This study presents an adaptive quantized control algorithm designed to address actuator faults and achieve obstacle avoidance for heterogeneous ships. Within this algorithm, a hysteresis quantizer is employed to minimize communication resource utilization. Novel adaptive compensation mechanism is introduced to counteract actuator faults. Additionally, the radial basis function neural networks (RBF-NNs) are utilized to approximate the uncertainties inherent in the ship model. Further adaptive parameters are incorporated to mitigate perturbation errors arising from mismatches between the quantizer and the ships’ unknown parameters. Stability is rigorously established through the construction of a direct Lyapunov function, demonstrating that all signals within the closed-loop system satisfy Semi-Globally Uniformly Ultimately Bounded (SGUUB). To validate the superiority of the proposed algorithm, simulation experiments are conducted for obstacle avoidance mission of marine heterogeneous system.

In the cruising mission of the unmanned surface vessel-unmanned aerial vehicle (USV-UAV) cooperative plant, although the UAV is capable of executing aerial maneuvers, it still faces inevitable obstacle avoidance and control challenges in narrow waterways and cross-bridge scenarios. As such, this study proposes a variable-speed multi-port segmented threshold event-triggered cooperative collision avoidance control algorithm based on the geometric velocity obstacle method (GVO). In the guidance module, first define the obstacle zone based on the detected bridge dimensions. Then, by setting safety thresholds and target points, implement a bridge collision avoidance guidance function with an automatic selection mechanism for the narrow waterway navigation mission. In the control module, in the presence of bridge obstacles, a variable-speed multi-port segmented threshold event triggering mechanism (MSETM) is designed for path tracking and collision avoidance missions to ensure the coordination of the USV-UAV cooperative plant at the beginning and end of collision avoidance. Furthermore, a multi-layer neural networks (MNNs) is adopted to approximate the system’s nonlinearities, achieving high approximation accuracy and computational efficiency. Stability of the algorithm is proven using Lyapunov stability theory, and its effectiveness is validated through simulation experiments.

To improve the trajectory tracking performance of unmanned surface vehicle (USV), this article investigates the USV optimal control problem with the consideration of actuator wear. In the proposed algorithm, the USV control system is divide into kinematic subsystem and kinetic subsystem. In particular, corresponding performance indexes that looking forward to be optimized are defined for each subsystem. The related value functions, Hamilton-Jacobi-Bellman equations and optimal control policies are approximated by actor-critic neural networks. To reduce the wear of propeller and rudder, the event-triggered problem is considered as a zero-sum game solving problem, where the best control inputs and worst thresholds are delivered via minmax strategy. Also, the nonlinear uncertainties of the USV are approximated and environment disturbances are compensated in the value functions for better control performance. The USV closed-loop control system is proved semi-globally uniformly ultimately bounded stability via Lyapunov theory. Finally, a simulation case and harbor experiment are illustrated to verify the superiorities and engineering application values of the proposed algorithm.

The autonomous and security are the crucial requirements in fields of the polar transportation. This paper proposes a newly iterative learning control framework for the autonomous surface vessels (ASV) to implement the path-following operation in the ice floes scenario. The proposed framework is divided into two parts: the guidance and control. For the former, the ice floes are firstly identified into threatening and non-threatening based on the size. Subsequently, the obstacle area of each threatening ice floe is programmed considering the underwater portion. Then the ice floes avoidance guidance with auto-select mechanism for ice-zone traversal mission is constructed by setting the hazard threshold and target point. For the latter, a robust adaptive iterative learning control (ILC) system is designed for the path-following mission, where the control accuracy increases with the number of iterations. The stability of the closed-loop control system is proved with utilization of the Lyapunov theorem. Finally, two numerical examples are provided to evaluate the advantages and accuracy of the proposed algorithm, where the ice floes are generated with irregular.

This article investigates an adaptive prescribed performance path-following control algorithm for rotor-assisted vehicles, incorporating reinforcement learning (RL) to execute energy-saving cruising missions. For obtaining a high-performance path-following controller, a concise prescribed performance control (PPC) algorithm is designed to tightly constrain the output errors within the defined boundaries, while a shifting function is introduced to solve the problem of initial condition restrictions. Furthermore, through integrating the Backstepping method and the optimal control technique, an improved RL with the form of actor-critic neural networks (AC-NNs) is proposed to offer an innovative approach to the challenges of the model uncertainties and external disturbances. In this approach, the actor NN is employed to create an appropriate control policy, while the critic NN is aimed at evaluating the cost-to-go function to modify the system action. Semi-global uniform ultimate bounded (SGUUB) stable properties of the proposed algorithm are guaranteed via the Lyapunov theory. Finally, the superiority and feasibility of the proposed algorithm are verified by two numerical experiments.

