Asymmetric time‐varying integral barrier Lyapunov control‐based trajectory tracking of autonomous vehicles with input magnitude and rate constraints

Abstract 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 scheme is developed to design the steering controller, longitudinal controller, and yaw moment controller, which is capable of constraining the system states and control signals within the predefined boundary. In addition, the radius basis function neural network 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. 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 Lyapunov theory. Finally, the quantitative and qualitative comparisons in different driving scenarios using the Carsim–Simulink joint environment are carried out to illustrate the effectiveness and validation of the proposed method.