This article investigates the distributed affine formation maneuver control for collaborative autonomous surface vehicles (ASVs) systems with the event-triggered data transmission mechanism among inter-ASVs. To achieve preset arbitrary configuration transformations in the case where the formation knowledge is assigned to only a part of ASVs, the affine image is employed to describe desired configurations. To ensure position and attitude configuration rigidities simultaneously, a synthetical constraint composed of the Laplacian matrices and the stress matrices is constructed. Besides, an algebraic relation between the synthetical constraint and the formation tracking error is established. The above tools enable the sliding mode technology to be used to the affine formation maneuver control of collaborative ASVs systems. It follows that a novel distributed event-triggered affine formation maneuver control scheme is developed for the collaborative ASVs systems. The advantages of the proposed control scheme are threefold: 1) the distributed affine formation maneuver control of collaborative second-order vehicles systems is achieved with changeable formation velocities and orientations; 2) the existing literatures adapt to developing the second-order distributed affine formation maneuver control based on the backstepping philosophy. In contrast, the sliding manifold replaces the backstepping philosophy here. Since the complexity explosion is circumvented, the proposed control scheme is relatively simple; and 3) the event-triggered data transmission mechanism among inter-ASVs is introduced, and neighbor velocities and accelerations are omitted for the control implementation. As a consequence, the communication resource usage can be reduced. Finally, the semi-physical simulations are conducted to illustrate the effectiveness of the proposed control scheme.

This article addresses the distributed formation control issue of cooperative unmanned surface vessels (USVs) under interleaved periodic event-triggered communications. First, an adaptive event-based control protocol is designed, where the event-based neural network (NN) scheme is developed to compensate for uncertain model dynamics. Upon the designed control protocol, an interleaved periodic event-triggered mechanism (IPETM) is subsequently proposed to achieve the communication objective. Unlike the common continuous event-triggered methods and periodic event-triggered methods, in which multiple nodes are allowed to trigger their events at the same time, the proposed IPETM ensures that USVs detect their events at different times to avoid the simultaneous event triggering of different nodes. By this virtue, traffic jamming in common wireless environments can be prevented, such that potential communication delays and faults are naturally avoided. In addition, the event detecting instants of the presented IPETM are also discrete and periodic, such that it can be performed under low-computational frequencies. Through Lyapunov-based analysis, it is verified that all closed-loop signals can converge to an arbitrary small compact set with exponential convergence rates. Simulation results demonstrate the effectiveness and superiority of the proposed control scheme.

In this article, we address the distributed optimal coordinated control (DOCC) problem for unmanned surface vehicles (USVs) suffering from limited network channels and communication interruptions. For networked USVs, ignoring network limitations can cause serious sailing incidents. Therefore, we developed a DOCC design with a limited number of communication channels and a restricted communication range. The proposed optimal control system includes two subsystems: coordinated trajectory optimization and local trajectory tracking control. The first employs a delicately designed auxiliary system to determine the optimal solution for each USV. To address the scarcity and intermittency of communication channels, we also develop an intermittent interleaved periodic event-triggered mechanism (IIPETM) in the auxiliary system. The second subsystem adopts a neural network-based backstepping approach to achieve the tracking objective. Compared with the existing methods, the developed algorithm can interleave the triggering instants for different USVs and ensure system stability, even under communication interruptions. We demonstrate the effectiveness and superiority of the proposed scheme through numerical simulations of USVs.