Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators

Küçük Resim Yok

Tarih

2021

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Elsevier Ltd

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

With increasing demand for using robotic manipulators in industrial applications, controllers specific for performing repeatable tasks are required. These controllers must also be robust to model uncertainties. To address this research issue, a repetitive learning control method fused with adaptive fuzzy logic techniques is designed. Specifically, modeling uncertainties are first modeled with a fuzzy logic network and an adaptive fuzzy logic strategy with online tuning is designed. The stability is investigated via Lyapunov type techniques where global uniform ultimate boundedness of closed loop system is guaranteed. Numerical simulation results obtained from a two degree of freedom robot manipulator model and experiments performed on a robot manipulator demonstrate the efficacy of the proposed control methodology. © 2021 Elsevier B.V.

Açıklama

Anahtar Kelimeler

Adaptive fuzzy logic, Fuzzy approximation, Lyapunov methods, Repetitive learning control, Robot manipulators, Universal fuzzy controller

Kaynak

Applied Soft Computing

WoS Q Değeri

Scopus Q Değeri

Q1

Cilt

104

Sayı

Künye