MOEA/D-DE based bivariate control sequence optimization of a variable-rate fertilizer applicator

•An improved GRNN was proposed to improve the fertilization rate prediction model.•A three-objective bivariate fertilization-rate optimization model was developed.•MOEA/D-DE algorithm was proposed to optimize the control sequence. To realize precise control for a bivariate control system of a variab...

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Vydáno v:	Computers and electronics in agriculture Ročník 167; s. 105063
Hlavní autoři:	Zhang, Jiqin, Liu, Gang, Luo, Chengming, Hu, Hao, Huang, Jiayun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.12.2019 Elsevier BV
Témata:	Algorithms applicators Bivariate analysis Coefficient of variation Differential evolution (DE) Evolutionary algorithms Fertilization fertilizers General Regression Neural Network (GRNN) General regression neural networks Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) Multiple objective analysis Optimal control Optimization Precision agriculture Response time Shafts (machine elements) Variable-rate fertilization Precision agriculture General Regression Neural Network (GRNN) Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) Variable-rate fertilization Differential evolution (DE)
ISSN:	0168-1699, 1872-7107
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Abstract	•An improved GRNN was proposed to improve the fertilization rate prediction model.•A three-objective bivariate fertilization-rate optimization model was developed.•MOEA/D-DE algorithm was proposed to optimize the control sequence. To realize precise control for a bivariate control system of a variable-rate applicator, it is essential to determine the optimal control sequence, which depends on quantifying the appropriate combination of the active feed-roll length (L) and the rotational speed of the drive shaft (N). This paper presents a novel method to optimize the control sequence (L, N) to improve fertilization accuracy and uniformity, while guaranteeing the rapidity of equipment adjustment. First, the variable-rate fertilization process model was formed using an improved General Regression Neural Network (GRNN), in which the optimum spread parameter (σ=2.0304) was calculated using a differential evolutionary (DE) algorithm. Next, a three-objective problem model was developed, and the Pareto set of the control sequence was obtained using a Multi-Objective Evolutionary Algorithm based on a Decomposition (MOEA/D) algorithm. Finally, a group of control sequences representing different target fertilization rates at the weight vector of (0.90, 0.08, 0.02) was chosen and an indoor test was conducted. Results revealed that the optimized control sequence overall outperformed the traditional method. It decreased the mean relative error (RE) from 8.239% to 5.977% and coefficient of variation (CV) from 13.512% to 13.187%, while constraining the response time to around two seconds.
AbstractList	To realize precise control for a bivariate control system of a variable-rate applicator, it is essential to determine the optimal control sequence, which depends on quantifying the appropriate combination of the active feed-roll length (L) and the rotational speed of the drive shaft (N). This paper presents a novel method to optimize the control sequence (L, N) to improve fertilization accuracy and uniformity, while guaranteeing the rapidity of equipment adjustment. First, the variable-rate fertilization process model was formed using an improved General Regression Neural Network (GRNN), in which the optimum spread parameter (σ=2.0304) was calculated using a differential evolutionary (DE) algorithm. Next, a three-objective problem model was developed, and the Pareto set of the control sequence was obtained using a Multi-Objective Evolutionary Algorithm based on a Decomposition (MOEA/D) algorithm. Finally, a group of control sequences representing different target fertilization rates at the weight vector of (0.90, 0.08, 0.02) was chosen and an indoor test was conducted. Results revealed that the optimized control sequence overall outperformed the traditional method. It decreased the mean relative error (RE) from 8.239% to 5.977% and coefficient of variation (CV) from 13.512% to 13.187%, while constraining the response time to around two seconds. •An improved GRNN was proposed to improve the fertilization rate prediction model.•A three-objective bivariate fertilization-rate optimization model was developed.•MOEA/D-DE algorithm was proposed to optimize the control sequence. To realize precise control for a bivariate control system of a variable-rate applicator, it is essential to determine the optimal control sequence, which depends on quantifying the appropriate combination of the active feed-roll length (L) and the rotational speed of the drive shaft (N). This paper presents a novel method to optimize the control sequence (L, N) to improve fertilization accuracy and uniformity, while guaranteeing the rapidity of equipment adjustment. First, the variable-rate fertilization process model was formed using an improved General Regression Neural Network (GRNN), in which the optimum spread parameter (σ=2.0304) was calculated using a differential evolutionary (DE) algorithm. Next, a three-objective problem model was developed, and the Pareto set of the control sequence was obtained using a Multi-Objective Evolutionary Algorithm based on a Decomposition (MOEA/D) algorithm. Finally, a group of control sequences representing different target fertilization rates at the weight vector of (0.90, 0.08, 0.02) was chosen and an indoor test was conducted. Results revealed that the optimized control sequence overall outperformed the traditional method. It decreased the mean relative error (RE) from 8.239% to 5.977% and coefficient of variation (CV) from 13.512% to 13.187%, while constraining the response time to around two seconds.
ArticleNumber	105063
Author	Liu, Gang Huang, Jiayun Zhang, Jiqin Luo, Chengming Hu, Hao
Author_xml	– sequence: 1 givenname: Jiqin surname: Zhang fullname: Zhang, Jiqin email: zhjq2010jasmine@163.com organization: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agriculture University, Beijing 100083, China – sequence: 2 givenname: Gang surname: Liu fullname: Liu, Gang email: pac@cau.edu.cn organization: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agriculture University, Beijing 100083, China – sequence: 3 givenname: Chengming surname: Luo fullname: Luo, Chengming email: chmluo@mail.hzau.edu.cn organization: Department of Agricultural Engineering, College of Engineering, Huazhong Agricultural University, Wuhan, Hubei 430070, China – sequence: 4 givenname: Hao surname: Hu fullname: Hu, Hao email: 13205600570@163.com organization: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agriculture University, Beijing 100083, China – sequence: 5 givenname: Jiayun surname: Huang fullname: Huang, Jiayun email: s20183081301@cau.edu.cn organization: Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agriculture University, Beijing 100083, China
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Keywords	Precision agriculture General Regression Neural Network (GRNN) Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) Variable-rate fertilization Differential evolution (DE)
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Snippet	•An improved GRNN was proposed to improve the fertilization rate prediction model.•A three-objective bivariate fertilization-rate optimization model was... To realize precise control for a bivariate control system of a variable-rate applicator, it is essential to determine the optimal control sequence, which...
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SubjectTerms	Algorithms applicators Bivariate analysis Coefficient of variation Differential evolution (DE) Evolutionary algorithms Fertilization fertilizers General Regression Neural Network (GRNN) General regression neural networks Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) Multiple objective analysis Optimal control Optimization Precision agriculture Response time Shafts (machine elements) Variable-rate fertilization
Title	MOEA/D-DE based bivariate control sequence optimization of a variable-rate fertilizer applicator
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