In this brief, an adaptive robust finite-time tracking control (ARFTTC) scheme for trajectory tracking of a fully actuated marine surface vehicle with unknown disturbances is proposed. A new finite-time disturbance observer is incorporated into the proposed finite-time tracking control (FTTC) structure that facilitates faster convergence and better robustness to disturbances. Hence, in the presence of unknown disturbances, the ARFTTC can cause tracking error to converge to zero in a finite time. Simulation studies and comprehensive comparisons with conventional backstepping technique demonstrate remarkable performance and superiority of the ARFTTC in terms of both tracking accuracy and robustness.

In this article, a novel reinforcement learning-based optimal tracking control (RLOTC) scheme is established for an unmanned surface vehicle (USV) in the presence of complex unknowns, including dead-zone input nonlinearities, system dynamics, and disturbances. To be specific, dead-zone nonlinearities are decoupled to be input-dependent sloped controls and unknown biases that are encapsulated into lumped unknowns within tracking error dynamics. Neural network (NN) approximators are further deployed to adaptively identify complex unknowns and facilitate a Hamilton-Jacobi-Bellman (HJB) equation that formulates optimal tracking. In order to derive a practically optimal solution, an actor-critic reinforcement learning framework is built by employing adaptive NN identifiers to recursively approximate the total optimal policy and cost function. Eventually, theoretical analysis shows that the entire RLOTC scheme can render tracking errors that converge to an arbitrarily small neighborhood of the origin, subject to optimal cost. Simulation results and comprehensive comparisons on a prototype USV demonstrate remarkable effectiveness and superiority.

An unmanned surface vehicle (USV) under complicated marine environments can hardly be modeled well such that model-based optimal control approaches become infeasible. In this article, a self-learning-based model-free solution only using input–output signals of the USV is innovatively provided. To this end, a data-driven performance-prescribed reinforcement learning control (DPRLC) scheme is created to pursue control optimality and prescribed tracking accuracy simultaneously. By devising state transformation with prescribed performance, constrained tracking errors are substantially converted into constraint-free stabilization of tracking errors with unknown dynamics. Reinforcement learning paradigm using neural network-based actor–critic learning framework is further deployed to directly optimize controller synthesis deduced from the Bellman error formulation such that transformed tracking errors evolve a data-driven optimal controller. Theoretical analysis eventually ensures that the entire DPRLC scheme can guarantee prescribed tracking accuracy, subject to optimal cost. Both simulations and virtual-reality experiments demonstrate the remarkable effectiveness and superiority of the proposed DPRLC scheme.

In this paper, accurate trajectory tracking problem of a surface vehicle disturbed by complex marine environments is solved by creating a finite-time control (FTC) scheme whereby the nonsingular fast terminal sliding mode (NFTSM) and finitetime disturbance observer (FDO) techniques are deployed. Salient features are as follows: (1) By devising an FDO, complex disturbances arising from marine environments can be exactly identified within a short time, in conjunction with the defined NFTSM manifold, and thereby contributing to exact trajectory tracking; (2) Finite-time disturbance observation is completely decoupled from the nominal FTC scheme, and achieves stronger disturbance rejection and faster transient response, simultaneously; (3) Both disturbance observation and trajectory tracking errors can exactly reach to zero in a finite time. Simulation results and comparisons are comprehensively conducted on CyberShip II and demonstrate remarkable superiority of the FTC scheme.

In this paper, a novel ship domain model termed quaternion ship domain (QSD) is proposed. Unlike other ship domains, the proposed QSD is more dependable and more flexible for navigators to use to make decisions. The main characteristics are that: the domain size is determined by the quaternion including four radii, i.e. fore, aft, starboard and port, which sufficiently take factors affecting the domain (i.e. ship manoeuvring capability, speeds and courses, etc.) into account; and that the domain shape is modelled by another parameter which makes the QSD more flexible since the ship boundary could not only be linear or nonlinear, but also be thin or fat. In order to reasonably relate the proposed QSD to practical applications, i.e. collision risk assessment, collision avoidance and trajectory planning, etc., a fuzzy QSD (FQSD) has been developed by using fuzzy sets. As a result, fuzzy boundaries rather than crisp ones in the FQSD are more practical and more convenient for navigators to understand and judge since uncertainty and fuzzy information have been merged into the FQSD. Furthermore, concepts of longitudinal and lateral risk based on the FQSD have been introduced to estimate the spatial collision risk (SCR) for the ships encountered. Finally, several computer simulations have been conducted on various encounter situations and comparative studies with other ship domains have been comprehensively analyzed. Simulation results demonstrate that the proposed QSD is more effective and more flexible than other ship domains, and that the intelligent SCR based on the FQSD are reasonable and dependable.

In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.