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Aggregation Strategies in Reachable Set Computation of Hybrid Systems

Published:08 October 2019Publication History
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Abstract

Computing the set of reachable states is a widely used technique for proving that a hybrid system satisfies its safety specification. Flow-pipe construction methods interleave phases of computing continuous successors and phases of computing discrete successors. Directly doing this leads to a combinatorial explosion problem, though, as with each discrete successor there may be an interval of time where the transition can occur, so that the number of paths becomes exponential in the number of discrete transitions. For this reason, most reachable set computation tools implement some form of set aggregation for discrete transitions, such as, performing a template-based overapproximation or convex hull aggregation. These aggregation methods, however, in theory can lead to unbounded error, and in practice are often the root cause of why a safety specification cannot be proven.

This paper proposes techniques for improving the accuracy of the aggregation operations performed for reachable set computation. First, we present two aggregation strategies over generalized stars, namely convex hull aggregation and template based aggregation. Second, we perform adaptive deaggregation using a data structure called Aggregated Directed Acyclic Graph (AGGDAG). Our deaggregation strategy is driven by counterexamples and hence has soundness and relative completeness guarantees. We demonstrate the computational benefits of our approach through two case studies involving satellite rendezvous and gearbox meshing.

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              • Published in

                cover image ACM Transactions on Embedded Computing Systems
                ACM Transactions on Embedded Computing Systems  Volume 18, Issue 5s
                Special Issue ESWEEK 2019, CASES 2019, CODES+ISSS 2019 and EMSOFT 2019
                October 2019
                1423 pages
                ISSN:1539-9087
                EISSN:1558-3465
                DOI:10.1145/3365919
                Issue’s Table of Contents

                Copyright © 2019 ACM

                © 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 8 October 2019
                • Accepted: 1 July 2019
                • Revised: 1 June 2019
                • Received: 1 April 2019
                Published in tecs Volume 18, Issue 5s

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