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ICONIP
2010

Learning Basis Representations of Inverse Dynamics Models for Real-Time Adaptive Control

9 years 5 months ago
Learning Basis Representations of Inverse Dynamics Models for Real-Time Adaptive Control
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. Key words: Learning Basis Representation, Inverse Dynamics, Adaptive Control
Yasuhito Horiguchi, Takamitsu Matsubara, Masatsugu
Added 26 Jan 2011
Updated 26 Jan 2011
Type Journal
Year 2010
Where ICONIP
Authors Yasuhito Horiguchi, Takamitsu Matsubara, Masatsugu Kidode
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