In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a series of auxiliary variables, and after linearly parameterizing the USV dynamic model, a parameter adaptive update law is developed based on Lyapunov’s second method to estimate unknown dynamic parameters in the USV dynamics model. This parameter adaptive update law enables online identification of all USV dynamic parameters during trajectory tracking while ensuring convergence of the estimation errors. Second, a radial basis function neural network (RBF-NN) is employed to approximate unmodeled dynamics in the USV system, and on this basis, a robust damping term is designed based on neural damping technology to compensate for environmental disturbances and unmodeled dynamics. Subsequently, a trajectory tracking controller with parameter adaptation law and robust damping term is proposed using Lyapunov theory and adaptive control techniques. In addition, finite-time auxiliary variables are also added to the controller to handle the actuator saturation problem. Signal delay compensators are designed to compensate for input signal delays in the control system, thereby enhancing controller reliability. The proposed controller ensures robustness in trajectory tracking under model uncertainties and time-varying environmental disturbances. Finally, the convergence of each signal of the closed-loop system is proved based on Lyapunov theory. And the effectiveness of the control system is verified by numerical simulation experiments.

In this study, an effective parametric 3D geometry model of a propeller was established with the aid of reverse engineering. The goal is to reduce the free parameters while automating the modelling of the propeller. The process of building the parametric model begins by generating an initial point cloud by defining the feature matrix associated with the propeller blade profile shape. Subsequently, the initial point cloud is deformed and refined by the deformation feature matrix and resampling. Finally, a 3D geometry model of the propeller is generated by surface reconstruction. The model can be built automatically by interactively modifying the feature matrices. Two numerical analyses illustrate the performance of the parametric 3D geometry model. Specifically, two propellers are constructed using the proposed model to estimate the shape error between the reconstructed propellers and the original offset of the propellers. These propellers are selected as research objects to determine the hydrodynamic performance error between the propeller constructed by the proposed model and a benchmark propeller. According to the results of the numerical study, the parametric 3D geometry model can precisely reconstruct the aforementioned geometry within a valid error range. The hydrodynamic error analysis demonstrates that the geometric inaccuracy from the reconstructed model has less impact on the propeller performance. This indicates that the model described in this study is generalised and robust. Moreover, some uncommon propeller CAD models were generated in batches using the parametric 3D geometry model.

Data-driven evolutionary algorithms (DDEA) have proven to be highly effective in solving expensive and computationally intensive problems. However, offline DDEA heavily relies on the availability of historical data. Additionally, existing methods that construct a single surrogate model are well-suited for specific issues but may not generalize well to others. Therefore, this paper proposes a data-driven evolutionary optimization method with clustering-aided ensemble learning and Taylor polynomial-based data generation (CELDDEA-TPDG). In CELDDEA-TPDG, we incorporate a data synthesis mechanism based on the Taylor polynomial. This method is inspired by the difference approach in the finite element method (FEM). By leveraging the first-order difference scheme to assist in derivative estimation and utilizing the principles of the Taylor polynomial, synthetic data are generated within the neighborhood of historical data. Furthermore, a clustering module is incorporated into the ensemble learning process to selectively filter base learners, thereby enhancing the performance of the ensemble learning model. To evaluate the performance of CELDDEA-TPDG, we first constructed multiple variants of the CELDDEA-TPDG method to investigate the effectiveness of its components. Furthermore, we demonstrated the advantages of CELDDEA-TPDG by comparing it with ten other methods (both traditional and State-of-the-art (SOTA)) on benchmark functions. The results indicate that, compared with the six other state-of-the-art algorithms, CELDDEA-TPDG consistently ranks first with higher stability. Finally, in a real-world hydrodynamic performance optimization problem, we compared CELDDEA-TPDG with the classical DDEA-SE method to illustrate its superiority in solving practical engineering problems. The results show that CELDDEA-TPDG can achieve better performance. The data used in this work is available at https://github.com/Tenhree/Marine-Propeller-Data.git.