Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models |
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Authors: | Meng-Ling Wang Ning Li Shao-Yuan Li |
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Affiliation: | 1. Institute of Automation, Shanghai Jiao Tong University, Shanghai200240, PRC2. Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, PRC |
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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. |
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Keywords: | Spatially-distributed system principal component analysis (PCA) time/space separation control (MPC)')" > dimension reduction model predictive control (MPC) |
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