Longitudinal Wheel Slip Regulation using Nonlinear Autoregressive-Moving Average (NARMA-L2) Neural Controller*

In this study, the implementation of a nonlinear autoregressive-moving average model ( NARMA-L2) neural network controller to maximize the tra ction of tires during braking scenarios was explored. The proposed controller and system dynamics was done in Simulink. All in all, the neural network controller shows good stability and good response in following the reference trajectory or desired slip ratio. It has experienced the peak worst error of around 2%, its best performance was reached after 89 epochs and it can reach around 99.5% of the reference trajectory or desired slip ratio. Further research should focus on hardware implementation, integration with slip estimation techniques , and, better sets of training data to make the controller more adaptive to different environment and road surface characteristics.