Neural network adaptive command filtered control of robotic manipulators with input saturation

Neural network adaptive command filtered control of robotic manipulators with input saturation

This paper investigates finite-time control of uncertain robotic manipulators with external disturbances by means of neural network control and backstepping technique. To solve the “explosion of terms” in traditional backstepping control, a second-order command filter is designed, and the virtual in...

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Main Authors: Lin Wang, Chunzhi Yang
Format: Article
Language:English
Published: SAGE Publishing 2019-12-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.1177/1729881419894779
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spelling doaj-786d5dd178ef4a71a7beb9f7c76cd8ea2020-11-25T04:03:35ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142019-12-011610.1177/1729881419894779Neural network adaptive command filtered control of robotic manipulators with input saturationLin Wang0Chunzhi Yang1 School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, China Department of Applied Mathematics, Huainan Normal University, Huainan, ChinaThis paper investigates finite-time control of uncertain robotic manipulators with external disturbances by means of neural network control and backstepping technique. To solve the “explosion of terms” in traditional backstepping control, a second-order command filter is designed, and the virtual input and its first-order derivative can be obtained accurately in a finite time. The parameters of the neural network are updated by using the tracking error signals. The proposed controller can guarantee that the tracking error converges to a small region of the origin in some finite time. Finally, we give a simulation study to show the effectiveness of the proposed method.https://doi.org/10.1177/1729881419894779
collection DOAJ
language English
format Article
sources DOAJ
author Lin Wang
Chunzhi Yang
spellingShingle Lin Wang
Chunzhi Yang
Neural network adaptive command filtered control of robotic manipulators with input saturation
International Journal of Advanced Robotic Systems
author_facet Lin Wang
Chunzhi Yang
author_sort Lin Wang
title Neural network adaptive command filtered control of robotic manipulators with input saturation
title_short Neural network adaptive command filtered control of robotic manipulators with input saturation
title_full Neural network adaptive command filtered control of robotic manipulators with input saturation
title_fullStr Neural network adaptive command filtered control of robotic manipulators with input saturation
title_full_unstemmed Neural network adaptive command filtered control of robotic manipulators with input saturation
title_sort neural network adaptive command filtered control of robotic manipulators with input saturation
publisher SAGE Publishing
series International Journal of Advanced Robotic Systems
issn 1729-8814
publishDate 2019-12-01
description This paper investigates finite-time control of uncertain robotic manipulators with external disturbances by means of neural network control and backstepping technique. To solve the “explosion of terms” in traditional backstepping control, a second-order command filter is designed, and the virtual input and its first-order derivative can be obtained accurately in a finite time. The parameters of the neural network are updated by using the tracking error signals. The proposed controller can guarantee that the tracking error converges to a small region of the origin in some finite time. Finally, we give a simulation study to show the effectiveness of the proposed method.
url https://doi.org/10.1177/1729881419894779
work_keys_str_mv AT linwang neuralnetworkadaptivecommandfilteredcontrolofroboticmanipulatorswithinputsaturation
AT chunzhiyang neuralnetworkadaptivecommandfilteredcontrolofroboticmanipulatorswithinputsaturation
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