Tractive performance prediction of intelligent air-cushion track vehicle: fuzzy logic approach (original) (raw)
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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 ...
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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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Intelligent Air-Cushion Tracked Vehicle (IACTV) is intended as an alternative to conventional off-road vehicles, which are driven by track system and air-cushion system. To make IACTV as efficient as possible, proper investigation of vehicle performance is essential. However, most relevant factors that affect the competitive efficiency of the air-cushion tracked vehicle are the tractive effort, motion resistance and power consumption. Therefore, an Artificial Neural-Network (ANN) model is proposed to investigate the vehicle performance. Cushion Clearance Height (CH), and Air-Cushion Pressure (CP) are used at the input layers while Power Consumption (PC), Tractive Effort (TE) and Motion Resistance (MR) are used at the output layers. Experiments are carried out in the field to investigate the vehicle performance and compared with the results obtained from ANN.
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This study presents a developed hybrid electrical air-cushion tracked vehicle (HETAV) for the transportation operation of agricultural and industrial goods on the swamp peat terrain bearing capacity of 5 kN/m2. The vehicle’s design parameters are optimized by using the developed mathematical models which are made based on the kinematics and dynamics behaviors of the vehicle. A set of sensors are used with this vehicle to activate the air-cushion system and battery pack recharging system. The vehicle’s air-cushion system is protected by a novel-design auto-adjusting supporting system. The air-cushion dragging motion resistance is overcome with additional thrust which is developed by a propeller. The vehicle is equipped with the air-cushion system to make the vehicle ground contact pressure 5 kN/m2.
Hybrid Electrical Air-Cushion Tracked Vehicle for Swamp Peat in Malaysia
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The aim of this paper is to present a hybrid electrical air-cushion tracked vehicle (HETAV) for the operation on swamp peat. Mathematical models are incorporated with accounting kinematics and dynamics behaviors of the vehicle. Sinkage of the HETAV is sensed by an ultrasonic displacement (UD) sensor, in order to operate the air-cushion system. The air-cushion of HETAV is protected with a novel-design auto-adjusting supporting (AAS) system. A propeller is equipped with the vehicle to develop additional thrust for overcoming the dragging motion resistance of the air-cushion system. The performance of the HETAV is defined by traction and motion resistance. The mean value of traction for the swamp terrain with propeller over without propeller increases 10.21% and 6.47% for the vehicle weight of 1.02 kN and 2.04 kN, respectively. Similarly, it was found that the mean values of vehicle's motion resistance decrease 12.63% and 25.81% for the vehicle weight of 2.45 kN and 3.43 kN, respectively.
Indian Journal of Science and Technology, 2015
Different types of off road vehicles are widely used in agriculture, oil industry, mining and military operations but none of them can effectively operate over the swamp peat terrain because of its low bearing capacity of 7kN/m 2 . Segmented rubber tracked vehicle and intelligent air-cushion system tracked vehicle were developed in Malaysia for swamp peat terrain. 16kN/m 2 of ground pressure was exerted by using the segmented rubber tracked vehicle during field operation therefore could not be operated efficiently. The air-cushion tracked vehicle increased the floatation capacity but at the same time increased the frictional effects therefore the tracks of the vehicle easily slipped out from the traction wheels during operation. Addressing these issues an intelligent additional track mechanism for tracked vehicle has been designed to improve the mobility over swamp peat terrain where the additional track would be increased the ground surface area and reduced the vehicle ground pressure. This paper presents the process involved in designing the intelligent additional track mechanism tracked vehicle for transportation of agricultural and industrial goods on the swamp peat terrain with bearing capacity of 7kN/m 2 . The mechanical design comprises of track vehicle frame with track mechanism. Additional track mechanism with Fuzzy expert system. The design parameters are optimized using developed mathematical model based on the dynamics and kinematics behavior of the vehicle. In order to increase the vehicle contact surface area and reduce the surface contact pressure the additional track mechanism is designed in such way that it can be folded and unfolded from its position by using the ball-screw scissor lift mechanism. While, Fuzzy expert system is used to control the movement of the lift mechanism based on 70mm critical sinkage of vehicle detected from a set of sensors. The completed to vehicle system would be used for off-road applications as required.
Fuzzy knowledge-based model for prediction of traction force of an electric golf car
The methods of artificial intelligence are widely used in soft computing technology due to its remarkable prediction accuracy. How- ever, artificial intelligent models are trained using large amount of data obtained from the operation of the off-road vehicle. In contrast, fuzzy knowledge-based models are developed by using the experience of the traction in order to maintain the vehicle traction as required with utilizing optimum power. The main goal of this paper is to describe fuzzy knowledge-based model to be practically applicable to a reasonably wide class of unknown nonlinear systems. Compared with conventional control approach, fuzzy logic approach is more effi- cient for nonlinear dynamic systems and embedding existing structured human knowledge into workable mathematics. The purpose of this study is to investigate the relationship between vehicle’s input parameters of power supply (PI) and moisture content (MC) and out- put parameter of traction force (TF). Experiment has been conducted in the field to investigate the vehicle traction and the result has been compared with the developed fuzzy logic system (FLS) based on Mamdani approach. Results show that the mean relative error of actual and predicted values from the FLS model on TF is found as 7%, which is less than the acceptable limit of 10%. The goodness of fit of the prediction value from FLS is found close to 1.0 as expected and hence shows the good performance of the developed system. Ó 2011 Published by Elsevier Ltd. on behalf of ISTVS.
Applied Soft Computing, 2014
Various methodologies of artificial intelligence have been recently used for estimating performance parameters of soil working machines and off-road vehicles. Due to nonlinear and stochastic features of soil-wheel interactions, application of knowledge-based Mamdani max-min fuzzy expert system for estimation of contact area and contact pressure is described in this paper. Fuzzy logic model was constructed by use of the experience of contact area and contact pressure utilizing data obtained from series of experimentations in soil bin facility and a single-wheel tester. Two paramount tire parameters: wheel load and tire inflation pressure are the input variables for our model, each has five membership functions. As a fundamental aspect of the fuzzy logic based prediction systems, a set of fuzzy if-then rules were used in accordance with fuzzy logic principles. 25 linguistic if-then rules were included to develop a complicated highly intelligent predicting model based on Centroid method at defuzzification stage. The model performance was assessed on the basis of several statistical quality criteria. Mean relative error lower than 10%, satisfactory scattering around unity-slope line (T), and high coefficient of determination, R 2 , were obtained by the fuzzy logic model proposed in this study.