research-article

Neuro-adaptive observer based control of flexible joint robot

Authors:

Chenguang Yang,

Chun-Yi SuAuthors Info & Claims

Neurocomputing, Volume 275, Issue C

Pages 73 - 82

https://doi.org/10.1016/j.neucom.2017.05.011

Published: 31 January 2018 Publication History

Abstract

Due to high nonlinearity, strong coupling and time-varying characteristics of flexible joint robot manipulators, their control design is generally a challenging problem. There are inevitable uncertainties associated with their kinematics and dynamics, so that accurate models would not be available for control design. Furthermore, practically we may face the problem that state variables required by the controller are not measurable. In this paper, we focus on the study of control system design using a neural network observer to solve the aforementioned unmeasurable problem. First, we propose an observer based on Radial Basis Function (RBF) neural network to estimate state variables of the normal system. We then design the controller based on dynamic surface control method for a single link flexible joint manipulator whose model is unknown. The unknown model of the manipulator is constructed by RBF neural network. The stability of the observer and controller is shown by Lyapunov method. Finally, simulation studies are performed to test and verify the effectiveness of the proposed controller.

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Cited By

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https://dl.acm.org/doi/10.1016/j.neucom.2023.127045
Jia P(2023)Design of a Novel NNs Learning Tracking Controller for Robotic Manipulator with Joints FlexibilityJournal of Robotics10.1155/2023/11867192023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/1186719
Zhuang XTian YWang HAli S(2023)Neural Network Adaptive Observer design for Nonlinear Systems with Partially and Completely Unknown Dynamics Subject to Variable Sampled and Delay Output MeasurementNeurocomputing10.1016/j.neucom.2023.126865561:COnline publication date: 7-Dec-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.126865
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cover image Neurocomputing

Neurocomputing Volume 275, Issue C

January 2018

2070 pages

ISSN:0925-2312

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 31 January 2018

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Cited By

Xu SWu Z(2024)Adaptive learning control of robot manipulators via incremental hybrid neural networkNeurocomputing10.1016/j.neucom.2023.127045568:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127045
Jia P(2023)Design of a Novel NNs Learning Tracking Controller for Robotic Manipulator with Joints FlexibilityJournal of Robotics10.1155/2023/11867192023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/1186719
Zhuang XTian YWang HAli S(2023)Neural Network Adaptive Observer design for Nonlinear Systems with Partially and Completely Unknown Dynamics Subject to Variable Sampled and Delay Output MeasurementNeurocomputing10.1016/j.neucom.2023.126865561:COnline publication date: 7-Dec-2023
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Chakrabarty AZemouche ARajamani RBenosman M(2022)Robust Data-Driven Neuro-Adaptive Observers With Lipschitz Activation Functions2019 IEEE 58th Conference on Decision and Control (CDC)10.1109/CDC40024.2019.9029394(2862-2867)Online publication date: 28-Dec-2022
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Shi HWang MWang C(2022)Pattern-based autonomous smooth switching control for constrained flexible joint manipulatorNeurocomputing10.1016/j.neucom.2022.04.031492:C(162-173)Online publication date: 1-Jul-2022
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Chakrabarty ABenosman M(2021)Safe learning-based observers for unknown nonlinear systems using Bayesian optimizationAutomatica (Journal of IFAC)10.1016/j.automatica.2021.109860133:COnline publication date: 1-Nov-2021
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Yang MHuang JYang LZhang M(2021)A Note on Adaptive Observer Design Method for One-Sided Lipschitz SystemsCircuits, Systems, and Signal Processing10.1007/s00034-020-01505-840:2(1021-1039)Online publication date: 1-Feb-2021
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Chen LWang Q(2021)Extended‐state‐observer‐based dynamic surface control of flexible‐joint robot systems with input saturationInternational Journal of Adaptive Control and Signal Processing10.1002/acs.332335:12(2372-2388)Online publication date: 3-Dec-2021
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