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Decentralized Neural Backstepping Control Applied to a Robot Manipulator
| Content Provider | SAGE Publishing |
|---|---|
| Author | Garcia-Hernandez, Ramon Ruz-Hernandez, Jose A. Rullan-Lara, Jose L. |
| Copyright Year | 2013 |
| Abstract | This paper presents a discrete-time decentralized control scheme for trajectory tracking of a two degrees of freedom (DOF) robot manipulator. A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The weights for each neural network are adapted online by an extended Kalman filter training algorithm. The motion for each joint is controlled independently using only local angular position and velocity measurements. The stability analysis for the closed-loop system via the Lyapunov approach is included. Finally, the real-time results show the feasibility of the proposed control scheme using a robot manipulator. |
| Related Links | https://journals.sagepub.com/doi/pdf/10.5772/54015?download=true |
| ISSN | 17298806 |
| Issue Number | 1 |
| Volume Number | 10 |
| Journal | International Journal of Advanced Robotic Systems (ARX) |
| e-ISSN | 17298814 |
| DOI | 10.5772/54015 |
| Language | English |
| Publisher | Sage Publications UK |
| Publisher Date | 2013-01-01 |
| Publisher Place | London |
| Access Restriction | Open |
| Rights Holder | © 2013 Garcia-Hernandez et al.; licensee InTech. |
| Subject Keyword | High-Order Neural Networks Decentralized control Backstepping Extended Kalman Filter |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Science Applications Software |
