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Affine Abstraction of Nonlinear Systems with Applications to Active Model Discrimination

Abstract This work considers the design of separating input signals in order to discriminate among a finite number of uncertain nonlinear models. Each nonlinear model corresponds to a system operating mode, unobserved intents of other drivers or robots, or to fault types or attack strategies, etc., and the separating inputs are designed such that the output trajectories of all the nonlinear models are guaranteed to be distinguishable from each other under any realization of uncertainties in the initial condition, model discrepancies or noise. I propose a two-step approach. First, using an optimization-based approach, we over-approximate nonlinear dynamics by uncertain affine models, as abstractions that preserve all its system behaviors such that an... (more)
Created Date 2018
Contributor Singh, Kanishka Raj (Author) / Yong, Sze Zheng (Advisor) / Artemiadis, Panagiotis (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Subject Systems science / Robotics / Automotive engineering / Active Model Discrimination / Affine Abstraction / Input Design / Nonlinear Systems
Type Masters Thesis
Extent 49 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Mechanical Engineering 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis