Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2012.2226577
Title: Direct adaptive neural control for a class of uncertain nonaffine nonlinear systems based on disturbance observer
Authors: Chen, M.
Ge, S.S. 
Keywords: Adaptive control
Disturbance observer
Input saturation
Neural networks (NNs)
Nonaffine nonlinear system
Issue Date: Aug-2013
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
URI: http://scholarbank.nus.edu.sg/handle/10635/55644
ISSN: 21682267
DOI: 10.1109/TSMCB.2012.2226577
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