Abstract
The resolved acceleration control method is one of the most effective and fundamental control methods for robot manipulators. However, friction of each joint of a manipulator impedes control accuracy. Therefore, friction has to be effectively compensated for in order to realize precise control of robot manipulators. Recently, soft computing, such as fuzzy reasoning, neural networks, and genetic algorithms, have been playing an important role in the control of robots. Applying soft computing, learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose an effective robot manipulator fuzzy-neuro control method based on the resolved acceleration control method in which joint friction is effectively compensated for using adaptive friction models.
| Original language | English |
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| Pages | II-656 - II-661 |
| DOIs | |
| Publication status | Published - Jan 1 1999 |
| Externally published | Yes |
| Event | Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 - Seoul, South Korea Duration: Aug 22 1999 → Aug 25 1999 |
Other
| Other | Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 |
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| City | Seoul, South Korea |
| Period | 8/22/99 → 8/25/99 |
All Science Journal Classification (ASJC) codes
- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics