Random uncertain motor parameters identification based on fourth-order moment method  and trust region management technology 

Abstract In many engineering practices, motor parameters are usually uncertain due to complex objective environments or various subjective perceptions. Using the fourth-order moment method and trust region model management technology, the uncertain motor parameter identification problem is transformed into a deterministic parameter identification problem for motor statistical characteristic parameters. Firstly, the probability distribution function is used to measure the uncertainty. The cumulative probability under different motor performance response thresholds is calculated by the fourth-order moment method to obtain the corresponding probability distribution function. In the subsequent framework, the micro multi-objective genetic algorithm is used to minimize the calculated and measured cumulative probability of motor performance response. To improve the optimization efficiency, the trust region management technology, is guided to gradually approach the region where the real fourth-order motor statistical characteristic parameters are located. At the same time, the genetic intelligent technology is introduced to reduce the calculation cost. Finally, the numerical results in the example show that the proposed method can effectively identify the uncertain motor parameters.