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Representation and learning of nonlinear compliance using neural nets

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1 Author(s)
Asada, H. ; Dept. of Mech. Eng., Massachusetts Inst. of Technol., Cambridge, MA, USA

A new approach to compliant motion control using neural networks is presented. In the paper, “compliance” is treated as a nonlinear mapping from a measured force to a corrected motion. The nonlinear mapping by a multilayer neural network is outlined, this allows one to deal with complex control strategies that cannot be represented by linear compliance, such as in stiffness and damping control

Published in:

Robotics and Automation, IEEE Transactions on  (Volume:9 ,  Issue: 6 )

Date of Publication:

Dec 1993

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