### 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