%0 Journal Article
%T Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models<br>
%A Meng-Ling Wang
%A Ning Li
%A Shao-Yuan Li
%A <br>
%J 国际自动化与计算杂志
%D 2011
%I 
%X 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.
%K Spatially-distributed system
%K principal component analysis (PCA)
%K time/space separation
%K dimension reduction model predictive control (MPC)&prev_q=control (MPC)')"> dimension reduction model predictive control (MPC)" target="_blank">control (MPC)')"> dimension reduction model predictive control (MPC)<br>
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7139AD613512F4F05F6D525B914296AA&aid=4328156B79FCE3CF4421F7F02E3AD309&yid=9377ED8094509821&vid=5D311CA918CA9A03&iid=CA4FD0336C81A37A&sid=CA4FD0336C81A37A&eid=DF92D298D3FF1E6E&journal_id=1476-8186&journal_name=国际自动化与计算杂志&referenced_num=0&reference_num=26