Real-time Model Predictive Control Framework for a Point Absorber Wave Energy Converter with Excitation Force Estimation and Prediction

• Published in: IEEE access Vol. 12; p. 1
• Main Authors: Elamin, Abdin Y., Wahyudie, Addy, Hashfi, Tuanku Bazlin, Shareef, Hussain, Errouissi, Rachid, Laghari, Mohammad Shakeel, Mubin, Marizan binti, Mekhilef, Saad
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Absorbers; Autoregressive model; Autoregressive models; Autoregressive processes; Cost function; Estimation; Force; Goodness of fit; hardware-in-the-loop; Kalman filter; Kalman filters; Laguerre functions; model predictive control; permanent magnet linear generator; power maximization; Predictive control; Quadratic programming; Real time; Real-time systems; Sea measurements; Sea states; Wave energy conversion; wave energy converter; Wave excitation; Wave power
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3347731

This study focuses on real-time model predictive control (MPC) implementation for a point-absorber-type wave energy converter (WEC) to achieve maximum wave power extraction. To address the MPC dependence on current and future wave excitation force knowledge, the control framework includes a square-root unscented Kalman filter (SR-UKF) to estimate the wave excitation force, whereas an autoregressive (AR) model forecasts the excitation force over the prediction horizon. The MPC utilizes a linear model of the point absorber, and its cost function is formulated as a quadratic programming (QP) problem with the objective of maximizing the wave energy extraction subject to physical limitations and constraints. A set of Laguerre functions was used to reduce the MPC computational complexity by decreasing the number of decision variables in the QP problem. The MATLAB/Simulink simulation results showed that the embodiment of point absorber nonlinearities by the SR-UKF enhanced the estimation accuracy compared with that of linear Kalman filters in terms of maximum error, root mean square error, and goodness of fit. Moreover, an AR model of order 100 provided satisfactory prediction performance for a 4 s prediction. The MPC strategy was simulated to evaluate its efficiency with respect to the absorbed wave power and generated electrical power under different sea-state conditions and disturbance uncertainties. Depending on sea state, it was found that the proposed MPC strategy generated 27.8% to 80% more electrical power than resistive loading control. Finally, a hardware-in-the-loop experimental setup was used to validate the computational viability of the proposed control framework in real-time WEC applications.