Beijing Key Laboratory of Field Bus Technology and Automation

Beijing, China

Beijing Key Laboratory of Field Bus Technology and Automation

Beijing, China
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Shi Y.-T.,North China University of Technology | Shi Y.-T.,Beijing Key Laboratory of Field Bus Technology and Automation | Yang Z.-A.,North China University of Technology | Li Z.-J.,North China University of Technology | And 5 more authors.
Xitong Fangzhen Xuebao / Journal of System Simulation | Year: 2013

A multi-input multi-output hybrid system model identification method based on data-driven and a real-time nonlinear predictive control method based on multi-parametric programming were proposed for a class of nonlinear and multivariable industrial processes. A clustering classification method of high- dimensional data space was adopted to solve a MIMO piecewise affine autoregressive exogenous (PWARX) model identification problem. With the help of HYSDEL modeling language of hybrid systems and its compiler tools, MIMO-PWARX model was transformed into nonlinear piecewise affine (PWA) model. Using multi-parametric programming method, a predictive controller based on PWA model was designed to achieve on-line control of nonlinear process. Through this method, the hybrid model information of systems was obtained from a set of process data and PWA model of complex process was automatically generated. Based on the PWA Process model, real-time nonlinear predictive controller was implemented. Finally, the hybrid identification and control method was applied to the typical MIMO nonlinear quadruple tank system. High identification accuracy and satisfied control performance were obtained.


Shi Y.-T.,North China University of Technology | Shi Y.-T.,Beijing Key Laboratory of Field Bus Technology and Automation | Yang Z.-A.,North China University of Technology | Li Z.-J.,North China University of Technology | And 3 more authors.
Chinese Control Conference, CCC | Year: 2012

A multi-input multi-output piecewise affine model identification method based on data-driven and a real-time predictive control method based on multi-parametric programming are proposed for a class of nonlinear and multivariable processes represented by quadruple tank system. A clustering-based algorithm is designed to solve a MIMO piecewise affine autoregressive exogenous (PWARX) model identification problem. With the use of HYSDEL modeling language and its compiler tools, MIMO-PWARX model is transformed into piecewise affine (PWA) model. A multi-parametric programming method is adopted to design a predictive controller based on PWA model. The experiment with quadruple tank shows the capability of obtaining PWARX model information directly through analysis of process data based on data-driven and the effect of real-time nonlinear predictive control based on the model in this scheme. © 2012 Chinese Assoc of Automati.

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