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Irrigation canals are large-scale systems, consisting of many  interacting components, and spanning vast geographical areas. For  safe and efficient operation of these canals, maintaining the levels  of the water flows close to pre-specified reference values is crucial,  both under normal operating conditions as well as in extreme situations.    Irrigation canals are equipped with local controllers, to control  the flow of water by adjusting the settings of control structures  such as gates and pumps. Traditionally, the local controllers  operate in a decentralized way in the sense that they use local  information only, that they are not explicitly aware of the  presence of other controllers or subsystems, and that no  communication among them takes place. Hence, an obvious drawback of  such a decentralized control scheme is that adequate performance at  a system-wide level may be jeopardized, due to the unexpected and  unanticipated interactions among the subsystems and the actions of  the local controllers.    In this paper we survey the state-of-the-art literature on  distributed control of water systems in general, and irrigation  canals in particular. We focus on the model predictive control  (MPC) strategy, which is a model-based control strategy in which  prediction models are used in an optimization to determine optimal  control inputs over a given horizon. We discuss how communication  among local MPC controllers can be included to improve the  performance of the overall system. We present a distributed  control scheme in which each controller employs MPC to determine  those actions that maintain water levels after disturbances close  to pre-specified reference values. Using the presented scheme the  local controllers cooperatively strive for obtaining the best  system-wide performance. A simulation study on an irrigation canal  with seven reaches illustrates the potential of the approach.

Distributed model predictive control of irrigation canals