Autonomous takeoff for unmanned seaplanes via fuzzy identification and generalized predictive control

Abstract

Autonomous takeoff and landing on water, unattended long-term operation capability are the typical characteristics of unmanned seaplanes. As the hydrodynamic forces estimation for unmanned seaplanes is very complicated and sea states are severe, the researches on the modeling, dynamic analysis and controller design are still a great challenge. In this paper, based on the nonlinear mathematic model of the unmanned seaplane, a design methodology via fuzzy identification and generalized predictive control (GPC) is proposed, aiming to improve the sea-keeping ability and avoid the unstable phenomenon in high sea states. A discrete-time model using T-S fuzzy identification is constructed according to the dynamic characteristics in different motion stages, and then GPC algorithm with wave forecasting is applied to achieve autonomous takeoff for the unmanned seaplane. The simulation results show that the proposed approach is capable of making the unmanned seaplane take off successfully with satisfactory performances in three different wave conditions.