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| Received:November 26, 2009Revised:July 26, 2010 |
| 基金项目:This work was supported by the National Nature Science Foundation of China (Nos. 60825302, 61074061), the High Technology Research and Development Program of China (No. 2007AA041403), the Program of Shanghai Subject Chief Scientist, and ‘Shu Guang’ Project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation. |
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| Online correction MPC strategy for spatially-distributed system based on PCA method |
| Mengling WANG,Ning LI,Shaoyuan LI |
| (Institute of Automation, Shanghai Jiao Tong University) |
| Abstract: |
| In this paper, the online correction model predictive control (MPC) strategy is presented for partial differential equation (PDE) unknown spatially-distributed systems (SDSs). The low-dimensional MIMO models are obtained using principal component analysis (PCA) method from the high-dimensional spatio-temporal data. Though the linear lowdimensional model is easy for control design, it is a linear approximation for nonlinear SDSs. Thus, the MPC strategy is proposed based on the online correction low-dimensional models, where the state at a previous time is used to correct the output of low-dimensional models and the spatial output is correct by the average deviation of the historical data. The simulations demonstrated show the accuracy and efficiency of the proposed methodologies. |
| Key words: Spatially-distributed system Principal component analysis (PCA) Model predictive control Time/space reconstruction Time/space projection |
