By deploying unmanned surface vehicles (USVs), the efficiency and reliability of mission execution can be improved. This paper is concerned with the problem of formation autonomous navigation system (FANS) for USVs. The decision layer of the system is composed of path planning subsystem and navigation control subsystem to realize fast and effective autonomous navigation system. The FANS is constructed by leader-follower structure and distributed control strategy, which make the individuals in the formation have certain autonomy. The dynamic domain tunable fast marching square algorithm proposed in the path planning subsystem can not only adjust the safety and length of the path planned, but also continuously re-plan the path according to the motion information of the USV formation and target-vessels. Navigation control subsystem based on finite control set model predictive control can quickly and safely guide and control formation in local sea environment. The simulation tests are carried out in static and dynamic harbor environments respectively, which verifies the ability of the FANS to achieve stable formation tracking and autonomous safe navigation.

In the presence of modeling uncertainties and input saturation, this paper proposes a practical adaptive sliding mode control scheme for an underactuated unmanned surface vehicle (USV) using neural network, auxiliary dynamic system, sliding mode control and backstepping technique. First, the radial basis function neural network with minimum learning parameter method (MLP) is constructed to online approximate the uncertain system dynamics, which uses single parameter instead of all weights online learning, leading to a reduction in the computational burdens. Then a hyperbolic tangent function is adopted to reduce the chattering phenomenon due to the sliding mode surface. Meanwhile, the auxiliary dynamic system and the adaptive technology are employed to handle input saturation and unknown disturbances, respectively. In addition, a neural shunting model is introduced to eliminate the “explosion of complexity” problem caused by the backstepping method for virtual control derivation. The stability of the closed-loop system is guaranteed by the Lyapunov stability theory. Finally, simulations are provided to validate the effectiveness of the proposed control scheme.

This paper presents a complete scheme for research on the three degrees of freedom model and response model of the vector propulsion of an unmanned surface vehicle. The object of this paper is “Lanxin”, an unmanned surface vehicle (7.02 m × 2.6 m), which is equipped with a single vector propulsion device. First, the “Lanxin” unmanned surface vehicle and the related field experiments (turning test and zig-zag test) are introduced and experimental data are collected through various sensors. Then, the thrust of the vector thruster is estimated by the empirical formula method. Third, using the hypothesis and simplification, the three degrees of freedom model and the response model of USV are deduced and established, respectively. Fourth, the parameters of the models (three degrees of freedom model, response model and thruster servo model) are obtained by system identification, and we compare the simulated turning test and zig-zag test with the actual data to verify the accuracy of the identification results. Finally, the biggest advantage of this paper is that it combines theory with practice. Based on identified response model, simulation and practical course keeping experiments are carried out to further verify feasibility and correctness of modeling and identification.

This paper presents an adaptive backstepping sliding mode tracking control method for underactuated unmanned surface vehicle. The tracking controller is designed by combining backstepping and adaptive sliding mode control technology. The virtual control law is used to replace the control strategy of position error, and the position tracking control is transformed into velocity control, which can effectively avoid the input saturation and singular value problem existing in the design of traditional backstepping control law. The neural shunt model method is used to solve the differential explosion problem caused by virtual control law. The adaptive technology based on backstepping sliding mode theory is introduced to compensate the model uncertainty and time-varying disturbances of the system, and the robustness of the underactuated USV is enhanced by this method in the unknown environment. Based on the Lyapunov stability theory, it is proved that the control system error is ultimately uniformly bounded. The simulation results show that the proposed adaptive backstepping sliding mode tracking control method can achieve stable tracking in the case of system parameter uncertainty and time-varying disturbances, so it can be concluded that this method is reasonable and effective.

This paper addresses three related issues concerning the path following control of a podded propulsion unmanned surface vehicle (USV), namely modeling, guidance and control. The pod is different from the general propeller-rudder propulsion device, and its essence is a vector thruster. Therefore, first, through various assumptions and simplification, the three-degree of freedom (DOFs) planar motion model of the podded propulsion USV is established. Then, the classical line-of-sight (LOS) guidance strategy is improved by adaptive sideslip angle and a time-varying lookahead distance. Based on the guidance system, the corresponding controllers for yaw rate and surge speed are presented, which are combined by backstepping technology, the neural network minimum parameter learning method and the neural shunting model. Specifically, the neural network minimum parameter learning method is proposed to compensate the uncertainty of the model and the immeasurability of external disturbances, and the neural shunting model is employed to cope with the “explosion of complexity” problem of backstepping. Meanwhile, an auxiliary dynamic system is introduced to prevent actuator saturation (input saturation). All error signals of the system are proven to be uniformly ultimately bounded (UUB) by employing Lyapunov stability theory. Finally, two numerical simulations are given to prove the correctness of the proposed scheme.

This paper deals with the problem of formation collision avoidance for unmanned surface vehicles (USVs). Compared with the general ship formation, the formation collision avoidance system (FCAS) need better responsiveness and stability because of faster speed and smaller volume for USVs. A method based on finite control set model predictive control (FCS-MPC) is proposed to solve this problem. The novelty of the method is that it can control formation quickly to avoid obstacles and reach the destination in accordance with the dynamics of each vessel in the formation, without the prior knowledge of the environment and reference trajectory. The thruster speed and propulsion angle of the USV form a finite control set, which is more practical. The FCAS adopts the leader-follower structure and distributed control strategy to ensure that the Followers have a certain autonomy. The first two simulation tests verify that the system has the formation stability, formation forming ability and the applicability in restricted water. The last simulation test shows that the system can control the USV formation to sail quickly and safely in complex sea scenarios with formation transformation task and multiple dynamic obstacles.