Adaptive Asymmetric Time-Varying Integral Barrier Lyapunov Control-based Trajectory Tracking of Autonomous Vehicles ☆

This paper investigates the lane following and changing maneuvers of autonomous vehicles in the presence of unknown disturbances, taking into account the dynamic system states and input constraints. The integrated longitudinal-lateral and yaw rate dynamics of the vehicle are simultaneously considered to improve the tracking accuracy and system stability when navigating under critical conditions. Then, a novel adaptive asymmetric time-varying integral barrier Lyapunov control and dynamic surface control scheme are developed to design the active front steering controller, longitudinal controller, and direct yaw moment control controller, which is capable of constraining the system states and control signals within the predefined boundary. In addition, the radius basis function neural network (RBFNN) is employed to estimate the lumped disturbances caused by the parametric uncertainties, external disturbances, and unmodeled dynamics, and the command filter system is used to avoid the explosion of terms phenomenon. Due to the fast and accurate torque response characteristics of the in-wheel motors, the optimization-based method is then implemented to effectively allocate the driving/braking torque to each in-wheel motor so as to improve vehicle performance. The stability of the closed-loop system is comprehensively demonstrated by means of the Lyanpynov theory. Finally, the quantitative and qualitative comparisons in different diving scenarios using the Carim-Simulink joint environment are carried out to illustrate the effectiveness and validation of the proposed method.