Particle swarm optimization (PSO) applied to fuzzy modeling in a thermal-vacuum system

A nonlinear identification approach based on particle swarm optimization (PSO) and Takagi-Sugeno (T-S) fuzzy model for describing dynamical behavior of a thermal-vacuum system is proposed in this paper. Identification of nonlinear systems is an important problem in engineering among what fuzzy models have received particular attention due to their potentialities to approximate nonlinear behavior. Meanwhile PSO is proposed as a method for optimizing the premise part of production rules, least mean squares technique is employed for consequent part of production rules of a T-S fuzzy model. Experimental application using a thermal-vacuum system, used for space environmental emulation and satellite qualification, is analyzed. Numerical results indicate that the PSO succeeded in constructing a T-S fuzzy model for nonlinear identification in this particular application.