Advanced process control of irrigation: the current state and an analysis to aid future development
Article
Article Title | Advanced process control of irrigation: the current state and an analysis to aid future development |
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ERA Journal ID | 3502 |
Article Category | Article |
Authors | McCarthy, Alison C. (Author), Hancock, Nigel H. (Author) and Raine, Steven R. (Author) |
Journal Title | Irrigation Science |
Journal Citation | 31 (3), pp. 183-192 |
Number of Pages | 10 |
Year | 2013 |
Place of Publication | Heidelberg, Germany |
ISSN | 0342-7188 |
1432-1319 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00271-011-0313-1 |
Web Address (URL) | http://link.springer.com/article/10.1007%2Fs00271-011-0313-1 |
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. |
Keywords | variable-rate; water productivity; management; automation |
ANZSRC Field of Research 2020 | 300205. Agricultural production systems simulation |
400799. Control engineering, mechatronics and robotics not elsewhere classified | |
300201. Agricultural hydrology | |
Public Notes | © 2011 Springer-Verlag. Published online 8 Dec 2011. Published version deposited in accordance with the copyright policy of the publisher. |
Byline Affiliations | National Centre for Engineering in Agriculture |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q1038/advanced-process-control-of-irrigation-the-current-state-and-an-analysis-to-aid-future-development
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