A robust self-organizing fuzzy-neural control (RSOFNC) system is proposed in this paper. The RSOFNC system is comprised of a self-structuring fuzzy neural network (SFNN) controller and a robust controller. The SFNN controller is the principal con-troller and the robust controller is designed to achieve tracking performance. In the SFNN controller design, a SFNN with the asymmetric Gaus-sian membership functions is used to online ap-proximate an ideal controller via the structure and parameter learning phases. The structure learning phase consists of the growing of membership func-tions and the pruning of fuzzy rules, and thus the SFNN can avoid the time-consuming trial-and-error tuning procedure for determining the network struc-ture of fuzzy neural network. Finally, the proposed RSOFNC system is applied to control a second-order chaotic system. The simulation results show that the proposed RSOFNC system can achieve favorable tracking performance.