System identification and control of an Arleigh Burke Class Destroyer using an extended Kalman Filter

Abstract

Maneuvering characteristics of surface combatants in the United States Navy are often ignored during the design process. Key maneuvering parameters such as tactical diameter and turning rate are determined during sea trials after the ship enters service. In the "Navy After Next" the study of maneuvering of surface combatants will become increasingly more important in efforts to reduce the number of personnel required to operate the ship and thus reduce life cycle costs. This thesis attempts to address this issue. The thesis presents an Extended Kalman Filtering (EKF) algorithm to estimate the linear damping hydrodynamic coefficients for an Arleigh Burke Class Destroyer. Actual data is generated by conducting maneuvers (with a nonlinear model of the ship developed in a separate study) where nonlinear effects are small. The EKF then uses that data to estimate the hull stability coefficients (Y(v), N(c), Y(r), and N(r)) on-line in real time. The coefficient values determined by the EKF are then used in a simulation model and the results are compared to the actual trajectories. Despite the nonlinearities present in the actual data, the EKF provides coefficient values that reproduce trajectories with only 15% error. The linear coefficients are then used to develop simple controllers to automate maneuvering for the actual ship. The parameters determined by the EKF are used to derive a linear time invariant (LTI) model of the ship. This LTI model then serves as the basis for model-based compensator designs to automatically control ship maneuvers. The first controller is an autopilot to regulate the ship's heading and the second is a regulator that ensures the ship remains on its intended track. The performance of the compensators is then evaluated by simulating the performance of the LTI controllers on the nonlinear plant