@inproceedings{44dc6a6bd66b43b8bc866b5e9c6d8b6f,
title = "Model predictive control by differential neural networks approach",
abstract = "In this paper a new model predictive neural control is suggested. It consists of the application of the model predictive method to control a nonlinear uncertain system where the information is reduced. The uncertain plant was approximated by a special class of dynamic neural network observer (projectional observer) that uses some sort of information regarding the set where the states remain. A novel method leads to construct an approximate model of the uncertain system where the controllability condition is ensured. The model predictive control was designed using the information obtained by the proposed observer. The upper bound for the tracking error was established if the controller is applied. Simulation regarding the control of a biotechnological process is carried out.",
author = "Isaac Chairez and Alejandro Garc{\'i}a and Alexander Poznyak and Tatyana Poznyak",
year = "2010",
doi = "10.1109/IJCNN.2010.5596521",
language = "Ingl{\'e}s",
isbn = "9781424469178",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010",
address = "Estados Unidos",
note = "2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 ; Conference date: 18-07-2010 Through 23-07-2010",
}