FLOC 2018: FEDERATED LOGIC CONFERENCE 2018
Input Design for Nonlinear Model Discrimination Via Affine Abstraction

Authors: Kanishka Raj Singh, Yuhao Ding, Necmiye Ozay and Sze Zheng Yong

Paper Information

Title:Input Design for Nonlinear Model Discrimination Via Affine Abstraction
Authors:Kanishka Raj Singh, Yuhao Ding, Necmiye Ozay and Sze Zheng Yong
Proceedings:ADHS Full papers
Editor: Alessandro Abate
Keywords:aaa, bbb, ccc
Abstract:

ABSTRACT. This paper 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. We 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 any discrimination guarantees for the affine abstraction also hold for the original nonlinear system. Then, we propose a novel solution in the form of a mixed-integer linear program (MILP) to the active model discrimination problem for uncertain affine models, which includes the affine abstraction and thus, the nonlinear models. Finally, we demonstrate the effectiveness of our approach for identifying the intention of other vehicles in a highway lane changing scenario.

Pages:6
Talk:Jul 12 12:05 (Session 73B: Observation and Estimation)
Paper: