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- W2894568673 abstract "This work considers the design of separating inputsignals in order to discriminate among a finite number of uncertainnonlinear models. Each nonlinear model corresponds to a systemoperating mode, unobserved intents of other drivers or robots, orto fault types or attack strategies, etc., and the separatinginputs are designed such that the output trajectories of all thenonlinear models are guaranteed to be distinguishable from eachother under any realization of uncertainties in the initialcondition, model discrepancies or noise. I propose a two-stepapproach. First, using an optimization-based approach, weover-approximate nonlinear dynamics by uncertain affine models, asabstractions that preserve all its system behaviors such that anydiscrimination guarantees for the affine abstraction also hold forthe original nonlinear system. Then, I propose a novel solution inthe form of a mixed-integer linear program (MILP) to the activemodel discrimination problem for uncertain affine models, whichincludes the affine abstraction and thus, the nonlinear models.Finally, I demonstrate the effectiveness of our approach foridentifying the intention of other vehicles in a highway lanechanging scenario. For the abstraction, I explore two approaches.In the first approach, I construct the bounding planes using aMixed-Integer Nonlinear Problem (MINLP) formulation of the givensystem with appropriately designed constraints. For the secondapproach, I solve a linear programming (LP) problem thatover-approximates the nonlinear function at only the grid points ofa mesh with a given resolution and then accounting for the entiredomain via an appropriate correction term. To achieve a desiredapproximation accuracy, we also iteratively subdivide the domaininto subregions. This method applies to nonlinear functions withdifferent degrees of smoothness, including Lipschitz continuousfunctions, and improves on existing approaches by enabling the useof tighter bounds. Finally, we compare the effectiveness of thisapproach with the existing optimization-based methods in simulationand illustrate its applicability for estimatordesign." @default.
- W2894568673 created "2018-10-12" @default.
- W2894568673 creator A5045043759 @default.
- W2894568673 date "2018-01-01" @default.
- W2894568673 modified "2023-09-23" @default.
- W2894568673 title "Affine Abstraction of Nonlinear Systems with Applications to Active Model Discrimination" @default.
- W2894568673 hasPublicationYear "2018" @default.
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