2026-05-29T23:04:14Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/163822026-02-03T11:57:12Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Pérez-Fernández, Javier author Cabrera-Carrillo, Juan Antonio author Castillo-Aguilar, Juan Jesús author 2018-07-30 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 process is 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 noise or computation delays. https://hdl.handle.net/10630/16382 Algoritmos genéticos A traction Control System based on Co-evolutionary Learning in Spiking Neural Networks (SNN)