RT Conference Proceedings
T1 A traction Control System based on Co-evolutionary Learning in Spiking Neural Networks (SNN)
A1 Pérez-Fernández, Javier
A1 Cabrera-Carrillo, Juan Antonio
A1 Castillo-Aguilar, Juan Jesús
K1 Algoritmos genéticos
AB A traction control system is designed and trained for different road conditions with co-evolutionary learning based on a genetic algorithm. Common solutions do not consider the variation and oscillation created in the transition between roads defining a control logic which is highly dependent on road accuracy and a speed estimator. To solve this problem, a co-evolutionary learning processis used. This procedure trains the control algorithm, a spiking neural network, on different roads and transitions looking for the worst-case scenario. We have developed a control algorithm with a good dynamic response to constant and changing roads. This control algorithm makes the system stable when the road estimation is delayed or unstable, solving a common flaw produced by sensor noiseor computation delays.
YR 2018
FD 2018-07-30
LK https://hdl.handle.net/10630/16382
UL https://hdl.handle.net/10630/16382
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 22 mar 2026