@inproceedings{8ab9bfe1354f41428bf180aefa1b7e7d,
title = "Adaptive neural control of non-affine pure-feedback systems",
abstract = "Controlling non-affine nonlinear systems is a challenging problem in the control community. In this paper, an adaptive neural control approach is presented for the completely non-affine pure-feedback system with only one mild assumption. By combining adaptive neural design with input-to-state stability (ISS) analysis and the small-gain theorem, the difficulty in controlling non-affine pure-feedback system is overcome by achieving the so-called {"}ISS-modularity{"} of the controller-estimator. The ISS-modular approach provides an effective way for controlling non-affine nonlinear systems with uncertainties. Simulation studies are included to demonstrate the effectiveness of the proposed approach.",
author = "Gong Wang and Hill, \{David J.\} and Ge, \{Shuzhi S.\}",
year = "2005",
doi = "10.1109/.2005.1467031",
language = "English",
isbn = "0780389360",
series = "Proceedings of the 20th IEEE International Symposium on Intelligent Control, ISIC '05 and the 13th Mediterranean Conference on Control and Automation, MED '05",
pages = "298--303",
booktitle = "Proceedings of the 20th IEEE International Symposium on Intelligent Control, ISIC '05 and the 13th Mediterranean Conference on Control and Automation, MED '05",
note = "20th IEEE International Symposium on Intelligent Control, ISIC '05 and the13th Mediterranean Conference on Control and Automation, MED '05 ; Conference date: 27-06-2005 Through 29-06-2005",
}