Improved ship roll motion performance with combined EKF parameter estimation and mpc control
Citation
Cakici, F., Jambak, A. I., Kahramanoglu, E., Karabuber, A. K., Kucukdemiral, I., Ogur, M. U., Peri, F., Sahin, O. S., & Ugur, M. A. (2024). Improved Ship Roll Motion Performance with Combined EKF Parameter Estimation and MPC Control. In 2024 IEEE Conference on Control Technology and Applications (CCTA) (pp. 477–482). IEEE. https://doi.org/10.1109/ccta60707.2024.10666525Abstract
Roll motion reduction is a critical operational challenge for ships operating in a seaway. This paper presents a nonlinear roll dynamics model for a gulet model ship equipped with active fins. We employ an Extended Kalman Filter (EKF) to accurately estimate model parameters from experimental roll test conducted in Hydrodynamic Research Laboratory at Yildiz Technical University. Subsequently, a disturbance rejection based velocity from Model Predictive Controller (MPC) actively drives the fins to minimize roll motion, explicitly incorporating real-world amplitude and rate saturations. Simulation results demonstrate the success of our parameter estimation approach and the promising potential of the MPC strategy for roll reduction.
