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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wen Yu Heredia, J.A. |
| Copyright Year | 2000 |
| Description | Author affiliation: Dept. de Control Autom., CINVESTAV-IPN, Mexico City, Mexico (Wen Yu) |
| Abstract | In this paper the popular PD controller of robot manipulator is modified. RBF neural networks are used to compensate the gravity and friction. No exact knowledge of the robot dynamics is required. The suggested learning law of neuro compensator is similar to the well-known backpropagation algorithm but with additional robust terms. Lyapunov-like analysis is used to derive the stability of learning algorithm. |
| Starting Page | 329 |
| Ending Page | 334 |
| File Size | 324477 |
| Page Count | 6 |
| File Format | |
| ISBN | 0769506194 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2000.861488 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-07-27 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | PD control Radial basis function networks Backpropagation algorithms Robot control Manipulator dynamics Neural networks Gravity Friction Robustness Algorithm design and analysis |
| Content Type | Text |
| Resource Type | Article |
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