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• W1992171281 endingPage "1225" @default.
• W1992171281 startingPage "1213" @default.
• W1992171281 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." @default.
• W1992171281 created "2016-06-24" @default.
• W1992171281 creator A5024678101 @default.
• W1992171281 creator A5069702292 @default.
• W1992171281 date "2013-08-01" @default.
• W1992171281 modified "2023-10-12" @default.
• W1992171281 title "Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer" @default.
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• W1992171281 doi "https://doi.org/10.1109/tsmcb.2012.2226577" @default.
• W1992171281 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26502431" @default.
• W1992171281 hasPublicationYear "2013" @default.
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