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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Water Management and Conservation Research » Research » Publications at this Location » Publication #145069


item Wahlin, Brian

Submitted to: Journal of Irrigation and Drainage Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/15/2003
Publication Date: 6/1/2004
Citation: Wahlin, B.T. 2004. Performance of model predictive control on asce test canal 1. Journal of Irrigation and Drainage Engineering. 130(3)227-238.

Interpretive Summary: Water is becoming a scarce resource, and irrigation districts are under pressure to use water more effectively. Computerized automatic control of irrigation canals has the potential of improving the operation efficiency of irrigation districts. Model Predictive Control (MPC) is a popular control algorithm in the process control industry. One of the reasons for the popularity of MPC is that the logic behind it is intuitive. In fact, the MPC control strategy is similar to the strategy that people use to drive a car. There are many other aspects of MPC that make it ideally suited for the automatic control of irrigation canals. The performance of MPC was examined using a set of standardized tests for control algorithms. These results will be of use to irrigation districts and consultants.

Technical Abstract: Model Predictive Control (MPC) has become increasingly popular in the process control industry. MPC is particularly suited to the automatic control of irrigation water delivery systems because it explicitly accounts for the long delay times encountered in open-channel flow. In addition, a feedforward routine is easy to implement in MPC and many of the constraints that canal operators face can be directly incorporated into the MPC scheme. The ASCE Task Committee on Canal Automation Algorithms developed a series of test cases to evaluate the performance of canal control algorithms. In this paper, simulation tests were performed on the ASCE test canal 1 using a remote downstream control implementation of MPC. The performance of the MPC algorithm on the ASCE test canal 1 is similar to other proposed controllers. When there are no minimum gate movement constraints, MPC is fairly robust because the controller performance did not significantly degrade under untuned conditions. In the presence of minimum gate movement constraints, oscillations can develop under untuned conditions at low flows. At high flows, the feedforward routine in MPC did not perform as well as expected. This underperformance is do the simplifications made by the underlying process model.