P. J. van Overloop

Real-time implementation of model predictive control on Maricopa-Stanfield Irrigation and Drainage District's WM Canal.

Water resources are limited in many agricultural areas. One method to improve the effective use of water is to improve delivery service from irrigation canals. This can be done by applying automatic control methods that control the gates in an irrigation canal. The model predictive control (MPC) is one such advanced control method. In this article, the MPC is used to deliver irrigation water to the WM Canal at the Maricopa-Stanfield Irrigation and Drainage District. The tests show that the water is efficiently delivered to the users and water level deviations at all locations are small. The control is compared to the results from an advanced Linear Quadratic Regulator control method, also tested on the actual canal.

Predictive Control for National Water Flow Optimization in The Netherlands

The river delta in The Netherlands consists of interconnected rivers and large water bodies. Structures, such as large sluices and pumps, are available to control the local water levels and flows. The national water board is responsible for the management of the system. Its main management objectives are: protection against overtopping of dikes due to high river flows and high sea tides, supply of water during dry periods, and navigation. The system is, due to its size, divided into several subsystems that are managed by separate regional divisions of the national water board. Due to changes in local land-use, local climate, and the need for energy savings, the currently existing control systems have to be upgraded from local manual control schemes to regional model predictive control (MPC) schemes. In principle, the national objectives for the total delta require a centralized control approach integrating all regional MPC schemes. However, such centralized control is on the one hand not feasible, due to computational limitations, and on the other hand unwanted, due to the existing regional structure of the organization of the national water board. In this chapter the application of MPC is discussed for both individual regional control and coordinated national control. Results of a local MPC scheme applied to the actual water system of the North Sea Canal/Amsterdam-Rhine Canal are presented and a framework for coordination between several distributed MPC schemes is proposed.

Application of an In-Line Storage Strategy to Improve the Operational Performance of Main Irrigation Canals Using Model Predictive Control

AbstractStoring water in main irrigation canal reaches could be an influential strategy to improve the existing operational activities in the irrigation canals. However, the control of such a canal system will become much more complicated due to freeboards of the reaches temporarily decreasing. In this paper, Model Predictive Control (MPC) is applied to control the water level of an accurate model of a realistic main canal, which consists of 13 canal reaches, using an in-line storage operational strategy. Four different test scenarios are selected to cover a range of conventional to unconventional operational strategies by imposing limitations on the head-gate opening. Different target bands are created between the predefined allowed maximum and minimum water level for the canal reaches and the MPC is obliged to keep the water levels within these ranges. The results show that the in-line storage improves current operational performance of the canal system by compensating the existing delay times of flow t...

Hybrid model predictive control using time-instant optimization for the Rhine-Meuse Delta

In order to provide safety against high sea water levels, in many low-lying countries on the one hand dunes are maintained at a certain safety level and dikes are built, while on the other hand large control structures that can be controlled dynamically are constructed. Currently, these structures are often operated purely locally, without coordination on actions between different structures. Automatically coordinating the actions is particularly difficult, since open water systems are complex, hybrid systems, in the sense that continuous dynamics (e.g., the evolution of the water levels) are mixed with discrete events (e.g., the opening or closing of barriers). In low-lands, this complexity is increased further due to bi-directional water flows resulting from backwater effects and interconnectivity of flows in different parts of river deltas. In this paper, we propose a model predictive control (MPC) approach that is aimed at automatically coordinating the different actions. Hereby, the hybrid nature is explicitly addressed. In order to reduce the computational effort required to solve the hybrid MPC problem we propose to use TIO-MPC, where TIO stands for time-instant optimization. A simulation study illustrates the potential of the proposed controller in comparison with the current setup in the Rhine-Meuse delta in The Netherlands.

Model reduction in model predictive control of combined water quantity and quality in open channels

Model predictive control (MPC) is an advanced real-time control technique that uses an internal model to predict the future system behavior and generates optimal control actions by solving an optimization problem. MPC has been more and more applied for controlling open water systems, especially open water channels. Most of the research however focuses on water quantity (water level) control. Since water quality management is recently attracting more attention, extending MPC on combined water quantity and quality management is a logical next step. In this paper, we study the application of complex models in MPC to control both water quantity and quality. However, because of the online optimization of MPC, the computational time becomes an issue. In order to reduce the computational time, a model reduction technique, Proper Orthogonal Decomposition (POD), is applied to reduce the model order. The method is tested on a Polder flushing case. The results show that POD can significantly reduce the model order for both water quantity and quality with high accuracy. The MPC using the reduced model performs well in controlling combined water quantity and quality in open water channels.

