@inproceedings{9fd31eb94f1642109925854a835820ee,
title = "STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego",
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.",
keywords = "technology, engineering and IT, flood, predictive control, real-time control, reinforcement learning, stormwater basin",
author = "Martijn Goorden and Peter Jensen and Kim Larsen and Mihhail Samusev and Ji{\v r}{\'i} Srba and Guohan Zhao",
year = "2022",
doi = "10.1007/978-3-031-19992-9\_21",
language = "English",
isbn = "9783031199912",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "327–333",
editor = "Ahmed Bouajjani and Luk{\'a}s Holik and Zhilin Wu",
booktitle = "International Symposium on Automated Technology for Verification and Analysis",
address = "United States",
}