Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints

This paper presents a control design for a robotic manipulator with uncertainties in both actuator dynamics and manipulator dynamics subject to asymmetric time-varying joint space constraints. Tangent-type time-varying barrier Lyapunov functionals (tvBLFs) are constructed to ensure no constraint vio...

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Main Authors: Rahimi, H.N., Howard, Ian, Cui, Lei
Format: Journal Article
Published: Academic Press 2018
Online Access:http://purl.org/au-research/grants/arc/DE170101062
http://hdl.handle.net/20.500.11937/66628
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author Rahimi, H.N.
Howard, Ian
Cui, Lei
author_facet Rahimi, H.N.
Howard, Ian
Cui, Lei
author_sort Rahimi, H.N.
building Curtin Institutional Repository
collection Online Access
description This paper presents a control design for a robotic manipulator with uncertainties in both actuator dynamics and manipulator dynamics subject to asymmetric time-varying joint space constraints. Tangent-type time-varying barrier Lyapunov functionals (tvBLFs) are constructed to ensure no constraint violation and to remove the need for transforming the original constrained system into an equivalent unconstrained one. Adaptive Neural Networks (NNs) are proposed to handle uncertainties in manipulator dynamics and actuator dynamics in addition to the unknown disturbances. Proper input saturation is employed, and it is proved that under the proposed method the stability and semi-global uniform ultimate boundedness of the closed-loop system can be achieved without violation of constraints. The effectiveness of the theoretical developments is verified through numerical simulations.
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format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T10:30:22Z
publishDate 2018
publisher Academic Press
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spelling curtin-20.500.11937-666282022-10-06T03:57:27Z Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints Rahimi, H.N. Howard, Ian Cui, Lei This paper presents a control design for a robotic manipulator with uncertainties in both actuator dynamics and manipulator dynamics subject to asymmetric time-varying joint space constraints. Tangent-type time-varying barrier Lyapunov functionals (tvBLFs) are constructed to ensure no constraint violation and to remove the need for transforming the original constrained system into an equivalent unconstrained one. Adaptive Neural Networks (NNs) are proposed to handle uncertainties in manipulator dynamics and actuator dynamics in addition to the unknown disturbances. Proper input saturation is employed, and it is proved that under the proposed method the stability and semi-global uniform ultimate boundedness of the closed-loop system can be achieved without violation of constraints. The effectiveness of the theoretical developments is verified through numerical simulations. 2018 Journal Article http://hdl.handle.net/20.500.11937/66628 10.1016/j.ymssp.2018.03.042 http://purl.org/au-research/grants/arc/DE170101062 Academic Press fulltext
spellingShingle Rahimi, H.N.
Howard, Ian
Cui, Lei
Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints
title Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints
title_full Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints
title_fullStr Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints
title_full_unstemmed Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints
title_short Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints
title_sort neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints
url http://purl.org/au-research/grants/arc/DE170101062
http://hdl.handle.net/20.500.11937/66628