TY - GEN
T1 - Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicles
AU - Pérez, Joshué
AU - Gajate, Agustín
AU - Milanés, Vicente
AU - Onieva, Enrique
AU - Santos, Matilde
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/78549267150
U2 - 10.1109/FUZZY.2010.5584208
DO - 10.1109/FUZZY.2010.5584208
M3 - Conference contribution
AN - SCOPUS:78549267150
SN - 9781424469208
T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010
Y2 - 18 July 2010 through 23 July 2010
ER -
