A robust passive non‐linear observer, utilizing the sliding mode concept and acceleration feedback (AFB) technique, is developed for ships. The main advantage of the proposed observer is that it is robust and that it takes the Coriolis‐centripetal matrix (C‐matrix) into account. The observer reconstructs velocities of ships and bias from slowly varying environmental disturbances. It also filters out the noise and wave frequency data from measurements to protect the actuators from wear and excessive fuel consumption. The sliding mode technique is introduced to improve robust performance against neglected disturbances, uncertainties, and unmodeled dynamics. The acceleration feedback technique and coordinate transformation are used for reshaping the inertia matrix and removing the C‐matrix from the mathematical model. Then, the observer design and stability analysis become simpler. An output feedback controller using observer backstepping and the Lyapunov redesign technique is derived, and the global stability of the observer and observer‐controller system is shown by Lyapunov stability theory. A set of simulations was carried out to verify the performance of the proposed observer and controller.

This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.

This paper address the problem of trajectory tracking control of an USV based on nonlinear adaptive observer using dynamic recurrent fuzzy neural network (DRFNN). In order to control an underactuated surface vehicle (USV) efficiently, knowledge about the position, velocity and attitude of the USV is needed. For low-cost USV, the sensor suit can only provide measurements of position and yaw. The proposed observer can estimates the unknown nonlinear terms in the USV's dynamics without exact knowledge about parameter of Coriolis-centripetal matrix and nonlinear damping matrix. The adaptive observer scheme is proved to be uniformly ultimately bounded. Furthermore, a new sliding manifold definition with exponentially stable combination of the conventional manifold and it derivation is presented. The new sliding model control algorithm guarantees the fast convergence rate and stability in tracking as well as robust against observer's estimation error. Simulation results demonstrate the effectiveness of the proposed approaches.

No abstract

No abstract