Dynamic controlled pattern extraction and pattern-based model predictive control

Abstract

Many latent variable modeling methods have been developed to extract the running pattern of industrial process, but the dynamic causality between control inputs and pattern is unmodeled or implicit, and thus the direct pattern control is impracticable. To overcome this limitation, the dynamic controlled pattern extraction and pattern-based model predictive control (MPC) are investigated in this paper. Firstly, a novel dynamic controlled principal component analysis (DCPCA) is proposed to extract the pattern of the industrial process from measured variables. Specially, the autoregressive with exogenous (ARX) model is introduced to characterize the dynamic relationships of the process. By maximizing the covariance of the ARX prediction and the spatial projection, the process running information can be captured by the pattern maximally with the minimum dimensions, and also benefiting from this way, both the free motions and the dynamic causality between the control inputs and pattern is established explicitly. Then, a well-designed robust tube-based MPC is implemented for optimal pattern tracking. Finally, case studies illustrate the effectiveness and advantages of the proposed DCPCA algorithm and pattern-based MPC strategy.