Piecewise robust barrier tubes for nonlinear hybrid systems with uncertainty

H. Kong, E. Bartocci, Y. Jiang, T.A. Henzinger, in:, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature, 2019, pp. 123–141.


Conference Paper | Published | English

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Author
Department
Series Title
LNCS
Abstract
Piecewise Barrier Tubes (PBT) is a new technique for flowpipe overapproximation for nonlinear systems with polynomial dynamics, which leverages a combination of barrier certificates. PBT has advantages over traditional time-step based methods in dealing with those nonlinear dynamical systems in which there is a large difference in speed between trajectories, producing an overapproximation that is time independent. However, the existing approach for PBT is not efficient due to the application of interval methods for enclosure-box computation, and it can only deal with continuous dynamical systems without uncertainty. In this paper, we extend the approach with the ability to handle both continuous and hybrid dynamical systems with uncertainty that can reside in parameters and/or noise. We also improve the efficiency of the method significantly, by avoiding the use of interval-based methods for the enclosure-box computation without loosing soundness. We have developed a C++ prototype implementing the proposed approach and we evaluate it on several benchmarks. The experiments show that our approach is more efficient and precise than other methods in the literature.
Publishing Year
Date Published
2019-08-13
Proceedings Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
11750
Page
123-141
Conference
FORMATS 2019: Formal Modeling and Anaysis of Timed Systems
Conference Location
Amsterdam, The Netherlands
Conference Date
2019-08-27 – 2019-08-29
ISSN
eISSN
IST-REx-ID

Cite this

Kong H, Bartocci E, Jiang Y, Henzinger TA. Piecewise robust barrier tubes for nonlinear hybrid systems with uncertainty. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 11750. Springer Nature; 2019:123-141. doi:10.1007/978-3-030-29662-9_8
Kong, H., Bartocci, E., Jiang, Y., & Henzinger, T. A. (2019). Piecewise robust barrier tubes for nonlinear hybrid systems with uncertainty. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11750, pp. 123–141). Amsterdam, The Netherlands: Springer Nature. https://doi.org/10.1007/978-3-030-29662-9_8
Kong, Hui, Ezio Bartocci, Yu Jiang, and Thomas A Henzinger. “Piecewise Robust Barrier Tubes for Nonlinear Hybrid Systems with Uncertainty.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11750:123–41. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-29662-9_8.
H. Kong, E. Bartocci, Y. Jiang, and T. A. Henzinger, “Piecewise robust barrier tubes for nonlinear hybrid systems with uncertainty,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Amsterdam, The Netherlands, 2019, vol. 11750, pp. 123–141.
Kong H, Bartocci E, Jiang Y, Henzinger TA. 2019. Piecewise robust barrier tubes for nonlinear hybrid systems with uncertainty. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). FORMATS 2019: Formal Modeling and Anaysis of Timed Systems, LNCS, vol. 11750. 123–141.
Kong, Hui, et al. “Piecewise Robust Barrier Tubes for Nonlinear Hybrid Systems with Uncertainty.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11750, Springer Nature, 2019, pp. 123–41, doi:10.1007/978-3-030-29662-9_8.
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