Preview Control with Dynamic Constraints for Autonomous Vehicles

Abstract

In this paper, a preview theory-based steering control approach considering vehicle dynamic constraints is presented. The constrained variables are predicted by an error states system and utilized to adjust the control law once the established dynamic constraints are violated. The simulated annealing optimization algorithm for preview length is conducted to improve the adaptability of the controller to varying velocities and road adhesion coefficients. The theoretical stability of a closed-loop system is guaranteed using Lyapunov theory, and further analysis of the system response in time domain and frequency domain is discussed. The results of simulations implemented on Carsim–Simulink demonstrate the favorable performance of the proposed control in tracking accuracy and system stability under extreme conditions.

Event-triggered model predictive-preview control strategy for trajectory tracking of autonomous vehicle

“…Yuan et al (2022) proposed a driver emergency steering model with adaptive preview information, which enable obstacle avoidance behavior. Subsequently, to improve the response speed of the vehicle at variable curves, Li et al (2021) , and improved the vehicle's adaptability to various curves and the accuracy of trajectory tracking. In recent years, for the vehicle tracking problem under variable speed, Yuan et al (2020) proposed an optimal heading angle reference model based on preview information, and utilized an upgraded model predictive controller to increase the vehicle's tracking accuracy at various speeds.…”

“…The MPC algorithm is employed to address the online receding-horizon problem with multiple constraints This process requires a lot of computing resources and time, which may result in excessive computation and hardware burden. Therefore, the combination of event-triggered and MPC is necessary to promote the calculation efficiency of the control algorithm, where the event-triggered means that the corresponding action is generated only when a specific event occurs (Li, 2021). Yang et al (2021) proposed an event-triggered MPC strategy, which reduces the computational complexity of the controller by designing appropriate triggering conditions.…”

Lateral Trajectory Tracking of Self-Driving Vehicles Based on Sliding Mode and Fractional-Order Proportional-Integral-Derivative Control

“…Zhang et al used a trajectory planning state hierarchy approach to design a controller based on vehicle kinematics to facilitate curve tracking [13]. Lin et al designed a preview controller based on a simulated annealing algorithm to optimize the preview distance, which can use the road curvature as preview information for feedforward control and feedback control, and it was verified that this method can improve the robustness of the controller under different working conditions [14]. Mata et al introduced a constant nominal longitudinal speed calculation method into the robustness control algorithm based on a linear time-invariant monorail model, which guarantees vehicle comfort criteria over a wide range of speeds [15].…”

SiamMFC: Visual Object Tracking Based on Mainfold Full Convolution Siamese Network

“…As a general object tracker, visual target tracking is one of the most important research contents in computer vision and has been widely concerned by more and more scholars. Visual object tracking technology has been widely used in security, video surveillance, and robot vision [ 1 , 2 ]. Although many high-performance trackers have been proposed in recent years, visual object tracking is still facing great challenges such as occlusion, deformation, and blur.…”