Model Predictive Control for Closed-Loop Irrigation

Lozoya, Camilo; Mendoza, Carlos Enrique Guzmán; Mejía, Leonardo; Quintana, Jesus; Mendoza, Gilberto; Bustillos, Manuel; Arras, Octavio; Solis, Luis Asencios

doi:10.3182/20140824-6-za-1003.02067

Cited by 58 publications

(35 citation statements)

References 8 publications

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“…The irrigation problem has input constraints in terms of optimal irrigation volume and output constraints in terms of soil moisture thresholds and the desired plant response to water deficits [135]. Ooi et al [139], Lozoya et al [101] and Saleem et al [135] described a model predictive control framework for irrigation scheduling based on a soil moisture balance model. They employed a system identification procedure to generate a grey box model of the soil-plant-atmosphere system with a network of soil moisture sensors providing real-time feedback to the control algorithm.…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…They do not incorporate any form of sensor feedback on plant water status, soil water status and climatic variables [101]. This 'open-loop' strategy is largely designed based on heuristics and historical data.…”

Section: Decision Supportmentioning

confidence: 99%

“…Model predictive control employs a plant model and optimization algorithm to calculate plant inputs in order to achieve a future value of a performance criterion. The system performance is predicted over a finite horizon subject to constraints on both the inputs and outputs of the plant [101]. Readers are directed to [135][136][137] for an in-depth review of the theory of model predictive control and its application in various industries.…”

Section: Model Predictive Controlmentioning

confidence: 99%

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Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil-plant-atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

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Section: Model Predictive Controlmentioning

confidence: 99%

Section: Decision Supportmentioning

confidence: 99%

Section: Model Predictive Controlmentioning

confidence: 99%

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Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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“…A daily plan model to the irrigation of a crop field using optimal control was developed in [9]. In [5,[10][11][12], a data-driven model predictive controller was proposed for automatic control of irrigation systems. However, conventional methods such as on-off or proportional control methods are not effective due to energy loss and productivity [13].…”

Section: Control Of Irrigation Systemsmentioning

confidence: 99%

Modeling and Control of Irrigation Systems: A Short Survey and Future Directions

Çetin¹,

Beyhan²

2019

acperpro

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Modeling and control of irrigation systems has importance for the yield of the crops and especially, saving the water sources has prominent importance for the world. In this paper, recent modeling and control methods for the irrigation systems are surveyed for the future applications. Even though a mathematical model is constructed for the amount of water in soil, there are too many unpredictable disturbances on the irrigation systems so that the mathematical model cannot be exact. Therefore, the accuracy of the literature control methods is less than expected results. As a solution, the unknown model-based control methods and also accurate prediction methods are going to be promising approaches for irrigation systems in future.

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“…This directly results in an increase in food demand by about 70% . Presently, the water‐use efficiency of current irrigation systems is around 50–60% due to poor irrigation strategies . Therefore, an important step toward managing the water crisis is to increase the water‐use efficiency in agriculture irrigation.…”

Section: Introductionmentioning

confidence: 99%

Optimal sensor placement for agro‐hydrological systems

2019

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The estimation of soil moisture is essential for developing advanced closed‐loop irrigation schemes. One associated problem is how to place the sensors appropriately in the soil to provide good measurements for state estimation. In this work, we address the problem of optimal sensor placement for state estimation of agro‐hydrological systems. A systematic approach is proposed to find the minimum number of sensors that ensures the observability of the entire system and then to find the best locations of the sensors in terms of degree of observability. The Richards equation that is used to describe the dynamics of the agro‐hydrological system is discretized into a large‐scale nonlinear state‐space model. In the proposed procedure, the key steps include order reduction of the large‐scale system model, exploration of the minimum number of sensors needed for state estimation and optimal placement of the sensors in the soil. Three different scenarios are considered and optimal sensor placement is addressed for all the scenarios using the proposed procedure. Simulation results show the effectiveness of the proposed procedure and methods.

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Model Predictive Control for Closed-Loop Irrigation

Cited by 58 publications

References 8 publications

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Modeling and Control of Irrigation Systems: A Short Survey and Future Directions

Optimal sensor placement for agro‐hydrological systems

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