Please use this identifier to cite or link to this item:
|Title:||Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time|
|Authors:||Ge, S.S. |
|Citation:||Ge, S.S., Yang, C., Lee, T.H. (2008). Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time. IEEE Transactions on Neural Networks 19 (9) : 1599-1614. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2008.2000446|
|Abstract:||In this paper, adaptive neural network (NN) control is investigated for a class of nonlinear pure-feedback discrete-time systems. By using prediction functions of future states, the pure-feedback system is transformed into an n-step-ahead predictor, based on which state feedback NN control is synthesized. Next, by investigating the relationship between outputs and states, the system is transformed into an input-output predictor model, and then, output feedback control is constructed. To overcome the difficulty of nonaffine appearance of the control input, implicit function theorem is exploited in the control design and NN is employed to approximate the unknown function in the control. In both state feedback and output feedback control, only a single NN is used and the controller singularity is completely avoided. The closed-loop system achieves semiglobal uniform ultimate boundedness (SGUUB) stability and the output tracking error is made within a neighborhood around zero. Simulation results are presented to show the effectiveness of the proposed control approach. © 2008 IEEE.|
|Source Title:||IEEE Transactions on Neural Networks|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 22, 2018
WEB OF SCIENCETM
checked on Sep 12, 2018
checked on Jun 1, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.