Parameter estimation of the Bouc-Wen hysteresis model using particle swarm optimization

Particle swarm optimization (PSO), which is a new robust stochastic evolutionary computational algorithm based on the movement and intelligence of swarms, is proposed to estimate parameters of the Bouc-Wen hysteresis model. The performance of the PSO method is compared with the more common genetic algorithms (GAS) in terms of parameter accuracy. Simulation results of the Bouc-Wen model with all the unknown parameters are illustrated to show that a higher quality solution with better computational efficiency than the GA method can be achieved by means of the PSO method. Furthermore, parameter estimation of the Bouc-Wen model with noisy data is considered. The results show that the proposed method is still effective even if the simulated data are corrupted by noise.