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This article researches the problem of trajectory tracking for unmanned marine vehicles (UMVs) under disturbances and denial‐of‐services (DoS) attacks in the wireless channel. An equivalent data‐driven model of UMVs with ocean disturbances is established by using partial form dynamic linearization algorithm. The disturbances and the input of UMVs have different pseudo partitioned Jacobean matrix, and the disturbances are estimated by using extended state observer, which improves the immunity of UMVs to disturbances in the environment. It is the first time that the DoS attacks are considered under the data‐driven model for UMVs, and a novel data‐driven adaptive trajectory tracking control framework is constructed. The article proposes an attack predictive compensation mechanism to mitigate their effects of DoS attacks, which follows the Bernoulli distribution. Based on it, the data‐driven adaptive trajectory tracking controller is designed such that the error of trajectory tracking is convergent under DoS attacks and external disturbances. Finally, the effectiveness of the proposed data‐driven control scheme and the predictive compensation mechanism is validated through the simulations. [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 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]