A neural network structure that has been particularly successful in robotic control is the cerebellar model articulation controller (CMAC). In this paper, a CMAC network controller that uses self-organization through competitive learning is presented. The concept consists in applying the self-organizing characteristic of a Kohonen map to a CMAC neural network. This allows the CMAC network to organize its neurons efficiently. The approach can be applied on a simple two-link robot arm model which approximates the agonist-antagonist activity of muscles in the human arm. The CMAC and SOCMAC controllers can be trained to learn the behavior of a conventional PI controller, and comparative results are presented. The training procedures and the parameters that are involved in the design of the self-organizing CMAC (SOCMAC) controller are discussed.
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