Abstract
The main bottleneck for the real-time implementation of a predictive controller based on a nonlinear process model is the complexity of the associated optimization problem. The computational costs can be lowered by linearizing the model at a current operating point and using linear predictive control. For long-range predictive control, however, this leads to the accumulation of linearization errors. In this paper, multi-step linearization around the working points within the prediction horizon is investigated. Takagi-Sugeno (TS) fuzzy models are chosen, as the model structure as local linear models can be derived from the linear rule consequents in a straightforward way.
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