Formulation, Analysis and Validation of Takagi-Sugeno Fuzzy Modeling For Robotic Monipulators

This paper proposes a methodology for analysis of the dynamic behavior of a robotic manipulator in continuous time. Initially this system (nonlinear system) will be decomposed into linear submodels and analyzed in the context of the Linear and Parameter Varying (LPV) Systems. The obtained linear submodels, which represent the local dynamic behavior of the robotic manipulator in some operating points were grouped in a Takagi-Sugeno fuzzy structure. The obtained fuzzy model was analyzed and validated through analog simulation, as universal approximator of the robotic manipulator.

Authors:

• Rafael Jorge Menezes Santos
• Ginalber Luiz de Oliveira Serra
• Carlos César Teixeira Ferreira

Keywords:

• modeling of nonlinear dynamic systems
• Takagi- Sugeno fuzzy model
• Linear and Parameter Varying (LPV) System.

References:

[1] R. Babuˇska, Fuzzy Systems, Modeling and Identification, GA Delft, Delft
University of Technology: 200-.
[2] R. Babuˇska, Fuzzy Modeling for Control, Massachusetts: Kluwer Academic
Publishers, 1998.
[3] C. V. Altrock, Fuzzy logic and neuro-fuzzy applications in business and
finance, Prentice Hall, 1997.
[4] E. H. Mandani, Application of fuzzy logic to approximate reasoning using
linguistic systems, Fuzzy Sets and Systems, 1977.
[5] R. S. Yager and R. T. Ovchinnoikov and H. Nguyen, Fuzzy sets and
applications, John Wiley, 1987.
[6] L. A. Zadeh, Fuzzy sets, Information and Control, 1965.
[7] C. L. Phillips and R. D. Harbor, Feedback Control Systems, 3rd ed., Upper
Saddle River: Prentice Hall, New Jersey, 1996.
[8] L. Wang, A Course in Fuzzy Systems and Control, Upper Saddle River:
Prentice Hall, New Jersey, 1996.
[9] L. A. Aguirre, Introdu├º├úo ├á Identifica├º├úo de Sistemas: Técnicas Lineares
e Não-Lineares Aplicadas a Sistemas Reais, 2a ed., Belo Horizonte:
Editora UFMG, 2004.
[10] S. I. Shaw and M. G. Sim├Áes, Controle e modelagem fuzzy, S├úo Paulo:
Edgard Bl├╝cher, 1999.
[11] G. L. O. Serra., Robust Adaptive ELS-QR Algorithm for Linear Discrete
Time Stochastic Systems Identification, Proceedings of World Academy
of Science, Engineering and Technology, v. 45, p. 469-474, 2008.
[12] G. L. O. Serra e C. P. BOTTURA, Métodos de Vari├ível Instrumental
Fuzzy para Identificação de Sistemas, Controle e Automação, v. 18,(4),
p. 410-422, 2007.