An improved particle swarm optimization for exponential stabilization of a singular linear time-varying system

This paper derives an optimization problem for exponential stabilization condition of a singular linear time-varying system governed by the second-order vector differential equations and proposes an improved particle swarm optimization (PSO) method, called the adaptive fuzzy PSO with a constriction factor (AFPSO-cf) algorithm, for solving the optimization problem of exponential stabilization. The proposed AFPSO-cf algorithm adaptively adjusts the accelerating coefficients of PSO by using the fuzzy set theory to improve global searching ability of controller parameters. Compared with the standard particle swarm optimization (SPSO), the PSO with a constriction factor (PSO-cf), the Quadratic Interpolation PSO (QIPSO), the unified PSO (UPSO), the fully informed particle swarm (FIPS) and the comprehensive learning PSO (CLPSO) algorithms, the experiment results show that the proposed method significantly performs better than those algorithms.