A comprehensive treatment of the cerebellar model articulation controller (CMAC) neural network (NN) for the control of robot manipulators is presented. The structure and localized learning properties of CMAC NN is exploited to design efficient controllers for nonlinear systems belonging to a given useful class. Continuous- and discrete-time implementation of these controllers is systematically examined. Novel weight update schemes are derived and the closed-loop stability of the controller and the system is rigorously proved. These weight update schemes are shown to be nonstandard modifications of adaptive techniques prevalent in the literature. Finally, the validity of these techniques is demonstrated through numerical studies.
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