Abstract
In robotic arm operations, after planning the desired motion trajectory, accurate and stable trajectory tracking control is essential. However, in actual control, there are issues such as the uncertainty of the dynamic parameters and susceptibility to external disturbances during motion. Addressing these issues, this paper employs RBF neural networks to estimate dynamic parameters and utilizes an adaptive controller to achieve online trajectory tracking. The dynamic approximation capability and adaptability of this method enhance the real-time performance and disturbance rejection of robotic arm trajectory tracking control. Finally, the availability of this method is verified by experiments in the simulation environment.
| Original language | English |
|---|---|
| Title of host publication | CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024 |
| Publisher | Institution of Engineering and Technology |
| Pages | 260-264 |
| Number of pages | 5 |
| Volume | 2024 |
| Edition | 13 |
| ISBN (Electronic) | 9781837242108 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024 - Xi�an, China Duration: 16 Aug 2024 → 19 Aug 2024 |
Conference
| Conference | 2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024 |
|---|---|
| Country/Territory | China |
| City | Xi�an |
| Period | 16/08/24 → 19/08/24 |
Keywords
- Dynamic parameters estimation
- RBF neural network
- robotic arm
- trajectory tracking control
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