This study proposes a novel adaptive fuzzy control methodology to remove disadvantages of traditional fuzzy approximation based control. Meanwhile, the highly uncertain robot manipulator is taken as an application with either guaranteed robust tracking performances or asymptotic stability in a global sense. First, the design concept, namely, feedforward fuzzy approximation based control, is introduced for a simple uncertain system. Here the desired commands are utilized as the inputs of the Takagi-Sugeno (T-S) fuzzy system to closely compensate the unknown feedforward term required during steady state. Different to traditional works, the assumption on bounded fuzzy approximation error is not needed, while this scheme allows easier implementation architecture. Next, the concept is extended to controlling manipulators and achieves global robust tracking performances. Note that a linear matrix inequality (LMI) technique is applied and provides an easier gain design. Finally, numerical simulations are carried out on a two-link robot to illustrate the expected performances.