Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Formulation, Analysis and Validation of Takagi-Sugeno Fuzzy Modeling For Robotic Monipulators

Authors: Rafael Jorge Menezes Santos, Ginalber Luiz de Oliveira Serra, Carlos César Teixeira Ferreira

Abstract:

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.

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

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062988

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2204

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 N├úo-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 Identifica├º├úo de Sistemas, Controle e Automa├º├úo, v. 18,(4), p. 410-422, 2007.