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To control complex dynamical systems, which are frequently coupled with unknown dynamics, modeling errors, nonlinearities, various sorts of disturbances, uncertainties and noise robust or model-free control methods should be employed. The features of a novel dynamical algorithm for robust adaptive learning in fuzzy rule-based neural networks of Takagi-Sugeno-Kang type with sigmoid membership functions and its application to the neuro-fuzzy adaptive nonlinear feedback control of systems with uncertain dynamics are presented. The proposed approach makes direct use of variable structure systems theory and the feedback-error-learning scheme. In the simulations, it has been tested on the control of Duffing oscillator and the analytical claims have been justified under the existence of uncertainty and large nonzero initial errors.