LTLC: Linear temporal logic for control

Young Min Kwon, Gul A Agha

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

Abstract

Linear systems are one of the most commonly used models to represent physical systems. Yet, only few automated tools have been developed to check their behaviors over time. In this paper, we propose a linear temporal logic for specifying complex properties of discrete time linear systems. The proposed logic can also be used in a control system to generate control input in the process of model checking. Although, developing a full feedback control system is beyond the scope of this paper, authors believe that a feedback loop can be easily introduced by adopting the receding horizon scheme of predictive controllers. In this paper we explain the syntax, the semantics, a model checking algorithm, and an example application of our proposed logic.

Original languageEnglish (US)
Title of host publicationHybrid Systems
Subtitle of host publicationComputation and Control - 11th International Workshop, HSCC 2008, Proceedings
Pages316-329
Number of pages14
DOIs
StatePublished - Dec 1 2008
Event11th International Workshop on Hybrid Systems: Computation and Control, HSCC 2008 - St. Louis, MO, United States
Duration: Apr 22 2008Apr 24 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4981 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Workshop on Hybrid Systems: Computation and Control, HSCC 2008
CountryUnited States
CitySt. Louis, MO
Period4/22/084/24/08

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kwon, Y. M., & Agha, G. A. (2008). LTLC: Linear temporal logic for control. In Hybrid Systems: Computation and Control - 11th International Workshop, HSCC 2008, Proceedings (pp. 316-329). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4981 LNCS). https://doi.org/10.1007/978-3-540-78929-1-23