Title page for etd-0205115-151732


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URN etd-0205115-151732
Author Ming-han Chung
Author's Email Address No Public.
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Department Electrical Engineering
Year 2014
Semester 2
Degree Master
Type of Document
Language English
Title Image Based Visual Servoing Using Proportional Controller with Compensator
Date of Defense 2015-02-16
Page Count 59
Keyword
  • robot Manipulator
  • CMAC
  • function approximation
  • visual seroing
  • Reinforcement learning
  • Abstract The main objective is to design a proportional controller of a robot manipulator using the fuzzy cerebellar model articulation controller based on Takagi–Sugeno (T–S) framework with a compensator. The controller and compensator apply in visual servoing, including system identification of image and kinematic Jacobians. The proposed approach is basically as a function of the visual error and extent from the error with respect to desire visual feature. This approach leads to enormous reduction on computational expense compared to the image-based approaches of model inverse kinematics. The design of the controller architecture will make it possible to implement in general case. Proportional control variable are learned offline with the help of FCMAC-T-S model, and online compensator scheme has been proposed for adapting possible uncertainties in the unknown system and environment. Fine-tuned scheme in the proposed compensator can compensate the controller output during the period of visual servoing, improving its stability while tracking the target. Stimulation results have shown that visual servoing for tracking static target can be achieved using the proposed controller with stochastic reinforcement learning compensator. The simulation of eye-in-hand configuration is implemented in Webots 7.0.3.
    Advisory Committee
  • Jin-Ling Lin - chair
  • Ming-Yi Ju - co-chair
  • Yu-Jen Chen - co-chair
  • Kao-Shing Hwang - advisor
  • Files
  • etd-0205115-151732.pdf
  • Indicate in-campus at 0 year and off-campus access at 1 year.
    Date of Submission 2015-03-05

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