Model predictive control of combined irrigation and water supply systems: Application to the Guadiana river

Puig, Vicenç; Ocampo‐Martínez, Carlos; Romera, Juli; Quevedo, Joseba; Negenborn, Rudy R.; Rodríguez, Pilar; Campos, S.

doi:10.1109/icnsc.2012.6204896

Cited by 17 publications

(6 citation statements)

References 5 publications

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“…Now, recursive least square is used to ease the implementation into a real-time algorithm. Recursive least square is an online implementation of least squares where the estimated parameter is predicted and corrected by using the current measurement [24], in the form of ( ℎ + ℎ) =̂( ℎ) + ( ℎ + ℎ) ( ℎ) ( ℎ + ℎ) , (17) where ( ℎ + ℎ) = ( ℎ + ℎ) −̂( ℎ) ( ℎ) is the a priori estimate error and ( ℎ + ℎ) is the adaptation gain that can be updated by…”

Section: Coefficients Estimationmentioning

confidence: 99%

“…By using (17) and (18), an offline algorithm can be implemented in order to estimate coefficients 1 , 2 , and 3 , as shown in Algorithm 1.…”

Section: Coefficients Estimationmentioning

confidence: 99%

“…For process identification grass was used instead of a specific crop, since grass = 1; that is, et = et . After data was captured, an offline recursive least square algorithm defined by (17) and (18) was executed on Matlab (http://www.mathworks.com/) in order to obtain the process dynamics coefficients 1 , 2 , and 3 , for each one of the three volumetric water levels:…”

Section: Plant Dynamics Validationmentioning

confidence: 99%

“…Model predictive control has been proposed as a suitable technique for large water distribution systems. In [17], MPC is used to generate flow control strategies from the sources of water to the consumer and irrigation areas to achieve safety volumes in dams and flow control stability. In [18], a hierarchical system to control an irrigation canal is proposed, where a centralized predictive controller controls the inflow to the canal and coordinates the local controllers by modifying their set-points.…”

Section: Introductionmentioning

confidence: 99%

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Sensor-Based Model Driven Control Strategy for Precision Irrigation

et al. 2016

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Improving the efficiency of the agricultural irrigation systems substantially contributes to sustainable water management. This improvement can be achieved through an automated irrigation system that includes a real-time control strategy based on the water, soil, and crop relationship. This paper presents a model driven control strategy applied to an irrigation system, in order to make an efficient use of water for large crop fields, that is, applying the correct amount of water in the correct place at the right moment. The proposed model uses a predictive algorithm that senses soil moisture and weather variables, to determine optimal amount of water required by the crop. This proposed approach is evaluated against a traditional irrigation system based on the empirical definition of time periods and against a basic soil moisture control system. Results indicate that the use of a model predictive control in an irrigation system achieves a higher efficiency and significantly reduce the water consumption.

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Section: Coefficients Estimationmentioning

confidence: 99%

“…By using (17) and (18), an offline algorithm can be implemented in order to estimate coefficients 1 , 2 , and 3 , as shown in Algorithm 1.…”

Section: Coefficients Estimationmentioning

confidence: 99%

Section: Plant Dynamics Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensor-Based Model Driven Control Strategy for Precision Irrigation

et al. 2016

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“…Our system is though proposed only for the proper control of gates but further it can be extended for proper supply of water for irrigation and to households too by implementing additional system which was proposed by Puig, V. , Ocampo-Martinez, C. Romera, J. Quevedo, J. Rodriguez, P. Campos, S. Negenborn. Their system [9] comprised of a model predictive control which considers the flow control strategies of water in dams. In reality there are many problems that occur in dams like deposition of silts, sediment souring which creates negative impact on flood control and water draining, water body quality, aquatic biology and marine navigation, etc.…”

Section: Literature Surveymentioning

confidence: 99%

Embedded Dam Gate Control System using 'C' and Visual Basic

Iyer¹,

Pai²,

Badri³

et al. 2013

IJCA

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Now-a-days water scarcity has become a serious problem in India and there are many factors responsible for this like improper supply of water from the dam, improper water saving systems, etc. but one major factor is the improper opening and closing of the dam gate according to the level of water in the dam. Also till date the control mechanism of the dam gates are done manually and using PLC. But there are lots of errors in manual method. Also the PLC based system is huge and hence suitable for major dams due to its cost. For medium and small dams like irrigation dams does not require such huge PLC systems. So to reduce these problems a mechatronic control system is proposed in this paper. This project is a AT89S51 microcontroller based dam gate control system which helps in keeping an eye on the frequent usage of water resources from dam for irrigation purposes and efficient operation of dam gate according to the level of water and also helps in indicating about flood to people living in the surrounding. This proposed mechanism of dam gate control reduces the water wastage and efficient usage of available water is ensured. Also there are heavy load shedding problems in the villages in almost all states of India. To overcome these problems the proposed dam gate control system can be combined with the operation of the geothermal and nuclear power plants for generation of electricity .

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Intelligent decision‐making for fertigation treatment of tomatoes cultivated in greenhouse: An experimental study

Li,

Hu,

et al. 2024

Irrigation and Drainage

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To verify the effectiveness of the intelligent decision method for fertigation, an automatic control system for fertigation in greenhouses was designed, and three intelligent decision methods based on evapotranspiration (T1), soil moisture (T2) and accumulated temperature (T3) were tested. Intelligent decisions included monitoring meteorological information, automatically monitoring soil moisture, utilizing fertigation application systems and using automated control modules. The system was stable and accurately controlled according to the decision scheme. The results showed that the average errors of the automated control system for decision‐making and irrigation were 1.1 and 0.8%, respectively. The study findings serve as a reference for the integration of intelligent irrigation decision‐making and control systems and for further improving the efficiency of water and fertilizer utilized. Compared with those of the control, the three intelligent decision‐making methods increased the tomato yield by 8, 12 and 7%, respectively. In addition, the irrigation water and fertilizer levels decreased significantly compared with those in the control treatment. Although the accuracy of the soil water content (SWC) estimated based on ET and temperature in irrigation decision‐making is low, the general trend is consistent with practice. In addition, the irrigation water use efficiency (IWUE) and partial factor productivity of fertilizer (PFP) were significantly improved. Similarly, the IWUE in T1 was the highest (60 kg m⁻3), and the PFP in T3 was the highest (669 kg kg⁻¹).

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Model predictive control of combined irrigation and water supply systems: Application to the Guadiana river

Cited by 17 publications

References 5 publications

Sensor-Based Model Driven Control Strategy for Precision Irrigation

Sensor-Based Model Driven Control Strategy for Precision Irrigation

Embedded Dam Gate Control System using 'C' and Visual Basic

Intelligent decision‐making for fertigation treatment of tomatoes cultivated in greenhouse: An experimental study

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Model predictive control of combined irrigation and water supply systems: Application to the Guadiana river

Cited by 17 publications

References 5 publications

Sensor-Based Model Driven Control Strategy for Precision Irrigation

Sensor-Based Model Driven Control Strategy for Precision Irrigation

Embedded Dam Gate Control System using 'C&apos; and Visual Basic

Intelligent decision‐making for fertigation treatment of tomatoes cultivated in greenhouse: An experimental study

Contact Info

Product

Resources

About

Embedded Dam Gate Control System using 'C' and Visual Basic