Interval-Boundary-Dependent Control for Interval Type-2 T–S Fuzzy Systems

Park, Bum Yong; Shin, JaeWook

doi:10.1007/s40313-018-0418-3

Cited by 3 publications

(1 citation statement)

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“…General type 2 fuzzy sets (GT2FS) and interval type 2 fuzzy sets (IT2FS) are the two main categories which are developed from T2FLS (Nodeh, Ghasemi, & Daniali, 2019). Various researches reveal that latter is more accurate and more efficient in coping with uncertainties (Larguech, Aloui, Pagès, El Hajjaji, & Chaari, 2015;Park & Shin, 2018;Ramesh, Panda, & Kumar, 2013;Shabaniniai, Etedali, & Ghadamyari, 2014;Zirkohi & Lin, 2015). Another advantage of IT2FS is that fewer fuzzy rules are needed as it uses foot of uncertainty (FOU) in its membership functions.…”

Section: Introductiónmentioning

confidence: 99%

Hybrid robust controller based on interval type 2 fuzzy neural network and higher order sliding mode for robotic manipulators

Nodeh

Ghasemi

Daniali

2019

Nexo Revista Científica

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Industrial arms should be able to perform their duties in environments where unpredictable conditions and perturbations are present. In this paper, controlling a robotic manipulator is intended under significant external perturbations and parametric uncertainties. Type-2 fuzzy logic is an appropriate choice in the face of uncertain environments, for various reasons, including utilizing fuzzy membership functions. Also, using the neural network (NN) can increase robustness of the controller. Although neural network does not basically need to build its type-2 fuzzy rules, the initial rules based on sliding surface of higher order sliding mode controller (HOSMC) can improve the system's performance. In addition, self-regulation feature of the controller, which is based on the existence of the neural network in the central type-2 fuzzy controller block, increases the robustness of the method even more. Effective performance of the proposed controller (IT2FNN-HOSMC) is shown under various perturbations in numerical simulations.

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