Integration of fuzzy CMAC and BELC networks for uncertain nonlinear system control

This paper develops a fuzzy adaptive control system consisting of a new type of fuzzy neural network and a robust controller for uncertain nonlinear systems. The new designed neural network contains the key mechanisms of a typical fuzzy CMAC network and a brain emotional learning controller network. First, the input values of the new network are delivered to a receptive field structure that is inspired from the fuzzy CMAC. Then, the values are divided into a sensory and an emotional channels; and the two channels interact with each other to generate the final outputs of the proposed network. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. In the experimentation, a “Duffing-Holmes” chaotic system and a simulated mobile robot are applied to verify the effectiveness and feasibility of the proposed control system. By comparing with the performances of other neural network based control systems, we believe our proposed network is capable of producing better control performances of complex uncertain nonlinear systems control.