A Model Predictive Controller for Autonomous Vehicle Path Tracking by Considering Dynamics of Automatic Steering System

다국어 초록 (Multilingual Abstract)

Path tracking control is an essential function for autonomous driving systems. These autonomous driving systems require high tracking accuracy, ride comfort, and smoothness of steering action in the controller’s design. In order to satisfy various requirements, model predictive control (MPC) approaches, which derive an optimal steering trajectory with respect to a pre-defined path, have been widely used. The conventional predictive controller is based on a simple bicycle model for estimation of vehicle motion. However, since there is the dissimilarity between the simplified model and the real autonomous vehicle, the conventional controller is limited in the improvement of vehicle maneuvers. To overcome the limitation, dynamics and constraints of an automatic steering system are taken into account as practical control challenges. In this paper, we propose a model predictive control technique by considering dynamics of the automatic steering system. The optimal trajectory of steering angle is obtained by using the quadratic programming (QP) optimization method. The proposed model predictive controller was verified by simulation using a commercial vehicle model. The simulation results show that an improved control performance can be achieved by considering dynamics of automatic steering system.

목차 (Table of Contents)

• Abstract
• 1. INTRODUCTION
• 2. MODELING
• 3. MPC BASED PATH TRACKINGSTRATEGY
• 4. SIMULATION
• 5. CONCLUSION
• 5. ACKNOWLEDGEMENTS
• References