Fuzzy expert system for controlling swamp terrain intelligent air-cushion tracked vehicle

A fuzzy expert system was used in this study to control an intelligent air-cushion tracked vehicle (IACTV) as it operated in a swamp peat terrain. The system was effective in controlling the intelligent air-cushion vehicle while measuring the vehicle traction (TE), motion resistance (MR), power cons...

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Bibliographic Details
Published in:International journal of automotive technology 2011, 12(5), , pp.745-753
Main Authors: Hossain, A., Rahman, A., Mohiuddin, A. K. M.
Format: Article
Language:English
Subjects:
Automobiles
Automotive Engineering
Consumption
Control systems
Design engineering
Energy consumption
Engineering
Expert systems
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Load
Mathematical models
Mechanical engineering
Power consumption
Sensors
Studies
Vehicles
자동차공학
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Summary:A fuzzy expert system was used in this study to control an intelligent air-cushion tracked vehicle (IACTV) as it operated in a swamp peat terrain. The system was effective in controlling the intelligent air-cushion vehicle while measuring the vehicle traction (TE), motion resistance (MR), power consumption (PC), cushion clearance height (CCH) and cushion pressure (CP). An ultrasonic displacement sensor, pull-in solenoid electromagnetic switch, pressure-control sensor, microcontroller, and battery pH sensor were incorporated into the fuzzy expert system (FES) to experimentally determine the TE, MR, PC, CCH, and CP. In this study, we provide an illustration of how an FES might play an important role in the prediction of the power consumption of the vehicle’s intelligent air-cushion system. The mean relative error in the actual and predicted values from the FES model with respect to tractive effort, total motion resistance and total power consumption were found to be 5.58 %, 6.78 % and 10.63 %, respectively. For all parameters, the relative error in the predicted values was found to be less than the acceptable limit (10%), except for the total power consumption. Furthermore, the goodness of fit of the predicted values was found to be close to 1.0 as expected and, hence, indicates the good performance of the developed system.
ISSN:1229-9138
1976-3832
DOI:10.1007/s12239-011-0086-9