Model Predictive Control Strategy for Smooth Path Tracking of Autonomous Vehicles with Steering Actuator Dynamics

Path tracking control is one of the most important functions for autonomous driving. In path tracking control, high accuracy and smooth tracking are required for safe and comfort driving. In order to meet these requirements, model predictive control approaches, which can obtain an optimized solution with respect to a predefined path, have been widely studied. Conventional predictive controllers have been studied based on a simple bicycle model. However, the conventional predictive controllers have a performance limitation in practical challenges due to the difference between the simple bicycle model and the actual vehicle. To overcome this limitation, the actuator dynamics of the steering system should be incorporated into the control design. In this paper, we propose a model predictive control based path tracking control algorithm to achieve the accurate and smooth tracking by incorporating the dynamic characteristics of the steering actuation system. In the proposed control algorithm, an optimal trajectory of the steering command is calculated by applying a quadratic programming optimization method. The proposed controller was verified by computer simulation with various driving scenarios. The simulation results show that the proposed controller can improve the tracking performance.