Robust Takagi-Sugeno fuzzy dynamic regulator for trajectory tracking of a pendulum-cart system
type: Item
instance of: scholarly article
title: Robust Takagi-Sugeno fuzzy dynamic regulator for trajectory tracking of a pendulum-cart system
zbMATH Open document ID: 1394.93159
DOI: 10.1155/2015/247682
author: Miguel A. Llama
author: Wilfredo De La Torre
author: Francisco Jurado
author: Ramon Garcia-Hernandez
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: Starting from a nonlinear model for a pendulum-cart system, on which viscous friction is considered, a Takagi-Sugeno (T-S) fuzzy augmented model (TSFAM) as well as a TSFAM with uncertainty (TSFAMwU) is proposed. Since the design of a T-S fuzzy controller is based on the T-S fuzzy model of the nonlinear system, then, to address the trajectory tracking problem of the pendulum-cart system, three T-S fuzzy controllers are proposed via parallel distributed compensation: (1) a T-S fuzzy servo controller (TSFSC) designed from the TSFAM; (2) a robust TSFSC (RTSFSC) designed from the TSFAMwU; and (3) a robust T-S fuzzy dynamic regulator (RTSFDR) designed from the RTSFSC with the addition of a T-S fuzzy observer, which estimates cart and pendulum velocities. Both TSFAM and TSFAMwU are comprised of two fuzzy rules and designed via local approximation in fuzzy partition spaces technique. Feedback gains for the three fuzzy controllers are obtained via linear matrix inequalities approach. A swing-up controller is developed to swing the pendulum up from its pendant position to its upright position. Real-time experiments validate the effectiveness of the proposed schemes, keeping the pendulum in its upright position while the cart follows a reference signal, standing out the RTSFDR.
Mathematics Subject Classification ID: 93C42
zbMATH DE Number: 6925857
Wikidata QID: Q59117720
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/247682
OpenAlex ID: W1554559761
cites work: Fuzzy identification of systems and its applications to modeling and control
cites work: Stability analysis and nonlinear observer design using Takagi-Sugeno fuzzy models.
description: scientific article; zbMATH DE number 6925857