Control of Antilock Braking System using Spiking Neural Networks

Abstract

Model-free approaches such as Artificial Neural Networks and Fuzzy Controllers are widely used in the control of Antilock Braking System (ABS) due to its strongly nonlinear structure and uncertainties involved. In this paper the design of a Spiking Neural Network (SNN) controller is considered for the regulation of the wheel slip value at its optimum value. For the training of the network a gradient descent based approach is followed. To formulate the generation of a new spike train from the incoming spikes, the Spike Response Model (SRM) is used. Delay coding is utilized to convert real numbers into spike times. The control algorithm is applied to a quarter vehicle model, and it is verified through simulations indicating fast convergence and good performance of the designed controller.