Optimal identification of Takagi-Sugeno fuzzy models for nonlinear FDI

Abstract

The design of Fault Detection and Isolation (FDI) systems is generally based on models identified by minimizing adjustment error. For nonlinear processes, the use of observers based on Takagi–Sugeno fuzzy models is proposed. However, from a detection and isolation point of view, models obtained using known identification algorithms are not necessarily optimal. This is showed through evaluations made using a benchmark hydraulic system simulator, with a confusion matrix, mean detection time, and mean time between false alarms as performance indicators. Based on these results, this chapter proposes a new design procedure employing multiobjective optimization that improves on the above-mentioned time indicators by a factor varying between 1.5 and 3.7.