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Particle Swarm Optimization (PSO) Fuzzy Systems and NARMAX Approaches Trade-Off Applied to Thermal-Vacuum Chamber Identification

[+] Author Affiliations
Ernesto Araujo, Ubiratan S. Freitas, Elbert A. N. Macau

National Institute for Space Research-INPE, São José Campos, SP, Brazil

Leandro S. Coelho

Pontifícia Universidade Católica do Paraná, Curitiba, PR, Brazil

Luis A. Aguirre

Universidade Federal de Minas Gerais-UFMG, Belo Horizonte, MG, Brazil

Paper No. PVP2006-ICPVT-11-93631, pp. 403-410; 8 pages
  • ASME 2006 Pressure Vessels and Piping/ICPVT-11 Conference
  • Volume 2: Computer Technology
  • Vancouver, BC, Canada, July 23–27, 2006
  • Conference Sponsors: Pressure Vessels and Piping Division
  • ISBN: 0-7918-4753-5 | eISBN: 0-7918-3782-3
  • Copyright © 2006 by ASME


Two nonlinear identification methods are employed in this paper in an experimental comparative approach to generate dynamical models for a thermal-vacuum system. Used for space environment emulation and satellite qualification, a thermal-vacuum chamber presents highly nonlinear and time-delay characteristics. While, in the first nonlinear identification approach, Particle Swarm Optimization (PSO) derive a Takagi-Sugeno fuzzy model, the second one was based on NARMAX polynomial identification technique. PSO is a stochastic global optimization technique that uses a population of particles, where the position and velocity of each particle represent a solution to the problem. It is employed as an auxiliary mechanism for finding out a T-S fuzzy model. The NARMAX polynomial identification technique uses a criterion called Error Reduction Ratio (ERR) computed by employing an orthogonal least squares method whose terms are selected in a forward-regression manner. Results indicate that both methods are feasible solutions for eliciting models from the available data.

Copyright © 2006 by ASME



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