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Automated verification and synthesis of stochastic hybrid systems

Lookup NU author(s): Dr Abolfazl Lavaei, Dr Sadegh SoudjaniORCiD



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Stochastic hybrid systems have received significant attentions as a relevant modelling framework describing many systems, from engineering to the life sciences: they enable the study of numerous applications, including transportation networks, biological systems and chemical reaction networks, smart energy and power grids, and beyond. Automated verification and policy synthesis for stochastic hybrid systems can be inherently challenging: this is due to the heterogeneity of their dynamics (presence of continuous and discrete components), the presence of uncertainty, and in some applications the large dimension of state and input sets. Over the past few years, a few hundred articles have investigated these models, and developed diverse and powerful approaches to mitigate difficulties encountered in the analysis and synthesis of such complex stochastic systems. In this survey, we overview the most recent results in the literature and discuss different approaches, including (in)finite abstractions, verification and synthesis for temporal logic specifications, stochastic similarity relations, (control) barrier certificates, compositional techniques, and a selection of results on continuous-time stochastic systems; we finally survey recently developed software tools that implement the discussed approaches. Throughout the manuscript we discuss a few open topics to be considered as potential future research directions: we hope that this survey will guide younger researchers through a comprehensive understanding of the various challenges, tools, and solutions in this enticing and rich scientific area.

Publication metadata

Author(s): Lavaei A, Soudjani S, Abate A, Zamani M

Publication type: Article

Publication status: Published

Journal: Automatica

Year: 2022

Volume: 146

Print publication date: 01/12/2022

Online publication date: 14/10/2022

Acceptance date: 10/08/2022

Date deposited: 29/09/2022

ISSN (print): 0005-1098

ISSN (electronic): 1873-2836

Publisher: Elsevier Ltd


DOI: 10.1016/j.automatica.2022.110617

ePrints DOI: 10.57711/ket7-6a12


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