Abstract
In this paper, we present a new scheme to design direct adaptive neural network controller for uncertain nonlinear systems in the presence of input saturation. By incorporating dynamic surface control (DSC) technique into a neural network based adaptive control design framework, the control design is achieved. With this technique, the problem of “explosion of complexity” inherent in the conventional backstepping method is avoided, and the controller singularity problem is removed, and the effect of input saturation constrains is considered. In addition, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed scheme.
This work was supported in part by the National Natural Science Foundation of China (Nos.51179019, 60874056, 61074014, 51009017), the Natural Science Foundation of Liaoning Province (No. 20102012), China Postdoctoral Special Science Foundation (No. 200902241) and the Fundamental Research Funds for the Central Universities (No. 2011QN097, 2012QN013).
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Li, J., Li, T., Li, Y., Wang, N. (2012). Direct Adaptive Neural Dynamic Surface Control of Uncertain Nonlinear Systems with Input Saturation. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_45
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DOI: https://doi.org/10.1007/978-3-642-31362-2_45
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