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.

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.

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 paper investigates the adaptive fuzzy outputfeedback path-following control of autonomous surface vessels (ASVs) via the event-triggered mechanism, in which the fuzzy logic systems (FLSs) are employed to approximate the uncertainties. By transforming the estimation errors, an adaptive fuzzy observer is established to estimate the kinematic states of the ASV, and a serial-parallel estimation model is fabricated based on this observer. By combining the tracking errors with the prediction errors between the observer and the serial-parallel estimation model, the composite adaptive laws are devised for the weights of the FLSs. Then, the event-triggered controller is developed in the channel of sensor to controller, in which the estimated kinematic states, the positional coordinates and the heading angle of the ASV are sampled only when the triggering condition is violated. In this paper, we put forward the problem of jumps of virtual control laws at the triggering instants, which commonly exists in the backstepping-based event-triggered control (ETC) in the channel of sensor to controller. To solve this problem, the dynamic surface filters are employed to generate the continuous substitutes for the virtual control laws. The proposed scheme can guarantee at least the similar control performance compared with its continuous alternatives while it importantly reduces the communication burden through the ETC, and can enhance the estimation accuracy of the observer by applying the composite learning. All the errors are ensured to be semiglobally uniformly ultimately bounded (SGUUB) in the closedloop system. Finally, the effectiveness of the proposed scheme is verified through the numerical experiment.