Nominal Model Selection for Control Plant Based on Hankel-Norm Model Reduction
基于Hankel范数模型降价的控制对象的名义模型选择

This paper investigates the problem concerning with plant nominal model selection when a batch of plant models have been supplied, with the nominal model intended use as robust controller design. A selection algorithm is proposed which is based on frequency weighted Hankel norm model reduction. An illustrative example shows that compared with the plant nominal model seleted intuitively, the approximation error of the plant nominal model obtained through the proposed algorithm is smaller, even though its complexity remains unchanged.