دانلود مقاله ISI انگلیسی شماره 136059
ترجمه فارسی عنوان مقاله

برآورد مطلوب و برنامه ریزی در مدیریت آبخوان با استفاده از روش کنترل بازخورد سریع

عنوان انگلیسی
Optimal estimation and scheduling in aquifer management using the rapid feedback control method
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
136059 2017 39 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Advances in Water Resources, Volume 110, December 2017, Pages 310-318

ترجمه کلمات کلیدی
فیلتر کلمن، رگولاتور درجه دوم خطی، بهینه سازی، مدیریت مخزن آب، کنترل گاوسی مربع خطی،
کلمات کلیدی انگلیسی
Kalman filter; Linear quadratic regulator; Optimization; Water reservoir management; Linear quadratic Gaussian control;
پیش نمایش مقاله
پیش نمایش مقاله  برآورد مطلوب و برنامه ریزی در مدیریت آبخوان با استفاده از روش کنترل بازخورد سریع

چکیده انگلیسی

Management of water resources systems often involves a large number of parameters, as in the case of large, spatially heterogeneous aquifers, and a large number of “noisy” observations, as in the case of pressure observation in wells. Optimizing the operation of such systems requires both searching among many possible solutions and utilizing new information as it becomes available. However, the computational cost of this task increases rapidly with the size of the problem to the extent that textbook optimization methods are practically impossible to apply. In this paper, we present a new computationally efficient technique as a practical alternative for optimally operating large-scale dynamical systems. The proposed method, which we term Rapid Feedback Controller (RFC), provides a practical approach for combined monitoring, parameter estimation, uncertainty quantification, and optimal control for linear and nonlinear systems with a quadratic cost function. For illustration, we consider the case of a weakly nonlinear uncertain dynamical system with a quadratic objective function, specifically a two-dimensional heterogeneous aquifer management problem. To validate our method, we compare our results with the linear quadratic Gaussian (LQG) method, which is the basic approach for feedback control. We show that the computational cost of the RFC scales only linearly with the number of unknowns, a great improvement compared to the basic LQG control with a computational cost that scales quadratically. We demonstrate that the RFC method can obtain the optimal control values at a greatly reduced computational cost compared to the conventional LQG algorithm with small and controllable losses in the accuracy of the state and parameter estimation.