Asymptotic system identification method based on particle swarm optimization

In general, structure of a system to be identified is unknown for users a priori. This makes the model complex and high order structure. In this paper, we introduce the asymptotic method (ASYM) to deal with the problem. ASYM calculates a high-order model using the well-known least squares method, then reduces the identified model to a simple one. For this model reduction, various model reduction techniques such as balanced realization and output error reduction were developed. In this paper, a new method to reduce the high-order model using the particle swarm optimization in the frequency domain is proposed. Effectiveness of the proposed method is examined through numerical examples.