Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input Saturation
This paper investigates an event-triggered adaptive neural tracking control issue for flexible-joint robot (FJR) systems subject to unknown dynamic and input saturation. To enable the backstepping design framework to be implemented, the input saturation nonlinearity is replaced by a smooth function....
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9760475/ |
| _version_ | 1828344522130587648 |
|---|---|
| author | Xinglei Xu Shiwei Xu |
| author_facet | Xinglei Xu Shiwei Xu |
| author_sort | Xinglei Xu |
| collection | DOAJ |
| description | This paper investigates an event-triggered adaptive neural tracking control issue for flexible-joint robot (FJR) systems subject to unknown dynamic and input saturation. To enable the backstepping design framework to be implemented, the input saturation nonlinearity is replaced by a smooth function. In the control design, the dynamic surface control (DSC) and adaptive neural techniques are used to handle the “explosion of complexity” issue and unknown dynamics, respectively. Furthermore, to reduce the calculated burden caused by the adaptive neural reconstruction technique, three virtual parameters are updated by using the single-parameter-learning approach. To decrease the frequency of actuator response to the control command for reducing the mechanical wear of actuator, an event triggering mechanism is established between the control law and actuator. Finally, an event-triggered adaptive neural tracking control solution is proposed, which is endowed the advantages as: (1) it does not need any <italic>priori</italic> knowledge of FJR systems; 2) it only needs to update three unknown parameters; 3) it can reduce the transmission frequency of the control commands and the response rate of the actuator. Using the Lyapunov stability theory, the proposed event-triggered control solution ensures that all signals of the closed-loop tracking control system are bounded. Finally, the simulation results verify the effectiveness and superiority of the proposed control scheme. |
| first_indexed | 2024-04-13T23:58:27Z |
| format | Article |
| id | doaj.art-8e8aee5986784e6fbad2b2fc652be188 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T23:58:27Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8e8aee5986784e6fbad2b2fc652be1882022-12-22T02:23:48ZengIEEEIEEE Access2169-35362022-01-0110433674337510.1109/ACCESS.2022.31690129760475Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input SaturationXinglei Xu0https://orcid.org/0000-0002-2989-1471Shiwei Xu1School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, ChinaSchool of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, ChinaThis paper investigates an event-triggered adaptive neural tracking control issue for flexible-joint robot (FJR) systems subject to unknown dynamic and input saturation. To enable the backstepping design framework to be implemented, the input saturation nonlinearity is replaced by a smooth function. In the control design, the dynamic surface control (DSC) and adaptive neural techniques are used to handle the “explosion of complexity” issue and unknown dynamics, respectively. Furthermore, to reduce the calculated burden caused by the adaptive neural reconstruction technique, three virtual parameters are updated by using the single-parameter-learning approach. To decrease the frequency of actuator response to the control command for reducing the mechanical wear of actuator, an event triggering mechanism is established between the control law and actuator. Finally, an event-triggered adaptive neural tracking control solution is proposed, which is endowed the advantages as: (1) it does not need any <italic>priori</italic> knowledge of FJR systems; 2) it only needs to update three unknown parameters; 3) it can reduce the transmission frequency of the control commands and the response rate of the actuator. Using the Lyapunov stability theory, the proposed event-triggered control solution ensures that all signals of the closed-loop tracking control system are bounded. Finally, the simulation results verify the effectiveness and superiority of the proposed control scheme.https://ieeexplore.ieee.org/document/9760475/Flexible-joint robotadaptive neural tracking controlevent triggering controlinput saturationdynamic surface control |
| spellingShingle | Xinglei Xu Shiwei Xu Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input Saturation IEEE Access Flexible-joint robot adaptive neural tracking control event triggering control input saturation dynamic surface control |
| title | Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input Saturation |
| title_full | Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input Saturation |
| title_fullStr | Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input Saturation |
| title_full_unstemmed | Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input Saturation |
| title_short | Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input Saturation |
| title_sort | event triggered adaptive neural tracking control of flexible joint robot systems with input saturation |
| topic | Flexible-joint robot adaptive neural tracking control event triggering control input saturation dynamic surface control |
| url | https://ieeexplore.ieee.org/document/9760475/ |
| work_keys_str_mv | AT xingleixu eventtriggeredadaptiveneuraltrackingcontrolofflexiblejointrobotsystemswithinputsaturation AT shiweixu eventtriggeredadaptiveneuraltrackingcontrolofflexiblejointrobotsystemswithinputsaturation |