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Design Framework for Achieving Guarantees with Learning-Based Observers

Author

Listed:
  • Balázs Németh

    (Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, H-1111 Budapest, Hungary)

  • Tamás Hegedűs

    (Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Stoczek u. 2, H-1111 Budapest, Hungary)

  • Péter Gáspár

    (Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, H-1111 Budapest, Hungary)

Abstract

The paper proposes a novel framework for state observer design, in which learning-based observers are incorporated. The aim of the method is to provide a framework, which is able to guarantee the limitation of the observation error, even if the error of the learning-based observer under all scenarios cannot be verified. The framework is based on the robust H ∞ design method, which is able to provide guarantees on the resulted observer. Moreover, the observer design process is extended with a controller design, which leads to a joint robust H ∞ controller-observer design. In this paper the proposed method is applied on a vehicle control problem, such as lateral path following. In this problem the goal of the observer is to provide an accurate lateral velocity signal for the vehicle, which is used in the controlled system for the generation of front wheel steering angle. The effectiveness of the method is illustrated through simulation examples on high-fidelity vehicle dynamic simulator CarMaker.

Suggested Citation

  • Balázs Németh & Tamás Hegedűs & Péter Gáspár, 2021. "Design Framework for Achieving Guarantees with Learning-Based Observers," Energies, MDPI, vol. 14(8), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2039-:d:531444
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    References listed on IDEAS

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    1. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.
    2. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    3. Caihong Zhang & Tairen Sun & Yongping Pan, 2014. "Neural Network Observer-Based Finite-Time Formation Control of Mobile Robots," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, July.
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