Smooth-Switching Gain Based Adaptive Neural Network Control of n-Joint Manipulator with Multiple Constraints
Modeling errors, external loads and output constraints will affect the tracking control of the n-joint manipulator driven by the permanent magnet synchronous motor. To solve the above problems, the smooth-switching for backstepping gain control strategy based on the Barrier Lyapunov Function and ada...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/11/5/127 |
| _version_ | 1797502371495936000 |
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| author | Qing Yang Haisheng Yu Xiangxiang Meng Wenqian Yu Huan Yang |
| author_facet | Qing Yang Haisheng Yu Xiangxiang Meng Wenqian Yu Huan Yang |
| author_sort | Qing Yang |
| collection | DOAJ |
| description | Modeling errors, external loads and output constraints will affect the tracking control of the n-joint manipulator driven by the permanent magnet synchronous motor. To solve the above problems, the smooth-switching for backstepping gain control strategy based on the Barrier Lyapunov Function and adaptive neural network (BLF-ANBG) is proposed. First, the adaptive neural network method is established to approximate modeling errors, unknown loads and unenforced inputs. Then, the gain functions based on the error and error rate of change are designed, respectively. The two gain functions can respectively provide faster response speed and better tracking stability. The smooth-switching for backstepping gain strategy based on the Barrier Lyapunov Function is proposed to combine the advantages of both gain functions. According to the above strategy, the BLF-ANBG strategy is proposed, which not only solves the influence of multiple constraints, unknown loads and modeling errors, but also enables the manipulator system to have better dynamic and steady-state performances at the same time. Finally, the proposed controller is applied to a 2-DOF manipulator and compared with other commonly used methods. The simulation results show that the BLF-ANBG strategy has good tracking performance under multiple constraints and model errors. |
| first_indexed | 2024-03-10T03:32:08Z |
| format | Article |
| id | doaj.art-3b15b37383344517b57f400b59b3b291 |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-10T03:32:08Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-3b15b37383344517b57f400b59b3b2912023-11-23T09:37:13ZengMDPI AGActuators2076-08252022-04-0111512710.3390/act11050127Smooth-Switching Gain Based Adaptive Neural Network Control of n-Joint Manipulator with Multiple ConstraintsQing Yang0Haisheng Yu1Xiangxiang Meng2Wenqian Yu3Huan Yang4College of Automation, Qingdao University, Qingdao 266071, ChinaCollege of Automation, Qingdao University, Qingdao 266071, ChinaCollege of Automation, Qingdao University, Qingdao 266071, ChinaState Grid Dongping Power Supply Company, State Grid, Taian 271000, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 271000, ChinaModeling errors, external loads and output constraints will affect the tracking control of the n-joint manipulator driven by the permanent magnet synchronous motor. To solve the above problems, the smooth-switching for backstepping gain control strategy based on the Barrier Lyapunov Function and adaptive neural network (BLF-ANBG) is proposed. First, the adaptive neural network method is established to approximate modeling errors, unknown loads and unenforced inputs. Then, the gain functions based on the error and error rate of change are designed, respectively. The two gain functions can respectively provide faster response speed and better tracking stability. The smooth-switching for backstepping gain strategy based on the Barrier Lyapunov Function is proposed to combine the advantages of both gain functions. According to the above strategy, the BLF-ANBG strategy is proposed, which not only solves the influence of multiple constraints, unknown loads and modeling errors, but also enables the manipulator system to have better dynamic and steady-state performances at the same time. Finally, the proposed controller is applied to a 2-DOF manipulator and compared with other commonly used methods. The simulation results show that the BLF-ANBG strategy has good tracking performance under multiple constraints and model errors.https://www.mdpi.com/2076-0825/11/5/127manipulatormultiple constraintsadaptive neural networksmooth-switching for gainBarrier Lyapunov Function |
| spellingShingle | Qing Yang Haisheng Yu Xiangxiang Meng Wenqian Yu Huan Yang Smooth-Switching Gain Based Adaptive Neural Network Control of n-Joint Manipulator with Multiple Constraints Actuators manipulator multiple constraints adaptive neural network smooth-switching for gain Barrier Lyapunov Function |
| title | Smooth-Switching Gain Based Adaptive Neural Network Control of n-Joint Manipulator with Multiple Constraints |
| title_full | Smooth-Switching Gain Based Adaptive Neural Network Control of n-Joint Manipulator with Multiple Constraints |
| title_fullStr | Smooth-Switching Gain Based Adaptive Neural Network Control of n-Joint Manipulator with Multiple Constraints |
| title_full_unstemmed | Smooth-Switching Gain Based Adaptive Neural Network Control of n-Joint Manipulator with Multiple Constraints |
| title_short | Smooth-Switching Gain Based Adaptive Neural Network Control of n-Joint Manipulator with Multiple Constraints |
| title_sort | smooth switching gain based adaptive neural network control of n joint manipulator with multiple constraints |
| topic | manipulator multiple constraints adaptive neural network smooth-switching for gain Barrier Lyapunov Function |
| url | https://www.mdpi.com/2076-0825/11/5/127 |
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