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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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Local factorization of trajectory lifting morphisms for single-input affine control systems ☆
TL;DR: This paper shows that any trajectory lifting map between two single-input control affine systems can be locally factored as the composition of two special trajectory lifting maps: a projection onto a quotient system followed by a differentially flat output with respect to another control system.
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Approximate reduction of dynamical systems
TL;DR: Using notions related to incremental stability, this paper gives conditions on when a dynamical system can be projected to a lower dimensional space while providing hard bounds on the induced errors, i.e., when it is behaviorally similar to a dynamicals system on a lowerdimensional space.
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Securing State Estimation Under Sensor and Actuator Attacks: Theory and Design
TL;DR: The notion of sparse strong observability is introduced and it is shown that is a necessary and sufficient condition for correctly reconstructing the state despite the considered attacks and an estimator is proposed to harness the complexity of this intrinsically combinatorial problem by leveraging satisfiability modulo theory solving.
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Compositional Transient Stability Analysis of Multi-Machine Power Networks
TL;DR: In this article, energy-based models derived from first principles that are not subject to hard-to-justify classical assumptions are used to derive intuitive conditions ensuring the transient stability of power systems with lossy transmission lines.
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Taming AI Bots: Controllability of Neural States in Large Language Models
TL;DR: In this article , the authors tackle the question of whether an agent can, by suitable choice of prompts, control an AI bot to any state, and derive necessary and sufficient conditions for controllability.