Abstract
The adaptive tracking control problem is discussed for a class of strict-feedback uncertain systems. RBF Neural Networks are used to approximate the uncertainties. A unified and systematic procedure is developed to derive a robust adaptive tracking controller with the fusion of dynamic surface control technique and small gain approach. The proposed algorithm can avoid both problems of 'explosion of complexity' and 'curse of dimension' synchronously, thus is convenient to implement in applications. The stability of the closed-loop system is proved. Finally, simulation results via two application examples validate the effectiveness and performance of the proposed scheme.
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