Adaptive Dynamic Programming Based Output-Feedback Optimal Control for Lever-Type Wave Energy Converter

Wave energy, as one of the promising renewable energy sources, is contained in the motion of the sea wave. The conversion efficiency of wave energy can be improved by the converter device and the corresponding control strategy. In this paper, an adaptive dynamic programming (ADP)-based output-feedback optimal control approach is proposed for lever-type wave energy converter (WEC) with mechanical motion rectification (MMR)-based power takeoff (PTO). The main advantage of the designed MMR-based PTO is that the bidirectional motion of the float can be obtained, which can directly convert irregular, bidirectional wave motions into regular, unidirectional generator rotations, accordingly. The Euler-Lagrange equation is employed to establish the dynamic model of the designed lever-type WEC. Moreover, the d-Iq-axis model of permanent magnet synchronous generator (PMSG) is incorporated with the dynamic model of lever-type WEC, by which a fair comprehensive WEC model can be characterised. Then, the extended state observer (ESO) is introduced to accommodate the unknown nonlinear dynamics. Different with the traditional control strategy designed for WEC that requires measurement of all the system states, an output-feedback optimal control based on ADP is suggested in this paper, in which a novel adaptive law based on parameter estimation error is applied to solve the modified algebraic Riccati equation (MARE) online. Finally, simulation results are provided to illustrate the feasibility of the developed WEC and efficiency of the proposed method.