Observer-Based Attack and Disturbance Mitigation for Heavy-Duty Vehicles in an Autonomous Docking Application

A heavy-duty vehicle (HDV) finds applications in various fields, particularly logistics and freight transportation, involving the movement of containers from ports to local warehouses and eventual delivery destinations. In smart ports, the process has been automated, necessitating the vehicle’s autonomous docking and undocking. Such a cyber-physical system faces the risk of cyberattacks and internal disturbances posing hazardous situations within the terminal. Therefore, it becomes crucial to identify and mitigate any cyberattacks as well as disturbances during the docking and undocking procedures. This research aims to develop an observer-based detection and mitigation system for actuator attacks and disturbances for autonomous docking of HDVs, employing a point stabilization-based control strategy. The methodology outlined in this study involves analytical proof of the asymptotic stability of the detection and mitigation system, complemented by numerical simulations and experiments to provide a quantitative evaluation. The proposed system design utilizes the Lyapunov direct method to establish the asymptotic stability of the detection system and subsequently integrates it with the point stabilization controller to design the mitigation system, ultimately contributing to the asymptotic stability of the desired configuration for docking the vehicles. The simulation of autonomous docking is performed, demonstrating the ability of the detection and mitigation system, in conjunction with the point stabilization controller, to detect simulated actuator attacks and mitigate their effects, resulting in a smooth docking process at the desired configuration. Experimental results validate the effectiveness of the controller, as the vehicle consistently achieves the desired configuration. Although this work primarily focuses on cyberattacks, it is worth noting that, for safety reasons within container ports, the experimental investigation tackles actuator disturbances instead of actuator attacks, effectively showcasing the system’s ability to detect internal actuator malfunctions.