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Design and Implementation of a Neuro-Fuzzy System for Longitudinal Control of Autonomous Vehicles

Abstract : The control of nonlinear systems has been putting especial attention in the use of Artificial Intelligent techniques, where fuzzy logic presents one of the best alternatives due to the exploit of human knowledge. However, several fuzzy logic real-world applications use manual tuning (human expertise) to adjust control systems. On the other hand, in the Intelligent Transport Systems (ITS) field, the longitudinal control (throttle and brake management) is an important topic because external perturbations can generate uncomfortable accelerations as well as unnecessary fuel consumption. In this work, we utilize a neuro-fuzzy system to use human driving knowledge to tune and adjust the input-output parameters of a fuzzy ifthen system. The neuro-fuzzy system considered in this work is ANFIS (Adaptive-Network-based Fuzzy Inference System). Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous manual tuned controller, mainly in comfort and efficient use of actuators.
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https://hal.inria.fr/hal-00738038
Contributor : Joshué Pérez Rastelli <>
Submitted on : Wednesday, October 3, 2012 - 11:50:42 AM
Last modification on : Saturday, December 26, 2020 - 1:46:06 PM
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  • HAL Id : hal-00738038, version 1

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Joshué Pérez Rastelli, Agustin Gajate, Vicente Milanés, Enrique Onieva, Matilde Santos. Design and Implementation of a Neuro-Fuzzy System for Longitudinal Control of Autonomous Vehicles. Proc. IEEE World Congress on Computational Intelligence WCCI, Jul 2010, Barcelona, Spain. ⟨hal-00738038⟩

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