A neural network based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input-output models with an unknown nonlinear function and unmodeled dynamics. By on-line approximating the unknown nonlinear functions and unmodeled dynamics by radial basis function (RBF) networks, the proposed approach does not require the unknown parameters to satisfy the linear dependence condition. It is proved that with the proposed control law, the closed-loop system is stable and the tracking error converges to zero in the presence of unmodeled dynamics and unknown nonlinearity. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Wang, D., & Wang, J. (2006). Neural network based robust adaptive control for a class of nonlinear systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3972 LNCS, pp. 898–903). Springer Verlag. https://doi.org/10.1007/11760023_132
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