Active Model Discrimination Using Partition-Based Output Feedback Input Design

Qiang Shen, Sze Zheng Yong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a partition-based output feedback active model discrimination approach that generates optimal output feedback inputs in a fixed time horizon for separating a set of discrete-time affine models subject to uncontrolled inputs, noises and uncertain initial conditions. Instead of computing the optimal input by solving a parametric mixed-integer linear program (MILP) at run time, we move this computationally demanding optimization task offline by partitioning the measurement domain and building a partition tree over the fixed time horizon. Since output measurements are available at each time instant during run time, we can update the separating input correspondingly and improve the model discrimination performance by reducing the input cost. The effectiveness of the proposed approach is demonstrated through simulations for identifying intention models of human-driven vehicles in a lane changing scenario.

Original languageEnglish (US)
Title of host publicationEuropean Control Conference 2020, ECC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages712-717
Number of pages6
ISBN (Electronic)9783907144015
StatePublished - May 2020
Event18th European Control Conference, ECC 2020 - Saint Petersburg, Russian Federation
Duration: May 12 2020May 15 2020

Publication series

NameEuropean Control Conference 2020, ECC 2020

Conference

Conference18th European Control Conference, ECC 2020
CountryRussian Federation
CitySaint Petersburg
Period5/12/205/15/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Systems Engineering
  • Mechanical Engineering
  • Computational Mathematics
  • Control and Optimization

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