Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization

Abstract

Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of birds. Currently, PSO has been widely used in optimization problems. It also can be used to identify the unknown parameters in a nonlinear system, if a parameter identification problem can be transformed into an optimization problem. This paper is concerned with solving the parameter identification problem for nonlinear dynamic systems through a novel social emotional particle swarm optimization (SEPSO), which is combined with social emotional model. The feasibility of this approach is demonstrated through application to parameters identification of manipulator control system. The performance of the proposed SEPSO is compared with genetic algorithm (GA) and standard particle swarm optimization (SPSO) in terms of parameter accuracy. It is illustrated in simulations that the proposed SEPSO is more successful than SPSO and GA. Hence, the proposed algorithm can also be applied to many other parameter identification and optimization problem.