An Online Parameter Estimation Method Based on Adaptive Unscented Kalman Filter for Unmanned Surface Vessel

Abstract

In this paper, an online parameter estimation method for unmanned surface vessels (USVs) is designed. The main idea is to establish an augmented system by viewing the parameters as system states, and then estimate the full states of the augmented system by using adaptive unscented Kalman filter (AUKF). Nine parameters including the inertial effects, the damping, the thrust allocation, and the current velocity can be online estimated accurately based on the measurements from real-time kinematic (RTK) Global Positioning System (GPS) and inertial measurement unit (IMU). The trajectory tracking control is further studied in the presence of input constraints, where the model predictive control (MPC) is introduced. The simulation results of parameter estimation demonstrate the effectiveness of the proposed method.