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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 923584, 14 pages
http://dx.doi.org/10.1155/2015/923584
Research Article

An Improved Generalized Predictive Control in a Robust Dynamic Partial Least Square Framework

State Key Lab of Industrial Control Technology, Department of Control Science & Engineering, Zhejiang University, Hangzhou 310027, China

Received 20 March 2015; Accepted 16 September 2015

Academic Editor: Qing Chang

Copyright © 2015 Jin Xin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.