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Stability analysis of fuzzy parametric uncertain systems.

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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Robust controller design for fuzzy parametric uncertain systems: an optimal control approach.

Balasaheb M Patre1, R J Bhiwani

  • 1Department of Instrumentation Engineering, SGGS Institute of Engineering and Technology, Vishnupuri, Nanded, India. bmpatre@ieee.org

ISA Transactions
|November 15, 2012
PubMed
Summary

A new robust controller design for fuzzy parametric uncertain systems (FPUS) is presented. This method converts FPUS into an interval state-space form, enabling a linear quadratic regulator (LQR) solution for enhanced controller robustness.

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Last Updated: May 16, 2026

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Published on: August 15, 2020

Area of Science:

  • Control Systems Engineering
  • Fuzzy Logic Systems
  • Robust Control Theory

Background:

  • Fuzzy parametric uncertain systems (FPUS) present challenges in robust controller design due to inherent uncertainties.
  • Existing methods may not fully address the complexities of systems with fuzzy coefficients.

Purpose of the Study:

  • To propose a novel methodology for designing robust controllers for fuzzy parametric uncertain systems (FPUS).
  • To demonstrate the effectiveness of the proposed approach through numerical examples and simulations.

Main Methods:

  • Conversion of FPUS into an uncertain (interval) state-space controllable canonical form using alpha cuts.
  • Formulation of the robust controller design problem as an optimal control problem with a minimized cost function.
  • Application of Linear Quadratic Regulator (LQR) techniques for systems with matched uncertainty.

Main Results:

  • The proposed method successfully designs a robust controller for FPUS.
  • Simulation results validate the effectiveness and robustness of the developed controller.
  • The conversion to an interval state-space form simplifies the controller design process.

Conclusions:

  • The presented approach offers an effective way to design robust controllers for FPUS.
  • The LQR-based solution provides a systematic method for achieving robust control in fuzzy systems.
  • This work contributes to advancing control strategies for uncertain dynamic systems.