MPC-based strategy for longitudinal and lateral stabilization of a vehicle under extreme conditions

Abstract

In recent decades, active safety-control systems have been developed to assist drivers. Wheel slip ratio, yaw rate, and side-slip angle are commonly considered as the control indices of longitudinal and lateral vehicle stabilities [1]. However, if systems that only contain vehicle dynamics in longitudinal or lateral directions work simultaneously, the system objectives may conflict. This conflict could cause degradation of the systems’ overall performance, particularly under extreme driving conditions [2]. When this occurs, the vehicle and its tires work in a nonlinear region, and the tire forces tend to be saturated, suggesting that the highly coupled characteristics of dynamics cannot be ignored. Therefore, a certain safety system cannot effectively stabilize the vehicle, and control strategies are necessary to integrate multiple system objectives. Model predictive control (MPC) is a suitable method to control longitudinal and lateral vehicle stabilities in a coordinated manner and has been widely utilized in this field [3]. Furthermore, to accurately express the nonlinearity of a tire, its forces are best described using the combined-slip tire model [4]. Four wheel independent motor drive (4WIMD) electric vehicles have presently become a key research topic with the advantages of adjusting vehicle motion without differential braking or intense steering interference from the driver [5], creating new possibilities for vehicle stability control under extreme conditions. Herein, we present an MPC-based strategy for longitudinal and lateral vehicle stabilization under extreme driving conditions. A LuGre combined-slip tire model is developed to capture the variations in tire longitudinal and lateral dynamics motions. Then, the variation of longitudinal velocity is considered as a disturbance in the proposed strategy. In the corresponding multiple objective optimization problem, tracking the reference states and satisfying both safety and actuator constraints are accomplished for better handling performance and overall stability. The control scheme is illustrated in Figure 1. Related models. The vehicle lateral and yaw motion dynamics are