This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers'understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a locally linear n-step ahead predictor; and developing model-based control algorithms according to the Generalized Predictive Control principles using Type-2 Fuzzy Sets.

Throughout the book, theory is always complemented with practical applications and readers are invited to take their learning process one step farther and implement their own applications using the algorithms' source codes (provided). As such, the book offers avaluable referenceguide for allengineers and researchers in the field ofcomputer science who are interested in intelligent systems, rule-based systems and modeling uncertainty.

Presents a simple and didactic introduction to the principles of Type-2 Fuzzy Logic and extends them to state-of-the art methods in model-based control techniques

Uses application scenarios based on process control engineering domains, which are commonly used as a benchmark in the literature, providing a comparative standpoint to other control algorithm's implementations

Provides an open-source software framework where the algorithms used in the book are available, written for Matlab/Simulink and in C Language for embedded systems