Intelligent air-cushion system for a swamp peat terrain vehicle /
This study describes the development of an intelligent air-cushion system for a swamp peat terrain vehicle. The intelligent air-cushion system increases the vehicle floatation capacity and decreases the vehicle ground contact pressure less than the bearing capacity of the swamp peat terrain of 7 kN/...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Gombak, Selangor:
Kulliyyah of Engineering, International Islamic University Malaysia,
2011
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| Subjects: | |
| Online Access: | Click here to view 1st 24 pages of the thesis. Members can view fulltext at the specified PCs in the library. |
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| Summary: | This study describes the development of an intelligent air-cushion system for a swamp peat terrain vehicle. The intelligent air-cushion system increases the vehicle floatation capacity and decreases the vehicle ground contact pressure less than the bearing capacity of the swamp peat terrain of 7 kN/m2. The vehicle can traverse on unprepared swamp peat terrain with the help of an air-cushion system. The air-cushion system is protected from external disturbances by using spring loaded auto adjusted supporting system. The hovering pressure in the air-cushion is provided by an air-compressor through microcontroller controlled proportional valve. The microcontroller is activated from the output reading of a distance sensor attached with the vehicle. The main contribution of this study has been focused on developing the mathematical model for swamp peat terrain air-cushion tracked vehicle development, simulating the model for optimizing the design parameters of the vehicle and intelligent air-cushion system, developing a fuzzy logic controller to control the air-cushion system in an uncertain environment, and investigating the vehicle performance. The vehicle vertical displacement and rate of position error have been used as the input variables and flow rate as output. For this system an appropriate control strategy has been developed by using fuzzy logic to actuate the proportional valve. The results of the developed fuzzy based model on flow rate have been compared with the experimental results to demonstrate the performance of the intelligent system. The means of measured and predicted flow rate have been found as 77.78 % and 70.29%, respectively. The correlation coefficient has been found as 0.971 which substantiates the model. Moreover, the mean relative error of actual and predicted values from the model has been found as 10.93%, which is almost equal to the acceptable limit of 10% based on terramechanics. The goodness of fit of the prediction values from the model has been found as 0.91 which is close to 1.0 as expected. Several experiments have been conducted to investigate the tractive performance of the vehicle without and with the intelligent controller. Experiment and simulation results show that the optimal power consumption can be obtained by using the developed model with the load distribution ratio of 0.26 and tractive efficiency of 56% based on terramechanics. From the variation of traction force of the air-cushion tracked vehicle, it has been found that traction force increases due to the addition of the intelligent air-cushion system to the vehicle. The results reveal that the vehicle equipped with the intelligent system has, overall, the best performance, giving about 51.6% increase in traction force as compared to without intelligent system. Furthermore, validation of the developed model has been carried out by making comparison between the measured tractive performance of the vehicle and the predicted data from the fuzzy based model. The correlation coefficients of traction force, motion resistance, tractive efficiency and total power consumption have been found as 0.993, 0.995, 0.991, and 0.945, respectively. The mean relative error of measured and predicted values from the model have been found as 5.07%, 4.50%, 5.79% and 3.52%, respectively. For all parameters, the relative error of predicted values are found to be less than the acceptable limits of 10%. The goodness of fit of the prediction values from the model have been found as 0.987, 0.913, 0.981 and 0.903, respectively. All values are found to be closed to 1.0 as expected. |
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| Item Description: | Abstracts in English and Arabic. "A thesis submitted in fulfilment of the requirement for the degree of Doctor of Philosophy."--On t.p. |
| Physical Description: | xxiv, 183 leaves : ill. charts ; 30cm. |
| Bibliography: | Includes bibliographical references (leaves 159-168). |