Quasi-min-max fuzzy model predictive control of direct methanol fuel cells

Fuzzy Model Predictive Control With Enhanced Robustness for Nonlinear System via a Discrete Disturbance Observer

“…Proof: The LMIs (24a)-(24e) are equivalent to the expression of (21), (19), (22a), (22b), (22c), respectively. Proofs of Theorem 3 can be found in [6], [44] and thus are not repeated here.…”

Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems

“…This imprecision that T1FSs represent is in the form of membership functions, or linguistic values, and it is a powerful tool which is still widely used, for example, in synthetic aperture radar image change detection (Gong, Su, Jia, & Chen, 2014), conditional density estimation by using probabilistic fuzzy systems (van den Berg, Kaymak, & Almeida, 2013), predictive control of direct methanol fuel cells (W. Yang, Feng, & Zhang, 2014), fuzzy clustering via a granular gravitational technique (Sanchez, Castillo, Castro, & Melin, 2014), a support system for sellers in e-marketplaces (Kolomvatsos, Anagnostopoulos, & Hadjiefthymiades, 2014), image segmentation (Othman, Tizhoosh, & Khalvati, 2014), distributed filtering in sensor networks (Su, Wu, & Shi, 2013), indoor localization using WiFi (Garcia-Valverde et al, 2013), etc. Research in T1FLS is still ongoing despite the existence of more advanced FS representations (IT2FS and GT2FS), partly due to the fact that not all decision making situations require such advanced representations and T1FS…”