Adaptive Boundary Control of Flexible Manipulators with Parameter Uncertainty Based on RBF Neural Network
In this paper, nonlinear dynamical equations of the flexible manipulator with a lumped payload at the free end are derived from Hamilton's principle. The obtained model consists of both distributed parameters and lumped parameters, namely, partial differential equations (PDEs) governing the fle...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8261423 |
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| _version_ | 1849387230257217536 |
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| author | Cang He Fang Zhang Jinhui Jiang |
| author_facet | Cang He Fang Zhang Jinhui Jiang |
| author_sort | Cang He |
| collection | DOAJ |
| description | In this paper, nonlinear dynamical equations of the flexible manipulator with a lumped payload at the free end are derived from Hamilton's principle. The obtained model consists of both distributed parameters and lumped parameters, namely, partial differential equations (PDEs) governing the flexible motion of links and boundary conditions in the form of ordinary differential equations (ODEs). Considering the great nonlinear approximation ability of the radial basis function (RBF) neural network, we propose a combined control algorithm that includes two parts: one is a boundary controller to track the desired joint positions and suppress the vibration of flexible links; another is a RBF neural network designed to compensate for the parametric uncertainties. The iteration criterion of the RBF neural network weight matrix is derived from the extended Lyapunov function. Stabilization analysis is further carried out theoretically via LaSalle’s invariance principle. Finally, the results of the numerical simulation verify that the proposed control law can realize the asymptotic convergence of tracking error and suppression of the elastic vibration as well. |
| format | Article |
| id | doaj-art-2c7ca55298d44e969d6878bb09b1f80b |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-2c7ca55298d44e969d6878bb09b1f80b2025-08-20T03:55:17ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/82614238261423Adaptive Boundary Control of Flexible Manipulators with Parameter Uncertainty Based on RBF Neural NetworkCang He0Fang Zhang1Jinhui Jiang2State Key Lab of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, ChinaState Key Lab of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, ChinaState Key Lab of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, ChinaIn this paper, nonlinear dynamical equations of the flexible manipulator with a lumped payload at the free end are derived from Hamilton's principle. The obtained model consists of both distributed parameters and lumped parameters, namely, partial differential equations (PDEs) governing the flexible motion of links and boundary conditions in the form of ordinary differential equations (ODEs). Considering the great nonlinear approximation ability of the radial basis function (RBF) neural network, we propose a combined control algorithm that includes two parts: one is a boundary controller to track the desired joint positions and suppress the vibration of flexible links; another is a RBF neural network designed to compensate for the parametric uncertainties. The iteration criterion of the RBF neural network weight matrix is derived from the extended Lyapunov function. Stabilization analysis is further carried out theoretically via LaSalle’s invariance principle. Finally, the results of the numerical simulation verify that the proposed control law can realize the asymptotic convergence of tracking error and suppression of the elastic vibration as well.http://dx.doi.org/10.1155/2020/8261423 |
| spellingShingle | Cang He Fang Zhang Jinhui Jiang Adaptive Boundary Control of Flexible Manipulators with Parameter Uncertainty Based on RBF Neural Network Shock and Vibration |
| title | Adaptive Boundary Control of Flexible Manipulators with Parameter Uncertainty Based on RBF Neural Network |
| title_full | Adaptive Boundary Control of Flexible Manipulators with Parameter Uncertainty Based on RBF Neural Network |
| title_fullStr | Adaptive Boundary Control of Flexible Manipulators with Parameter Uncertainty Based on RBF Neural Network |
| title_full_unstemmed | Adaptive Boundary Control of Flexible Manipulators with Parameter Uncertainty Based on RBF Neural Network |
| title_short | Adaptive Boundary Control of Flexible Manipulators with Parameter Uncertainty Based on RBF Neural Network |
| title_sort | adaptive boundary control of flexible manipulators with parameter uncertainty based on rbf neural network |
| url | http://dx.doi.org/10.1155/2020/8261423 |
| work_keys_str_mv | AT canghe adaptiveboundarycontrolofflexiblemanipulatorswithparameteruncertaintybasedonrbfneuralnetwork AT fangzhang adaptiveboundarycontrolofflexiblemanipulatorswithparameteruncertaintybasedonrbfneuralnetwork AT jinhuijiang adaptiveboundarycontrolofflexiblemanipulatorswithparameteruncertaintybasedonrbfneuralnetwork |