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Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach

Author

Listed:
  • Dániel Fényes

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

  • Tamás Hegedus

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

  • Balázs Németh

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

In this paper, a novel neural network-based robust control method is presented for a vehicle-oriented problem, in which the main goal is to ensure stable motion of the vehicle under critical circumstances. The proposed method can be divided into two main steps. In the first step, the model matching algorithm is proposed, which can adjust the nonlinear dynamics of the controlled system to a nominal, linear model. The aim of model matching is to eliminate the effects of the nonlinearities and uncertainties of the system to increase the performances of the closed-loop system. The model matching process results in an additional control input, which is computed by a neural network during the operation of the control system. Furthermore, in the second step, a robust H ∞ is designed, which has double purposes: to handle the fitting error of the neural network and ensure the accurate tracking of the reference signal. The operation and efficiency of the proposed control algorithm are investigated through a complex test scenario, which is performed in the high-fidelity vehicle dynamics simulation software, CarMaker.

Suggested Citation

  • Dániel Fényes & Tamás Hegedus & Balázs Németh & Péter Gáspár, 2021. "Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach," Energies, MDPI, vol. 14(21), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7438-:d:674713
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    References listed on IDEAS

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    1. Tamás Hegedűs & Dániel Fényes & Balázs Németh & Péter Gáspár, 2021. "Improving Sustainable Safe Transport via Automated Vehicle Control with Closed-Loop Matching," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
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    Cited by:

    1. Balázs Németh, 2022. "Analysis and Synthesis of Coordinated Control Systems for Automated Road Vehicles," Energies, MDPI, vol. 15(20), pages 1-2, October.

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