摘要
In this paper, the online correction model predictive control （MPC） strategy is presented for partial dif- ferential 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 low- dimensional 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.
In this paper, the online correction model predictive control （MPC） strategy is presented for partial dif- ferential 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 low- dimensional 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.
基金
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
‘Shu Guang’ Project of Shanghai Municipal Education Commission
Shanghai Education Development Foundation
