Novel leaning feed-forward controller for accurate robot trajectory tracking
- Authors
- Bi Daowei; Wang, Gaoli; Zhang, Jun; Xue, Qiang
- Issue Date
- Aug-2005
- Publisher
- Springer Verlag
- Citation
- Advances in Natural Computation First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II, v.3611, pp 266 - 269
- Pages
- 4
- Indexed
- SCIE
SCOPUS
- Journal Title
- Advances in Natural Computation First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II
- Volume
- 3611
- Start Page
- 266
- End Page
- 269
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115931
- DOI
- 10.1007/11539117_39
- Abstract
- This paper presents a novel learning feed-forward controller design approach for accurate robotics trajectory tracking. Based on the joint nonlinear dynamics characteristics, a model-free learning algorithm based on Support Vector Machine (SVM) is implemented for friction model identification. The experimental results verified that SVM based learning feed-forward controller is a good approach for high performance industrial robot trajectory tracking, It can achieve low tracking error comparing with traditional trajectory tracking control method.
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Collections - COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles
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