Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 43, Issue 4, Pages 1213-1225Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2012.2226577
Keywords
Adaptive control; disturbance observer; input saturation; neural networks (NNs); nonaffine nonlinear system
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Funding
- National Basic Research Program of China (973 Program) [2011CB707005]
- National Natural Science Foundation of China [61174102]
- Jiangsu Natural Science Foundation of China [SBK2011069]
- Program for New Century Excellent Talents in University of China [NCET-11-0830]
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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.
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