An improved generalized predictive control in a robust dynamic partial least square framework (Q1666953)

Summary: To tackle the sensitivity to outliers in system identification, a new robust dynamic partial least squares (PLS) model based on an outliers detection method is proposed in this paper. An improved radial basis function network (RBFN) is adopted to construct the predictive model from inputs and outputs dataset, and a hidden Markov model (HMM) is applied to detect the outliers. After outliers are removed away, a more robust dynamic PLS model is obtained. In addition, an improved generalized predictive control (GPC) with the tuning weights under dynamic PLS framework is proposed to deal with the interaction which is caused by the model mismatch. The results of two simulations demonstrate the effectiveness of proposed method.