Online Derivation Method of Control Parameters for Switched Reluctance Motor by Strength Pareto Evolutionary Algorithm 2

摘要

In order to drive switched reluctance motor (SRM) with higher performance, several control parameters must be tuned at the several operating conditions in consideration of the constraint condition. For that reason, in order to derive control parameters efficiently, a previous research has used Strength Pareto Evolutionary Algorithm 2 (SPEA2), which is one of optimization methods. However, the previous research uses an offline derivation method that searches the control parameters by simulation. Therefore, this paper proposes a derivation method which can derive the control parameters for actual machine online. In addition, the validation of the proposed online derivation method was conducted. As a result, it was confirmed that the proposed online derivation method could obtain the appropriate parameters in a short time of about 1/6 times of the offline derivation method.