@inproceedings{3860829e55c443be845029e0c5e22cf0,
title = "A generalized policy iteration adaptive dynamic programming algorithm for optimal control of discrete-time nonlinear systems with actuator saturation",
abstract = "In this study, a nonquadratic performance function is introduced to overcome the saturation nonlinearity in actuators. Then a novel solution, generalized policy iteration adaptive dynamic programming algorithm, is applied to deal with the problem of optimal control. To achieve this goal, we use two neural networks to approximate control vectors and performance index function. Finally, this paper focuses on an example simulated on Matlab, which verifies the excellent convergence of the mentioned algorithm and feasibility of this scheme.",
keywords = "Adaptive dynamic programming, Neural network, Optimal control, Saturating actuators",
author = "Qiao Lin and Qinglai Wei and Bo Zhao",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 14th International Symposium on Neural Networks, ISNN 2017 ; Conference date: 21-06-2017 Through 26-06-2017",
year = "2017",
doi = "10.1007/978-3-319-59081-3_8",
language = "English",
isbn = "9783319590806",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "60--65",
editor = "Fengyu Cong and Qinglai Wei and Andrew Leung",
booktitle = "Advances in Neural Networks - ISNN 2017 - 14th International Symposium, ISNN 2017, Proceedings",
address = "Germany",
}