Abstract
Feature patterns, which reflect the present running conditions of a process, both qualitatively and quantitatively, play an important role in the description of the process dynamic behavior. A novel predictor based on the process feature patterns is presented here for a chemical process with large dead time. Through analyzing the closed-loop response behavior of a typical single-input single-output process in detail, four simple feature patterns reflecting the dynamic characteristics of the process are determined and extracted online from the recent history of the time series of the controlled and manipulated process variables. The predictive function is realized through a set of fuzzy logic rules, which are activated by the extracted feature patterns. The proposed predictive strategy, unlike traditional model-based predictive techniques, avoids mathematical models of the process and is computationally efficient. The effectiveness of the proposed strategy is substantiated by simulations. It is also shown that the strategy provides a promising alternative to online compensation for considerable dead time in a chemical process.
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