Data-Driven Economic MPC of a Point Absorber Wave Energy Converter
Yubin Jia, Jia Sun, Zhao Xu, Changyin Sun, Ke Meng, Zhao Yang Dong
Abstract
This paper presents a data-driven economic model predictive control (EMPC) framework for maximizing the output power of the nonlinear wave energy converter (WEC). The linear representation of the nonlinear WEC is realized by the Koopman operator theory, and Extended Dynamic Mode Decomposition (EDMD) is used to approximate the infinite-dimensional Koopman operator to a finite-dimensional linear model. The lifted linear model plays a role in designing controllers for the nonlinear WEC system using model-based linear controller strategies. EMPC method is adopted to realize the economic optimization and process control of the WEC. The designed EMPC controller is capable of translating the optimal control of the Koopman linear system to the original nonlinear WEC system. The asymptotic stability of the system is achieved by using the stage-cost function and auxiliary optimization problem. The simulations show the economy and effectiveness of the proposed method.