6533b821fe1ef96bd127afcd
RESEARCH PRODUCT
Robust adaptive neural backstepping control for a class of nonlinear systems with dynamic uncertainties
Hamid Reza KarimiHongyan YangHuanqing Wangsubject
Article SubjectArtificial neural networklcsh:MathematicsApplied MathematicsSIGNAL (programming language)Basis functionAnalysis; Applied Mathematicslcsh:QA1-939Class (biology)VDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411Nonlinear systemControl theoryBacksteppingNeural controlAnalysisMathematicsdescription
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/658671 Open Access This paper is concerned with adaptive neural control of nonlinear strict-feedback systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. To overcome the difficulty from the unmodeled dynamics, a dynamic signal is introduced. Radical basis function (RBF) neural networks are employed to model the packaged unknown nonlinearities, and then an adaptive neural control approach is developed by using backstepping technique. The proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. A simulation example is given to show the effectiveness of the presented control scheme.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-01-01 |