Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7893
Title: Neural network-based repetitive learning control of euler lagrange systems: An output feedback approach
Authors: Tatlıcıoğlu, Evren
Çobanoğlu, Necati
Zergeroğlu, Erkan
Keywords: Lyapunov methods
Neural networks
Nonlinear output feedback
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: In this letter, position tracking control problem of a class of fully actuated Euler Lagrange (EL) systems is aimed. The reference position vector is considered to be periodic with a known period. Only position measurements are available for control design while velocity measurements are not. Furthermore, the dynamic model of the EL systems has parametric and/or unstructured uncertainties which avoid it to be used as part of the control design. To address these constraints, an output feedback neural network-based repetitive learning control strategy is preferred. Via the design of a dynamic model independent velocity observer, the lack of velocity measurements is addressed. To compensate for the lack of dynamic model knowledge, universal approximation property of neural networks is utilized where an online adaptive update rule is designed for the weight matrix. The functional reconstruction error is dealt with the design of a novel repetitive learning feedforward term. The outcome is a dynamic model independent output feedback neural network-based controller with a repetitive learning feedforward component. The stability of the closed-loop system is investigated via rigorous mathematical tools with which semi-global asymptotic stability is ensured. © 2017 IEEE.
URI: https://doi.org/10.1109/LCSYS.2017.2720735
https://hdl.handle.net/11147/7893
ISSN: 2475-1456
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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