摘要：AbstractIn this paper, an observer-based model predictive control (MPC) strategy is presented for distributed parameter systems (DPSs). First, principal component analysis (PCA) is used for dimension reduction by transforming the high-dimensional spatio-temporal data into a low-dimensional time domain. Then an observer is builded to estimate the low-dimensional temporal output using the real-time measurable spatiotemporal output. Finally, the MPC strategy is proposed based on the low-dimensional estimation models. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.
