New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

Al-Hadithi, Basil M. and Jiménez Avello, Agustín and Matía Espada, Fernando (2012). New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems. "Optimal Control Applications and Methods", v. 33 (n. 5); pp. 552-575. ISSN 1099-1514. https://doi.org/10.1002/oca.1014.

Description

Title: New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems
Author/s:
  • Al-Hadithi, Basil M.
  • Jiménez Avello, Agustín
  • Matía Espada, Fernando
Item Type: Article
Título de Revista/Publicación: Optimal Control Applications and Methods
Date: September 2012
ISSN: 1099-1514
Volume: 33
Subjects:
Freetext Keywords: Nonlinear systems, Fuzzy systems, Takagi–Sugeno fuzzy model, Extended Kalman filter, Linear quadratic regulator
Faculty: E.U.I.T. Industrial (UPM)
Department: Electrónica, Automática e Informática Industrial [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use.

More information

Item ID: 11800
DC Identifier: http://oa.upm.es/11800/
OAI Identifier: oai:oa.upm.es:11800
DOI: 10.1002/oca.1014
Official URL: http://onlinelibrary.wiley.com/doi/10.1002/oca.1014/abstract
Deposited by: Memoria Investigacion
Deposited on: 06 Nov 2012 14:13
Last Modified: 01 Mar 2016 14:07
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