Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for large-scale systems. Nonetheless, a critical obstacle, which needs to be overcome in MPC, is the large computational burden when a large-scale system is considered or a long prediction horizon is involved. In order to solve this problem, we use an adaptive prediction accuracy (APA) approach that can reduce the computational burden almost by half. The proposed MPC scheme with this scheme is tested on the northern Dutch water system, which comprises Lake IJssel, Lake Marker, the River IJssel and the North Sea Canal. The simulation results show that by using the MPC-APA scheme, the computational time can be reduced to a large extent and a flood protection problem over longer prediction horizons can be well solved

Offset Free Model Predictive Control Of An Open Water Reach

Model predictive control (MPC) is a powerful tool which is used more and more to managing water systems such as reservoirs over a short-term prediction horizon. However, due to unknown disturbances present in the water system and other uncertainties, there is always a mismatch between the model and the actual system. To overcome this mismatch and achieve offset free control of the water system, the internal model of the MPC is updated by adding the disturbance dynamics of the actual system by means of a disturbance model. In this paper, the conditions to achieve offset free control for an open water reach are provided. A disturbance model is designed and used to achieve offset free control in a test canal assessed from simulation results

Effect of uncertainties on the real-time operation of a lowland water system in The Netherlands

Due to the limited pumping capacity in lowland water systems, reduction of system failure requires anticipation of extreme precipitation events. This can be done by Model Predictive Control that optimizes an objective function over a certain time horizon, for which the system behaviour is calculated by a model and a prediction of the inputs to the system. The forecast inputs usually contain large uncertainties. Because the pump constraints make the optimization problem non-certainty equivalent, uncertainties need to be considered to adequately control the water system. In this paper, the way uncertainties influence the control decision is investigated. An information-control horizon and an information-prediction horizon are introduced as time-limits for the sensitivity to future input information and the value of predictions. These horizons need to be considered in the design of a controller. Multiple Model Predictive Control is suggested to deal with the uncertainties in a risk based way

Short Term Reservoirs Operation On The Seine River: Performance Analysis Of Tree-Based Model Predictive Control

The Seine River, in France, flows through territories of large economic value, among which the metropolitan area of Paris. A system of four reservoirs operates upstream to regulate the river flows in order to protect the area against extreme events, such as floods and droughts. Current reservoirs management is based on reactive filling curves, designed from an analysis of historical hydrological regimes. The efficiency of this management strategy is jeopardized when inflows are significantly different from their seasonal average. To improve the current management strategy, we investigated the use of Tree-Based Model Predictive Control (TB-MPC). TB-MPC is a proactive and centralized method that uses information available in real-time, as ensemble weather forecasts. Reservoir management is tested under past hydro-climatic conditions using time series of ensemble weather forecasts produced by ECMWF (European Centre for Medium-Range Weather Forecasts) and weather observations. The performance of TB-MPC is compared to that of deterministic Model Predictive Control (MPC), showing the benefits of considering forecasts uncertainty by using ensemble forecasts

Model predictive control based on an integrator resonance model applied to an open water channel

This paper describes a new simplified model for controller design of open water channels that are relatively short, flat and deep: the integrator resonance model (IR model). The model contains an integrator and the first resonance mode of a long reflecting wave. The paper compares the integrator resonance model to the simplified models: integrator delay, integrator delay zero and filtered integrator delay and to the high-order linearized Saint-Venant equations model. Results of using the integrator resonance model in a model predictive controller applied in closed loop on a high-order non-linear Saint-Venant model of the first pool of the laboratory canal at Technical University of Catalonia, Barcelona are compared to the results of using the other simplified models in MPC. This comparison shows that the IR model has less model mismatch with the high order model regarding the relevant dynamics of these typical channels compared to the other simplified models. It is demonstrated that not considering the resonance behavior in the controller design may result in poor performance of the closed loop behavior. In order to demonstrate the validity of the simulation model used in this study, the controller using the IR model is also tested on the actual open water channel and compared to the results of the high-order non-linear Saint-Venant simulation model. The results of this comparison show a close resemblance between simulation model and real world system. (C) 2014 Elsevier Ltd. All rights reserved.Peer ReviewedPostprint (published version

Model predictive control based on an integrator resonance model applied to an open water channel

This paper describes a new simplified model for controller design of open water channels that are relatively short, flat and deep: the integrator resonance model (IR model). The model contains an integrator and the first resonance mode of a long reflecting wave. The paper compares the integrator resonance model to the simplified models: integrator delay, integrator delay zero and filtered integrator delay and to the high-order linearized Saint-Venant equations model. Results of using the integrator resonance model in a model predictive controller applied in closed loop on a high-order non-linear Saint-Venant model of the first pool of the laboratory canal at Technical University of Catalonia, Barcelona are compared to the results of using the other simplified models in MPC. This comparison shows that the IR model has less model mismatch with the high order model regarding the relevant dynamics of these typical channels compared to the other simplified models. It is demonstrated that not considering the resonance behavior in the controller design may result in poor performance of the closed loop behavior. In order to demonstrate the validity of the simulation model used in this study, the controller using the IR model is also tested on the actual open water channel and compared to the results of the high-order non-linear Saint-Venant simulation model. The results of this comparison show a close resemblance between simulation model and real world system. (C) 2014 Elsevier Ltd. All rights reserved.Peer Reviewe

Coordinated Distributed Model Predictive Reach Control of Irrigation Canals

Coordinated distributed model predictive reach control of irrigation canals ∗ R.R. Negenborn, P.-J. van Overloop, and B. De Schutter If you want to cite this report, please use the following reference instead

of Irrigation Canals

Coordinated model predictive reach control for irrigation canal