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
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. (C) 2021 Elsevier B.V. All rights reserved.
Açıklama
Anahtar Kelimeler
Fuzzy approximation, Universal fuzzy controller, Adaptive fuzzy logic, Robot manipulators, Lyapunov methods, Repetitive learning control, Neural-Network, Disturbance, System
Kaynak
Applied Soft Computing
WoS Q Değeri
Q1
Scopus Q Değeri
Cilt
104
