Yuchi Cao

With the increasing proportion of ships in logistics and the growing prosperity of traffic in maritime, negotiation and cooperative collision avoidance between ships is becoming more and more essential for navigational safety. This paper proposes a Model Predictive Control method that utilizes intention data of the target ship and a quaternion ship domain model to achieve collision avoidance while considering COLREGs, named IQMPC. Firstly, by utilizing the intention data of other ships, trajectories of the own ship and the target ship are well predicted to detect potential collision risks and take optimal avoidance actions in advance while risks exist. Secondly, the quaternion ship domain with its adjacent area is divided into four different parts to reflect the urgency of ship encounters. Collision risk evaluation functions are designed to determine avoidance actions conforming to COLREGs. Thirdly, several different ship encounter scenarios were simulated based on IQMPC to verify its capability.

In this paper, an innovative method is proposed to improve the global path planning of Unmanned Surface Vehicles (USV) in complex sea areas, combining fluid mechanic calculations with an improved A* algorithm. This method not only generates smooth paths but also ensures feasible global solutions, significantly enhancing the efficiency and safety of path planning. Firstly, in response to the water depths limitation, this study set up safe water depths, providing strong guarantees for the safe navigation of USVs in complex waters. Secondly, based on the hydrological and geographical characteristics of the study sea area, an accurate ocean environment model was constructed using Ansys Fluent software and computational fluid dynamics (CFD) technology, thus providing USVs with a feasible path solution on a global scale. Then, the local sea area with complex obstacles was converted into a grid map to facilitate detailed planning. Meanwhile, the improved A* algorithm was utilized for meticulous route optimization. Furthermore, by combining the results of local and global planning, the approach generated a comprehensive route that accounts for the complexities of the maritime environment while avoiding local optima. Finally, simulation results demonstrated that the algorithm proposed in this study shows faster pathfinding speed, shorter route distances, and higher route safety compared to other algorithms. Moreover, it remains stable and effective in real-world scenarios.

In this article, the dynamic positioning (DP) control problem of constrained unmanned marine vehicles with thruster faults is investigated. To deal with the system constraints and thruster faults effect simultaneously, a codesign framework based on integral sliding mode control (ISMC) and model predictive control (MPC) is proposed. The main design challenge comes from the multiplicative uncertainty introduced by thruster faults, which may cause failures in the DP control with traditional MPC. To solve this problem, we propose to use an improved tube-based MPC in which a fault uncertain set can be characterized and the terminal set containing the fault factor can be derived. Combining the ISMC we show that, all possible trajectories of the unmanned marine vehicle are constrained in the tube for all possible realizations of uncertainties. Finally, the simulation results demonstrate the effectiveness of the proposed codesign framework.

For shipboard boom cranes with the influence of ship roll motion, we develop a new nonlinear antiswing control scheme with a model predictive control (MPC) based auto-tuning mechanism. In this scheme, the superiority of MPC in handling constraints is retained in acquiring time-varying control gains. Compared with trial and error, the efficiency of searching for proper control gains can be improved dramatically while considering optimization. Specifically, by binding the ship rolling angle to the original state variables of shipboard cranes, a coordination transformation is first carried out to facilitate the controller design by excluding the specific expression of ship rollings. Then an energy-based nonlinear controller is proposed, and the system stability can be rigorously guaranteed based on the Lyapunov theory and LaSalle’s invariance principle. To improve the performance of the energy-based controller with gains derived through trial and error tuning, a novel MPC-based auto-tuning mechanism is further established so the optimal and time-varying control gains can be determined more effectively. The other merit of the proposed scheme lying in the state constraints and input saturation are taken into account to enhance the safe operation of shipboard cranes. Finally, numerical simulations are elaborately made, and comparison results are presented to demonstrate the superiority of the proposed scheme, especially in optimality, coordination control performance, and dealing with constraints.

As important equipment in offshore engineering and freight transportation, shipboard cranes, working in non-inertial coordination systems, are complicated nonlinear systems with strong couplings and typical underactuation. To tackle the challenges in the controller design for shipboard boom cranes, which is a representative type of shipboard cranes, a comprehensive framework embedding moving horizon estimation (MHE) in model predictive control (MPC) is constructed in this paper while considering disturbances and noise. By utilizing MHE, velocity information can be estimated with high precision even though this is influenced by disturbances and measurement noises. This expected superiority can greatly ease the difficulties in directly measuring all states of shipboard boom cranes. Then, the estimated information can be passed to MPC to derive the optimal control law by solving a constrained optimal problem. During this process, the physical limits of shipboard boom cranes are fully considered. Therefore, the practicability of the proposed framework is highly suitable for the actual requirements of shipboard boom cranes. Finally, the framework is verified by designing three typical scenarios with different disturbances and/or noises. Comparisons with other control approaches are also performed to demonstrate the effectiveness.

A Lyapunov-based model predictive control (MPC) scheme is firstly developed for shipboard boom cranes with ship rolling, while considering optimization and input saturation. By integrating the ship rolling into original state variables, the shipboard boom crane model is transformed to facilitate the successive controller design. A Lyapunov-based MPC framework is then constructed for the new dynamical model, while respecting actuator capability. To ensure the recursive feasibility and stability of the established framework, a contractive constraint with a bounded energy-based controller is further introduced, for which the bounds are intrinsical with the utilization of the arctan function. Stability analysis for the bounded controller is also discussed based on Lyapunov theory and LaSalle’s invariance principle. Compared with the bounded controller, optimization is taken into account by the Lyapunov-based MPC. Suboptimal solutions are also accepted to ease the computational burden. Moreover, the conservation existing in the bounded controller can be entirely circumvented by the Lyapunov-based MPC, so the capacity of actuators can be fully exploited while obeying the input saturation. Finally, numerical simulations are elaborately devised and implemented to show the effectiveness of the bounded controller and superiority of the Lyapunov-based MPC controller. Note to Practitioners—Shipboard crane is an important industrial equipment in ocean transportation and offshore engineering. Cranes are installed on ship deck, so the movements of ship, such as rolling, and pitching, may introduce difficulties, inefficiency, and unsafety to the manual operation. The conditions mentioned above motivate the study of automation control for shipboard cranes. A Lyapunov-based model predictive control (MPC) framework is constructed in this paper. Based on this framework, the payload can be conveyed to the desired location accurately and eliminate the swing quickly. Simultaneously, optimization is considered, thus, better control performance can be achieved by consuming less energy. Moreover, the physical limits of the actuators can be satisfied all the time. The present research only considers 3 degrees of freedom (DOF) shipboard boom cranes, for which the slew movement of boom and payload tangential swing are not involved. In the future research, the 5-DOF shipboard boom cranes will be studied to promote the application of the method proposed in this paper.

This paper presents a course following control method for ships based on optimized backstepping (OB) technology. The backstepping technology is employed as the main control framework since the ship course can be modeled in the strict feedback form. Based on the actor-critic architecture and radial basis function (RBF) neural network (NN), the reinforcement learning (RL) strategy is introduced to avoid the difficulty in solving the traditional Hamilton-Jacobi-Bellman (HJB) equation directly. The actor NNs are used for carrying out the control law, while the critic NNs aim at evaluating the tracking performance. An auxiliary design system and Gaussian error function are employed to handle the practical problem of input saturation. The stability of the closed-loop system can be guaranteed via Lyapunov theory. Finally, simulation examples and comparison are provided to demonstrate and verify the superior performance and advantages on course following and energy saving of the control scheme proposed in this paper.