EKF-DRNN autopilot for VLCC heading hybrid control

Abstract

An online Extended Kalman Filter (EKF)-Dynamic Recurrent Neural Network (DRNN) autopilot implementation strategy for Very Large Crude Carrier (VLCC) heading hybrid control with uncertain dynamics is designed in this paper. The autopilot scheme is based on a DRNN control model, which learns VLCC dynamic characteristics, while the VLCC heading control is estimated by the EKF to minimize squared course error. The online EKF-DRNN autopilot provides optimal control on the basis of fuel-saving evaluation criteria using the heading deviation and rudder angle. Therefore, the autopilot output is guaranteed to converge to the desired VLCC trajectory asymptotically. The proposed strategy is evaluated by applying it to VLCC Yuan Kun Yang from COSCO Shipping, and works excellently under different loads, speed and weather conditions. The VLCC heading hybrid controller is also assessed by ‘Z’ manoeuvring and turning test, and the superiority of the online EKF-DRNN autopilot is demonstrated. The remote online monitoring of Yuan Kun Yang’s main navigation data shows that it improved fuel-saving properties despite worsening weather conditions causing increased yawing.