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Fuzzy Logic Controlled Robotic Arm System

KrishiKosh

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Title Fuzzy Logic Controlled Robotic Arm System
 
Creator Alanabi, Naser Abdallah
 
Contributor Srivastava, Jyoti
 
Subject Robotic arm system ; fuzzy logic ; ANFIS ;Neuro-Fuzzy Controller ;matlab; Arduino UNO
 
Description Thesis titled “Fuzzy Logic Controlled Robotic Arm System” submitted in partial fulfillment of the requirements for the award of the degree of Doctor of Philosophy in Electrical and Electronics Engineering by Naser Abdallah Alanabi.
Robot manipulators have become increasingly important in the field of flexible automation. So modeling and control of robots in automation will be very important. But Robots, as complex systems, must detect and isolate faults with high probabilities while doing their tasks with humans or other robots with high precision and they should tolerate the fault with the controller. A Neuro-Fuzzy Controller (NFC) for position control of robot arm is presented . A five layer neural network is used to adjust input and output parameters of membership function in a fuzzy logic controller. The hybrid learning algorithm is used for training this network. In this algorithm, the least square estimation method is applied for the tuning of linear output membership function parameters and the error back propagation method is used to tune the nonlinear input membership function parameters. The simulation results show that neuro fuzzy controller is better and more robust than the PID controller for robot trajectory control . An adaptive fuzzy gain scheduling scheme for robotic arm has been proposed. In this thesis , an adaptive fuzzy based tracking and gain controller algorithm for obtaining the joints position relative to the desired trajectory has been done , which drives static and different time variations on the environmental changes, to estimate them at any point of time, when t >0 or alternatively, starting from the fuzzy initial states, Fuzzy initial states along with system dynamic equations provide us particular fuzzy differential equations (FDEs), referred as fuzzy Cauchy problem, we construct the fuzzy Cauchy problems during the initial time, two different ways have been applied , the non-fuzzy differential equations with initial conditions of data based. And the second, with ANFIS with Cauchy membership function for robotic arm to apply with two angles. The proposed adaptive fuzzy based tracking and gain controller algorithm requires much less training patterns than a neural net based adaptive scheme does and hence avoiding excessive training time. Results of simulation show that the proposed adaptive fuzzy controller offers better performance than fixed gain controllers at different operating conditions .
 
Date 2017-01-13T16:06:14Z
2017-01-13T16:06:14Z
2016
 
Identifier http://krishikosh.egranth.ac.in/handle/1/96365
 
Language en_US
 
Format application/pdf
 
Publisher Sam Higginbottom Institute of Agriculture, Technology & Sciences (SHIATS)