RT Journal Article
T1 On-line learning applied to spiking neural network for antilock braking systems
A1 Pérez-Fernández, Javier
A1 Alcázar-Vargas, Manuel Gonzalo
A1 Sánchez-Andrades, Ignacio
A1 Cabrera-Carrillo, Juan Antonio
A1 Nybacka, Mikael
A1 Castillo-Aguilar, Juan Jesús
K1 Automóviles - Frenos
K1 Ingeniería mecánica
K1 Redes neuronales (Informática)
AB Computationally replicating the behaviour of the cerebral cortex to perform the control tasks of daily life in a human being is a challenge today. First, … Finally, a suitable learning model that allows adapting neural network response to changing conditions in the environment is also required. Spiking Neural Networks (SNN) are currently the closest approximation to biological neural networks. SNNs make use of temporal spike trains to deal with inputs and outputs, thus allowing a faster and more complex computation. In this paper, a controller based on an SNN is proposed to perform the control of an anti-lock braking system (ABS) in vehicles. To this end, two neural networks are used to regulate the braking force. The first one is devoted to estimating the optimal slip while the second one is in charge of setting the optimal braking pressure. The latter resembles biological reflex arcs to ensure stability during operation. This neural structure is used to control the fast regulation cycles that occur during ABS operation. Furthermore, an algorithm has been developed to train the network while driving. On-line learning is proposed to update the response of the controller. Hence, to cope with real conditions, a control algorithm based on neural networks that learn by making use of neural plasticity, similar to what occurs in biological systems, has been implemented. Neural connections are modulated using Spike-Timing-Dependent Plasticity (STDP) by means of a supervised learning structure using the slip error as input. Road-type detection has been included in the same neural structure. To validate and to evaluate the performance of the proposed algorithm, simulations as well as experiments in a real vehicle were carried out. The algorithm proved to be able to adapt to changes in adhesion conditions rapidly. This way, the capability of spiking neural networks to perform the full control logic of the ABS has been verified.
PB Elsevier
YR 2023
FD 2023-09-18
LK https://hdl.handle.net/10630/27827
UL https://hdl.handle.net/10630/27827
LA eng
NO Javier Pérez, Manuel Alcázar, Ignacio Sánchez, Juan A. Cabrera, Mikael Nybacka, Juan J. Castillo, On-line learning applied to spiking neural network for antilock braking systems, Neurocomputing, Volume 559, 2023, 126784, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2023.126784
NO Funding for open access charge: Universidad de Málaga / CBUAThis work was partly supported by the Ministry of Science and Innovation under grant PID2019-105572RB-I00, partly by the Regional Government of Andalusia under grant UMA18-FEDERJA-109, and partly by the University of Malaga as well as the KTH Royal Institute of Technology and its initiative, TRENoP.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026