In this work, an adaptive fuzzy cerebellar model articulation control scheme (AFCMAC) is proposed to solve the tracking problem for a class of nonlinear systems. Firstly, a FCMAC approximator, based on the trapezoidal membership function, is designed to approach a nonlinear function. The proposed method supplies a simple control architecture that mixes CMAC and fuzzy logic, such that the complicated configuration in CMAC can be reduced. Furthermore, an AFCMAC with adaptive laws are evolved to tune all of the control gains on-line, thus conform the uncertainty of nonlinear systems without any learning phase. Especially, a robust compensator is adopted to reduce approximation errors and increases the precision of control. By Lyapunov stability analysis, it is guaranteed that all of the closed-loop signals are bounded, and the tracking errors can converge to zero asymptotically. Finally, simulation results based on tracks of the four-wheeled omnidirectional mobile robot are given to illustrate the performance of the proposed methodology. ? 2014 Taylor & Francis Group.