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

• Type: Journal
• Year: 2010
• Where: ICONIP
• Authors: Yasuhito Horiguchi, Takamitsu Matsubara, Masatsugu Kidode
• Keywords: Adaptive Control, ICONIP 2010, Information Technology, Inverse Dynamics Models, Multiple Conditions