Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/21/7438 |
| _version_ | 1797512491879628800 |
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| author | Dániel Fényes Tamás Hegedus Balázs Németh Péter Gáspár |
| author_facet | Dániel Fényes Tamás Hegedus Balázs Németh Péter Gáspár |
| author_sort | Dániel Fényes |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="script">H</mi><mo>∞</mo></msub></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-10T06:02:35Z |
| format | Article |
| id | doaj.art-55abd812283943cab89d2a4bf439e01c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T06:02:35Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-55abd812283943cab89d2a4bf439e01c2023-11-22T20:47:59ZengMDPI AGEnergies1996-10732021-11-011421743810.3390/en14217438Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching ApproachDániel Fényes0Tamás Hegedus1Balázs Németh2Péter Gáspár3Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, H-1111 Budapest, HungaryInstitute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, H-1111 Budapest, HungaryInstitute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, H-1111 Budapest, HungaryInstitute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, H-1111 Budapest, HungaryIn 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="script">H</mi><mo>∞</mo></msub></semantics></math></inline-formula> 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.https://www.mdpi.com/1996-1073/14/21/7438vehicle controlmodel-matchingrobust controlneural networks |
| spellingShingle | Dániel Fényes Tamás Hegedus Balázs Németh Péter Gáspár Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach Energies vehicle control model-matching robust control neural networks |
| title | Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach |
| title_full | Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach |
| title_fullStr | Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach |
| title_full_unstemmed | Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach |
| title_short | Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach |
| title_sort | robust control design for autonomous vehicles using neural network based model matching approach |
| topic | vehicle control model-matching robust control neural networks |
| url | https://www.mdpi.com/1996-1073/14/21/7438 |
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