Please use this identifier to cite or link to this item:
|Title:||Robust adaptive neural control of SISO nonlinear systems with unknown dead-zone and completely unknown control gain||Authors:||Zhang, T.
|Issue Date:||2006||Citation:||Zhang, T.,Ge, S.S. (2006). Robust adaptive neural control of SISO nonlinear systems with unknown dead-zone and completely unknown control gain. IEEE International Symposium on Intelligent Control - Proceedings : 88-93. ScholarBank@NUS Repository. https://doi.org/10.1109/ISIC.2006.285583||Abstract:||In this paper, robust adaptive neural tracking control is developed for a class of uncertain SISO nonlinear systems in a Brunovsky form with unknown nonlinear deadzone and unknown control gain & its sign. The design is based on the principle of sliding mode control and the use of Nussbaum-type function in solving the problem of the completely unknown function control gain. A novel description of general nonlinear dead-zone, which makes the control system design possible, is introduced by using the mean value theorem. The approach removes the condition of the equal slope with defined region for the dead-zone. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation for the upper bound of the optimal approximation error and the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. © 2006 IEEE.||Source Title:||IEEE International Symposium on Intelligent Control - Proceedings||URI:||http://scholarbank.nus.edu.sg/handle/10635/71653||ISBN:||0780397983||DOI:||10.1109/ISIC.2006.285583|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 22, 2020
checked on Mar 30, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.