@inproceedings{6399f6bb35814cbaa0ad24c7e4e01b23,
title = "A new characterization of stable neural network control for discrete-time uncertain systems",
abstract = "A novel neuro adaptive control framework for discrete-time multivariable nonlinear uncertain systems is developed. The proposed framework is Lyapunov-based and guarantees, instead of ultimate boundedness, partial asymptotic stability of the closed-loop system; that is, Lyapunov stability of the closed-loop system states and attraction with respect to the plant states. Unlike standard neural network approximation, we assume that the approximation error can be confined in a small gain-type norm-bounded conic sector over a compact set. This helps to couple tools from robust control with adaptive laws in discrete time to prove partial asymptotic stability of the closed-loop system. Finally, an illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.",
keywords = "Adaptive control, Asymptotic stability, Discrete-time systems, Lyapunov method, Neural network, Sector-bounded nonlinearities",
author = "Tomohisa Hayakawa and Haddad, {Wassim M.} and Naira Hovakimyan",
note = "Funding Information: This research was supported in part by the Air Force Office of Scientific Research under Grants F49620-03-1-0178 and F49620-03-1-0443.",
year = "2005",
doi = "10.3182/20050703-6-cz-1902.00275",
language = "English (US)",
isbn = "008045108X",
series = "IFAC Proceedings Volumes (IFAC-PapersOnline)",
publisher = "IFAC Secretariat",
pages = "324--329",
booktitle = "Proceedings of the 16th IFAC World Congress, IFAC 2005",
}