Development and Experimental Analysis of a Fuzzy Grey Control System on Rapeseed Cleaning Loss

Chai, Xiaoyu; Xu, Lizhang; Li, Yang; Qin, Jie; Li, Yaoming; Lv, L. Y.; Zhu, Yahui

doi:10.3390/electronics9111764

Cited by 13 publications

(4 citation statements)

References 19 publications

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“…Common control methods include optimal control [ 30 ], sliding mode control [ 31 ], PID control [ 32 ], and fuzzy control [ 33 ], etc. Gao [ 34 ] used the concept of frequency scale to linearize the traditional active disturbance rejection control and proposed linear active disturbance rejection control (LADRC).…”

Section: Vehicle Height Control Based On Third-order Ladrcmentioning

confidence: 99%

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Research and application of a new method for adjusting the vehicle body height

Yao

2022

PLoS ONE

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The use of active suspension for vehicle height adjustment has problems of high cost, high energy consumption, slow response, and complex structure. This paper proposes a new method for adjusting the vehicle body using the damping asymmetric characteristic of semi-active suspensions, which is based on the idea that the dampers with damping asymmetric characteristics will cause a change in the mean position of the vehicle body vibration. To verify the feasibility of this method, a single-wheel vehicle model containing asymmetric damping is established. The system’s responses under the sinusoidal and random roads excitation are obtained by the fourth-order Runge-Kutta method, the influences of key parameters on the vehicle body’s shifting height are analyzed, and the mechanism of vehicle body’s shift is explained from the perspective of energy conservation. Then a vehicle body height controller based on third-order linear active disturbance rejection control (LADRC) is designed. Simulation results show that the proposed method for controlling the vehicle height with asymmetric damping can quickly adjust the vehicle to the expected height whether under the sinusoidal road or random road. In addition, no additional hardware and energy consumption are required, providing a new idea for vehicle height control.

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Section: Vehicle Height Control Based On Third-order Ladrcmentioning

confidence: 99%

“…Common control methods include optimal control [30], sliding mode control [31], PID control [32], and fuzzy control [33], etc. Gao [34] used the concept of frequency scale to linearize…”

Section: Vehicle Height Control Based On Third-order Ladrcmentioning

confidence: 99%

Research and application of a new method for adjusting the vehicle body height

Yao

2022

PLoS ONE

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“…Fuzzy modeling is a black-box-type framework for modeling and identification of nonlinear systems and mechanisms. Fuzzy modeling techniques have proven useful for predicting inefficiencies in agricultural machinery [10][11][12] and identifying the most relevant parameters in optimization [13,14].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Simulator: Redesign of Chickpea Harvester Reels

Golpira

Sola-Guirado

2022

Agriculture

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Conventional redesign methodologies applied on the grain harvester headers for the mechanical harvesting of chickpeas cause its progress to not be as rapid and technological. This paper presents a hybrid modeling-optimization methodology to design harvester reels for efficient chickpea harvesting. The five fabricated headers were tested in both real and virtual modeling environments to optimize the operational parameters of the reel for minimum losses. Harvesting losses data gathered from chickpea fields over ten years of trials were fed into a fuzzy logic model, which in turn was merged with simulated annealing to develop a simulator. To this end, simulated annealing was used to produce combinations of reel diameter and number of bats, to be fed into the fuzzy model until achieving a minimum harvesting loss. The proposed model predicts the reel structure measured in-field evaluation, which fits well with the previously established mathematical model. A significant improvement in harvesting performance, 71% pod harvesting, validates the benefits of the proposed fuzzy-simulated annealing approach to optimize the design of grain harvester headers.

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“…Manual adjustment is usually carried out according to the experience, which could hardly cope with the variable conditions and maintain the steady feed-rate. The advance estimation of the crop density is an effective way to support drivers; however, it has always been a difficult problem in the field of agricultural engineering, restricting the development of agricultural mechanization, automation and intelligence [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Double-Threshold Segmentation of Panicle and Clustering Adaptive Density Estimation for Mature Rice Plants Based on 3D Point Cloud

et al. 2021

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Crop density estimation ahead of the combine harvester provides a valuable reference for operators to keep the feeding amount stable in agriculture production, and, as a consequence, guaranteeing the working stability and improving the operation efficiency. For the current method depending on LiDAR, it is difficult to extract individual plants for mature rice plants with luxuriant branches and leaves, as well as bent and intersected panicles. Therefore, this paper proposes a clustering adaptive density estimation method based on the constructed LiDAR measurement system and double-threshold segmentation. The Otsu algorithm is adopted to construct a double-threshold according to elevation and inflection intensity in different parts of the rice plant, after reducing noise through the statistical outlier removal (SOR) algorithm. For adaptively parameter adjustment of supervoxel clustering and mean-shift clustering during density estimation, the calculation relationship between influencing factors (including seed-point size and kernel-bandwidth size) and number of points are, respectively, deduced by analysis. The experiment result of density estimation proved the two clustering methods effective, with a Root Mean Square Error (RMSE) of 9.968 and 5.877, and a Mean Absolute Percent Error (MAPE) of 5.67% and 3.37%, and the average accuracy was more than 90% and 95%, respectively. This estimation method is of positive significance for crop density measurement and could lay the foundation for intelligent harvest.

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Development and Experimental Analysis of a Fuzzy Grey Control System on Rapeseed Cleaning Loss

Cited by 13 publications

References 19 publications

Research and application of a new method for adjusting the vehicle body height

Research and application of a new method for adjusting the vehicle body height

Data-Driven Simulator: Redesign of Chickpea Harvester Reels

Double-Threshold Segmentation of Panicle and Clustering Adaptive Density Estimation for Mature Rice Plants Based on 3D Point Cloud

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