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.; Jiménez Avello, Agustín y 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.

Descripción

Título: New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems
Autor/es:
  • Al-Hadithi, Basil M.
  • Jiménez Avello, Agustín
  • Matía Espada, Fernando
Tipo de Documento: Artículo
Título de Revista/Publicación: Optimal Control Applications and Methods
Fecha: Septiembre 2012
Volumen: 33
Materias:
Palabras Clave Informales: Nonlinear systems, Fuzzy systems, Takagi–Sugeno fuzzy model, Extended Kalman filter, Linear quadratic regulator
Escuela: E.U.I.T. Industrial (UPM) [antigua denominación]
Departamento: Electrónica, Automática e Informática Industrial [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 11800
Identificador DC: http://oa.upm.es/11800/
Identificador OAI: oai:oa.upm.es:11800
Identificador DOI: 10.1002/oca.1014
URL Oficial: http://onlinelibrary.wiley.com/doi/10.1002/oca.1014/abstract
Depositado por: Memoria Investigacion
Depositado el: 06 Nov 2012 14:13
Ultima Modificación: 01 Mar 2016 14:07
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