Improved DEA for motor’s model identification

Purpose The purpose of this paper is to propose an improved differential evolution algorithm (DEA) suitable for motor’s model identification. Design/methodology/approach The mutation operation of the standard DEA is improved, and the adaptive coefficient is designed to adjust the optimization process. Findings The application of motor model identification shows that the proposed improved DEA is more robust, with higher modeling accuracy and efficiency, and is more suitable for motor identification modeling applications. Compared with the ultrasonic motor model established by using particle swarm algorithm, the model established in this paper has higher precision. Originality/value This paper explores an improved DEA suitable for motor identification modeling. The algorithm can not only obtain the optimal solution but also effectively reduce the iterative generations and time required in the process of optimization identification.