•  
  •  
 

Document Type

Original Study

Keywords

Control Engineering

Abstract

In this paper, direct neural controller for braking system is proposed. Learning of the presented controller depends on the training data that comes from running the switching gain controller at different conditions of drive. The training data consist of relative velocity error, distance error and braking force. The feed-forward neural network is used to build direct neural controller with two hidden layers and using back-propagation training algorithm. The performance of the presented controller is validated using nonlinear braking model. Simulation results show the presented controller is able to prevent the collision of vehicles at different driving conditions. Also, the results show superiority of the direct neural controller in comparison with the switching gain controller at all drive cases that are tested in this work.

Download

Share

COinS