A Receding Horizon Control algorithm for adaptive management of soil moisture and chemical levels during irrigation

Abstract

The capacity to adaptively manage irrigation and associated contaminant transport is desirable from the perspectives of water conservation, groundwater quality protection, and other concerns. This paper introduces the application of a feedback-control strategy known as Receding Horizon Control (RHC) to the problem of irrigation management.The RHC method incorporates sensor measurements, predictive models, and optimization algorithms to maintain soil moisture at certain levels or prevent contaminant propagation beyond desirable thresholds. Theoretical test cases are first presented to examine the RHC scheme performance for the control of soil moisture and nitrate levels in a soil irrigation problem. Then, soil moisture control is successfully demonstrated for a centerpivot system in Palmdale, CA where reclaimed water is used for agricultural irrigation.Real-time soil moisture, temperature, and meteorological data are streamed wirelessly to a field computer to enable autonomous execution of the RHC algorithm. The RHC scheme is demonstrated to be a viable strategy for achieving water reuse and agricultural objectives while minimizing negative impacts on environmental quality.

Feedback control of soil moisture in precision-agriculture systems: Incorporating stochastic weather forecasts

“…Many of the scheduling strategies do employ feedback [4], [8], [9], but these feedback algorithms largely do not explicitly use models of soil-moisture dynamics. An important recent study [1] has introduced a receding-horizon-control methodology for managing soil moisture, which does use predictive models and sensor measurements to optimize irrigation plans. The receding-horizon methodology appears to be very promising for irrigation control (scheduling), especially given the non-linear and highly constrained form of the control problem and the need for shaping transient dynamics at low cost.…”

“…In particular, there is a growing recognition that precise control of agricultural parameters such as soil moisture can significantly increase agriculture-system performance (in terms of crop yields, resource use, environmental impact, etc); concurrently, the new technologies being embedded in agriculture systems can provide the means to achieve such precise control. Indeed, based on this recognition, a vibrant research and development effort is in progress to develop new control and management technologies for irrigation systems, and to exploit ubiquitous sensor data for soil-moisture monitoring and control (e.g., [1], [2], [3], [4]). Here, we introduce a new thrust in this research effort, on using stochastic weather-forecast data to anticipate and better manage soilmoisture profiles through irrigation.…”

“…The exploratory research that we present here builds on an extensive literature on modeling soil-moisture dynamics [2], [5], using sensor data (both to parameterize these models and to monitor the dynamics) [2], [3], [6], [7], and controlling/scheduling irrigation systems [1], [4], [8], [9]. It is important to stress, in particular, that irrigation of agricultural systems has long depended on dynamical systems and control-theory concepts.…”

Distributed programming framework for fast iterative optimization in networked cyber-physical systems

“…Actuator control problems in several important CPS applications, such as water-efficient irrigation [Park et al 2009], energy-efficient HVACs, selfreconfiguring visual surveillance [Kansal et al 2006], and personalized light control [Singhvi et al 2005], are often expressed as optimization of a cost function, involving control inputs to actuators and sensor data, through techniques proposed in model predictive control. Programmers implement these applications by selecting an optimization algorithm that satisfies their performance requirements given energy constraints and network characteristics of the deployment.…”

A Sustainable Model For Optimal Dynamic Allocation Of Patrol Tugs To Oil Tankers

“…Model predictive control (MPC) or receding horizon control (RHC) is a class of control algorithms that uses explicit process models to predict the future response of a system and guide a system to a desired output using optimization as an intermediate step (Park et al 2009). Receding horizon optimization is widely recognized as a highly practical approach with high performance (Zheng et al 2011).…”