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As an important component of automated guided vehicle (AGV) active safety systems, the active rollover prevention (ARP) control has been widely studied. In this paper, a fixed-time adaptive sliding mode (FTASM) control strategy based on nested adaptive law (NAL) and improved super-twisting observer (ISTO) is developed for the AGV. Firstly, the lateral and roll motion dynamics of AGV are established, and the ARP problem is transformed into the yaw stability control with constraints. Subsequently, a faster fixed-time stable system-based FTASM composite controller is proposed to guarantee a fixed and faster convergence time of the yaw rate tracking error, such that the rollover stability will reach a region of permissible risk. The adoption of NAL enables the adaptive controller to automatically search the minimum switching gain satisfying the reaching condition of sliding mode, thus the conservative assumption constraint of the disturbance upper bound is released. In addition, an ISTO is developed to estimate the unknown disturbance to further improve the control robustness and attenuate the control chattering. The closed-loop stability analysis is rigorously given by Lyapunov theory. Finally, the performance of the proposed control strategy is validated by hardware-in-loop simulations with Adams and Matlab software and an actual steer-by-wire plant.

To assure safety and comfort for autonomous vehicles (AVs) in the mixed traffic environment, a longitudinal potential field-based car-following model considering the cut-in behavior from an adjacent vehicle is established in this study. The longitudinal potential field (LPF), which includes the cut-in potential field, the hazard potential field and the velocity potential field, is constructed to quantitatively characterize the influence of the surrounding environment on the AVs. The cut-in potential field is created to indicate the possibility of cut-in behavior from the adjacent vehicle, the hazard potential field is intended to depict the risk of colliding with the preceding vehicle, and the velocity potential field is created to show how velocity impacts the following behavior. The proposed model is obtained by calculating the field force in the LPF, and its parameters are calibrated using the genetic algorithm. Comparative simulations are executed to verify the remarkable performance of the proposed model, in terms of acceleration, velocity, and headway tracking as well as responding to the cut-in behavior under three different driving conditions.