Abstract
This paper provides a suite of optimization techniques for
the verification of safety properties of linear hybrid
automata with large discrete state spaces, such as
naturally arising when incorporating health state
monitoring and degradation levels into the controller
design. Such models can -- in contrast to purely functional
controller models -- not analyzed with hybrid verification
engines relying on explicit representations of modes, but
require fully symbolic representations for both the
continuous and discrete part of the state space. The
optimization techniques shown yield consistently a speedup
of about 20 against previously published results for a
similar benchmark suite, and complement these with new
results on counterexample guided abstraction refinement. In
combination with the methods guaranteeing preciseness of
abstractions, this allows to significantly extend the class
of models for which safety can be established, covering in
particular models with 23 continuous variables and 2 to the
71 discrete states, 20 continuous variables and 2 to the
199 discrete states, and 9 continuous variables and 2 to
the 271 discrete states.
BibTeX
@techreport{AlthausBeberDammEtAl2016ATR, TITLE = {Verification of Linear Hybrid Systems with Large Discrete State Spaces: Exploring the Design Space for Optimization}, AUTHOR = {Althaus, Ernst and Beber, Bj{\"o}rn and Damm, Werner and Disch, Stefan and Hagemann, Willem and Rakow, Astrid and Scholl, Christoph and Waldmann, Uwe and Wirtz, Boris}, LANGUAGE = {eng}, ISSN = {1860-9821}, NUMBER = {ATR103}, INSTITUTION = {SFB/TR 14 AVACS}, YEAR = {2016}, DATE = {2016}, ABSTRACT = {This paper provides a suite of optimization techniques for the verification of safety properties of linear hybrid automata with large discrete state spaces, such as naturally arising when incorporating health state monitoring and degradation levels into the controller design. Such models can -- in contrast to purely functional controller models -- not analyzed with hybrid verification engines relying on explicit representations of modes, but require fully symbolic representations for both the continuous and discrete part of the state space. The optimization techniques shown yield consistently a speedup of about 20 against previously published results for a similar benchmark suite, and complement these with new results on counterexample guided abstraction refinement. In combination with the methods guaranteeing preciseness of abstractions, this allows to significantly extend the class of models for which safety can be established, covering in particular models with 23 continuous variables and 2 to the 71 discrete states, 20 continuous variables and 2 to the 199 discrete states, and 9 continuous variables and 2 to the 271 discrete states.}, TYPE = {AVACS Technical Report}, VOLUME = {103}, }
Endnote
%0 Report %A Althaus, Ernst %A Beber, Björn %A Damm, Werner %A Disch, Stefan %A Hagemann, Willem %A Rakow, Astrid %A Scholl, Christoph %A Waldmann, Uwe %A Wirtz, Boris %+ Algorithms and Complexity, MPI for Informatics, Max Planck Society Algorithms and Complexity, MPI for Informatics, Max Planck Society External Organizations External Organizations Automation of Logic, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society External Organizations External Organizations Automation of Logic, MPI for Informatics, Max Planck Society External Organizations %T Verification of Linear Hybrid Systems with Large Discrete State Spaces: Exploring the Design Space for Optimization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4540-0 %Y SFB/TR 14 AVACS %D 2016 %P 93 p. %X This paper provides a suite of optimization techniques for the verification of safety properties of linear hybrid automata with large discrete state spaces, such as naturally arising when incorporating health state monitoring and degradation levels into the controller design. Such models can -- in contrast to purely functional controller models -- not analyzed with hybrid verification engines relying on explicit representations of modes, but require fully symbolic representations for both the continuous and discrete part of the state space. The optimization techniques shown yield consistently a speedup of about 20 against previously published results for a similar benchmark suite, and complement these with new results on counterexample guided abstraction refinement. In combination with the methods guaranteeing preciseness of abstractions, this allows to significantly extend the class of models for which safety can be established, covering in particular models with 23 continuous variables and 2 to the 71 discrete states, 20 continuous variables and 2 to the 199 discrete states, and 9 continuous variables and 2 to the 271 discrete states. %B AVACS Technical Report %N 103 %@ false %U http://www.avacs.org/fileadmin/Publikationen/Open/avacs_technical_report_103.pdf