Cerebellar Model Articulation Controller Neural Networks (CMAC NN) is one of the intelligent systems used for modeling, identification, classification, and controlling of nonlinear systems. In this paper, the mathematical model of CMAC is presented. CMAC is implemented using Simulink environment and its parameters are tuned to get the best CMAC control action. Three different learning algorithms are tested, using a constant learning rate, a variable learning rate, and learning by the control action of the conjugate conventional controller. The effect of varying CMAC parameters is studied and discussed. The simulation results showed that the learning algorithm based on constant learning rate gives the best performance.