Abstract
In this paper, we propose a novel approach for adaptive control of robotic manipulators. Our approach uses a representation of inverse dynamics models learned from a varied set of training data with multiple conditions obtained from a robot. Since the representation contains various inverse dynamics models for the multiple conditions, adjusting a linear coefficient vector of the representation efficiently provides real-time adaptive control for unknown conditions rather than solving a high-dimensional learning problem. Using this approach for adaptive control of a trajectory-tracking problem with an anthropomorphic manipulator in simulations demonstrated the feasibility of the approach.
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Horiguchi, Y., Matsubara, T., Kidode, M. (2010). Learning Basis Representations of Inverse Dynamics Models for Real-Time Adaptive Control. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_82
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DOI: https://doi.org/10.1007/978-3-642-17534-3_82
Publisher Name: Springer, Berlin, Heidelberg
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