Fuzzy controller, designed by reinforcement learning, for vehicle traction system application
| Autor: | Vladimir V. Vantsevich, Sviatoslav Klos, David Gorsich, M. D. Letherwood, Andriy Lozynskyy, V. V. Lytvyn, Lyubomyr Demkiv |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computational Mathematics 020901 industrial engineering & automation Computational Theory and Mathematics Control theory Computer science 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing 02 engineering and technology Traction system Fuzzy logic |
| Zdroj: | Mathematical Modeling and Computing. 8:168-183 |
| ISSN: | 2415-3788 2312-9794 |
| Popis: | In this article, a fuzzy controller tuned by reinforcement learning is proposed. The developed algorithm utilizes a fuzzy logic theory and a reinforcement learning for fine-tuning parameters of the membership function for the fuzzy controller. Apart from the fuzzy controller developed, a fuzzy corrector of reference input (set-point) signal to the controller is applied. The fuzzy corrector changes the input (reference) signal of the system and takes into account an original reference input and type of external disturbances. Thus, the designed fuzzy control that is tuned by reinforcement learning is capable to ensure the stable, optimal, and safe performance of the system and takes into account external disturbances. To verify the performance of the proposed controller, the adaptive fuzzy controller tuned by reinforcement learning is applied to the mathematical model of a wheel locomotion module of an electric vehicle to advance a traction control system. Therefore, the effectiveness of the proposed adaptive fuzzy controller is proven through the simulation results. |
| Databáze: | OpenAIRE |
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