Tracking Control of Autonomous Car with Attention to Obstacle Using Model Predictive Control

Abstract

Previous research of MPC for path tracking and obstacle avoidance showed the car was able to evade obstacles while tracking the path but ineffectively and path tracking tests show an oscillating movement of the car. The research was done by varying cost function weights and the car was assumed to have a constant velocity. The best performance was obtained when the error weight is greater than the input weight. This research aims to use MPC for trajectory tracking and obstacle avoidance by using Linear Time Variant MPC (LTV MPC), where the trajectory tracking problem is defined by using a time-varying reference. MPC parameter is varied to find the best performing design. In the obstacle avoidance system, obstacle detection is done by measuring the distance between the instant car position and the obstacle position. While an obstacle is detected, a new lateral position constraint is calculated. Trajectory tracking tests are done using 2 types of tracks, sine wave, and lane changing. Obstacle avoidance tests are done using 1 obstacle and 2 obstacles. Results are evaluated using RMSE of car position, cost function, and the nearest distance between car and obstacle. Results show that MPC was able to evade obstacles while tracking the time-varying reference with 0.4 s delay. However, some variations were not able to meet the safe zone constraints for obstacle avoidance.