The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly u...
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ISBN:
(纸本)9781713872344
The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly uncertain disturbance on the system. When model predictive control (MPC) is employed, the optimal water release is usually computed based on the (predicted) trajectory of the inflow. This choice may jeopardize the closed-loop performance when the actual inflow differs from its forecast. In this work, we consider - for the first time - a stochastic MPC approach for water reservoirs, in which the control is optimized based on a set of plausible future inflows directly generated from past data. Such a scenario-based MPC strategy allows the controller to be more cautious, counteracting droughty periods (e.g., the lake level going below the dry limit) while at the same time guaranteeing that the agricultural water demand is satisfied. The method's effectiveness is validated through extensive Monte Carlo tests using actual inflow data from Lake Como, Italy. Copyright 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly u...
详细信息
The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly uncertain disturbance on the system. When model predictive control (MPC) is employed, the optimal water release is usually computed based on the (predicted) trajectory of the inflow. This choice may jeopardize the closed-loop performance when the actual inflow differs from its forecast. In this work, we consider - for the first time - a stochastic MPC approach for water reservoirs, in which the control is optimized based on a set of plausible future inflows directly generated from past data. Such a scenario-based MPC strategy allows the controller to be more cautious, counteracting droughty periods (e.g., the lake level going below the dry limit) while at the same time guaranteeing that the agricultural water demand is satisfied. The method's effectiveness is validated through extensive Monte Carlo tests using actual inflow data from Lake Como, Italy.
water level regulation of irrigation canals represents a major challenge for controlsystems design. Those systems exhibit large dynamic variations in their operating conditions. To overcome this fact, robust controll...
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water level regulation of irrigation canals represents a major challenge for controlsystems design. Those systems exhibit large dynamic variations in their operating conditions. To overcome this fact, robust controllers should be applied. The sliding mode control paradigm reveals this ability which make it a suitable candidate to be incorporated in the irrigation canal control loop. Moreover, its flexibility can be further potentiated by extending the ordinary formulation by adding fractional-order integro-differential operations. In this work, fractional-order sliding mode control is applied to the above mentioned problem. This application represents a novelty and, according to the obtained simulation results, leads to an accurate and proper performance when compared to its integer-order counterpart and to a fractional proportional-integrative controller, recently proposed for this problem. (C) 2017, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
water level regulation of irrigation canals represents a major challenge for controlsystems design. Those systems exhibit large dynamic variations in their operating conditions. To overcome this fact, robust controll...
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