@inproceedings{db26d940da0c48d1b4aefc3a4cd9b8ad,
title = "Stability and convergence of neurologic model based robotic controllers",
abstract = "The authors investigate the local convergence properties of an artificial-neural-network (ANN)-based learning controller, using linearization techniques. The controller utilizes generic multilayer ANNs to adaptively approximate the manipulator dynamics over a specified region of the state space for a given desired trajectory. This generic neural network structure can be viewed as a nonlinear extension of a deterministic auto-regressive model which is commonly used in model matching problems for linear systems.",
author = "Ciliz, {M. Kemal} and Can Isik",
year = "1992",
month = dec,
day = "1",
language = "English (US)",
isbn = "0818627204",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "IEEE Computer Society",
pages = "2051--2056",
booktitle = "Proceedings - IEEE International Conference on Robotics and Automation",
address = "United States",
note = "Proceedings of the 1992 IEEE International Conference on Robotics and Automation ; Conference date: 12-05-1992 Through 14-05-1992",
}