Bayesian fuzzy logic-based estimation of electron cyclotron heating (ECH) power deposition in MHD control systems

Davoudi, Mehdi

doi:10.1016/j.fusengdes.2017.04.105

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…To implement the fuzzy logic-based controller for the line follower robot a Fuzzy System Designer function has been used in the LabVIEW environment which is similar to the Fuzzy logic tool in MATLAB [11]. The first step in designing the Fuzzy controller is to introduce the input and output membership functions.…”

Section: Fuzzy Controllermentioning

confidence: 99%

Real-time Fuzzy Image Processing Method for Visual feedback of a Follower Robot

Davoudi

Heidarian

2020

Wseas Transactions on Circuits and Systems

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In this paper, an adaptive visual feedback system and controller has been designed and implemented in real-time to control the movements of a line follower robot to be smoother and faster. The robot consists of a couple of motorized wheels, the real-time controller and a CMOS camera as the only sensor for detecting line and feedback. The measurement based on real-time image processing and motor drive feedback used in this robot makes it robust to the obstacles and surface disturbances that may deviate robot. The image processing algorithm is adaptive to the line’s color and width too. Image processing techniques have been implemented in real-time to detect the line in the image frame and extract the necessary information (like line’s edge, coordinates and angle). A NI myRIO module is used as a stand-alone hardware unit and RT (Real-Time) target for implementation of controllers and image processing in LabVIEW environment. Both results of real-time and non-real-time implementation of controllers have been compared. To show the performance of real-time image processing in the control of this robot, three types of controllers (i.e. P, PI and Fuzzy controllers) have been implemented for line following tests and the results have been compared. At the end, it was found that the fuzzy controller controls the robot movements smoother, faster, with less errors and quicker response time compare to the other controllers

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Section: Fuzzy Controllermentioning

confidence: 99%