Abstract
The development and application to a physical system of an adaptive predictive fuzzy controller is presented. The target process is a small electric furnace used in ceramic manufacturing. The proposed controller attempts to minimize a multi-step quadratic cost under the assumption that the control actions are all free over the prediction horizon. The control law relies on a simplified fuzzy relational model identified on-line. A convenient selection of the triangular norms used in the composition operator is made for allowing the application of Recursive Least Squares (RLS) to fuzzy relational structures, thus speeding up identification. It is shown that for a particular selection of norms, relational structures require less parameters to be described, and they can be interpreted as a set of simplified fuzzy rules. Examples are presented in which the controller developed outperforms other controllers with identification procedures based on gradient.
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