Design of a Path-Tracking Steering Controller for Autonomous Vehicles

Abstract

This paper presents a linearization method for the vehicle and tire models under the model predictive control (MPC) scheme, and proposes a linear model-based MPC path-tracking steering controller for autonomous vehicles. The steering controller is designed to minimize lateral path-tracking deviation at high speeds. The vehicle model is linearized by a sequence of supposed steering angles, which are obtained by assuming the vehicle can reach the desired path at the end of the MPC prediction horizon and stay in a steady-state condition. The lateral force of the front tire is directly used as the control input of the model, and the rear tire's lateral force is linearized by an equivalent cornering stiffness. The course-direction deviation, which is the angle between the velocity vector and the path heading, is chosen as a control reference state. The linearization model is validated through the simulation, and the results show high prediction accuracy even in regions of large steering angle. This steering controller is tested through simulations on the CarSim-Simulink platform (R2013b, MathWorks, Natick, MA, USA), showing the improved performance of the present controller at high speeds.

A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking

“…Considering the nonlinear characteristics of the tire under limited operating conditions and the online calculation burden of the MPC control, the affine approximation linearization method is applied to the lateral rear tire force in prediction horizon [26,27,30]. The linear tire model can be expressed as: are initial tire lateral force in prediction horizon and stead-state tire lateral force, respectively.…”

“…Existing research has held the assumption that the vehicle longitudinal speed is a constant in prediction horizon [25][26][27][28][29], so that the optimization problem becomes convex optimization; thus it becomes easy for the optimization problem to obtain its solution that satisfies the constraints. In fact, the speed of the vehicle is constantly changing, which will produce corresponding error during predicting vehicle states in prediction horizon.…”

“…Model predictive control (MPC) has the capability of handling system constraints and future prediction in the design process. It minimizes the gap between the reference path and the actual path by the vehicle dynamics model in a prediction horizon, and it has become a popular method in the control of autonomous vehicles [26][27][28][29][30]. A differential evolution algorithm was introduced into the MPC path tracking controller in order to improve the computational efficiency [31].…”

Recent Advances in the Analysis of Sustainable Energy Systems

“…In addition, for N = 1.15, pressure drop significantly increased Thus, the packed bed with dimpled spheres should be used only when the need to enhance heat transfer performance dominates the problem of pressure drop increase. Sun et al [189] designed a path tracking steering controller for autonomous vehicles. Autonomous vehicle is a new challenging technology which aims to increase driving safety, reduce traffic congestion and emissions and to improve vehicle overall efficiency.…”

Hierarchical Control of Nonlinear Active Four-Wheel-Steering Vehicles

“…The active four-wheel-steering (4WS) system is studied widely to improve the handling stability at high speeds, and the maneuverability at low speeds. With the emergence of intelligent vehicle systems (IVS), the 4WS system can also be used to solve the path tracking problem to ensure that the vehicle can follow the scheduled route, and its algorithm is mainly about determining the required steering angle to adjust the dynamic turning point on the road curvature center [1][2][3]. Hitherto, some control strategies and methods have been proposed to improve the aforementioned control goals, such as fuzzy, adaptive, feedforward, feedback, optimal, H ∞ , sliding mode, decoupling, and neural network controls, as well as µ synthesis.…”