Particle swarm approaches using Lozi map chaotic sequences to fuzzy modelling of an experimental thermal-vacuum system

Abstract

Particle Swarm Optimization (PSO) approach intertwined with Lozi map chaotic sequences to obtain Takagi–Sugeno (TS) fuzzy model for representing dynamical behaviours are proposed in this paper. The proposed method is an alternative for nonlinear identification approaches especially when dealing with complex systems that cannot always be modelled using first principles to determine their dynamical behaviour. Since modelling nonlinear systems is normally a difficult task, fuzzy models have been employed in many identification problems due its inherent nonlinear characteristics and simple structure, as well. This proposed chaotic PSO (CPSO) approach is employed here for optimizing the premise part of the IF–THEN rules of TS fuzzy model; for the consequent part, least mean squares technique is used. The proposed method is utilized in an experimental application; a thermal-vacuum system which is employed for space environmental emulation and satellite qualification. Results obtained with a variety of CPSO's are compared with traditional PSO approach. Numerical results indicate that the chaotic PSO approach succeeded in eliciting a TS fuzzy model for this nonlinear and time-delay application.