Input Design for Nonlinear Model Discrimination via Affine Abstraction

Kanishka Singh, Yuhao Ding, Necmiye Ozay, Sze Zheng Yong

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)175-180
Number of pages6
Journal6th IFAC Conference on Analysis and Design of Hybrid Systems ADHS 2018: Oxford, United Kingdom, 11—13 July 2018
Issue number16
StatePublished - Jan 1 2018


  • Input design
  • affine abstraction
  • model discrimination
  • nonlinear systems

ASJC Scopus subject areas

  • Control and Systems Engineering


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