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This article proposes a robust trajectory tracking controller for underactuated unmanned surface vehicles under disturbances. An adaptive integral terminal super‐twisting algorithm is designed for guidance and control, following a cascade strategy. The properties of such algorithm include practical finite‐time (PFT) convergence, robustness against bounded disturbances/uncertainties with unknown boundaries, and chattering attenuation via adaptive gains. A stability analysis is addressed to prove the PFT stability of the overall system. Moreover, the vehicle is subject to wind, waves, and water currents disturbance models to verify the robustness with numerical simulations. Furthermore, real‐time experiments under an extra payload and environmental perturbations with unknown boundaries validate the performance with a prototype. Finally, a comparison against the adaptive super‐twisting algorithm with conventional sliding surface further demonstrates the advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

In order to construct an efficient and reliable global path for unmanned surface vehicles (USVs), this paper proposes a USV global path planning algorithm based on an optimized ant colony algorithm (OACA). The algorithm constructs a USV global path planning model by comprehensively considering energy consumption costs and turning control costs. In order to ensure the adaptability of the model to the task environment, the USV's motion characteristics are analyzed with full consideration of external interference, and on this basis, the characterization of energy consumption costs and turning control costs is completed. In order to obtain the global optimal solution of the model and accelerate the convergence speed of the model, in the initialization process of the ant colony algorithm, the initial pheromone is distributed unevenly by introducing the distance relationship among the intermediate node, the starting point, and ending point to improve the initial search efficiency. In the iterative process of searching for the optimal solution, enhancing the guidance effect of the current optimal solution on the offspring ants by introducing a weight factor to improve the update rule of pheromone, which accelerates the convergence speed of the algorithm. In the last two steps of the deadlock path, the number of lost ants is reduced by introducing a penalty factor to punish the pheromone which can guarantee the diversity of ants, and the algorithm's premature convergence is overcome. The simulation results prove the effectiveness of this algorithm. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this article, subject to both pose and velocity constraints within narrow waters, a self‐learning‐based optimal tracking control (SLOTC) scheme is innovatively created for an unmanned surface vehicle (USV) by deploying actor‐critic reinforcement learning (RL) mechanism and backstepping technique. To be specific, the barrier Lyapunov function (BLF) is devised to uniformly limit the states within a predefined region pertaining to a smoothly feasible reference trajectory. By virtue of a constrained Hamilton–Jacobi–Bellman (HJB) function, an actor‐critic control structure under backstepping is established by employing adaptive neural network identifiers which recursively updates actor and critic, simultaneously. Eventually, theoretical analysis proves that the entire SLOTC scheme can render all the states remain in the predefined compact set while tracking errors converge to an arbitrarily small neighborhood of the origin. Simulation results on a prototype USV demonstrate remarkable effectiveness and superiority. [ABSTRACT FROM AUTHOR]

This paper is concerned with the path following control for an underactuated unmanned surface vehicle (USV) subject to modeling error and input saturation under time‐varying external disturbances. In this note, a novel trajectory linearization control (TLC) method is first introduced in the design of path following controller for an unmanned surface vessel. Adaptive line‐of‐sight (LOS) algorithm is used in the navigation strategy, which not only solves the sideslip angle problem caused by external disturbances but also optimizes the convergence speed. The neural network minimum learning parameter (MLP) method is used to compensate for modeling errors and external disturbances. Compared with multi‐layer neural networks, MLP solves the problem of 'dimension disaster'. Meanwhile, in order to increase the robustness of the system, adaptive technology is employed to compensate for the compensation error of MLP. In addition, an auxiliary dynamic system is introduced into the controller design to address potential input saturation issue. Considering the above practical conditions, it is more convenient for the engineering implementation of the proposed control path following the control system. Using Lyapunov stability analysis theory, it is proven that all error signals in the system are uniformly ultimately bounded. Finally, two simulation experiments are presented to demonstrate the feasibility of the proposed control strategy. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposed a novel disturbance observer‐based control state‐error port‐controlled Hamiltonian (DOBC‐SEPCH) control strategy for optimization of energy consumption and enhancement of tracking performance for unmanned surface vehicle (USV) with unknown environmental disturbances via the PCH system techniques. Firstly, an observer is constructed to estimate disturbances. Then, an energy‐based controller is constructed by using the SEPCH system method. In addition, the SEPCH controller provided that the tracking errors converged exponentially to zero. The SEPCH technique is augmented by a disturbance observer. Due to the controller need to be designed in two stages, the stability of the whole system will be difficult to be discussed. We give the proof of the stability of the desired target dynamic system. The robustness and control performance of the system are enhanced. The simulation and comparison results illustrate the performance of this controller. [ABSTRACT FROM AUTHOR]