Advanced process control of irrigation: the current state and an analysis to aid future development

McCarthy, Alison C. and Hancock, Nigel H. and Raine, Steven R. (2013) Advanced process control of irrigation: the current state and an analysis to aid future development. Irrigation Science, 31 (3). pp. 183-192. ISSN 0342-7188

Abstract

Control engineering approaches may be applied to irrigation management to make better use of available irrigation water. These methods of irrigation decision-making are being developed to deal with spatial and temporal variability in field properties, data availability and hardware constraints. One type of control system is advanced process control which, in an irrigation context, refers to the incorporation of multiple aspects of optimisation and control. Hence, advanced process control is particularly suited to the management of site-specific irrigation. This paper reviews applications of advanced process control in irrigation: mathematical programming, linear quadratic control, artificial intelligence, iterative learning control and model predictive control. From the literature review, it is argued that model-based control strategies are more realistic in the soil-plant-atmosphere system using process simulation models rather than using ‘black-box’ crop production models. It is also argued that model-based control strategies can aim for specific end of season characteristics and hence may achieve optimality. Three control systems are identified that are robust to data gaps and deficiencies and account for spatial and temporal variability in field characteristics, namely iterative learning control, iterative hill climbing control and model predictive control: from consideration of these three systems it is concluded that the most appropriate control strategy depends on factors including sensor data availability and grower’s specific performance requirements. It is further argued that control strategy development will be driven by the available sensor technology and irrigation hardware, but also that control strategy options should also drive future plant and soil moisture sensor development.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2011 Springer-Verlag. Published online 8 Dec 2011. Published version deposited in accordance with the copyright policy of the publisher.
Depositing User: Ms Alison McCarthy
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - No Department
Date Deposited: 17 Oct 2012 02:59
Last Modified: 14 Oct 2014 01:52
Uncontrolled Keywords: variable-rate; water productivity; management; automation
Fields of Research (FOR2008): 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070103 Agricultural Production Systems Simulation
09 Engineering > 0913 Mechanical Engineering > 091302 Automation and Control Engineering
07 Agricultural and Veterinary Sciences > 0799 Other Agricultural and Veterinary Sciences > 079901 Agricultural Hydrology (Drainage, Flooding, Irrigation, Quality, etc.)
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970107 Expanding Knowledge in the Agricultural and Veterinary Sciences
Identification Number or DOI: doi: 10.1007/s00271-011-0313-1
URI: http://eprints.usq.edu.au/id/eprint/20211

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