Leveraging digital twin for autonomous docking of a container truck with stabilization control

Abstract

This paper describes the design, development, and implementation of a high‐precision autonomous docking control system for a container truck based on a digital twin approach. The digital twin is used to simulate the dynamic behavior of the physical truck and to design the controller by providing a virtual platform to test, validate, and optimize control strategies and algorithms before their deployment in the actual system. To this end, a cascade of a nonlinear observer and an unscented Kalman filter is used to estimate the state variables of the physical truck for point‐stabilization and orientation controls during the autonomous docking process. The docking motion involves two stabilization problems: point stabilization for smooth motion from the initial configuration to the docking slot, and orientation control to deliver the container truck to the final docking position with a margin of error of 5 cm for position and 0.0087 rad for orientation. The stability of both controllers is investigated, and simulations and experiments are conducted to demonstrate the accuracy of the proposed method in a container terminal environment.