Feedback Linearization using CMAC Neural Networks

The objective of this paper is to achieve tracking control of a class of unknown nonlinear dynamical systems using a Cerebellar Model Articulation Controller (CMAC). With mild assumptions on the state-feedback linearizable nonlinear systems, using this CMAC the uniform boundedness of the closed-loop signals is presented and that the controller achieves tracking. In fact, the CMAC system designed is a universal CMAC that can applied for any system in the given class of systems. New passivity properties of CMAC systems are examined and the relationship between passivity and closed-loop stability is derived. The utility of the CMAC NN in controlling a nonlinear system with unknown dynamics is demonstrated through numerical examples.