Prediction of soil compaction under pneumatic tires a using fuzzy logic approach

Çarman, Kazım

doi:10.1016/j.jterra.2008.10.001

Cited by 57 publications

(43 citation statements)

References 10 publications

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“…Because of its importance, fuzzy logic techniques were proposed for cushion pressure control thus minimizes the total power consumption [6][7][8]. To improve the performance on wheeled air-cushion and semi-tracked air-cushion vehicles, fuzzy logic system (FLS) has been applied successfully to a large number of expert applications.…”

Section: Introductionmentioning

confidence: 99%

Cushion pressure control system for an intelligent air-cushion track vehicle

2011

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This paper presents the control system of cushion pressure for the developed intelligent air-cushion track vehicle (IACTV) for operating on swamp terrain and wet fields. A novel auto-adjusting supporting system is designed for the vehicle's intelligent air-cushion system. Focusing on minimizing the total power demand of the vehicle, an optimization model has been established, for examining the effects of vehicle parameters and load distribution on power consumption by controlling air-cushion pressure. Then optimum cushion pressure is determined based on the developed optimum pressure -sinkage relationship and the pressure in the cushion chamber is controlled by the Fuzzy controller by maintaining volume flow rate and continuously monitored by the pressure sensor attached with the cushion chamber. The ultrasonic displacement sensor is used to measure the sinkage of the vehicle. The output voltages of the ultrasonic displacement are used to operate the pull-in solenoid switch through the microcontroller which closes the circuit of the compressor motor. Distribution of vehicle load to the air-cushion system is controlled by Fuzzy Logic controller by maintaining the inside pressure of the cushion.

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Section: Introductionmentioning

confidence: 99%

Cushion pressure control system for an intelligent air-cushion track vehicle

2011

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“…The predictive ability of the developed systems (ANN and TM) were investigated according to mathematical and statistical methods. In order to determine the relative error (  ) of the system, the following equation was used [21]. Where n is the number of observations, y is the measured value and ŷ is the predicted value.…”

Section: Methodsmentioning

confidence: 99%

“…The relative error gives the deviation between the predicted and experimental values and it is required to reach zero. In addition, goodness of fit ( ) of the predicted system was calculated by the following equation [21,22]. Where y is the mean of measured values.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Tire Tractive Performance by Using Artificial Neural Networks

Çarman

Taner

2012

MCA

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Abstract-The purpose of this study was to investigate the relationship between travel reduction and tractive performance and to illustrate how artificial neural networks (ANNs) could play an important role in the prediction of these parameters. The experimental values were taken in a soil bin. A 1-4-6-2 artificial neural network (ANN) model with a back propagation learning algorithm was developed to predict the tractive performance of a driven tire in a clay loam soil under varying operating and soil conditions. The input parameter of the network was travel reduction. The output parameters of the network were net traction ratio and tractive efficiency. The relationships were investigated using non-linear regression analysis and ANNs. The performance of the neural network-based model was compared with the performance of a non linear regression-based model using the same observed data. It was found that the ANN model consistently gave better predictions compared to the non linear regressionbased model. Based on the results of this study, ANNs appear to be a promising technique for predicting tire tractive performance.

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“…Accordingly, fuzzy modeling as a powerful tool has been used to evaluate different aspects of sustainable agriculture; i.e. farm machinery and tillage systems [35,36], fertilizer and herbicide application [37], energy utilization and sustainability [38][39][40], and postharvest technologies [41]. Nonetheless, a large number of studies on energy inputs for crop production have used a computational framework without any modeling [42,11,22,[43][44][45][46][47][48].…”

Section: Why Using Fuzzy Modeling and Dea Technique?mentioning

confidence: 99%

Sustainable and efficient energy consumption of corn production in Southwest Iran: Combination of multi-fuzzy and DEA modeling

Houshyar

Azadi

Almassi

et al. 2012

Energy

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Abstract:The goal of this study was to evaluate the sustainability and efficiency of corn production with regard to energy consumption in the Fars province, Southwest Iran. To reach the goal, fuzzy modeling and Data Envelopment Analysis (DEA) were employed. The fuzzy model included three sub-models which assessed the sustainability use of energy inputs, i.e. machinery, fuel, fertilizer, chemical, human power, seed, and electricity. Some DEA models like CCR (Charnes, Cooper and Rhodes), BCC (Banker, Charnes and Cooper) and SBM (Slack Based Measure) were applied in assessment of efficiency scores. Using non-hierarchical cluster analysis, 89 farmers were chosen in two clusters including 47 and 42 farmers as the first and second cluster, respectively. The farmers in cluster 2 used 44238.235 MJ/ha energy input which was 2500 MJ/ha more than the first cluster. The fuzzy modeling revealed that the sustainability indices were higher in the first cluster. However, the indices were in the ranges of very low to medium for all the farmers, representing that the current corn production system is not sustainable. Also, the DEA models revealed that the first cluster also held more efficient farmers. Since DEA defines a reference set of efficient for inefficient farmers to follow, lead inefficient farmers to improve their sustainability up to efficient ones. Therefore, efficient and inefficient farmers must change the trends of energy utilization for approaching even more sustainable production.

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Prediction of soil compaction under pneumatic tires a using fuzzy logic approach

Cited by 57 publications

References 10 publications

Cushion pressure control system for an intelligent air-cushion track vehicle

Cushion pressure control system for an intelligent air-cushion track vehicle

Prediction of Tire Tractive Performance by Using Artificial Neural Networks

Sustainable and efficient energy consumption of corn production in Southwest Iran: Combination of multi-fuzzy and DEA modeling

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