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

Yoonsuk Choi,Jinwoo Yoo,Wonwoo Lee,Jeesu Kim

doi:10.3390/s21206845

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.

Highlights

Driving vehicles and electrification of vehicle parts have been hot topics in the automobile industry over the past few years, and many parts of vehicles have been replaced with electric devices
This paper proposes a variable sampling-time model predictive control algorithm (VST-MPC) for improving the path tracking performance of a vehicle, and the sampling time is adjusted based on the optimal steering angle and lateral acceleration inputs calculated with MPC
MPC, a MPC, low sampling time waswas selected for the driving section,section, and a high time was selected pling time selected forcurved the curved driving andsampling a high sampling time was sefor the driving section

Summary

Introduction

Driving vehicles and electrification of vehicle parts have been hot topics in the automobile industry over the past few years, and many parts of vehicles have been replaced with electric devices. Researchers are studying model predictive control (MPC) algorithms, which they apply to autonomous vehicles to track the vehicle’s driving route or optimize the efficiency of the engine, transmission, exhaust gas consumption, and motor performance. In 1978 [1], chemical engineers applied MPC in chemical industrial control processes, thereby demonstrating its advantages over other control technologies. When user convenience became increasingly important and autonomously driving vehicles started to emerge, researchers studied optimal trajectories or collision avoidance trajectories by extending the use of MPC algorithms to the fields of advanced driver assistance systems (ADAS) [5,6,7] and autonomous driving [8,9]

Methods

Results

Conclusion