Numerically-Robust Inductive Proof Rules for Continuous Dynamical Systems
Name
Gao2019_Chapter_Numerically-RobustInductivePro.pdf
Description
Published version
Size
573.84 KB
Format
Unknown
Checksum (MD5)
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Author(s)
Gao, Sicun
Kapinski, James
Deshmukh, Jyotirmoy
Roohi, Nima
Solar Lezama, Armando
Arechiga, Nikos
Kong, Soonho
Date Issued
July 2019
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer International Publishing
Citation
2019. "Numerically-Robust Inductive Proof Rules for Continuous Dynamical Systems." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11562.
Version
Final published version
Abstract
© The Author(s) 2019. We formulate numerically-robust inductive proof rules for unbounded stability and safety properties of continuous dynamical systems. These induction rules robustify standard notions of Lyapunov functions and barrier certificates so that they can tolerate small numerical errors. In this way, numerically-driven decision procedures can establish a sound and relative-complete proof system for unbounded properties of very general nonlinear systems. We demonstrate the effectiveness of the proposed rules for rigorously verifying unbounded properties of various nonlinear systems, including a challenging powertrain control model.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-030-25543-5_9
