2026-06-01T12:20:36Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/163822026-02-03T11:57:12Zcom_10630_2254col_10630_37959
A traction Control System based on Co-evolutionary Learning in Spiking Neural Networks (SNN)
Pérez-Fernández, Javier
Cabrera-Carrillo, Juan Antonio
Castillo-Aguilar, Juan Jesús
Algoritmos genéticos
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.
2018-07-30T11:41:18Z
2018-07-30T11:41:18Z
2018
2018-07-30
conference output
https://hdl.handle.net/10630/16382
eng
AVEC18- 14th International Symposium on Avanced Vehicle Control
Beijing (CHINA)
16-20 Julio 2018
open access