Predefined-time Neural Sliding Mode Control based Trajectory Tracking of Autonomous Surface Vehicle

Abstract

With the progress and development of modern industry, it is urgent to develop a more general and mature predefined time control theory system for uncertain nonlinear systems and apply it to practical engineering systems. Therefore, a novel predefined-time sliding mode control is designed to improve the slow convergence speed of asymptotically convergent controllers and finite-time convergent controllers affected by the initial state of the system. The proposed control approach is proved to converge from the initial value to the balance position using Jensen's inequality and Liapunov theorem. The proposed control method can deal with the ship motion system having six Degree-Of-Freedom (DOF) under external disturbance and parameter uncertainty. Furthermore, there are several application scenarios to simulate the proposed algorithm, such as the ship which is required to achieve ideal control accuracy in a short and fixed time. Alternatively, an unmanned ship is required to arrive at the desired location within the specified time. To sum up, this algorithm can extend some existing theoretical results on finite-time and fixed-time control to the predefined-time case.