Fuzzy Control Model Study on Precision Irrigation System for Water Stress in Crops

Wang, Xiuqing; You, Guodong; Yang, Songfan

doi:10.4304/jcp.6.5.955-962

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…The former system is to control systems and the latter is to correct the three parameters of PID control automatically. Normally, the digital PID controller can be expressed as the following function [15][16][17][18]:…”

Section: Grey Prediction Fuzzy Pid Control and Design Principle 241mentioning

confidence: 99%

MBD of grey prediction fuzzy-PID irrigation control technology

Zhang¹,

Wei²,

Lin³

et al. 2018

Desalination and Water Treatment

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a b s t r a c tFor the water and fertilizer ratio adjustment, irrigation control precision and problems and development cycle in the agricultural water-fertilizer irrigation, the control model of the combination of proportion-integral-derivative (PID) control, fuzzy control and grey prediction control were designed, and the controller design was carried out based on the model design method. It can achieve the engineering code to the embedded hardware requirements of the whole process of automation, efficiency of water and fertilizer irrigation controller, rapid development and application. PID control was based on fuzzy rules and query fuzzy matrix table to obtain parameter self-turning, then it could adjust PID parameters in real time, furthermore, joined multi-factors grey forecasting multi-variable grey model (1, n), so as to effectively predict the water requirement of the crop and achieve the precise irrigation of the system. The running results the system reduces the overshoot by 14.68% compared with the conventional PID control, and the adjustment time of the system is reduced by 86.62%. Compared with the fuzzy PID control, the overshoot is slightly increased by 1.9%, but the adjustment time is reduced by 177.71%. Meanwhile, in the water and fertilizer concentration control system, the controller is developed quickly, the code quality is high, the system response speed is good, the real time is good and stable, and the effect of water and fertilizer is greatly improved.

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Section: Grey Prediction Fuzzy Pid Control and Design Principle 241mentioning

confidence: 99%

MBD of grey prediction fuzzy-PID irrigation control technology

Zhang¹,

Wei²,

Lin³

et al. 2018

Desalination and Water Treatment

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“…With a qualitative approach, fuzzy systems offer a methodology to simulate a human expert operational behaviour and allow using available data from these experts' knowledge. Fuzzy expert systems have been largely used in control systems (Benyakhlef&Radouane, 2008;Chiu &Lian, 2009;Yu et al, 2010;Feng, 2010;Wang et al, 2011), since when Mamdani and Assilian developed a fuzzy controller for a boiler (Mamdani&Assilan, 1975).…”

Section: Usage Of Fuzzy Logic In Aluminum Industrymentioning

confidence: 99%

Fuzzy Control Applied to Aluminum Smelting

Pereira¹,

Oliveira²,

Soares³

2012

Fuzzy Logic - Controls, Concepts, Theories and Applications

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Fuzzy Logic-Controls, Concepts, Theories and Applications 254 20 smelters are responsible for 65% or world production. Most companies work only with Aluminum, but 20% of them work with Aluminum with other metals or mines. Half of Aluminum production is done by companies vertically integrated, from bauxite mining to metal recycling (IAI, 2010). For all these reasons, the Aluminum can be considered a highly important metal, and therefore, its production is a target for many research activities. Researchers all around the world make efforts in making Aluminum production a less costly process, since it spends a lot of energy, and is very complex. In this chapter, we are going to present the whole context, and why and where fuzzy control is important to assist plant operators. The impact and consequences of this work is the use of rules defined by process operators indirectly through the huge database which provides historic information including control decisions made by them. Since this strategy emulates process operator, it can be said that an expert system can provide this personnel more time to concentrate on other activities. Moreover, this technique will be continually improved by revising its rules and evaluation, provided that fuzzy decisions will also have an impact, and this should be analysed and adjusted. 1.1 Aluminum production process Aluminum has been produced through the Hall-Héroult process, named after its inventors. So far, this is the only industrial way to produce this metal. Primary Aluminum is produced in a liquid form, through an electrolytic reduction of alumina (Al 2 O 3) in a cryolite bath (Na 3 AlF 6). This reaction takes place in electrolytic pots, as shown in Figure 2. Fig. 2. Sketch of an Alumina reduction pot of prebake type (adapted from Kola & Store, 2009). www.intechopen.com Fuzzy Control Applied to Aluminum Smelting 255 Inside these pots, also often called cells, Alumina is fed through silo and it is electrically consumed by the carbon anodes (Solheim, 2005), and as shown in the equation (1), the anode is also consumed during the electrolytic process.

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“…Feng [22] constructed the agricultural mechanization decision support system based on multi-agent technology by designing multi-agent development platform in combination with the system analysis and evaluation methods, which could analysis, evaluate, simulate and predict agricultural mechanization systems of Henan province. Wang et al [23] designed the precision irrigation fuzzy control system based on crop water stress acoustic monitoring, which could realize safety and efficiency in precision irrigation. Ning and Kuang [24] developed the agricultural machinery information inquiry system of Yunnan province by using Access.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Decision Support System for Equipping Farm Machines

Wang¹,

Yang²,

Yan³

2014

JNW

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Equipping farm machines is the key link of agricultural production process. The decision support system of equipping farm machines is able to aid managers to make scientific and effective decision. In this paper, the decision support system of equipping farm machines is designed and developed based on the related theories and the thought of prototype. The system chooses Delphi 7.0 as development language, and uses three classic equipping methods to establish system models. For the complex linear programming model, firstly it is established by M-file of Matlab, then COM components are generated; finally Delphi calls the COM components to solve. The database of the system is established and managed by SQL Server 2005. It can be seen from the result of the system application study that the system could assist users to equip farm machines more scientifically and dynamically.

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Fuzzy Control Model Study on Precision Irrigation System for Water Stress in Crops

Cited by 8 publications

References 5 publications

MBD of grey prediction fuzzy-PID irrigation control technology

MBD of grey prediction fuzzy-PID irrigation control technology

Fuzzy Control Applied to Aluminum Smelting

Design and Development of Decision Support System for Equipping Farm Machines

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