Road Type Recognition Using Neural Networks for Vehicle Seat Vibration Damping
In a modern vehicle systems one of the main goals to achieve is driver's safety, and many sophisticated systems are made for that purpose. Vibration isolation for the vehicle seats, and at the same time for the driver, is one of the challenging problems. Parameters of the controller used for the isolation can be tuned for a different road types, making the isolation better (specially for the vehicles like dampers, tractors, field machinery, bulldozers, etc.). In this paper we propose the method where neural networks are used for road type recognition. The main goal is to obtain a good road recognition for the purpose of better vibration damping of a driver's semi active controllable seat. The recognition of a specific road type will be based on the measurable parameters of a vehicle. Discrete Fourier Transform of measurable parameters is obtained and used for the neural network learning. The dimension of the input vector, as the main parameter that decides the speed of road recognition, is varied.