Nonlinear Constrained Optimal Control of Wave Energy Converters With Adaptive Dynamic Programming

In this paper, we address the energy maximization problem of wave energy converters (WEC) subject to nonlinearities and constraints, and present an efficient online control strategy based on the principle of adaptive dynamic programming (ADP) for solving the associated Hamilton-Jacobi-Bellman equation. To solve the derived constrained nonlinear optimal control problem, a critic neural network (NN) is used to approximate the time-dependant optimal cost value and then calculate the practical suboptimal causal control action. The proposed novel WEC control strategy leads to a simplified ADP framework without involving the widely used actor NN. The significantly improved computational efficacy of the proposed control makes it attractive for its practical implementation on a WEC to achieve a reduced unit cost of energy output, which is especially important when the dynamics of a WEC are complicated and need to be described accurately by a high-order model with nonlinearities and constraints. Simulation results are provided to show the efficacy of the proposed control method.