论文标题
基于学习的水库系统的分层控制
Learning-based hierarchical control of water reservoir systems
论文作者
论文摘要
由于水文输入不确定，并且需要适应不断变化的环境和改变控制目标，因此对水库系统的最佳控制代表了一个具有挑战性的问题。在这项工作中，我们提出了基于层次预测控制体系结构的基于实时学习的控制策略。实施了两个控制循环：内部循环旨在通过数据驱动的控制设计与分配的线性相似，然后外部经济模型预测控制器补偿模型错配，实施适当的约束，并提高跟踪性能。与传统的动态编程策略相比，所提出的方法的有效性在越南HOA BINH水库的精确模拟器上说明了。结果表明，所提出的方法的性能比基于随机动态编程的方法更好。
The optimal control of a water reservoir systems represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying control objectives. In this work, we propose a real-time learning-based control strategy based on a hierarchical predictive control architecture. Two control loops are implemented: the inner loop is aimed to make the overall dynamics similar to an assigned linear through data-driven control design, then the outer economic model-predictive controller compensates for model mismatches, enforces suitable constraints, and boosts the tracking performance. The effectiveness of the proposed approach as compared to traditional dynamic programming strategies is illustrated on an accurate simulator of the Hoa Binh reservoir in Vietnam. Results show that the proposed approach performs better than the one based on stochastic dynamic programming.
