Please use this identifier to cite or link to this item:
|Title:||Direct adaptive neural control for a class of uncertain nonaffine nonlinear systems based on disturbance observer|
Neural networks (NNs)
Nonaffine nonlinear system
|Source:||Chen, M., Ge, S.S. (2013-08). Direct adaptive neural control for a class of uncertain nonaffine nonlinear systems based on disturbance observer. IEEE Transactions on Cybernetics 43 (4) : 1213-1225. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2012.2226577|
|Abstract:||In this paper, the direct adaptive neural control is proposed for a class of uncertain nonaffine nonlinear systems with unknown nonsymmetric input saturation. Based on the implicit function theorem and mean value theorem, both state feedback and output feedback direct adaptive controls are developed using neural networks (NNs) and a disturbance observer. A compounded disturbance is defined to take into account of the effect of the unknown external disturbance, the unknown nonsymmetric input saturation, and the approximation error of NN. Then, a disturbance observer is developed to estimate the unknown compounded disturbance, and it is established that the estimate error converges to a compact set if appropriate observer design parameters are chosen. Both state feedback and output feedback direct adaptive controls can guarantee semiglobal uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis. Numerical simulation results are presented to illustrate the effectiveness of the proposed direct adaptive neural control techniques. © 2012 IEEE.|
|Source Title:||IEEE Transactions on Cybernetics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 15, 2018
WEB OF SCIENCETM
checked on Jan 29, 2018
checked on Feb 19, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.