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A model predictive control approach with relevant identification in dynamic PLS framework

Chi, Q. et al.

Control engineering practice. VOL 22, ; 2014, 181-193 -- Elsevier Science B.V., Amsterdam. Part; (pages 181-193) -- 2014

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  • Title:
    A model predictive control approach with relevant identification in dynamic PLS framework
  • Author: Chi, Q.;
    Fei, Z.;
    Zhao, Z.;
    Zhao, L.;
    Liang, J.
  • Found In: Control engineering practice. VOL 22, ; 2014, 181-193
  • Journal Title: Control engineering practice.
  • Subjects: Electrical and Electronic Engineering; Mechanical Engineering; Civil Engineering; LCC: TJ212; Dewey: 629.8
  • Publication Details: Elsevier Science B.V., Amsterdam.
  • Language: English
  • Abstract: In this paper, a generalized predictive control (GPC) scheme under a dynamic partial least squares (PLS) framework is proposed. At the modeling stage, a model predictive control relevant identification (MRI) approach is used to improve the identification of the model. Within PLS framework, the MIMO system can be automatically decomposed into several SISO subsystems in the latent space. For each subsystem, MRI is implemented and GPC is designed independently. With the advantage of MRI and PLS framework, fewer parameters are needed to be estimated in the identification stage, nonsquare and ill-conditioned system can be handled naturally, control parameters tuning is easier and better control performance can be obtained. Furthermore, the computing time of control action which is very crucial for GPC on-line application decreases since each GPC is running in SISO subsystem in parallel. The results of two simulation examples and a laboratory experiment demonstrate the merit of the proposed method.
  • Identifier: Journal ISSN: 0967-0661
  • Publication Date: 2014
  • Physical Description: Physical
  • Accrual Information: Monthly
  • Shelfmark(s): 3462.020000
  • UIN: ETOCRN343711231

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