Command filtered backstepping control of constrained flexible joint robotic manipulator
Abstract Here, an adaptive radial basis function (RBF) neural network (NN) backstepping controller is proposed for a class of input‐constrained flexible joint robotic manipulators represented by strict‐feedback form with unknown terms, external stochastic disturbance, and output disturbance. The pro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
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| Series: | IET Control Theory & Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cth2.12528 |
| _version_ | 1827587818018308096 |
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| author | Mohammad Mehdi Arefi Navid Vafamand Behrouz Homayoun Mohammadreza Davoodi |
| author_facet | Mohammad Mehdi Arefi Navid Vafamand Behrouz Homayoun Mohammadreza Davoodi |
| author_sort | Mohammad Mehdi Arefi |
| collection | DOAJ |
| description | Abstract Here, an adaptive radial basis function (RBF) neural network (NN) backstepping controller is proposed for a class of input‐constrained flexible joint robotic manipulators represented by strict‐feedback form with unknown terms, external stochastic disturbance, and output disturbance. The proposed approach is robust against both deterministic and stochastic uncertainties and disturbances and copes with the control input amplitude saturation. Moreover, by deploying the minimal learning parameter method and command filter technique, the computational burden of derivative terms and adaptive terms greatly decreases. Considering the mean‐value theorem assists us to avoid the need for having the input saturation bounds in prior. The suggested tracking control scheme mandates the closed‐loop system states to be semi‐globally bounded‐in‐probability. Also, a quartic Barrier Lyapunov function is utilized to force the tracking error to be confined within a pre‐chosen small region around the origin. Eventually, a numerical simulation of a flexible joint robot manipulator with a single link is performed to show the effectiveness and performance of the developed control method. |
| first_indexed | 2024-03-09T00:23:27Z |
| format | Article |
| id | doaj.art-8ccf7b6a67cf44e6852a84e147df05ec |
| institution | Directory Open Access Journal |
| issn | 1751-8644 1751-8652 |
| language | English |
| last_indexed | 2024-03-09T00:23:27Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Control Theory & Applications |
| spelling | doaj.art-8ccf7b6a67cf44e6852a84e147df05ec2023-12-12T04:52:11ZengWileyIET Control Theory & Applications1751-86441751-86522023-12-0117182506251810.1049/cth2.12528Command filtered backstepping control of constrained flexible joint robotic manipulatorMohammad Mehdi Arefi0Navid Vafamand1Behrouz Homayoun2Mohammadreza Davoodi3Department of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University ShirazIranDepartment of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University ShirazIranDepartment of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University ShirazIranDepartment of Electrical and Computer Engineering The University of Memphis MemphisTennesseeUSAAbstract Here, an adaptive radial basis function (RBF) neural network (NN) backstepping controller is proposed for a class of input‐constrained flexible joint robotic manipulators represented by strict‐feedback form with unknown terms, external stochastic disturbance, and output disturbance. The proposed approach is robust against both deterministic and stochastic uncertainties and disturbances and copes with the control input amplitude saturation. Moreover, by deploying the minimal learning parameter method and command filter technique, the computational burden of derivative terms and adaptive terms greatly decreases. Considering the mean‐value theorem assists us to avoid the need for having the input saturation bounds in prior. The suggested tracking control scheme mandates the closed‐loop system states to be semi‐globally bounded‐in‐probability. Also, a quartic Barrier Lyapunov function is utilized to force the tracking error to be confined within a pre‐chosen small region around the origin. Eventually, a numerical simulation of a flexible joint robot manipulator with a single link is performed to show the effectiveness and performance of the developed control method.https://doi.org/10.1049/cth2.12528adaptive neural tracking controlcommand filterflexible joint robotic manipulatorinput saturationminimal learning parameterstochastic non‐linear systems |
| spellingShingle | Mohammad Mehdi Arefi Navid Vafamand Behrouz Homayoun Mohammadreza Davoodi Command filtered backstepping control of constrained flexible joint robotic manipulator IET Control Theory & Applications adaptive neural tracking control command filter flexible joint robotic manipulator input saturation minimal learning parameter stochastic non‐linear systems |
| title | Command filtered backstepping control of constrained flexible joint robotic manipulator |
| title_full | Command filtered backstepping control of constrained flexible joint robotic manipulator |
| title_fullStr | Command filtered backstepping control of constrained flexible joint robotic manipulator |
| title_full_unstemmed | Command filtered backstepping control of constrained flexible joint robotic manipulator |
| title_short | Command filtered backstepping control of constrained flexible joint robotic manipulator |
| title_sort | command filtered backstepping control of constrained flexible joint robotic manipulator |
| topic | adaptive neural tracking control command filter flexible joint robotic manipulator input saturation minimal learning parameter stochastic non‐linear systems |
| url | https://doi.org/10.1049/cth2.12528 |
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