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.

中文翻译：

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具有自适应动态规划的波浪能转换器的非线性约束优化控制

在本文中，我们解决了受非线性和约束的波浪能转换器 (WEC) 的能量最大化问题，并提出了一种基于自适应动态规划 (ADP) 原理的高效在线控制策略，用于解决相关的 Hamilton-Jacobi-Bellman方程。为了解决导出的约束非线性最优控制问题，使用批评神经网络 (NN) 来逼近与时间相关的最优成本值，然后计算实际的次优因果控制动作。所提出的新颖 WEC 控制策略导致简化的 ADP 框架，而无需涉及广泛使用的参与者神经网络。所提出的控制的显着提高的计算效率使其在 WEC 上的实际实施具有吸引力，以实现降低能量输出的单位成本，当 WEC 的动力学很复杂并且需要通过具有非线性和约束的高阶模型准确描述时，这尤其重要。仿真结果显示了所提出的控制方法的有效性。