Uncertain parameters estimation of time-delayed chaotic systems with initial random noises using adaptive PSO

Abstract

Chaotic systems are nonlinear deterministic systems that display complex and unpredictable behavior. Chaos has been applied in many academic and engineering fields, such as communication, economic system, chemical processes and optimization. A fundamental part of control engineering is the identication and estimation parameters of system being controlled. In this paper, the parameter identification problem with random initial noises for a general class of time-delay chaotic systems with the unknown parameters and time-delays is considered. The parameter estimation problem can be formulated as a multi-dimensional optimization problem. In this paper we propose an efficient optimization method called Adaptive Particle Swarm Optimization (APSO) for parameters estimation and synchronization of time-delay chaotic systems. The effectiveness of the method is tested on time-delay logistic chaotic system, and the results are compared with other population based methods. Simulation results show that the proposed algorithm is robust and efficient for multi parameter estimation in presence of noise.