Unmanned surface vessels (USVs) are supposed to be able to adapt unstructured environments by means of multi-sensor active perception without any human interference, and high-accuracy path following is achieved for USVs by effective control strategies and intelligent devices of e-navigation. This paper proposes a finite-time predictor line-of-sight (LOS)-based integral sliding-mode adaptive neural (FPISAN) scheme for the path following of USVs in the presence of unknown dynamics and external disturbances, which copies with the problem of merging with the kinematic level and the kinetic level of USVs. From the point of view of USVs' practical engineering, the inertia matrix of USVs maintains nonzero off-diagonal. In order to ensure that USVs can converge to and follow a defined path, a novel LOS-based guidance law that can acquire sideslip angles by error predictors within a finite time is presented, called finite-time predictor-based LOS (FPLOS). Then, the path-following control laws are designed by combining the neural network (NN) technique with the integral sliding-mode method, where radial basis function NN (RBFNN) is applied to approximate lumped unknown dynamics induced by nonparametric uncertainties and external disturbances. The theoretical analysis verifies that the path-following guidance-control system of USVs is semiglobally uniformly ultimately bounded (SGUUB) with the aid of Lyapunov stability theory. The effectiveness and performance of this presented scheme are illustrated by simulation experiments with the comparison.

In this article, a finite-time line-of-sight-based control (FTLC) scheme is presented for path following of unmanned surface vessels (USV) subject to unknown time-varying sideslip angles and input saturation as well as uncertainties induced by unknown internal dynamics and external disturbances. Specifically, finite-time observers (FTOBs) are introduced to acquire the sideslip angle and kinetic uncertainties. Finite-time line-of-sight guidance is developed by incorporating the FTOB and fractional power techniques. The profile of desired surge velocity is designed based on the hyperbolic tangent function of cross-track error. Nonlinear feedback is then introduced to attenuate the overshoot of surge velocity. Finite-time stability auxiliary systems are designed using fractional power to compensate for actuator constraints with finite-time convergence. The proposed FTLC scheme enables a USV to reach and follow a predefined path while satisfying spatial and dynamical specifications and obtaining finite-time stability and small overshoot in the presence of actuator limitations and uncertainties. All errors in the closed-loop system of USV converge to an arbitrarily small neighborhood of the origin within a finite settling time, which still holds in the presence of unpredictable errors. Three USV cases and ten simulation groups are included to demonstrate the effective performance of the FTLC.

In the presence of unknown dynamics and input saturation, a finite-time predictor line-of-sight–based adaptive neural network scheme is presented for the path following of unmanned surface vessels. The proposed scheme merges with the guidance and the control subsystem of unmanned surface vessels together. A finite-time predictor–based line-of-sight guidance law is developed to ensure unmanned surface vessels effectively converging to and following the referenced path. Then, the path-following control laws are designed by combining neural network-based minimal learning parameter technique with backstepping method, where minimal learning parameter is applied to account for system nonparametric uncertainties. The key features of this scheme, first, the finite-time predictor errors are guaranteed; second, designed controllers are independent of the system model; and third, only required two parameters update online for each control law. The rigorous theory analysis verifies that all signals in the path-following guidance-control system are semi-globally uniformly ultimately bounded via Lyapunov stability theory. Simulation results illustrate the effectiveness and performance of the presented scheme.

To reduce the fuel consumption of ships’ oceanic voyages, this study incorporates the influence of ocean currents into the traditional level set algorithm and proposes a route planning algorithm capable of identifying energy-efficient routes in complex and variable sea conditions. The approach introduces the influence factor of ocean current to optimize routing in dynamically changing marie environments. First, models for the energy consumption of ships and flow fields are established. The level set curve is then evolved by integrating the flow environment and energy consumption gradient, solving the Hamilton–Jacobi equation with energy consumption parameters. The optimal path is subsequently determined through backtracking along the energy consumption gradient, enabling energy-efficient route planning from the starting point to the endpoint in complex ocean conditions. To verify the effectiveness of the proposed algorithm, its performance is evaluated through two case studies, comparing energy consumption under different environmental conditions. The experimental results demonstrate that, compared to the shortest path method based on the level set algorithm, the proposed approach achieves an energy saving rate of approximately 2.1% in obstacle-free environments and 1.4% in environments with obstacles.