Abstract
In random uncertain motor parameter identification field, there is low identification efficiency and ill-conditioned data coming from the second iteration involved in the uncertainty propagation and surrogate model between the motor parameters and the performance response. In this paper, the fourth-order moment method and trust region model management technology are combined to reduce the dependence of the surrogate model accuracy and improve the computational efficiency. In the framework, the inner layer calculates the cumulative probability under different motor performance response thresholds based on the fourth-order moment method, and obtain the probability density function of the calculated motor performance response. The outer layer transforms the random uncertain motor parameters identification into a deterministic optimization problem by minimizing the probability distribution between the calculated and the measured motor performance response. In the outer layer optimization, the trust region model management technology is used to divide the search interval of the entire parameter to be identified into a series of trust regions, construct a surrogate model on the trust region to calculate the motor performance response, and continuously update the trust region by comparing the response residuals between the calculated and measured motor performance response, so that the motor parameters to be identified continue to approach the interval where true values are located. At the same time, the genetic intelligent technology is introduced to further reduce the calculation cost. Finally, the probability distribution of random uncertain motor parameters is obtained according to the identified mean, standard deviation, skewness and kurtosis coefficients. The numerical results in the example verify that the random uncertain motor parameter identification can be effectively achieved by the proposed method.