Volume 8 Number 1
February 2011
Article Contents
Meng-Ling Wang, Ning Li and Shao-Yuan Li. Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models. International Journal of Automation and Computing, vol. 8, no. 1, pp. 1-7, 2011. doi: 10.1007/s11633-010-0547-z
Cite as: Meng-Ling Wang, Ning Li and Shao-Yuan Li. Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models. International Journal of Automation and Computing, vol. 8, no. 1, pp. 1-7, 2011. doi: 10.1007/s11633-010-0547-z

Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models

Author Biography:
  • Meng-Ling Wang graduated from Jiangsu Polytechnic University,PRC in 2002.

  • Corresponding author: Shao-Yuan Li graduated from Hebei University of Technology,PRC in 1987.
  • Received: 2010-01-24
Fund Project:

supported by National High Technology Research and Development Program of China (863 Program)(No.2009AA04Z162);National Nature Science Foundation of China(No.60825302, No.60934007, No.61074061);Program of Shanghai Subject Chief Scientist,Shu Guang project supported by Shang-hai Municipal Education Commission and Shanghai Education Development Foundation;Key Project of Shanghai Science and Technology Commission, China (No.10JC1403400)

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Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models

  • Corresponding author: Shao-Yuan Li graduated from Hebei University of Technology,PRC in 1987.
Fund Project:

supported by National High Technology Research and Development Program of China (863 Program)(No.2009AA04Z162);National Nature Science Foundation of China(No.60825302, No.60934007, No.61074061);Program of Shanghai Subject Chief Scientist,Shu Guang project supported by Shang-hai Municipal Education Commission and Shanghai Education Development Foundation;Key Project of Shanghai Science and Technology Commission, China (No.10JC1403400)

Abstract: In this paper, a low-dimensional multiple-input and multiple-output (MIMO) model predictive control (MPC) configuration is presented for partial differential equation (PDE) unknown spatially-distributed systems (SDSs). First, the dimension reduction with principal component analysis (PCA) is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.

Meng-Ling Wang, Ning Li and Shao-Yuan Li. Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models. International Journal of Automation and Computing, vol. 8, no. 1, pp. 1-7, 2011. doi: 10.1007/s11633-010-0547-z
Citation: Meng-Ling Wang, Ning Li and Shao-Yuan Li. Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models. International Journal of Automation and Computing, vol. 8, no. 1, pp. 1-7, 2011. doi: 10.1007/s11633-010-0547-z
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