Abstract
In this paper, a novel robust adaptive tracking control approach is presented for a class of strict-feedback single-input single-output nonlinear systems. By employing radial basis function neural network to account for system uncertainties, the proposed scheme is developed by combining “command filter” and “minimal learning parameter” techniques. The main advantages of the proposed controller are that: (1) the problem of “explosion of complexity” inherent in the conventional backstep** method is avoided; (2) the problem of “dimensionality curse” is solved, and only one adaptive parameter needs to be updated online. These advantages result in a much simpler adaptive control algorithm, which is convenient to implement in applications. In addition, stability analysis shows that uniform ultimate boundedness of the solution of the closed-loop system can be guaranteed. Simulation results demonstrate the effectiveness of the proposed scheme.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (Nos. 51179019, 61374114, 61001090), the Natural Science Foundation of Liaoning Province (No. 20102012), the Program for Liaoning Excellent Talents in University (LNET) (Grant No. LR 2012016), the Applied Basic Research Program of Ministry of Transport of P. R. China (Nos. 2011-329-225-390 and 2013-329-225-270), and the National Fundamental Research 973 Program of China under Grant 2011CB302801, the Macau Science and Technology Development Foundation under Grant 008/2010/A1, and Multiyear Research Grants.
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Miao, B., Li, T. A novel neural network-based adaptive control for a class of uncertain nonlinear systems in strict-feedback form. Nonlinear Dyn 79, 1005–1013 (2015). https://doi.org/10.1007/s11071-014-1717-2
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DOI: https://doi.org/10.1007/s11071-014-1717-2