A design of CMAC-based FLC with fast learning and accurate approximation

Proposes a CMAC-based FLC (fuzzy logic controller) with a fast learning capability and an accurate approximation ability. The proposed CMAC-based FLC has the fast learning capability because it pursues the local generalization and only a small number of activated units in the network participate in the forward and backward computation. It also produces an accurate input-output approximation ability because it adjusts the membership function´s model parameters of the input and output variables simultaneously and it considers both centers and widths of output membership functions to compute a crisp defuzzified value. Application to the truck backer-upper control problem of the proposed CMAC-based FLC is presented. Simulation results validate the fast learning and the accurate approximation of the proposed CMAC-based FLC.