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

• Published in: IEEE transactions on industrial electronics (1982) Vol. 66; no. 10; pp. 7904 - 7915
• Main Authors: Na, Jing, Wang, Bin, Li, Guang, Zhan, Siyuan, He, Wei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive control; Adaptive dynamic programming (ADP); Computational modeling; Computer simulation; constrained optimal control; Constraint modelling; Converters; Dynamic programming; Force; Hydraulic systems; Neural networks; Nonlinear control; Open area test sites; Optimal control; Optimization; wave energy converters (WEC); Wave power
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2018.2880728

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.