Active Collision Avoidance System Design Based on Model Predictive Control with Varying Sampling Time

In active collision avoidance, the trajectory tracking controller determines the deviation from the reference path and the vehicle stability. The main objective of this study was to reduce the tracking error and improve the tracking performance in collision avoidance. Unlike the previously proposed model predictive control (MPC) strategies with constant sampling time, an improved MPC controller with varying sampling time based on the hierarchical control framework was proposed in this paper. Compared with the original MPC tracking controller, the improved MPC controller demonstrated better adaptive capability for the varying road adhesion coefficients and vehicle speed on a curved road. The simulation results revealed that the hierarchical control framework generated an optimal trajectory for collision avoidance in real-time by minimizing the potential field energy.