This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding mode control technique and predictive control scheme. By leveraging a first-order forward Euler discretization approach, a discrete-time model of USVs was initially formulated. Then, a virtual velocity controller was developed to convert the position tracking into expected velocity tracking, which was achieved by utilizing a sliding mode control. Subsequently, a networked predictive control technique was performed to compensate for the time-varying delays. Finally, theoretical analysis and extensive comparative simulation tests demonstrated that the proposed control scheme guaranteed complete compensation for time-varying delays while ensuring the stability of the closed-loop system.

Remote Control Maritime Autonomous Surface Ships (RC-MASS) have become promising in the near future, while the delivery of the perception data from the ship to the shore suffers from communication issues, such as packet delay, reordering, and loss. In order to improve the accuracy of RC-MASS state estimation in complex communication environments and ensure the performance of control, two effective real-time state estimation methods are proposed. First, a comprehensive model is established by integrating the state of acceleration into the traditional ship motion model; Secondly, a simplified augment CKF (A-CKF) framework is proposed, in which the system state is augment by the previous states; Third, the State Estimation Algorithm with Last Data (SEA-LD) and the SEA with Retrodicted States (SEA-RS) are proposed, which are built upon the A-CKF framework and incorporate a novel state update strategy to accurately estimate the ship’s true state and reduce the impact of accumulated state errors caused by packet delay, reordering, and loss. To validate the proposed method, real-ship and simulation experiments under different communication conditions are introduced. The results indicate that the proposed method has significant improvements in state estimation performance and control performance of RC-MASS under complex communication scenarios. Under a normal delay distribution with means of 200ms, 500ms, and 1000ms, a variance of 10% of the mean, and a packet loss rate of 0.1%, both the SEA-RS and SEA-LD algorithms achieve errors below 0.047 m/s in velocity and 1.25m in position. In terms of control performance, the proposed methods can maintain stable control under severe delay conditions of 2500ms.

This paper studies the issues of attack detection and secure state estimation for nonlinear cyber-physical systems (CPSs) with unknown-but-bounded (UBB) noises. First, a nonlinear estimation approach based on first-order Taylor expansion with Lagrangian remainder is employed, and a zonotopic observer is proposed for state estimation. Then, a novel attack detection mechanism is presented to improve detection sensitivity. It consists of two steps: 1) judging the intersection between the predicted state set and the strip, and 2) checking whether the measurement state set is empty set. In addition, a secure state estimation method is proposed to protect the estimated state against cyber attacks. Finally, an unmanned vehicle system is leveraged to demonstrate the validity of the main results.