In this paper, the formation control of unmanned surface vehicles (USVs) is addressed considering actuator saturation and unknown nonlinear items. The algorithm can be divided into two parts; one is steering the leader USV to trace along the desired path and the other is steering the follower USV to follow the leader in the desired formation. In the proposed formation control framework, a virtual USV is first constructed so that the leader USV can be guided to the desired path. To solve the input constraint problem, an auxiliary is introduced, and the adaptive fuzzy method is used to estimate the unknown nonlinear items in the USV. To keep the desired formation, the desired velocities of follower USVs are deduced by using geometry and Lyapunov stability theories. The stability of the closed-loop system is also proved. Finally, the effectiveness of the proposed approach is demonstrated by the simulation and experimental results.

This article presents a prescribed-time tracking control method for underactuated unmanned surface vessels (USVs) using a neural reinforcement learning (RL) approach. First, the hand position approach, addressing the underactuated characteristic, is employed to convert the model of USV into the integral cascade form. Second, inheriting the advantages of prescribed performance control (PPC), the proposed controller not only stabilizes the tracking error within an asymmetric prescribed-time range, but also removes the limitation of initial conditions. Subsequently, the identifier—actor-critic architecture is introduced in the optimized backstepping design, which gives the solution of the Hamilton–Jacobi–Bellman (HJB) equation. Meanwhile, the relative threshold event-triggered mechanism is also considered to reduce the communication burden and executive frequency of actuators. Finally, employing the Lyapunov stability theory, it is proven that all signals in the closed-loop system are bounded, and the developed control scheme is demonstrated to be effective through simulation and experimental results.

While sailing along an expected route, unmanned surface vehicles (USVs) may encounter the static or dynamic obstacles. Therefore, from the perspective of safety, USVs must have the ability of automatic collision avoidance. In this paper, an obstacle avoidance and path point tracking control frame for USVs is proposed. The frame includes two parts. One is the decision-making module, in which the desired sailing speed and course angular velocity are generated. More specifically, through adopting the velocity obstacle approach (VOA), the feasible obstacle avoidance action set is obtained. Then, considering the maneuvering characteristics of USVs, the discrete optional obstacle avoidance actions are obtained by using the dynamic window approach (DWA). Finally, by introducing the International Regulations for Preventing Collisions at Sea (COLREGs), the obstacle avoidance actions that meet the rules can be screened out. An evaluation function is designed to select the final practical obstacle avoidance action. The second part is the dynamic controller module. A radial basis function (RBF) neural network-based path point tracking controller design is given in this part. The RBF neural network is designed to estimate the unknown model nonlinearity, and the stability of the closed-loop system is proved. Finally, simulations and experiments are carried out to illustrate the effectiveness of the presented algorithm.

As underwater operation equipment, autonomous underwater vehicles (AUVs) have received widespread attention in the trend of unmanned and intelligent operations. They play an increasingly important role in offshore oil exploration, underwater rescue, military reconnaissance, seabed salvage, and marine scientific research. The research and application of AUVs is a systematic engineering project that includes task planning, scheme design, prototype development, and launch and recovery (L&R), among which L&R is the key technology to achieve the safe and continuous operation of AUVs.