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interval Type-2 fuzzy logic; type-reduction; Type-2 fuzzy control; Type-2 fuzzy edge detection
Nowadays Fuzzy logic in control applications is a well-recognized alternative, and this is thanks to its inherent advantages as its robustness. However, the Type-2 Fuzzy Logic approach, allows managing uncertainty in the model. Type-2 Fuzzy Logic has recently shown to provide significant improvement in image processing applications, however it is also important to analyze its impact in controller performance. This paper is presenting a comparison in the robustness of Interval Type-2 and Generalized Type-2 Fuzzy Logic Controllers, in order to generate criteria to decide which type of controller is better in specific applications. The plants considered in the experimentation are two benchmark control plants and we report the Integral Squared Error (ISE), Integral Absolute Error (IAE) and Integral Time-weighted Absolute Error (ITAE) performance metrics, and also another important metric reported is the execution time. Based on the experimental results, Fuzzy Logic Controller selection criteria are proposed according to the performance and execution time requirements.
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