Learning basis representations of inverse dynamics models for real-time adaptive control

Authors:
Yasuhito Horiguchi;Takamitsu Matsubara;Masatsugu Kidode
Affiliations:
Graduate School of Information Science, Nara Institute of Science and Technology, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Japan
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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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.