) are explored.