Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation
This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/7413642 |
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| _version_ | 1850232953335971840 |
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| author | Rong Mei ChengJiang Yu |
| author_facet | Rong Mei ChengJiang Yu |
| author_sort | Rong Mei |
| collection | DOAJ |
| description | This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators. |
| format | Article |
| id | doaj-art-0cc82319ad8d46d3bc5369edcd6c459f |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0cc82319ad8d46d3bc5369edcd6c459f2025-08-20T02:03:01ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/74136427413642Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input SaturationRong Mei0ChengJiang Yu1Criminal Investigation Department, Nanjing Forest Police College, Nanjing 210023, ChinaCriminal Investigation Department, Nanjing Forest Police College, Nanjing 210023, ChinaThis paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.http://dx.doi.org/10.1155/2017/7413642 |
| spellingShingle | Rong Mei ChengJiang Yu Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation Complexity |
| title | Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation |
| title_full | Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation |
| title_fullStr | Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation |
| title_full_unstemmed | Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation |
| title_short | Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation |
| title_sort | adaptive neural output feedback control for uncertain robot manipulators with input saturation |
| url | http://dx.doi.org/10.1155/2017/7413642 |
| work_keys_str_mv | AT rongmei adaptiveneuraloutputfeedbackcontrolforuncertainrobotmanipulatorswithinputsaturation AT chengjiangyu adaptiveneuraloutputfeedbackcontrolforuncertainrobotmanipulatorswithinputsaturation |