Autonomous path planning and collision avoidance (CA) in complex maritime environments are critical challenges for Maritime Autonomous Surface Ships (MASSs). This paper presents a novel hierarchical motion planning framework that integrates global and local path planning algorithms. For global path planning in static obstacle environments, a hybrid approach combining A*, Bezier curves, and particle swarm optimization (PSO) is proposed to enhance path search efficiency and ensure smooth trajectory optimization. For local path planning and collision avoidance with dynamic vessels, a multi-ship CA algorithm based on velocity prediction potential fields is introduced. A new collision-risk identification model is developed to ensure compliance with COLREGS and enable collaborative CA in complex multi-ship scenarios. The framework also addresses environments with both dynamic and static obstacles through a dynamic path planner that integrates global and local planning. A key innovation is the dynamic CA decision-making mechanism with alteration of course and/or speed (ACS), which provides robust real-time navigation strategies. Validation through simulations, real-ship model tests, and AIS-data analysis demonstrates the framework’s robustness and adaptability for MASS navigation. Compared to existing methods, the proposed approach significantly reduces collision-avoidance path length while maintaining safety, real-time performance, and COLREGS compliance. This work advances autonomous maritime navigation by addressing complex scenarios with a unified framework that balances efficiency, safety, and regulatory adherence.

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.

This article studies the 3-D dynamic rendezvous control problem for coordinated heterogeneous marine vehicles, including an uncrewed underwater vehicle (UUV) and an autonomous surface vehicle (ASV). An observer-based safety-preserving rendezvous control approach is proposed to robustly stabilize the rendezvous errors under the port-Hamiltonian (PH) framework. First, an interconnection and damping assignment passivity-based control (IDA-PBC) method is adopted to provide a basic stabilizing control framework. In this problem, both vehicles are faced with hydrodynamic model uncertainties and unknown external disturbances. Then, to preserve the rendezvous safety under uncertain dynamics, the prescribed performance control (PPC) transformation is implemented for the ascending motion to get the equivalent approaching-constrained PH system. The intuitive design procedure provided by the IDA-PBC method, along with the collision-free rendezvous safety guaranteed by the auxiliary PPC technique, reduces the controller design complexity while providing a smooth rendezvous trajectory. Besides, a structure-keeping uncertainty observer algorithm is designed and incorporated to simultaneously handle model uncertainties and environmental disturbances without destroying the interconnection structure. Under the proposed approach, the UUV-ASV rendezvous errors can be effectively stabilized with rigorous closed-loop stability analysis. Finally, both simulations and comparative experiments are conducted to demonstrate the effectiveness and advantages of the proposed approach.

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.

This paper investigates the event-triggered train formation control problem for multiple autonomous surface vehicles (ASVs) formation system in polar communication interference environment. Firstly, a distributed resilient guidance algorithm is introduced to generate the reference route based on waypoints. In the guidance algorithm, the distributed resilient leader predictor (RLP) is applied to obtain the states of ice-breaking ship when communication fails, and the resilient train formation scheme is designed to compute the reference signals for ASVs. Subsequently, an adaptive neural event-triggered train formation control algorithm is developed. In the control algorithm, the neural networks (NNs) are conducted to approximate model uncertainties, and event-triggered control (ETC) is employed to minimize controller updates. Furthermore, the threshold of the event-triggered mechanism (ETM) can be dynamically adjusted by states of system. It is proved that the formulated algorithm can ensure the prediction errors converge and multiple ASVs system is stable in polar communication interference environment. Finally, two simulation experiments are adopted to illustrate the effectiveness 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 proposes an event-triggered quantitative prescribed performance neural adaptive control method for autonomous underwater vehicles (AUVs). At kinematic level, to achieve a quantitative predetermined tracking performance without violating user-defined transient indices, a quantitative prescribed performance control (QPPC) scheme is devised, where the overshoot of the transient tracking response can be specified by a quantitative design relationship. To pursue a tradeoff between tracking accuracy and resource saving, a hybrid threshold-based event-triggered mechanism (HTETM) is designed and incorporated into the AUV controller design procedure. Additionally, a modified echo state neural network (MESNN) is employed for disturbance estimation, where intermittent system information produced by the HTETM is used for online learning, resulting in that both the communication data throughput between the controller and actuators and the online computational load can be diminished synchronously. Finally, a control law is devised at dynamic level to compensate for the triggered error induced by the aperiodic sampling of HTETM. Simulation results are provided and analyzed to validate the effectiveness of the proposed control strategy with application to an omni directional intelligent navigator.

This article concerns the co-design scheme of the adaptive event-triggered mechanism (AETM) and asynchronous$H_{\infty }$control for two-dimensional (2-D) Markov jump systems. First, we introduce a hidden Markov model with the observation that the asynchronous phenomenon is inevitable between the plant mode and the controller mode. Besides, for economizing the communication times, an innovative 2-D AETM is constructed, which can dynamically regulate the event-triggered thresholds to strive for better system performance. Then, by utilizing the 2-D Lyapunov stability theory, nonlinear matrix inequalities are built to ensure the asymptotic mean-square stability with an$H_{\infty }$performance for the closed-loop 2-D system. To avoid introducing any conservatism when handling the above nonlinear matrix inequalities, a binary-based genetic algorithm (BGA) is exploited to treat some variables as known, such that derive some directly solvable linear matrix inequalities. Finally, a simulation example is provided to verify the effectiveness of the proposed 2-D AETM-based asynchronous controller strategy with a BGA.

