Robust adaptive neural backstepping control for a class of nonlinear systems with dynamic uncertainties
Journal article, Peer reviewed
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Original versionYang, H., Wang, H., & Karimi, H. R. (2014). Robust adaptive neural backstepping control for a class of nonlinear systems with dynamic uncertainties. Abstract and Applied Analysis, 2014, 1-12. doi: 10.1155/2014/658671 10.1155/2014/658671
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.
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