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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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. |
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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 |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Gao, Hui Song, Yongduan Wen, Changyun |
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Article |
author |
Gao, Hui Song, Yongduan Wen, Changyun |
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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 |
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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 |
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2020 |
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https://hdl.handle.net/10356/140987 |
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1681058997829894144 |