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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© 2005 Springer-Verlag Berlin Heidelberg
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Bi, D., Wang, G.L., Zhang, J., Xue, Q. (2005). Novel Leaning Feed-Forward Controller for Accurate Robot Trajectory Tracking. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_39
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DOI: https://doi.org/10.1007/11539117_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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