For fully actuated marine surface vessels (MSVs) with model uncertainties, unknown external disturbance, and unmeasurable velocity, this paper proposes an adaptive non-singular fast terminal sliding mode (NFTSM) finite-time trajectory tracking control scheme based on an improved nonlinear disturbance observer (INDO) and radial basis function neural networks (RBFNN). Firstly, this paper proposes a novel form of second-order nonlinear tracking differentiator (NTD) to generate more accurate virtual position and velocity signals. Then, this paper designs an INDO for more accurate estimation and compensation of unknown external disturbance problems. By combining the NTD-based virtual signals with desired signals, an NFTSM surface is constructed to accelerate system convergence and improve tracking accuracy. In addition, this paper designs RBFNN to approximate model uncertainties with added estimation velocity error. Furthermore, an improved adaptive law is used to estimate the upper bound of neural networks approximation error and INDO error to enhance the system’s adaptability to complex uncertainties and improve the robustness of the system. Lyapunov stability analysis shows that all system signals remain globally bounded in finite time. Finally, the comparative simulation verifies the accuracy and superiority of the proposed scheme (INDO-RBFNN-ANFTSMC).
Zheng et al. (Wed,) studied this question.