@inproceedings{5d511a825bda4b5597ea00e7d8a70e87,
title = "Neural network learning control of multi-input system with unknown dynamics",
abstract = "There are few studies on the optimal control of the multi-input system with different input dynamics in the literature. For this problem, the learning Nash controllers are obtained with a simplified-reinforcement learning (SRL) scheme and Nonzero-sum game theory. A neural network (NN) identifier is first established to approximate the unknown multi-input system. Then SRL NNs are used to approximate the optimal performance index of each input, which is used to learn the optimal control policies for the multi-input system. The weights of the NN architecture are tuned with a novel algorithm, and the parameter convergences are analyzed to be uniformly ultimately bounded. Finally, one two-input nonlinear system is introduced to verify the proposed learning control scheme.",
keywords = "Multi-input System, Neural Networks, Optimal Control, Reinforcement Learning",
author = "Yongfeng Lv and Xuemei Ren and Siqi Li and Huichao Li and Hengxing Lv",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 6th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2019 ; Conference date: 19-12-2019 Through 21-12-2019",
year = "2019",
month = dec,
doi = "10.1109/CCIS48116.2019.9073736",
language = "English",
series = "Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "169--173",
editor = "Xizhao Wang and Weining Wang and Xiangnan He",
booktitle = "Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2019",
address = "United States",
}