Abstract：Accurate identification of nonlinear model parameters is a prerequisite to precisely control precise displacement of giant magnetostrictive actuator, Aiming at the shortcomings of standard PSO algorithm such as existing premature convergence and easily falling into local optimum in later iteration. An improved PSO identification algorithm is proposed in this paper, which can dynamically change inertia weighting, study factors and genetic variation, so it can balance global and local search capability to improve the convergence speed and identification accuracy. Moreover, it is applied to parameters identification of nonlinear model for giant magnetostrictive actuator. Then the results show that: the improved algorithm can effectively identify the nonlinear model parameters of giant magnetostrictive actuator. It also has a higher degree of agreement between calculations and experiments and a better anti-interference ability.
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