Data-Based Guaranteed Trajectory Estimation for Unmanned Surface Vehicles
Xudong Wang, Hui Zhang, Yuan Wang, Yaonan Wang, Hanlin Dong, Jitao Li
Abstract
This article concerns the guaranteed trajectory estimation problem for unmanned surface vehicles (USVs) via set-membership estimation technique. Taking both rigid-body and hydrostatics kinetics into consideration, the nonlinear dynamic model of USV system is derived, where the parameters of system are all unknown. Considering external disturbance and nonlinearities, an offline data-based set-membership estimation algorithm of unknown system parameters is proposed to obtain the set representation of system parameters, which contain the actual parameters of USV. Then, based on the obtained parameter sets, an online guaranteed trajectory estimation algorithm of USV is constructed to provide guaranteed sets enclosing actual trajectory points of USV, which consists of a time-update step and measurement-update step. To tackle the nonlinear transformation of zonotopes, both interval arithmetic and Taylor model are utilized to provide rigorous bounds for nonlinear function. Finally, simulation results on a USV dynamic are provided to demonstrate the effectiveness of the proposed data-based guaranteed trajectory estimation method for USVs.