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STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego

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Automated Technology for Verification and Analysis (ATVA 2022)

Abstract

We present the new co-simulation and synthesis integrated-framework STOMPC for stochastic model-predictive control (MPC) with Uppaal Stratego . The framework allows users to easily set up MPC designs, a widely accepted method for designing software controllers in industry, with Uppaal Stratego as the controller synthesis engine, which provides a powerful tool to synthesize safe and optimal strategies for hybrid stochastic systems. STOMPC provides the user freedom to connect it to external simulators, making the framework applicable across multiple domains.

This work is partly supported by the Villum Synergy project CLAIRE and the ERC Advanced Grant LASSO.

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Notes

  1. 1.

    https://github.com/DEIS-Tools/strategoutil.

  2. 2.

    https://strategoutil.readthedocs.io/en/latest/.

  3. 3.

    https://doi.org/10.5281/zenodo.6519909.

  4. 4.

    https://strategoutil.readthedocs.io.

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Correspondence to Martijn A. Goorden .

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Goorden, M.A., Jensen, P.G., Larsen, K.G., Samusev, M., Srba, J., Zhao, G. (2022). STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego. In: Bouajjani, A., Holík, L., Wu, Z. (eds) Automated Technology for Verification and Analysis. ATVA 2022. Lecture Notes in Computer Science, vol 13505. Springer, Cham. https://doi.org/10.1007/978-3-031-19992-9_21

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  • DOI: https://doi.org/10.1007/978-3-031-19992-9_21

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