A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots

By Hani Hagras. IEEE transactions on fuzzy systems, 2004

1,019 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fd9d6ff54ca9b6be731fb33dc9019bf9eb8fc616

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Citations in the corpus

Optimization of Interval Type-2 Fuzzy Logic Controllers for a Perturbed Autonomous Wheeled Mobile Robot Using Genetic Algorithms (2009, background): “Mobile robots have attracted considerable interest in the robotics and control research community, because they have non-holonomic properties caused by non-integrable differential constrains [16,19].”
A Historical Account of Types of Fuzzy Sets and Their Relationships (2016, methodology): “Hani Hagras (M’03–SM’05–”
Geometric Type-1 and Type-2 Fuzzy Logic Systems (2007, background): “Digital Object Identifier 10.1109/TFUZZ.2006.889764 has curtailed their deployment and type-2 applications predominately deploy interval valued fuzzy sets where the secondary membership is an interval set [2]–[4].”
Toward General Type-2 Fuzzy Logic Systems Based on zSlices (2010, background): “This has been largely attributed to the ability of interval type-2 sets to better model the faced uncertainty and the fact that an interval type-2 set can be seen to possess an uncountable number of embedded type-1 sets [1]–[3].”
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means (2007, background): “For example, applications include the classification of coded video streams [5], co-channel interference elimination from nonlinear time-varying communication channels [6], connection admission control [7], control of mobile robots [8], [9], decision making [10], [11], equalization of nonlinear fadi”
Type-2 FLCs: A New Generation of Fuzzy Controllers (2007, background): “Therefore, FLCs that are based on type-2 fuzzy sets will have the potential to produce a better performance than the type-1 FLCs when dealing with uncertainties [7].”
Stability Analysis of Interval Type-2 Fuzzy-Model-Based Control Systems (2008, methodology): “The applications include adaptive filtering [14], autonomous mobile robots [15], and dc–dc power converters [16].”
Advances in type-2 fuzzy sets and systems (2007, background): “Keywords: Type-2 fuzzy sets; Type-2 fuzzy systems; Interval type-2 fuzzy sets; Interval type-2 fuzzy logic systems; Centroid; KM algorithms; Computing with words; Fuzzy weighted average”
On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers (2012, methodology): “Despite having a name that carries the connotation of uncertainty, research has shown that there are limitations in the ability of T1 FSs to model and minimize the effect of uncertainties [20], [21], [31], [60].”
Interval Type-2 Fuzzy Logic Systems Made Simple (2006, background): “The resulting type-2 fuzzy logic systems (T2 FLS) have the potential to provide better performance than a type-1 (T1) FLS (e.g., [4], [10]–[15], [21]–[23]).”
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems (2006, neutral): “[102], [116], [185], [205], [218], [222], [260]; industrial/chemical processes [22], [41], [90], [111], [129], [137], [146], [153], [162], [199], [200], [229], [248], [258], [271], [279], [288]; aircrafts [58], [73], [130], [161]; motors [9], [100], [143]; medical”
Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching (2014, background): “It has been shown in the literature that type-2 fuzzy sets [30], which extend the capability of type-1 fuzzy sets, are good in representing and capturing uncertainties, supported by a number of applications such as adaptive filtering [31], analog module implementation and design [32], [33], active s”