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 ...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.展开更多
Multivariate statistical process control (MSPC) has been successfully applied to performance monitoring and fault diagnosis for chemical processes However, traditional MSPC are based upon the assumption that the separ...Multivariate statistical process control (MSPC) has been successfully applied to performance monitoring and fault diagnosis for chemical processes However, traditional MSPC are based upon the assumption that the separated latent variables must be subject to normal probability distribution, which sometimes can not be satisfied In this paper, a novel method combining principal component analysis (PCA) and independent component analysis (ICA) is proposed to model non Gaussian data from industry and improve the monitoring performance of process In order to deal with the uncertainty of probability distribution within the independent component, a kind of classifier referred to as support vector classifier is used for classifying the abnormal modes Simulation result for a nonisothermal continuous stirred tank reactor (CSTR) by the presented method verifies the effectiveness of ICA based展开更多
基金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)+1 种基金Program of Shanghai Subject Chief Scientist,"Shu Guang" project supported by Shang-hai Municipal Education Commission and Shanghai Education Development FoundationKey Project of Shanghai Science and Technology Commission, China (No. 10JC1403400)
文摘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.
文摘Multivariate statistical process control (MSPC) has been successfully applied to performance monitoring and fault diagnosis for chemical processes However, traditional MSPC are based upon the assumption that the separated latent variables must be subject to normal probability distribution, which sometimes can not be satisfied In this paper, a novel method combining principal component analysis (PCA) and independent component analysis (ICA) is proposed to model non Gaussian data from industry and improve the monitoring performance of process In order to deal with the uncertainty of probability distribution within the independent component, a kind of classifier referred to as support vector classifier is used for classifying the abnormal modes Simulation result for a nonisothermal continuous stirred tank reactor (CSTR) by the presented method verifies the effectiveness of ICA based
