Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters
This study addresses the tracking control issue for n-link robotic manipulators with largely jumping parameters. Based on radial basis function neural networks (RBFNNs), we propose weighted multiple-model neural network adaptive control (WMNNAC) approach. To cover the variation ranges of the paramet...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/3172431 |
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| _version_ | 1832567536999202816 |
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| author | Jiazhi Li Weicun Zhang Quanmin Zhu |
| author_facet | Jiazhi Li Weicun Zhang Quanmin Zhu |
| author_sort | Jiazhi Li |
| collection | DOAJ |
| description | This study addresses the tracking control issue for n-link robotic manipulators with largely jumping parameters. Based on radial basis function neural networks (RBFNNs), we propose weighted multiple-model neural network adaptive control (WMNNAC) approach. To cover the variation ranges of the parameters, different models of robotic are constructed. Then, the corresponding local neural network controller is constructed, in which the neural network has been used to approximate the uncertainty part of the control law, and an adaptive observer is implemented to estimate the true external disturbance. The WMNNAC strategy with improved weighting algorithm is adopted to ensure the tracking performance of the robotic manipulator system when parameters jump largely. Through the Lyapunov stability theory and the method of virtual equivalent system (VES), the stability of the closed-loop system is proved. Finally, the simulation results of a two-link manipulator verify the feasibility and efficiency of the proposed WMNNAC strategy. |
| format | Article |
| id | doaj-art-a42286f42b0740d099098d193ecf42a3 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a42286f42b0740d099098d193ecf42a32025-02-03T01:01:22ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/31724313172431Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping ParametersJiazhi Li0Weicun Zhang1Quanmin Zhu2Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaDepartment of Engineering Design and Mathematics, University of the West of England, Bristol BS161QY, UKThis study addresses the tracking control issue for n-link robotic manipulators with largely jumping parameters. Based on radial basis function neural networks (RBFNNs), we propose weighted multiple-model neural network adaptive control (WMNNAC) approach. To cover the variation ranges of the parameters, different models of robotic are constructed. Then, the corresponding local neural network controller is constructed, in which the neural network has been used to approximate the uncertainty part of the control law, and an adaptive observer is implemented to estimate the true external disturbance. The WMNNAC strategy with improved weighting algorithm is adopted to ensure the tracking performance of the robotic manipulator system when parameters jump largely. Through the Lyapunov stability theory and the method of virtual equivalent system (VES), the stability of the closed-loop system is proved. Finally, the simulation results of a two-link manipulator verify the feasibility and efficiency of the proposed WMNNAC strategy.http://dx.doi.org/10.1155/2020/3172431 |
| spellingShingle | Jiazhi Li Weicun Zhang Quanmin Zhu Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters Complexity |
| title | Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters |
| title_full | Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters |
| title_fullStr | Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters |
| title_full_unstemmed | Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters |
| title_short | Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters |
| title_sort | weighted multiple model neural network adaptive control for robotic manipulators with jumping parameters |
| url | http://dx.doi.org/10.1155/2020/3172431 |
| work_keys_str_mv | AT jiazhili weightedmultiplemodelneuralnetworkadaptivecontrolforroboticmanipulatorswithjumpingparameters AT weicunzhang weightedmultiplemodelneuralnetworkadaptivecontrolforroboticmanipulatorswithjumpingparameters AT quanminzhu weightedmultiplemodelneuralnetworkadaptivecontrolforroboticmanipulatorswithjumpingparameters |