Identification of unfalsified plant model sets based on low-correlated bounded noise model

We propose a new model set identification method for robust control, which determines both nominal models and uncertainty bounds in frequency-domain using periodgrams obtained from experimental data. This method also gives less conservative model sets when we have more experimental data, which is one of the distinguished features compared with the existing model set identification methods. We construct a new noise model set in terms of periodgrams, which consists of hard-bounded (or deterministic) noises but takes into account of a low correlation property of noise signals, simultaneously. Then, based on the noise model, we show how to compute the nominal models and the upper bounds of modeling error via convex optimization, which minimize the given cost functions. Numerical examples show the effectiveness of the proposed method.