Chaotic Ant Swarm Designed T-S Fuzzy System for Adaptive Control of Dynamical Systems

Abstract

The chaotic ant swarm (CAS) algorithm is inspired by the chaotic and selforganization behavior of ants. It belongs to the categories of chaos optimization methods and swarm intelligence approaches. This paper presents the CASdesigned fuzzy system can be used effectively to achieve the adaptive control of dynamical systems. The design of the fuzzy system is comprised of the extraction of "if-then" rules that is followed by the identification of their parameters. The involved parameters include those that determine the membership function of fuzzy sets and the certainty factors of fuzzy if-then rules. The position vector of each ant in the CAS algorithm corresponds to the parameter vector of the selected T-S fuzzy system. At each learning time step, the CAS algorithm is iterated to give the optimal parameters of fuzzy system based on the fitness theory. Then the corresponding CAS-designed T-S fuzzy system is built and applied to the adaptive control of the unknown nonlinear dynamical systems. Numerical simulations are provided to verify the effectiveness and feasibility of the developed CAS-designed T-S fuzzy system.

Hydrothermal synthesis of Pb(Zr0·52Ti0·48)O3 powders at low temperature and low alkaline concentration

“…After 15 h of hydrothermal treatment at low temperature of 140°C, the pure PZT powders are also obtained, but the particles are inhomogenous in size distribution, and small amount of slices appear. At low temperature and low alkaline concentration, the perovskite PZT grows more quickly in two dimensions than in the third dimension to form the slices (Li et al 2007). The slices grow layer by layer to form cubic shaped particles as shown in figures 5 (c) and (d).…”