Tracking improvement for stable robot control using neural networks
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Pages | 2391-2396 |
| Volume | 5 |
| Publication status | Published - 1995 |
| Externally published | Yes |
Publication series
| Name | |
|---|---|
| Volume | 5 |
Conference
| Title | Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) |
|---|---|
| City | Perth, Aust |
| Period | 27 November - 1 December 1995 |
Link(s)
Abstract
This paper considers tracking control of robots in joint space. A new control algorithm is proposed based on the well known computed torque method and a compensating controller. The compensating controller is realized by using an switch-type structure and an RBF neural network. It is shown that stability of the closed loop system and better tracking performance can be established based on Lyapunov theory. Simulation results are also provided to support our analysis.
Citation Format(s)
Tracking improvement for stable robot control using neural networks. / Feng, Gang; Palaniswami, M.; Han, Z. X. et al.
IEEE International Conference on Neural Networks - Conference Proceedings. Vol. 5 1995. p. 2391-2396.
IEEE International Conference on Neural Networks - Conference Proceedings. Vol. 5 1995. p. 2391-2396.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