Estimating Canal Pool Resonance with Auto Tune Variation

The integrator-delay (ID) model is commonly used to model canal pools that do not exhibit resonance behavior. Simple step tests are often used to estimate ID model parameters; namely, delay time and backwater surface area. These step tests change the canal inflow at the upstream end of the pool and observe water depth variations at the downstream end. Some knowledge of the canal pool characteristics are needed to determine the amount of flow change and its duration. Auto tune variation (ATV) is one method for determining the duration of these step tests. Pools that are under backwater over their entire length tend to exhibit oscillations attributable to resonance waves. Random Binary Sequence (RBS) tests have been used to determine the resonance frequency of such pools, for which step tests with different durations are used. RBS tests are difficult to implement in practice and may not provide the resonance frequency. The intent of this paper is to dem- onstrate on a real canal that the ATV method can determine both the resonance frequency and the resonance-peak height for canal pools whose water levels oscillate. DOI: 10.1061/(ASCE)IR.1943-4774.0000384.

Hierarchical Operation of Water Level Controllers: Formal Analysis and Application on a Large Scale Irrigation Canal

We introduce a hierachical controller, the purpose of which is to speed up the water delivery process as compared to the standard method applied currently in the field. The lower layer of the hierarchical control consists of local proportional integral filter controllers (PIF controllers) for upstream control at each gate; specifically they are proportional integral controllers with a low-pass filter. In contrast, the higher layer is composed of a centralized model-based predictive controller, which acts by controlling the head gate and by coordinating the local PIF controllers by modifying their setpoints when needed. The centralized controller is event-driven and is invoked only when there is a need for it (a water delivery request) and as such it contributes scarcely to the communication burden. The scheme is robust to temporary communication losses as the local PIF controllers are fully able to control the canal in their normal independent automatic upstream control mode until the communication links are restored. We discuss the application of the hierarchical controller to a precise numerical model of the Central California Irrigation District Main Canal. This shows the improved performance of the new hierarchical controller over the standard control method.

Equitable Water Distribution in Main Irrigation Canals with Constrained Water Supply

In this study a novel configuration of the Water Level Difference Error method is introduced to speed up the error sharing in the context of Model Predictive Control (MPC). The potential application of the controller is examined. The main objective of this controller is fair distribution of water between upstream and downstream users in main canals suffering from water shortages. The scheme uses the Integrator-Delay (ID) model for canal pool responses in a model predictive controller. The designed controller is tested on an accurate simulation model of a large canal system, using four test scenarios. The scenarios suffer from limited water supply conditions that are imposed by a limitation on the canal inflow. The results show fast reactions in equitable sharing of water level deviations from target throughout the canal. Since, all the pools are involved in optimally managing the water shortage, significant improvements in operational performance of the canal are achieved. In addition, the operational performance of the designed controller is remarkably improved by applying a new strategy of target-bands instead of target-levels in the canal pools as it increases the flexibility of the controller in making appropriate decisions.

Applying Decentralized Water Level Difference Control for Operation of the Dez Main Canal under Water Shortage

AbstractDue to water scarcity, providing reliable amounts of water is not possible for most of the irrigation districts in arid regions. In such conditions, equitable water delivery becomes one of the main concerns. Applying a centralized controller to achieve the high level of equity in water delivery along the main canal is not a practical solution in many irrigation districts due to high costs of the implementation, maintenance and inspections; and vandalism of equipment. In this paper, a water level difference control strategy is applied to keep the water level errors equal in adjacent reaches by using decentralized controllers. Local downstream and distant downstream configurations of the control method are designed for the Dez main canal, located in the southwest of Iran, consisting of 13 canal reaches. The designed controllers are tested on two extreme water off-taking scenarios for normal operation and operation under water shortage. The tests show the capability of the difference control strategy...

Human-in-the-Loop Model Predictive Control of an Irrigation Canal [Applications of Control]

Until now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including industrial applications [1]-[3].