Cerebellar model articulation controller neural networks is one of the computational intelligence tools that can be applied for modeling, classification, and control. Proportional velocity controller is a servo-type controller, which is commonly applied to motion control systems. This paper presents a novel combination of cerebellar model articulation controller neural networks and optimal proportional velocity controller. A simple mathematical model for applying and studying cerebellar model articulation controller is introduced, and a study of its parameters is presented individually. The effect of parameters variation on cerebellar model articulation controller performance is identified. Learning algorithms highly affect the cerebellar model articulation controller behavior even when the parameters are optimized, and proper selection of the learning scheme must be taken under consideration. Three different learning algorithms are studied for evaluating transient and steady-state cerebellar model articulation controller responses. The results showed that the change of cerebellar model articulation controller generalization size and scale of the control signal has a marked effect on the performance of cerebellar model articulation controller. Furthermore, the constant learning rate algorithm gives the best overall performance.