STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego

Martijn Goorden; Peter Jensen; Kim Larsen; Mihhail Samusev; Jiří Srba; Guohan Zhao

doi:10.1007/978-3-031-19992-9_21

STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego

Martijn Goorden
, Peter Jensen
, Kim Larsen
, Mihhail Samusev
, Jiří Srba
, Guohan Zhao

Aalborg University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution to proceeding › peer-review

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.

Original language	English
Title of host publication	International Symposium on Automated Technology for Verification and Analysis : Automated Technology for Verification and Analysis
Editors	Ahmed Bouajjani, Lukás Holik, Zhilin Wu
Number of pages	7
Publisher	Springer
Publication date	2022
Pages	327–333
ISBN (Print)	9783031199912
DOIs	https://doi.org/10.1007/978-3-031-19992-9_21
Publication status	Published - 2022
Externally published	Yes

Series	Lecture Notes in Computer Science
Volume	13505
ISSN	0302-9743

Keywords

technology, engineering and IT
flood
predictive control
real-time control
reinforcement learning
stormwater basin

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10.1007/978-3-031-19992-9_21Licence: CC BY-ND

Cite this

Goorden, M., Jensen, P., Larsen, K., Samusev, M., Srba, J., & Zhao, G. (2022). STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego. In A. Bouajjani, L. Holik, & Z. Wu (Eds.), International Symposium on Automated Technology for Verification and Analysis: Automated Technology for Verification and Analysis (pp. 327–333). Springer. https://doi.org/10.1007/978-3-031-19992-9_21

Goorden, Martijn ; Jensen, Peter ; Larsen, Kim et al. / STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego. International Symposium on Automated Technology for Verification and Analysis: Automated Technology for Verification and Analysis. editor / Ahmed Bouajjani ; Lukás Holik ; Zhilin Wu. Springer, 2022. pp. 327–333 (Lecture Notes in Computer Science, Vol. 13505).

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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",

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editor = "Ahmed Bouajjani and Luk{\'a}s Holik and Zhilin Wu",

booktitle = "International Symposium on Automated Technology for Verification and Analysis",

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Goorden, M, Jensen, P, Larsen, K, Samusev, M, Srba, J & Zhao, G 2022, STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego. in A Bouajjani, L Holik & Z Wu (eds), International Symposium on Automated Technology for Verification and Analysis: Automated Technology for Verification and Analysis. Springer, Lecture Notes in Computer Science, vol. 13505, pp. 327–333. https://doi.org/10.1007/978-3-031-19992-9_21

STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego. / Goorden, Martijn; Jensen, Peter; Larsen, Kim et al.
International Symposium on Automated Technology for Verification and Analysis: Automated Technology for Verification and Analysis. ed. / Ahmed Bouajjani; Lukás Holik; Zhilin Wu. Springer, 2022. p. 327–333 (Lecture Notes in Computer Science, Vol. 13505).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution to proceeding › peer-review

TY - GEN

T1 - STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego

AU - Goorden, Martijn

AU - Jensen, Peter

AU - Larsen, Kim

AU - Samusev, Mihhail

AU - Srba, Jiří

AU - Zhao, Guohan

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - technology, engineering and IT

KW - flood

KW - predictive control

KW - real-time control

KW - reinforcement learning

KW - stormwater basin

U2 - 10.1007/978-3-031-19992-9_21

DO - 10.1007/978-3-031-19992-9_21

M3 - Conference contribution to proceeding

SN - 9783031199912

T3 - Lecture Notes in Computer Science

SP - 327

EP - 333

BT - International Symposium on Automated Technology for Verification and Analysis

A2 - Bouajjani, Ahmed

A2 - Holik, Lukás

A2 - Wu, Zhilin

PB - Springer

ER -

Goorden M, Jensen P, Larsen K, Samusev M, Srba J, Zhao G. STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego. In Bouajjani A, Holik L, Wu Z, editors, International Symposium on Automated Technology for Verification and Analysis: Automated Technology for Verification and Analysis. Springer. 2022. p. 327–333. (Lecture Notes in Computer Science, Vol. 13505). doi: 10.1007/978-3-031-19992-9_21

STOMPC: Stochastic Model-Predictive Control with Uppaal Stratego

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