In recent years, marine autonomous surface vessels (MASS) have grown into a ship research issue to increase the level of autonomy of ship behavior decision-making and control while sailing at sea. This paper focuses on the MASS motion control module design that aims to improve the accuracy and reliability of ship steering control systems. Nevertheless, the stochastic sea and wind environment have led to the extensive use of filters and state observers for estimating the ship-motion-related parameters, which are important for ship steering control systems. In particular, the ship maneuverability Nomoto index, which primarily determines the designed ship steering controller’s performance, cannot be observed directly due to the model errors and the external environment disturbance in the process of sailing. Hence, an adaptive robust ship steering controller based on a closed-loop gain shaping (CGS) scheme and an extended Kalman filter (EKF) on-line identification method is explored in this paper. To verify the effectiveness of the proposed steering controller design scheme, the motor vessel YUKUN was taken as the control plant and a series of simulation experiments were carried out. The results show the advantages of the dynamic response performance of the proposed steering controller compared with the classical PD and traditional CGS controllers. Therefore, the proposed adaptive CGS steering controller would be a good solution for MASS motion control module design.

This article is devoted to the optimal protocol for the leader-following consensus of second-order multiagent systems. Remarkably, the global consensus cost functional and the directed topology are pregiven, and the optimal protocol to be sought is distributed. This makes the Riccati-based strategies inapplicable, by which merely centralized protocol can be derived. As the main contribution of this article, an effective strategy of seeking a distributed optimal protocol is proposed for second-order agents over the digraph of the directed tree. Detailedly, the feasibility of distributed optimal protocol is first affirmed, that is, the existence of optimal gain parameters can be guaranteed. Then, by recursively deriving the completely explicit formulas of the consensus errors of relative position and velocity, an online implementable algorithm is developed to achieve the parameterization of the cost functional, that is, to obtain the explicit formula of the cost functional with respect to gain parameters of all agents. Lastly, the desired optimal gain parameters are obtained by minimizing the explicit formula.

In this paper, we propose a hybrid algorithm that integrates an improved artificial potential field method (IAPF), model predictive control (MPC), and an extended state observer (ESO) to address the obstacle avoidance problem in multi-unmanned surface vehicle (Multi-USV) formations, including both dynamic and static obstacles, as well as navigation through narrow waterways. Initially, the virtual structure method was applied for formation control. Next, the traditional potential field method was enhanced by employing a saturated attractive potential field and a partitioned repulsive potential field, which improve formation stability and obstacle avoidance accuracy in complex environments. The extended state observer was then employed to estimate and compensate for unknown system dynamics and external disturbances from the marine environment in real time, improving system robustness. On this basis, by leveraging the multi-step predictive optimization capabilities of model predictive control, the proposed algorithm dynamically adjusts control inputs based on the desired trajectories generated from potential field forces, which ensures the stability of formation control and effective obstacle avoidance. The simulation results demonstrate that the proposed algorithm effectively avoids both dynamic and static obstacles in multi-unmanned surface vehicle formations and enables successful navigation through narrow waterways by altering the formation.

The integration of cognitive radio networks (CRNs) and unmanned aerial vehicles (UAVs)-assisted communications has become an effective approach to addressing the challenges of spectrum scarcity, difficulties in equipment setup, and high communication costs in maritime communications. For sake of security and energy consumption issues of UAV-assisted communications, we propose a maritime dual-UAV-assisted cognitive relay communication system in the presence of an unknown-position eavesdropper (Eve). Considering the large-scale time-varying channels of the Marine network and the presence of Eve, one of the UAVs is used to relay information and the other one is used to interfere with Eve. Meanwhile, an error estimation model is employed to approximately estimate the position of Eve. Energy harvesting (EH) under time segmentation (TS) is applied by UAVs with periodic elliptic trajectories to compensate their transmit power for maximizing security energy efficiency (SEE) of the proposed system. Due to complex non-convex constraints and fractional form of objective function of the proposed optimization problem, it is challenging to solve using conventional methods. The Dinkelbach algorithm for fractional transformation and the Lagrangian dual method are introduced to overcome the aforementioned difficulties in solving the problem. Simulations verify the effectiveness of the proposed algorithm in improving SEE.