Adaptive Neural Network Variable Structure Control for Liquid-Filled Spacecraft under Unknown Input Saturation
This study addresses the problem of attitude maneuver control for a three-axis stabilized liquid-filled spacecraft using an adaptive neural network variable structure control algorithm in the presence of parametric uncertainty, external disturbances, and control input saturation. The liquid fuel is...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/6515626 |
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| _version_ | 1832560000304676864 |
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| author | Hongwei Wang Shufeng Lu Xiaojuan Song |
| author_facet | Hongwei Wang Shufeng Lu Xiaojuan Song |
| author_sort | Hongwei Wang |
| collection | DOAJ |
| description | This study addresses the problem of attitude maneuver control for a three-axis stabilized liquid-filled spacecraft using an adaptive neural network variable structure control algorithm in the presence of parametric uncertainty, external disturbances, and control input saturation. The liquid fuel is equivalent to a spherical pendulum model, and the coupled dynamic model of liquid-filled spacecraft is derived using the conservation law of angular momentum moment. Then, adaptive variable structure control technique is designed, which contains hyperbolic tangent function that preserves control smoothness at all times. The proposed control algorithm has the properties that state variables converge to the origin asymptotically under parametric uncertainty and external disturbance. Furthermore, the controller derived here is extended by adding a feed-forward saturation compensation scheme to reduce the influence of unknown control input saturation on the system. Also, the saturation compensation scheme is derived by using a radial basis function neural network to approximate the unknown saturation nonlinearity. The associated stability proof of the resulting closed-loop system is presented based on Lyapunov analysis, and asymptotic convergence of the state variables is guaranteed via the Barbalat lemma. Numerical simulations are presented to illustrate the spacecraft performance obtained by using the proposed controllers. |
| format | Article |
| id | doaj-art-cef48c5253ea44cf9e5b6466006a98b1 |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-cef48c5253ea44cf9e5b6466006a98b12025-02-03T01:28:43ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742020-01-01202010.1155/2020/65156266515626Adaptive Neural Network Variable Structure Control for Liquid-Filled Spacecraft under Unknown Input SaturationHongwei Wang0Shufeng Lu1Xiaojuan Song2College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaDepartment of Mechanics, Inner Mongolia University of Technology, Hohhot 010051, ChinaCollege of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaThis study addresses the problem of attitude maneuver control for a three-axis stabilized liquid-filled spacecraft using an adaptive neural network variable structure control algorithm in the presence of parametric uncertainty, external disturbances, and control input saturation. The liquid fuel is equivalent to a spherical pendulum model, and the coupled dynamic model of liquid-filled spacecraft is derived using the conservation law of angular momentum moment. Then, adaptive variable structure control technique is designed, which contains hyperbolic tangent function that preserves control smoothness at all times. The proposed control algorithm has the properties that state variables converge to the origin asymptotically under parametric uncertainty and external disturbance. Furthermore, the controller derived here is extended by adding a feed-forward saturation compensation scheme to reduce the influence of unknown control input saturation on the system. Also, the saturation compensation scheme is derived by using a radial basis function neural network to approximate the unknown saturation nonlinearity. The associated stability proof of the resulting closed-loop system is presented based on Lyapunov analysis, and asymptotic convergence of the state variables is guaranteed via the Barbalat lemma. Numerical simulations are presented to illustrate the spacecraft performance obtained by using the proposed controllers.http://dx.doi.org/10.1155/2020/6515626 |
| spellingShingle | Hongwei Wang Shufeng Lu Xiaojuan Song Adaptive Neural Network Variable Structure Control for Liquid-Filled Spacecraft under Unknown Input Saturation International Journal of Aerospace Engineering |
| title | Adaptive Neural Network Variable Structure Control for Liquid-Filled Spacecraft under Unknown Input Saturation |
| title_full | Adaptive Neural Network Variable Structure Control for Liquid-Filled Spacecraft under Unknown Input Saturation |
| title_fullStr | Adaptive Neural Network Variable Structure Control for Liquid-Filled Spacecraft under Unknown Input Saturation |
| title_full_unstemmed | Adaptive Neural Network Variable Structure Control for Liquid-Filled Spacecraft under Unknown Input Saturation |
| title_short | Adaptive Neural Network Variable Structure Control for Liquid-Filled Spacecraft under Unknown Input Saturation |
| title_sort | adaptive neural network variable structure control for liquid filled spacecraft under unknown input saturation |
| url | http://dx.doi.org/10.1155/2020/6515626 |
| work_keys_str_mv | AT hongweiwang adaptiveneuralnetworkvariablestructurecontrolforliquidfilledspacecraftunderunknowninputsaturation AT shufenglu adaptiveneuralnetworkvariablestructurecontrolforliquidfilledspacecraftunderunknowninputsaturation AT xiaojuansong adaptiveneuralnetworkvariablestructurecontrolforliquidfilledspacecraftunderunknowninputsaturation |