Proportional integral (PI) control is the most frequently used method for controlling water levels in irrigation canals. For design purposes, simplified linear models are typically used to describe the series of pools that, separated by control gates, compose the whole canal. Common models are made by series connection of integrator plus time delay or integrator with time delay plus a zero, these models being obtained from the Saint-Venant equations. However, it is known that hydraulic processes in irrigation canals are strongly nonlinear and present dynamics that are time varying depending on the operating point and the hydraulic conditions of the pool, so that the performance of PI controls may be reduced as the operating point moves far from the nominal design state. This paper presents the application of an adaptive predictive expert control (ADEX) for the distant downstream level control in irrigation canals trying to exploit their adaptive capability to efficiently achieve changes in the setpoint levels and disturbance rejection. Both PI and ADEX controllers are compared in simulation and in real field tests in the control of the end pool of the Canal Imperial de Aragón in Spain.KEY WORDS: predictive control; adaptive predictive control; irrigation canal control; water resources During the last 20 years, a significant number of researchers from different groups, mainly in academic or management centers and research environments related to water, have developed, for this problem, various methods of control in the theoretical context. A state of the art prior to 2000 can be seen in [1,2]. Some significant further studies are summarized below. A robust tuning method for proportional-integral (PI) classical control is presented in [3]. In [4], fractional PI control is applied.
Predictive control is one of the most commonly used control methods in a variety of application areas, including hydraulic processes such as water distribution canals for irrigation. This article presents the design and application of predictive control for the water discharge entering into an irrigation canal located in Spain. First, a discrete time linear model of the process is described and its parameters are experimentally identified. The model is well validated within the usual canal operating range and is used to formulate a predictive control law with an incremental formulation. Finally, experimental and simulation results are presented in which predictive control has shown better performance than a well-tuned proportional, integral and derivative controller to automatically manage demanded water discharges.
Efficient flood management requires accurate real time forecasts to allow early warnings, real time control of hydraulics structures or other actions. Commercially available\ud computing tools typically use, for flow or level forecasting, hydraulic models derived from the numerical approximation of Saint-Venant equations. These tools need powerful computers, accurate knowledge of the riverbed topography and skilled operators with some hydraulic background. This paper presents an alternative approach in which the river network is modeled as a cascade of interconnected input-output systems. Each system is modeled by an adaptive predictive expert model, which provides real-time fast and accurate forecasts over a moving prediction horizon. The main advantages of the approach are: (1) simplicity in the formulation and low computational burden; (2) no need of topographic information on the river waterbeds; (3) operators do\ud not need strong hydraulic knowledge and the forecast may be done autonomously. The approach is evaluated using real data from the Ebro river basin in Spain.Postprint (published version
Efficient flood management requires accurate real-time forecasts to allow early warnings, real-time control of hydraulics structures, or other actions. Commercially available computing tools typically use hydraulic models derived from the numerical approximation of Saint-Venant equations. These tools need powerful computers, accurate knowledge of the riverbed topography, and skilled operators with a not insignificant hydraulic background. This paper presents an alternative approach in which the river basin is modeled as a network of cascade interconnected input-output systems. Each system is modeled by an adaptive predictive expert model, which provides real-time fast and accurate forecasts over a moving prediction horizon. The approach is evaluated using real data from the Ebro river basin in Spain. The main concluded advantages of the new approach are: (1) the formulation is simple with low computational burden; (2) it does not require topographic information on the river waterbeds; (3) the forecast may be performed autonomously. et al. | An adaptive predictive approach for river level forecasting Journal of Hydroinformatics | 15.2 | 2013 J. V. Aguilar et al. \ An adaptive predictive approacii for river ievel forecasting Journal of Hydroinformatics | 15.2 | 2013 J. V. Aguilar eí al. | An adaptive predictive approach for river level forecasting Journal of Hydroinformatics \ 15.2 | 2013 J. V. Aguilar et al. An adaptive predictive approacin for river ievei forecasting Journal of Hydroinformatics | 15.2 | 2013 J. V. Aguilar et al. An adaptive predictive approach for river ievel forecasting Journal of Hydroinformatics | 15.2 | 2013 J. V. Aguilar ef a/. | An adaptive predictive approach for river level forecasting Journal of Hydroinformatics | 15.2 | 2013 J. V. Aguilar et al. \ An adaptive predictive approacii for river level forecasting Journal of Hydroinformatics | 15.2 | 2013 J. V. Aguilar eí al. An adaptive predictive approach for river level forecasting Journal of Hydroinformatics | 15.2 { 2013
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.