When a group of autonomous surface vehicles (ASVs) sail from a wide waterway to a narrow waterway, one difficulty is to keep relative formation with collision avoidance. Scaling the formation sizes with formation shapes invariant is a promising way. This article investigates such a formation scaling control problem of ASVs with uncertainties and input saturation. A novel bearing-based adaptive neural formation scaling control scheme for ASVs is developed. The main idea of this formation scheme is as follows. Choose a small number of leader ASVs based on bearing rigidity theory and program their trajectories according to the kinematics of formation scaling maneuver. Steer remaining ASVs to follow leader ASVs via adaptive neural techniques and the formation sizes can be scaled only by leaders without redesigning control inputs of followers. To deal with the uncertainties of ASVs, weights updating of neural networks is simplified into one-parameter estimation in each control channel. Auxiliary systems are introduced for each ASV to reduce the effect of limited actuator capability. It is shown that desired formation scaling maneuver of ASVs can be achieved with the proposed formation scheme if the augmented formation is infinitesimally bearing rigid. Formation errors are guaranteed to be uniformly ultimately bounded. The main advantage of our scheme over existing results is that directional, computational, and actuator constraints are satisfied simultaneously in the formation scaling control of ASVs. Simulations and comparisons are provided to illustrate the effectiveness of theoretical results.

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 paper investigates the leader–follower formation problem of underactuated surface vessels. Velocities of both leader and follower vessels are unavailable. Model uncertainties and ocean disturbances are also considered. By incorporating adaptive control, neural networks (NNs), the high-gain observer (HGO) and the minimal learning parameter (MLP) algorithm in the backstepping procedure, a novel adaptive output-feedback formation control scheme is developed. We show that formation errors can be guaranteed to be semiglobally uniformly ultimately bounded (SGUUB) with the proposed controller. Compared with existing methods, the formation can be achieved only with position and yaw angle of both leader and follower. Meanwhile, the developed scheme can enhance the robustness of the closed-loop system with less computational effort, where only two online parameters need to be tuned. Simulation and comparison results are provided to illustrate the effectiveness of theoretical results.