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Study on Integrated Neural Networks and Fuzzy Logic Control for Autonomous Electric Vehicles
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Author(s): J. Vimala Devi (Department of Computer Science Engineering, Dayananda Sagar College of Engineering, India), Rajesh Vyankatesh Argiddi (Department of Computer Science and Engineering, Walchand Institute of Technology, Solapur, India), P. Renuka (Department of Mathematics, KPR Institute of Engineering and Technology, India), K. Janagi (Department of Mathematics, KPR Institute of Engineering and Technology, India), B. S. Hari (Department of Mechanical Engineering, Kongu Engineering College, India)and S. Boopathi (Muthayammal Engineering College, India)
Copyright: 2024
Pages: 24
Source title:
Semantic Web Technologies and Applications in Artificial Intelligence of Things
Source Author(s)/Editor(s): Fernando Ortiz-Rodriguez (Tamaulipas Autonomous University, Mexico), Amed Leyva-Mederos (Universidad Central "Marta Abreu" de Las Villas, Cuba), Sanju Tiwari (Tamaulipas Autonomous University, Mexico), Ania R. Hernandez-Quintana (Universidad de La Habana, Cuba)and Jose L. Martinez-Rodriguez (Autonomous University of Tamaulipas, Mexico)
DOI: 10.4018/979-8-3693-1487-6.ch006
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Abstract
This chapter presents a comprehensive study on the integration of neural networks and fuzzy logic control techniques for enhancing the autonomy of electric vehicles (EVs). The integration of these two paradigms aims to overcome the limitations of traditional control approaches by leveraging the complementary strengths of neural networks in learning complex patterns and fuzzy logic in handling uncertainty and imprecision. The chapter discusses the design, implementation, and evaluation of an autonomous EV control system that utilizes neural networks for learning vehicle dynamics and fuzzy logic for decision-making in various driving scenarios. Through extensive simulations and experiments, the effectiveness and robustness of the proposed integrated approach are demonstrated, showcasing its potential for improving the safety, efficiency, and adaptability of autonomous EVs in real-world environments.
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