Journal of Irrigation and Drainage Engineering

A variety of methods are in use for the design of controllers for adjusting canal gate positions to maintain a constant water level immediately upstream from check gates. These methods generally rely on a series of tests on the water level’s response to changes in canal gate position or flow, either by simulation or on the canal itself. This paper presents a method for tuning these controllers based on wave celerity through use of the integrator delay zero (IDZ) model. These equations can be used to determine the resonance peak height and resonance frequency. Unsteady-flow canal simulation models are used to show the response of controller design using these theoretical equations with a test case for ASCE Test Canal 1. A novel method is presented for avoiding disturbance amplification by considering the delay times in all canal pools downstream.

The main objective of this study is to determine the optimal distribution of water between upstream and downstream users in a main irrigation canal with a limited supply of water in order to maximize the net revenue derived from existing farming activities. To this end, an economic positive mathematic programming (PMP) model is employed to determine the economic value of the water for each delivery point along the main canal in which there is agricultural activity. This information is added to a model that accounts for the operational aspects of a realistic, large irrigated district in the center of Iran, which is used by a model predictive controller to prioritize the reaches in the main canal according to their potential profit in economic terms. The results show the satisfactory operation of the canal reaches, such that the water levels in the reaches with high economic water value are kept closer to the operational target levels. Accordingly, the water deficit is proportionally divided along the main canals to maximize economic profits from the irrigated district.

Model predictive control (MPC) can be employed for optimal operation of adjustable hydraulic structures. MPC selects the control to be applied to the system by solving an optimization problem over a finite horizon in real-time. The horizon finiteness is both the reason for MPC’s success and its main limitation. MPC has in fact been successfully employed for short-term reservoir management. Short-term reservoir management deals effectively with fast processes, such as flood, but it is not capable of looking sufficiently ahead to handle long-term issues, such as drought. This study proposes an infinite horizon MPC solution that deals with both short and long-term objectives, tailored for reservoir management. In the proposed solution, the control signal is structured by the use of basis functions. Basis functions reduce the optimization argument to a small number of variables, making the control problem solvable in a reasonable time. The solution is tested for the operational management of Manantali reservoir, in the Senegal River. The long-term horizon offered by infinite horizon MPC is necessary to deal with the strongly seasonal climate of the region for both flood and drought prevention.

Model predictive control (MPC) is an efficient approach to regulate water systems, for both water quantity and quality. It generates optimal control trajectories based on model predictions over a finite horizon. In this research, the focus is on nonlinear MPC with a nonlinear internal model and the comparison of a sequential and simultaneous optimization setup, referred to as sequential and simultaneous nonlinear model predictive control (SeNMPC, SiNMPC), for the solution of the optimum control problem. The representation of the water system in the internal model is based on the diffusive wave model. The model is integrated in time by an unconditionally stable Backward Euler scheme to avoid model instabilities and time step restrictions. This numerical robustness is essential in real-time control applications where large control time steps should not be jeopardized by local grid refinements owing to the system topology. In order to speed up the optimization, an adjoint model is set up to calculate analytical derivatives of the objective function with respect to the optimization variables. Both SeNMPC and SiNMPC are successfully tested on a drainage canal network to regulate water levels and lead to identical results. The SiNMPC shows some advantages over the SeNMPC approach in terms of a higher computational performance and easier options to constrain the optimum control problem.

Model predictive control (MPC) has been extensively studied for controlling irrigation canals. Most of the studies use lumped models and fixed target for the controller. This setup works fine if the water delivery is on demand. However, in the case of on-supply delivery, balancing mismatches between demand and supply and spreading mismatches among all users are crucial. A fixed control target will not meet the request due to the mismatches. In this paper, a dynamic target trajectory approach is proposed to calculate changes of control targets that are used by MPC. The dynamic target trajectory calculates the percentage change of each setpoint based on the total volume mismatch spreading over the available capacity in each canal pool. The approach is applied to the Central Main Canal in Arizona. The results demonstrate that the dynamic trajectory can help the optimization find the optimum more quickly and facilitate tuning of the controller. Moreover, the mismatches can be better distributed over canal pools.

Open water systems such as irrigation canals are used to transport and deliver water from the source to the user. Water loss in these systems by seepage, leakage, evaporation, or unknown water offtakes can be large. If this loss is unknown to the model used, it will not be considered by the controller and create a real system model mismatch. This mismatch will affect the water level directly and create an offset from the reference set point of the water level. A control configuration for open water canals, model predictive control (MPC) based on moving horizon estimation (MHE-MPC), to deal with offset problems resulting from real system-model mismatch is described in this paper. MHE uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and systematically removes the offset in the controlled water level. This control configuration is numerically tested on an accurate hydrodynamic model of the Control Algorithms Test Canal of the Technical University of Catalonia (UPC-PAC). The results presented in this paper show that MHE-MPC can realize offset-free control and the results are better than those of the well-known disturbance modelling offset-free control scheme.

Model predictive control (MPC) is one of the most popular control techniques that has been widely used in many fields of water resources management, such as canal control for drainage, irrigation, and navigation. MPC uses an internal mathematical model to describe system dynamics over a given prediction horizon and then minimizes a hard-constrained optimization problem based on actual objectives. Due to the use of hard constraints, the optimization problem may occasionally be infeasible. A compromise is sometimes made to look for a feasible solution by softening the hard constraints, which means that the limit on water levels or flows is allowed to be violated to a certain extent. For example, water in a canal may go above the top of a dike during a high-discharge event, resulting in a spill. This amount of spilling water leaves the water system and does not flow back, which therefore should be deducted in the mathematical model of the water system. To deal with this spill, past studies often utilized a hybrid model and an integer optimization. However, the system in the hybrid model is usually nonlinear and nonsmooth, especially when it transits from one discrete state to another. In this paper, an alternative way is proposed to link the spill with the softened constraint, still maintaining the linearity of the water system. Results show that the proposed way to tackle the spilling water is easy to implement and the water level is more accurately regulated around the setpoint in a canal control problem.

Although canal control automation has been generally disregarded in the irrigation modernization projects in Spain, regulation reservoirs formed part of most projects. Recent projects combining storage reservoirs with automatic canal control lack clear design criteria, although they are influenced by the evident benefits from the construction of reservoirs at the tail of large canals with upstream control. A case study is presented herein that evaluates the effects of enlarging the downstream pool of one of the few irrigation canals that is operated automatically in Spain using distant downstream control. The study used an unsteady flow hydraulic model that proved to be a very useful tool for demonstrating to district managers the advantages and disadvantages of alternative control strategies, including the construction of a reservoir at the canal tail.

Dutch water management is considered highly efficient, but it faces a lack of public awareness and other certain physical challenges. One proposed strategy to deal with these challenges includes increasing citizen participation and citizen science using mobile devices in particular. Such mobile crowd sensing (MCS) can be used to enhance canal operations and model predictive control (MPC) by nonexperts. The data collector often pushes implementations, and little knowledge and experience from the field of product design is used. This can lead to underperformance both with regards to the technology and the volunteer citizens. This study uses an adapted Technology Acceptance Model 3 (TAM3) to survey Dutch citizens’ intentions while operating a mock-up smartphone application to identify key drivers of their acceptance in an early design phase. Included among the important drivers of citizens’ behavioral intentions (BI) are usefulness, relevance to the task, and the demonstrability of benefits. These insights can possibly unveil validated design criteria for future MCS applications. Such validated criteria can not only prevent the underperformance of citizen science from a volunteer point of view, but it can also affect the performance of the MCS as well.