@inproceedings{823c2fe6da244cf8bc35721d6233b3de,
title = "Data-driven robust optimal control design for uncertain cascaded systems using value iteration",
abstract = "In this paper, a new non-model-based control design is proposed to solve the H∞ control problem for linear continuous-time systems. Our first contribution is to develop a robust control design by combining the zero-sum differential game theory with the gain assignment technique together. Compared with traditional game theory-based approaches, the obtained result allows us to assign arbitrarily the input-to-output L2 gain for a class of continuous-time linear cascaded systems. Moreover, the presence of dynamic uncertainty is tackled using the small-gain theory. Our second contribution is to give a new non-model-based robust adaptive dynamic programming (RADP) algorithm. In sharp contrast to the existing methods, the obtained algorithm is based on continuous-time value iteration (VI), and an initial stabilizing control policy is no longer required. Finally, an example of a power system is adopted to illustrate the effectiveness of the obtained algorithm.",
keywords = "Gain, Game theory, Games, Optimal control, Power system dynamics, Robustness, Symmetric matrices",
author = "Tao Bian and Jiang, {Zhong Ping}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 54th IEEE Conference on Decision and Control, CDC 2015 ; Conference date: 15-12-2015 Through 18-12-2015",
year = "2015",
month = feb,
day = "8",
doi = "10.1109/CDC.2015.7403422",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7610--7615",
booktitle = "54rd IEEE Conference on Decision and Control,CDC 2015",
}