Neural network and terminal sliding mode control for controlling an uncertain autonomous vehicle.

In this article, we present a combination of neural network and terminal sliding mode control (TSMC) strategy to model and control an autonomous vehicle with an uncertain dynamic. The first step is for modelling the vehicle using bicycle model. Then, the second step is for adding a new term to the control law to reduce the external disturbances and the error modelling parameters. The final step, we propose a neural network to model the unknown dynamics of the model. The control law and the stability of this autonomous automobile system is resulting from the study of Lyapunov function. There is a simulation to prove the performance of our proposed strategy taking into account the stability of the system by using the Lyapunov function to achieve the specified follow-up performance. [ABSTRACT FROM AUTHOR]