Neural Network Training for Feedback Error Learning Using Space State Controller
Julio‐Ariel Romero‐Pérez, Luis Diago, I. Hagiware · 대한기계학회 춘추학술대회 · 2015
In robotics, one of the most difficult task is to perform a precisely and fast movement of a robotic arm. For paper-folding robots, it is still extremely difficult to execute the required manipulations of the paper mainly because the difficulties in modeling and control of the paper. In this paper two control models are proposed to resolve this problem. One of the closest approaches to solve this problem comes from Neuroscience, where using a human’s brain inspired control system known as Cerebellar control model, precisely and fast movements of a robotic arm can be performed. In this paper a Feedback controller motor command is used as a target signal to train an Artificial Neural Network (ANN), in order to use the output of the ANN as a Feed-forward signal.