@inproceedings{c2f29682754443fbb89fa74467887620,
title = "Active disturbance rejection control based on differential neural networks",
abstract = "This study addresses the problem of designing an output model reference control for non-linear systems in the presence of parametric disturbances/uncertainties in the state model and output noise measurements. A state observer based on a differential neural network (DNN) estimates the unknown states and the unknown disturbance simultaneously. The control design includes the estimated disturbance to provide a better tracking performance. The second result optimizes the gains of the controller and observer in order to obtain a reduced convergence zone for the tracking error based on the attractive ellipsoid method approach (AEM). Numerical results point out the advantages obtained by the nonlinear control based on the DNN observer when it is compared with a classical Luenberger structure.",
keywords = "Active input rejection, Differential neural networks, Lyapunov control, State estimation",
author = "Ivan Salgado and Manuel Mera and Isaac Chairez",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Joint Conference on Neural Networks, IJCNN 2017 ; Conference date: 14-05-2017 Through 19-05-2017",
year = "2017",
month = jun,
day = "30",
doi = "10.1109/IJCNN.2017.7966392",
language = "Ingl{\'e}s",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4236--4243",
booktitle = "2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings",
address = "Estados Unidos",
}