Backstepping design of adaptive neural fault-tolerant control for MIMO nonlinear systems

In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded f...

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Main Authors: Gao, Hui, Song, Yongduan, Wen, Changyun
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/140987
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1409872020-06-03T05:53:02Z Backstepping design of adaptive neural fault-tolerant control for MIMO nonlinear systems Gao, Hui Song, Yongduan Wen, Changyun School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Fault-tolerant Control Filter-based Modification In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller. 2020-06-03T05:53:02Z 2020-06-03T05:53:02Z 2016 Journal Article Gao, H., Song, Y., & Wen, C. (2017). Backstepping design of adaptive neural fault-tolerant control for MIMO nonlinear systems. IEEE Transactions on Neural Networks and Learning Systems, 28(11), 2605-2613. doi:10.1109/tnnls.2016.2599009 2162-237X https://hdl.handle.net/10356/140987 10.1109/TNNLS.2016.2599009 28113647 2-s2.0-85037051880 11 28 2605 2613 en IEEE Transactions on Neural Networks and Learning Systems © 2016 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Fault-tolerant Control
Filter-based Modification
spellingShingle Engineering::Electrical and electronic engineering
Fault-tolerant Control
Filter-based Modification
Gao, Hui
Song, Yongduan
Wen, Changyun
Backstepping design of adaptive neural fault-tolerant control for MIMO nonlinear systems
description In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Gao, Hui
Song, Yongduan
Wen, Changyun
format Article
author Gao, Hui
Song, Yongduan
Wen, Changyun
author_sort Gao, Hui
title Backstepping design of adaptive neural fault-tolerant control for MIMO nonlinear systems
title_short Backstepping design of adaptive neural fault-tolerant control for MIMO nonlinear systems
title_full Backstepping design of adaptive neural fault-tolerant control for MIMO nonlinear systems
title_fullStr Backstepping design of adaptive neural fault-tolerant control for MIMO nonlinear systems
title_full_unstemmed Backstepping design of adaptive neural fault-tolerant control for MIMO nonlinear systems
title_sort backstepping design of adaptive neural fault-tolerant control for mimo nonlinear systems
publishDate 2020
url https://hdl.handle.net/10356/140987
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