Tractable model discrimination for safety–critical systems with disjunctive and coupled constraints

Qiang Shen, Ruochen Niu, Sze Zheng Yong

Research output: Contribution to journalArticlepeer-review

Abstract

This paper considers the model discrimination problem among a finite number of models in safety–critical systems that are subjected to constraints that can be disjunctive and where state and input constraints can be coupled with each other. In particular, we consider both the optimal input design problem for active model discrimination that is solved offline as well as the online passive model discrimination problem via a model invalidation framework. To overcome the issues associated with non-convex and generalized semi-infinite constraints due to the disjunctive and coupled constraints, we propose some techniques for reformulating these constraints in a computationally tractable manner by leveraging the Karush–Kuhn–Tucker (KKT) conditions and introducing binary variables, thus recasting the active and passive model discrimination problems into tractable mixed-integer linear/quadratic programming (MILP/MIQP) problems. When compared with existing approaches, our method is able to obtain the optimal solution and is observed in simulations to also result in less computation time. Finally, we demonstrate the effectiveness of the proposed active model discrimination approach for estimating driver intention with disjunctive safety constraints and state–input coupled curvature constraints, as well as for fault identification.

Original languageEnglish (US)
Article number101217
JournalNonlinear Analysis: Hybrid Systems
Volume46
DOIs
StatePublished - Nov 2022

Keywords

  • Autonomous driving
  • Fault diagnosis
  • Input design
  • Intention identification
  • Model discrimination

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Analysis
  • Computer Science Applications

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