Terminal Sliding Mode Control Using Adaptive Fuzzy-Neural Observer
We propose a terminal sliding mode control (SMC) law based on adaptive fuzzy-neural observer for nonaffine nonlinear uncertain system. First, a novel nonaffine nonlinear approximation algorithm is proposed for observer and controller design. Then, an adaptive fuzzy-neural observer is introduced to i...
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2013-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2013/958958 |
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doaj-1fb729a8b7e04977a4649f4d255706902020-11-24T22:13:52ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/958958958958Terminal Sliding Mode Control Using Adaptive Fuzzy-Neural ObserverDezhi Xu0Bin Jiang1Moshu Qian2Jing Zhao3College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaWe propose a terminal sliding mode control (SMC) law based on adaptive fuzzy-neural observer for nonaffine nonlinear uncertain system. First, a novel nonaffine nonlinear approximation algorithm is proposed for observer and controller design. Then, an adaptive fuzzy-neural observer is introduced to identify the simplified model and resolve the problem of the unavailability of the state variables. Moreover, based on the information of the adaptive observer, the terminal SMC law is designed. The Lyapunov synthesis approach is used to guarantee a global uniform ultimate boundedness property of the state estimation error and the asymptotic output tracking of the closed-loop control systems in spite of unknown uncertainties/disturbances, as well as all the other signals in the closed-loop system. Finally, using the designed terminal sliding mode controller, the simulation results on the dynamic model demonstrate the effectiveness of the proposed new control techniques.http://dx.doi.org/10.1155/2013/958958 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dezhi Xu Bin Jiang Moshu Qian Jing Zhao |
spellingShingle |
Dezhi Xu Bin Jiang Moshu Qian Jing Zhao Terminal Sliding Mode Control Using Adaptive Fuzzy-Neural Observer Mathematical Problems in Engineering |
author_facet |
Dezhi Xu Bin Jiang Moshu Qian Jing Zhao |
author_sort |
Dezhi Xu |
title |
Terminal Sliding Mode Control Using Adaptive Fuzzy-Neural Observer |
title_short |
Terminal Sliding Mode Control Using Adaptive Fuzzy-Neural Observer |
title_full |
Terminal Sliding Mode Control Using Adaptive Fuzzy-Neural Observer |
title_fullStr |
Terminal Sliding Mode Control Using Adaptive Fuzzy-Neural Observer |
title_full_unstemmed |
Terminal Sliding Mode Control Using Adaptive Fuzzy-Neural Observer |
title_sort |
terminal sliding mode control using adaptive fuzzy-neural observer |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2013-01-01 |
description |
We propose a terminal sliding mode control (SMC) law based on adaptive fuzzy-neural observer for nonaffine nonlinear uncertain system. First, a novel nonaffine nonlinear approximation algorithm is proposed for observer and controller design. Then, an adaptive fuzzy-neural observer is introduced to identify the simplified model and resolve the problem of the unavailability of the state variables. Moreover, based on the information of the adaptive observer, the terminal SMC law is designed. The Lyapunov synthesis approach is used to guarantee a global uniform ultimate boundedness property of the state estimation error and the asymptotic output tracking of the closed-loop control systems in spite of unknown uncertainties/disturbances, as well as all the other signals in the closed-loop system. Finally, using the designed terminal sliding mode controller, the simulation results on the dynamic model demonstrate the effectiveness of the proposed new control techniques. |
url |
http://dx.doi.org/10.1155/2013/958958 |
work_keys_str_mv |
AT dezhixu terminalslidingmodecontrolusingadaptivefuzzyneuralobserver AT binjiang terminalslidingmodecontrolusingadaptivefuzzyneuralobserver AT moshuqian terminalslidingmodecontrolusingadaptivefuzzyneuralobserver AT jingzhao terminalslidingmodecontrolusingadaptivefuzzyneuralobserver |
_version_ |
1725799721892577280 |