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A Modelica-Based Simulation Method for Black-Box Optimal Control Problems with Level-Set Dynamic Programming

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Machine Learning, Optimization, and Data Science (LOD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11331))

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Abstract

A system model in practice may be a black-box function. However, most of the current research on optimal control problems is conducted under the condition that the specific expression of the model is known, and there is a lack of research on the optimal control problem of black-box models. Based on Modelica language and corresponding simulation platform, this paper gets the simulation data from a Modelica model with the serialization of parallel simulation and uses level-set dynamic programming (DP) algorithm to calculate the cost-to-go function recursively. In order to retrieve the sequence of optimal control variables and corresponding optimal state trajectory, two methods are proposed, namely the method based on continuous simulation and the method that approximates state transfer equations locally with a sequence of Radial Basis Functions (RBFs). As an example, an academic case is analyzed. The result proves the effectiveness of the proposed method in solving the optimal control problem of the black-box models.

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Acknowledgments

Financial support from the National Natural Science Foundation of China under Grant No. 51575205 is gratefully acknowledged.

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Ping Qiao, Yizhong Wu & Qi Zhang

Authors
  1. Ping Qiao
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  2. Yizhong Wu
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  3. Qi Zhang
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Corresponding author

Correspondence to Ping Qiao .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy and University of Reading, Reading, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

  5. IBM, Tivoli Research Lab, Rome, Italy

    Vincenzo Sciacca

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Qiao, P., Wu, Y., Zhang, Q. (2019). A Modelica-Based Simulation Method for Black-Box Optimal Control Problems with Level-Set Dynamic Programming. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_31

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  • DOI: https://doi.org/10.1007/978-3-030-13709-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13708-3

  • Online ISBN: 978-3-030-13709-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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