A novel nonlinear model predictive control approach to minimize losses in open-channel irrigation systems

Abstract

Open-channel irrigation systems (OCIS) are widely recognized as the simplest, most widely adopted, and economically advantageous method of water transportation in agriculture. However, these systems often experience substantial water losses due to seepage and leaks, which are closely correlated with the channel levels. The control mechanisms commonly discussed for OCIS primarily emphasize preserving constant channel levels, leading to constant losses. In order to overcome this limitation, the present study offers a paradigm change in control objectives, thereby facilitating the management of channel levels and flow variability. The new objectives seek to address user water demands, mitigate channel water levels, prevent overflow instances, and limit ecological and infrastructural impacts. A request-based operational approach is utilized in implementing a nonlinear model predictive control (NMPC) technique. The NMPC employs a simple modeling framework that can effectively describe the behavior of OCIS in various operational settings. It also integrates a receding-horizon cost function to optimize water allocation and minimize water levels. The provided design of the control strategy includes a set of feasible conditions that ensure the operability and stability of the controlled system. The efficacy of the proposed control technique has been verified by experimentation on a well-established testbed documented in previous academic papers. This validation process has demonstrated a notable 50% decrease in water waste while simultaneously ensuring sufficient water supply to users.