Fuzzy model-based DMC for nonlinear process control

This paper proposes a new approach to predictive control of highly nonlinear processes based on a fuzzy model of the Takagi-Sugeno form. Standard Model Based Predictive Control (MBPC) methods use linear process models and are therefore unable to deal with strong process nonlinearities. But, the advantage of linear MBPC is in implementation due to fast optimization algorithms and guaranteed convergence within each time sample. In our approach, step responses for different operating points are extracted on-line from the nonlinear fuzzy model and a modified linear DMC algorithm is used. In this way, all the advantages of both fuzzy modeling of nonlinear processes and DMC control are accomplished. Proposed approach is simple and efficient also for real-time control. In the paper two illustrative examples are shown: a highly nonlinear simulated pH-process and a laboratory scale thermal plant. Copyright (C) 1998 IFAC.