Neural Network Approach Super-Twisting Sliding Mode Control for Path-Tracking of Autonomous Vehicles

Abstract

This paper proposes a neural network approach adaptive super-twisting sliding mode control algorithm for autonomous vehicles. An adaptive and robust control algorithm in autonomous vehicles is needed to compensate for disturbance and parametric uncertainty from the variable environment and vehicle conditions. The sliding mode control (SMC) is a robust controller that compensates for robust and reasonable control performance against disturbance and parametric uncertainty. However, the inherent limitation of the sliding mode control, namely the chattering phenomenon, has a negative effect on the system. Additionally, when the disturbance exceeds the defined boundaries, the control stability is compromised. To overcome these limitations, this study incorporates the radial basis function neural network (RBFNN) and Lyapunov function to estimate disturbance and parametric uncertainty. The estimated disturbance is reflected in the super-twisting sliding mode control (STSMC) to reduce the chattering phenomenon and achieve enhanced robust performance. The performance evaluation of the proposed neural network approach control algorithm is conducted using the double lane change (DLC) scenario and rapid path-tracking (RPT) scenario, implemented in the CarMaker and Matlab/Simulink environments, respectively.