Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

An iterative solution to dynamic output stabilization and comments on "dynamic output feedback controller design for

Min-Long Lin1, Ji-Chang Lo

  • 1Department of Mechanical Engineering, National Central University, Jung-Li, Taiwan, ROC.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 17, 2004
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015
Same journal

Meta-Analysis of the First Facial Expression Recognition Challenge.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Adjustable model-based fusion method for multispectral and panchromatic images.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012

This study reveals that existing output feedback controller gains are approximations, potentially failing linear matrix inequality constraints. An iterative approach is proposed for accurate dynamic output stabilization in fuzzy systems.

Area of Science:

  • Control Systems Engineering
  • Fuzzy Systems Theory
  • Applied Mathematics

Background:

  • The paper examines output feedback controller design for fuzzy systems.
  • Existing methods approximate controller gains (K) using a formula involving the Moore-Penrose inverse (K* = QP(-1)C0(dagger)).
  • This approximation may lead to violations of crucial linear matrix inequality (LMI) constraints.

Purpose of the Study:

  • To demonstrate that the controller gains (K) in prior work are approximations, not exact solutions.
  • To highlight the potential failure of these approximated gains to satisfy LMI constraints.
  • To propose a novel iterative LMI approach for precise dynamic output stabilization.

Main Methods:

  • Mathematical analysis to show the approximated nature of existing controller gains.

Related Experiment Videos

  • Identification of the discrepancy between the approximated gains (K) and the exact solution (K*).
  • Development of an iterative linear matrix inequality (LMI) algorithm for dynamic output stabilization.

    • Main Results:

    • The output feedback controller gains (K) are shown to be an approximation of the true solution (K*).
    • The approximated gains (K) may not satisfy the required linear matrix inequality (LMI) constraints.
    • The proposed iterative LMI method offers a more accurate solution for dynamic output stabilization.

    Conclusions:

    • The approximated controller gains are insufficient for guaranteeing LMI constraint satisfaction in fuzzy systems.
    • An iterative LMI approach provides a robust and accurate method for dynamic output stabilization.
    • This work refines controller design for fuzzy systems by addressing limitations in existing approximation methods.