Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot
This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. The...
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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/10.1155/2017/7683785 |
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| author | Min Wang Huiping Ye Zhiguang Chen |
| author_facet | Min Wang Huiping Ye Zhiguang Chen |
| author_sort | Min Wang |
| collection | DOAJ |
| description | This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme. |
| format | Article |
| id | doaj-art-92215aed97a3436dbd3d56f5bf9eae11 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-92215aed97a3436dbd3d56f5bf9eae112025-08-20T02:10:03ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/76837857683785Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter RobotMin Wang0Huiping Ye1Zhiguang Chen2School of Automation Science and Engineering, Guangzhou Key Laboratory of Brain Computer Interaction and Applications, South China University of Technology, Guangzhou 510641, ChinaSchool of Automation Science and Engineering, Guangzhou Key Laboratory of Brain Computer Interaction and Applications, South China University of Technology, Guangzhou 510641, ChinaSchool of Automation Science and Engineering, Guangzhou Key Laboratory of Brain Computer Interaction and Applications, South China University of Technology, Guangzhou 510641, ChinaThis paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.http://dx.doi.org/10.1155/2017/7683785 |
| spellingShingle | Min Wang Huiping Ye Zhiguang Chen Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot Complexity |
| title | Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot |
| title_full | Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot |
| title_fullStr | Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot |
| title_full_unstemmed | Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot |
| title_short | Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot |
| title_sort | neural learning control of flexible joint manipulator with predefined tracking performance and application to baxter robot |
| url | http://dx.doi.org/10.1155/2017/7683785 |
| work_keys_str_mv | AT minwang neurallearningcontrolofflexiblejointmanipulatorwithpredefinedtrackingperformanceandapplicationtobaxterrobot AT huipingye neurallearningcontrolofflexiblejointmanipulatorwithpredefinedtrackingperformanceandapplicationtobaxterrobot AT zhiguangchen neurallearningcontrolofflexiblejointmanipulatorwithpredefinedtrackingperformanceandapplicationtobaxterrobot |