Robust adaptive fuzzy control (RAFC) for ship steering with uncertain nonlinear systems

A novel robust adaptive fuzzy control algorithm is presented for ship steering with an uncertain system and a gain function, which are all the unstructured (or non-repeatable) state-dependent unknown nonlinear functions. The Takagi-Sugeno type fuzzy logic systems are used to approximate uncertain functions and the RAFC algorithm is designed by the use of input-to-state stability (ISS) approach and small gain theorem. The closed-loop system is proven to be semi-globally, uniformly, ultimately bounded. In addition, the possible controller singularity problem in some of the existing adaptive control schemes met with feedback linearization techniques can be removed and the adaptive mechanism with only one learning parameterization can be achieved. The proposed methodology, which is applied to design a ship steering autopilot to maintain the ship on a pre-determined heading, is verified using the simulation mode of Dalian Maritime University's ocean-going training ship, 'Yulong'. Simulation results show the effectiveness of the control scheme.