Based on the nominal model of the system, Cerebellum Model Articulation Controller (CMAC) is used for the variable structure control of a class of state feedback linearizable multiple-input multiple-output (MIMO) continuous-time nonlinear systems. By using adaptive law to estimate the error of estimation, the uncertainty of the system is reduced. The variable structure gain is tuned by the fuzzy logic. We design a controller that exploits the advantages of CMAC neural network, variable structure control (VSC) and fuzzy control theory, which improved the performance of the system. For this scheme, stable update laws are determined by using the Lyapunov theory, and the boundedness of all signals in the closed loop system is guaranteed. No prior offline-training phase is necessary. The simulation results verify the efficiency of the proposed approach.
