A Variable-Sampling Time Model Predictive Control Algorithm for Improving Path-Tracking Performance of a Vehicle

Yoonsuk Choi; Wonwoo Lee; Jeesu Kim; Jinwoo Yoo

doi:10.3390/s21206845

A Variable-Sampling Time Model Predictive Control Algorithm for Improving Path-Tracking Performance of a Vehicle

Sensors (Basel). 2021 Oct 14;21(20):6845. doi: 10.3390/s21206845.

Authors

Yoonsuk Choi¹, Wonwoo Lee¹, Jeesu Kim^{2

3}, Jinwoo Yoo⁴

Affiliations

¹ The Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Korea.
² Department of Congno-Mechatronics Engineering, Pusan National University, Busan 46241, Korea.
³ Department of Optics and Mechatronics Engineering, Pusan National University, Busan 46241, Korea.
⁴ Department of Automobile and IT Convergence, Kookmin University, Seoul 02707, Korea.

Abstract

This paper proposes a novel model predictive control (MPC) algorithm that increases the path tracking performance according to the control input. The proposed algorithm reduces the path tracking errors of MPC by updating the sampling time of the next step according to the control inputs (i.e., the lateral velocity and front steering angle) calculated in each step of the MPC algorithm. The scenarios of a mixture of straight and curved driving paths were constructed, and the optimal control input was calculated in each step. In the experiment, a scenario was created with the Automated Driving Toolbox of MATLAB, and the path-following performance characteristics and computation times of the existing and proposed MPC algorithms were verified and compared with simulations. The results prove that the proposed MPC algorithm has improved path-following performance compared to those of the existing MPC algorithm.

Keywords: autonomous driving; autonomous vehicle; model predictive control; path tracking; variable sampling time.