Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

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Publisher: search.filters.publisher.Institute of Electrical and Electronics Engineers Inc.Subject: search.filters.subject.Lyapunov methods

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Now showing 1 - 4 of 4
  • Article
    Citation - WoS: 39
    Citation - Scopus: 43
    Self-Adjusting Fuzzy Logic Based Control of Robot Manipulators in Task Space
    (Institute of Electrical and Electronics Engineers Inc., 2021) Yılmaz, Bayram Melih; Tatlıcıoğlu, Enver; Savran, Aydoğan; Alcı, Musa
    End effector tracking control of robot manipulators subject to dynamical uncertainties is the main objective of this work. Direct task space control that aims minimizing the end effector tracking error directly is preferred. In the open loop error system, the vector that depends on uncertain dynamical terms is modeled via a fuzzy logic network and a self-adjusting adaptive fuzzy logic component is designed as part of the nonlinear proportional derivative based control input torque. The stability of the closed loop system is investigated via Lyapunov based arguments and practical tracking is proven. The viability of the proposed control strategy is shown with experimental results. Extensions to uncertain Jacobian case and kinematically redundant robots are also presented. IEEE
  • Article
    Citation - WoS: 1
    Citation - Scopus: 3
    Euclidean Position Estimation of Static Features Using a Moving Uncalibrated Camera
    (Institute of Electrical and Electronics Engineers Inc., 2012) Nath, Nitendra; Dawson, Darren M.; Tatlıcıoğlu, Enver
    In this paper, a novel Euclidean position estimation technique using a single uncalibrated camera mounted on amoving platform is developed to asymptotically recover the 3-D Euclidean position of static object features. The position of the moving platform is assumed to be measurable, and a second object with known 3-D Euclidean coordinates relative to theworld frame is considered to be available a priori. To account for the unknown camera calibration parameters and to estimate the unknown 3-D Euclidean coordinates, an adaptive least squares estimation strategy is employed based on prediction error formulations and a Lyapunovtype stability analysis. The developed estimator is shown to recover the 3-D Euclidean position of the unknown object features despite the lack of knowledge of the camera calibration parameters. Numerical simulation results along with experimental results are presented to illustrate the effectiveness of the proposed algorithm. © 2011 IEEE.
  • Conference Object
    Citation - Scopus: 1
    Range Identification for Nonlinear Parameterizable Paracatadioptric Systems
    (Institute of Electrical and Electronics Engineers Inc., 2009) Nath, Nitendra; Dawson, Darren M.; Tatlıcıoğlu, Enver
    In this paper, a new range identification technique for a calibrated paracatadioptric system mounted on a moving platform is developed to recover the range information and the three-dimensional (3D) Euclidean coordinates of a static object feature. The position of the moving platform is assumed to be measurable. To identify the unknown range, first a function of the projected pixel coordinates is related to the unknown 3D Euclidean coordinates of an object feature. This function is nonlinearly parameterized (i.e., the unknown parameters appear nonlinearly in the parameterized model). An adaptive estimator based on a min-max algorithm is then designed to estimate the unknown 3D Euclidean coordinates of an object feature relative to a fixed reference frame which facilitates the identification of range. A Lyapunov-type stability analysis is used to show that the developed estimator provides an estimation of the unknown parameters within a desired precision.
  • Conference Object
    Citation - Scopus: 1
    Euclidean Position Estimation of Static Features Using a Moving Uncalibrated Camera
    (Institute of Electrical and Electronics Engineers Inc., 2009) Nath, Nitendra; Dawson, Darren M.; Tatlıcıoğlu, Enver
    In this paper, a novel Euclidean position estimation technique using a single uncalibrated camera mounted on a moving platform is developed to asymptotically recover the three-dimensional (3D) Euclidean position of static object features. The position of the moving platform is assumed to be measurable, and a second object with known 3D Euclidean coordinates relative to the world frame is considered to be available a priori. To account for the unknown camera calibration parameters and to estimate the unknown 3D Euclidean coordinates, an adaptive least squares estimation strategy is employed based on prediction error formulations and a Lyapunov-type stability analysis. The developed estimator is proven to recover the 3D Euclidean position of the unknown object features despite the lack of knowledge of the camera calibration parameters.