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Efficient finite abstraction of mixed monotone systems

Published: 14 April 2015 Publication History

Abstract

We present an efficient computational procedure for finite abstraction of discrete-time mixed monotone systems by considering a rectangular partition of the state space. Mixed monotone systems are decomposable into increasing and decreasing components, and significantly generalize the well known class of monotone systems. We tightly overapproximate the one-step reachable set from a box of initial conditions by computing a decomposition function at only two points, regardless of the dimension of the state space. We apply our results to verify the dynamical behavior of a model for insect population dynamics and to synthesize a signaling strategy for a traffic network.

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cover image ACM Conferences
HSCC '15: Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control
April 2015
321 pages
ISBN:9781450334334
DOI:10.1145/2728606
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 April 2015

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  1. finite state abstractions
  2. mixed monotone systems
  3. monotone systems

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  • (2024)Compositional Synthesis of Safety Barrier Certificates for Infinite Networks2024 European Control Conference (ECC)10.23919/ECC64448.2024.10591247(1234-1239)Online publication date: 25-Jun-2024
  • (2024)Run Time Assurance for Spacecraft Attitude Control Under Nondeterministic AssumptionsIEEE Transactions on Control Systems Technology10.1109/TCST.2023.334062432:3(862-873)Online publication date: May-2024
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