Acquisition of Adaptive Behavior for the SMA-Net Robot Using Chaotic Neural Networks
Ikuo Suzuki*, Masaru Fujii**, Keitaro Naruse**,
Hiroshi Yokoi***, and Yukinori Kakazu**
*Life Oriented Software Laboratory, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran 050-8585, Japan
**Graduate School of Engineering, Hokkaido University, Kita-13 Nishi-8, Kita-ku, Sapporo 060-8628, Japan
***School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
In this paper, we propose control for the SMA-Net Robot, a flexible structure consisting of many units and shape memory alloy (SMA) springs. To generate greater force for movement, more than one SMA spring is required. Since SMA springs are driven by thermal transition, controlling individual spring heating patterns is important in SMA-Net Robot behavior. It is a problem in controlling SMA spring that its detailed control is difficult because of the nonlinearity. We propose methodology that arranges heating and cooling as a rhythm pattern memorized by many chaotic neural networks (CNNs). To renew connecting weights in the network, we use the modified dynamic learning method (DLM) in online learning. The results of computational experiments showed that the SMA-Net Robot with the proposed control generates movement automatically.
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