Adaptive control strategy for heavy-duty AGVs considering four-wheel steering and roll dynamics

Abstract In this study, an adaptive control strategy integrating trajectory tracking and stability control is proposed to address the significant challenge of sideslip and potential rollover faced by heavy-duty automated guided vehicles (AGVs) operating on low-adhesion surfaces, such as flooded roads in port areas. To simultaneously address both longitudinal and lateral control, a dynamic model for heavy-duty AGVs is developed, accounting for the accurate nonlinear characteristics of four-wheel steering and roll dynamics. Building upon this foundation, an adaptive model predictive controller is proposed that enables the adaptation of control parameters and internal state parameters. This controller addresses the multi-objective optimization problem of simultaneously managing trajectory tracking and stability by considering constraints related to yaw stability, rollover prevention, trajectory tracking, and actuator output limitations. Additionally, real-time estimators are developed for vehicle mass using recursive least squares and for state parameters using an extended Kalman filter, which are used to update the controller parameters. Co-simulations are performed using Matlab/Trucksim to compare the proposed strategy with other control strategies and methods and to validate its performance under diverse road conditions. The results show that the proposed control strategy effectively improves the trajectory tracking capability and handling stability of heavy-duty AGVs, particularly on low-adhesion surfaces.