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首页> 外文期刊>IEEE transactions on industrial informatics >Robust Policy Learning Control of Nonlinear Plants With Case Studies for a Power System Application
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Robust Policy Learning Control of Nonlinear Plants With Case Studies for a Power System Application

机译：电力系统应用案例研究的非线性工厂的强大政策学习控制

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In view of the prevalence of dynamic uncertainties, we study the robust policy learning control of nonlinear plants in this paper. The auxiliary system and policy learning techniques are integrated to accomplish robust stabilization of mismatched nonlinear systems. First, the uncertain dynamics is handled by proper transformation, so as to construct an optimal regulation problem with respect to an augmented auxiliary system. Then, the integral policy iteration algorithm is employed for optimal control design without requiring system dynamics. The equivalence results involved in problem transformation and algorithm improvement are analyzed. After that, the actor-critic structure is adopted with least squares implementation for approximate calculation. Finally, the experimental simulation with an application to a power system is provided, which demonstrates the validity of the adaptive robust control strategy. The present policy learning algorithm does not rely on whole information of system dynamics and the established robust control technique is applicable for nonlinear plants subjected to mismatched uncertainties.
机译：鉴于动态不确定性的普及，我们研究了本文中非线性植物的强大政策学习控制。辅助系统和策略学习技术被集成以实现不匹配的非线性系统的鲁棒稳定性。首先，通过适当的变换处理不确定的动态，以便在增强辅助系统方面构建最佳调节问题。然后，用于最佳控制设计的积分策略迭代算法，而无需系统动态。分析了问题变换和算法改进所涉及的等价结果。之后，采用参与者 - 批评结构，以实现近似计算的最小二乘实现。最后，提供了应用于电力系统的实验模拟，其展示了自适应稳健控制策略的有效性。本策略学习算法不依赖于系统动态的全部信息，并且建立的鲁棒控制技术适用于经受不匹配的不确定性的非线性工厂。

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