A State Observer-based Robust Control Approach for Surface Vessel Using Fractional-order Calculus and Neural Network

The tracking problem in surface vessels is one of the prominent tasks that need to be thoroughly resolved to ensure ships operate safely and stably in unstructured environments and sensor failures. This paper proposes a state observer based on a robust trajectory controller for surface vessels suffering from disturbances. The robust controller is constructed by integrating fractional-order terms into the hierarchical sliding surfaces to stabilize the system. To minimize the installation costs of sensors and simplify the complexity of the setup and calibration of the control system, we design a state observer using neural network theory to approximate velocity of the vessel. The stability of the robust controller under the influence of time-varying disturbances and state observer is analyzed based on Lyapunov stability theory. Furthermore, numerical simulation results validate the effectiveness of our proposed controller in improving the vessel's performance under time-varying disturbances.