This paper presents a novel approach of controlling a mobile robot using Generalized Dynamic Fuzzy Neural Networks (GDFNN). Using the GDFNN learning algorithm, not only the parameters of the controller can be optimized online, but also the structure of the controller can be self-adaptive. In comparison to the state-of-the-art neuro-fuzzy controller which predefines the rules, the proposed approach is more flexible. Moreover, the learning speed of this approach is very fast and fuzzy rules can be automatically generated online. This is in contrast with the state-of-the-art neuro-fuzzy controller which requires offline learning process. Simulations studies on a Khepera II robot show that the performance of the proposed approach is more superior.