In this paper, with the aim of meeting the requirements of car following, safety, comfort, and economy for adaptive cruise control (ACC) system, an ACC algorithm based on model predictive control (MPC) using constraints softening is proposed. A higher-order kinematics model is established based on the mutual longitudinal kinematics between the host vehicle and the preceding vehicle that considers the changing characteristics of the inter-distance, relative velocity, acceleration, and jerk of the host vehicle. Performance indexes are adopted to represent the multi-objective demands and constraints of the ACC system. To avoid the solution becoming unfeasible because of the overlarge feedback correction, the constraint softening method was introduced to improve robustness. Finally, the proposed ACC method is verified in typical car-following scenarios. Through comparisons and case studies, the proposed method can improve the robustness and control precision of the ACC system, while satisfying the demands of safety, comfort, and economy.
With the rapid development of intelligent transportation system, the research on vehicle stability can be a theoretical basis for realizing autonomous driving technology. The previous stability control strategies have not taken into account the tire force saturation factor, the slip rate, and the robustness of the control system sufficiency. According to the characteristic that the torque of each wheel can be distributed independently, a torque distribution algorithm under emergency conditions is proposed. The proposed torque distribution algorithm is constructed using three hierarchical controllers. The upper controller attempts to judge whether the vehicle is in stable state using the phase plane method. Also, it judges whether the wheels are slipping. The middle controller aims to calculate the demands for the desired traction force and yaw moment, whereas the lower controller is designed to translate those virtual signals into actual actuator commands. When designing the middle controller, a sliding mode control method is utilized to guarantee system stability and robustness by taking into account various factors, including lateral wind, and sensor noise. For the lower controller, the control allocation optimization method is utilized to determine an appropriate control input for each in-wheel motor by considering the road conditions, adhesion utilization, and maximum output torque of the motor. The numerical simulation studies are conducted to evaluate the performance of the torque distribution algorithm. Comparison results indicate that the proposed algorithm presents better performance to distribute the appropriate torque for each wheel and ensure the stability of the vehicle under emergency conditions.
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