This paper proposes an intelligent controller design for an assisted electric wheelchair using interval type-2 fuzzy logic controller. The controller design has been structured using cascaded fuzzy inference system wi...
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The Middle Eastern and North African region is highly reliant on the oil and gas industry. Subsequently, the need for pipeline inspection and fault diagnosis has become paramount. Current inspection methods rely on ma...
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This paper proposes an intelligent controller design for an assisted electric wheelchair using interval type-2 fuzzy logic controller. The controller design has been structured using cascaded fuzzy inference system wi...
This paper proposes an intelligent controller design for an assisted electric wheelchair using interval type-2 fuzzy logic controller. The controller design has been structured using cascaded fuzzy inference system with feedback loop to avoid obstacles. In addition, to control the dynamics of the wheelchair, a speed fuzzy inference system has been added to the proposed controller. The controller is designed so that the user input is firstly obtained from the wheelchair joystick. The surrounding obstacles, thereafter, are detected using the input of three sensors attached to the wheelchair. At each time step, and until reaching the target point, the performed controller output generates both the motion angle and the wheelchair speed commands. The performance of the controller is tested by simulating the wheelchair travelling in different navigation scenarios. The simulation results confirm the effectiveness of the presented controller.
The Middle Eastern and North African region is highly reliant on the oil and gas industry. Subsequently, the need for pipeline inspection and fault diagnosis has become paramount. Current inspection methods rely on ma...
The Middle Eastern and North African region is highly reliant on the oil and gas industry. Subsequently, the need for pipeline inspection and fault diagnosis has become paramount. Current inspection methods rely on manual procedures that introduce sources of error into the results. In this paper, the design for a hybrid rolling and flying unmanned aerial vehicle (UAV) for pipeline inspection is proposed. The UAV was developed with a rolling frame capable of landing on pipelines to externally inspect them for cracks, leaks, or faults by traversing along the pipeline's surface while maintaining its stability. The hybrid UAV uses a camera to classify cracks along the surface of the pipeline using deep learning models. The paper then tests several models and deploys the best model found during testing -MobileNet100 along with the proposed design for the UAV. Finally, the prototype that was presented in this paper has been tested within a controlled environment to verify the compliance of the flying, rolling and fault detection subsystems.
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