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.

To improve the autonomy of maritime search and rescue (SAR) operation, this paper concentrates on the formation control problem for autonomous surface vehicles (ASVs) with the limited communication resource. A novel parallel search guidance, considering the maneuvering characteristics of ASVs, is developed to guide the formation to execute the automatic SAR operation. That can guarantee that the corresponding guidance law is highly efficient, self-driving and suitable for the large-scale formation. Combined with the guidance principle, a formation control algorithm is proposed by fuse of the event-triggered control and neural networks (NNs). In the proposed scheme, the gain uncertainty of actuators is effectively compensated requiring no prior information around the model structure. Unlike the existing results, the proposed event-triggered mechanism can activate synchronously both the controller and the NNs weight estimator. Considerable effort has been made to guarantee the semi-global uniform ultimate bounded (SGUUB) stability. Finally, two examples are illustrated to verify the effectiveness of the algorithm.

This article presents a novel composite neural learning fault-tolerant algorithm to implement the path-following activity of underactuated vehicles with event-triggered input. With the input event-triggered mechanism, the dominant superiority is to reduce the communication burden in the channel from the controller to actuators. In the proposed scheme, the system uncertainties are dealt with in the fusion of the neural networks (NNs) and the dynamic surface control (DSC) method. The serial-parallel estimation model (SPEM) is constructed to estimate the error dynamics, where the derived prediction error could improve the compensation effect of the NNs. As for the gain uncertainties and the unknown actuator faults, four adaptive parameters are designed to stabilize the related perturbation and not be affected by the triggering instants. Based on the direct Lyapunov theorem, considerable efforts have been made to guarantee the semiglobal uniformly ultimately bounded (SGUUB) stability of the closed-loop system. Finally, comparison and practical experiments are illustrated to verify the superiority of the proposed algorithm.

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.

This article studies the model-based event-triggered control (ETC) for the tracking activity of the underactuated surface vessel (USV). Following this ideology, the continuous acquisition of states is no longer needed, and the communication traffic is reduced in the channel of sensor to controller. The control laws are fabricated in the frame of an adaptive model, which is renewed with the states of the original system whenever the triggering condition is violated. In the scheme, both internal and external uncertainties are approximated by the neural networks (NNs). To decrease the computing complexity, the minimum learning parameters (MLPs) are involved both in the adaptive model and the derived controller. The adaptive laws of only two MLPs are devised, and their updating only happens at triggering instants. Using the MLPs, an adaptive triggering condition is further derived. To avoid the "Zeno" phenomenon in small tracking errors, a dead-zone operator is designed for the triggering condition. Furthermore, we incorporate the dynamic surface control (DSC) into the controller design, such that the jumping of virtual control laws at triggering instants is smoothed and the problem of "complexity explosion" is circumvented. Through the techniques of the impulsive dynamic system and the direct Lyapunov function, the parameter setting for the DSC is derived to guarantee the semiglobal uniformly ultimate boundedness (SGUUB) of all the error signals in the closed-loop system. Finally, the effectiveness of the proposed scheme is validated through the simulation.

For leader-following multiagent systems with input saturation, the existing protocols use a low gain feedback approach to achieve semi-global consensus. The main drawback of this approach is the ineffective utilization of the actuator potential, resulting in bad performance. To improve the transient performance of the consensus tracking, this paper proposes a gain scheduled approach for multiagent systems subject to the saturator saturations. A novel kind of scheduler-based protocols are proposed, which consists of state feedback controllers with time-varying gain and parameter schedulers. The role of the controllers is to achieve the consensus tracking, while the schedulers can accelerate this consensus progress by enlarging the gain parameter. To remove the dependence of the schedulers on global information, a minimum-value-based consensus algorithm is put forward, with idea of driving all values of agents throughout the network to their minimum value. Its implementation is guaranteed by the network-topology connectivity. Finally, our approach is further extended to the case where the leader's control input is nonzero, time-varying, and bounded. The discontinuous protocol and its continuous approximation counterpart are designed, yielding the exactand quasi-consensus tracking, respectively. Simulation results verify the theoretical analysis.

This paper deals with the optimum dynamic positioning control problem for marine ships in the presence of actuator gain uncertainties and unknown environmental disturbances. The proposed approach is formulated as two modules, i.e., the guidance part and the control part. By utilizing the improved extremum seeking algorithm, the optimum-seeking guidance is developed in this note to generate the reasonable heading guidance for dynamic positioning ships. The main purpose of this design is to ensure the closed-loop system running efficiently and environment-friendly in practice. Combined with the proposed guidance principle, a robust neural control algorithm is developed based on the dynamic surface control, neural networks, and the robust neural damping technique. In this algorithm, the strong couplings of state variables and the gain uncertainty of actuators are tackled, and the system uncertainties are compensated requiring less (or no) information of the hydrodynamic structure, the actuator model and the external disturbances. Considerable effort is made to guarantee the semiglobal uniform ultimate bounded stability by employing the Lyapunov theory. The advantages of the proposed control scheme could be summarized as two points. First, the control approach is with the properties of optimization and energy-saving, which is meaningful for applying the theoretical algorithm. Second, the pitch ratio of thrusters is selected as the control inputs of interest, which is measurable in the practical plant. These characteristics would facilitate the implementation of the algorithm in engineering. Two examples are provided to verify the performance of the proposed scheme.