We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination\nof two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle\ndynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove\nthe vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order\nto follow the desired trajectory as close as possible while rejecting the effects of wind gusts.We compared the controller based on\nboth simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control\nmanoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for\nboth forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive\ncontrol is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.
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