The sensor faults and the communication burden are the core issues in fields of the intelligent maritime search control. In this paper, a robust adaptive event-triggered control strategy is presented for the underactuated surface vessel-unmanned aerial vehicles (USV-UAVs) cooperative system to implement the maritime parallel search mission. The proposed scheme is comprised of two parts, i.e., the three-dimensional (3D) search guidance principle and the cooperative formation control law. The developed guidance principle can generate the reference signals for the USV and UAVs, which the maneuvering characteristics of the heterogeneous agents are considered at the waypoints. Linked with the guidance term, a robust adaptive event-triggered control algorithm is designed for the cooperative system by fusing the dynamic event-triggered mechanism and sensor-tolerant technique. The dynamic triggered threshold is constructed on basis of the state error rather than the predefined parameters. Besides, the constrains of the sensor faults and the model uncertainties are tackled by constructing the adaptive parameter and robust neural damping term. Through the Lyapunov theorem, the semi-global uniform ultimate bounded (SGUUB) stability is guaranteed for all state variables. Finally, the advantages of the proposed scheme are evaluated on simulation platform, exhibiting the good tracking accuracy and tolerant performance in presence of the external disturbances.

To improve the autonomy of marine ranching operations, this paper addresses the cooperative formation control and multi-task switching problem of ranch autonomous marine vehicles (AMVs) with the structure synchronized dynamic event-triggered mechanism (DETM). In the proposed algorithm, adaptive potential ship (APS) technique is adopted to guarantee the integrity and continuity of the guidance signal. Combined with the guidance principle, a cooperative formation control algorithm is proposed by employing the DETM and neural networks (NNs). The communication burden in the channel from the sensor to the controller and from the controller to actuator has been reduced for the merits of the proposed DETM. Unlike the existing results, the proposed DETM can activate the threshold parameters, adaptive parameters and NNs weight estimators at the triggering times synchronously. This releases the computation burden greatly. Considerable effort has been made to guarantee the semi-globally uniformly ultimately bounded (SGUUB) stability via the Lyapunov theorem. Finally, two simulations consist of the marine ranching path following and comparative example are carried out to evaluate the advantages of the proposed strategy.

This article presents a robust adaptive neural control algorithm for the wing-sail-assisted vehicle to track the desired waypoint-based route, where the event-triggered mechanism is with the multiport form. The main features of the proposed algorithm are three-fold: 1) the communication burden, in the channel from the sensor to the controller as well as the actuator, has been reduced for the merits of the multiport event-triggered approach. The feedback error signals and the control input will be updated only on the event-triggered time point; 2) for the wing-sail-assisted vehicle, the thrust force is provided by devices with the propeller and the sail. From this consideration, the proper sail force compensation is derived on the basis of information about the current heading angle and the wind direction. The corresponding control law can guarantee the energy-saving for the propeller; and 3) in the algorithm, the system uncertainties are remodeled by the neural-network approximator. Furthermore, by fusion of the robust neural damping and dynamic surface control (DSC) techniques, the corresponding gain-related adaptive law is developed to address constraints of the gain uncertainty and the environmental disturbances. Through the Lyapunov theorem, all signals of the closed-loop control system have been proved to be with the semiglobal uniform ultimate bounded (SGUUB) stability, including the triggered time point and the intermediate triggered interval. Finally, the numerical simulation and the practical experiment are illustrated to verify the effectiveness of the proposed strategy.

This article investigates the adaptive fuzzy event-triggered control for the underactuated surface vessels (USVs), considering constraints of the International Regulations for Preventing Collisions at Sea (COLREGs) and the actuator failures. The proposed scheme can be divided into the guidance module and the control module. An improved logic virtual ship guidance principle, considering the ship-to-ship collision avoidance, is developed to generate the real-time reference signal for USVs. The main characteristic of the guidance principle is to ensure USVs sailing in the path following mode and collision avoidance mode. Especially for the collision avoidance mode, the collision avoidance guidance is targetedly designed for three sailing situations (the head-on situation, the overtaking situation, and the crossing situation), which is consistent with the COLREGs. Furthermore, an adaptive fuzzy event-triggered law is designed to control the USVs to converge to the desired path. The model unknown terms and the basic fault of the actuator are identified by the fuzzy logic system, and the fuzzy logic state observer is designed to estimate the unmeasured states of the USVs. Unlike the existing results, the communication burden from the controller to the actuators is reduced for the merits of the input event-triggered rule. Through the Lyapunov theory, it is proved that all the signals of the closed-loop control system are the semiglobal uniform ultimate bounded. Finally, the simulated examples are provided to illustrate the validity of the proposed control approach.

This note focuses on the application of the event-triggered mechanism into the formation control system. For this purpose, a novel fleet control model is established in the Cartesian coordinate system. Through this structure, a model-based event-triggered control (ETC) is designed by utilizing the radial basic function neural networks (RBF NNs) and the minimum learning parameter (MLP) technique. Thus, the continuous acquisition of the formation state does not take longer, and the communication load of the resource-limited fleet is largely reduced. In addition, the semi-global uniformly ultimately bounded (SGUUB) of all signals are proved by the Lyapunov candidate function. And the corresponding simulation results can be used to verify the effectiveness and robustness of the proposed control scheme.