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Adaptive Hierarchical Fuzzy CMAC Controller with Stable Learning Algorithm for Unknown Nonlinear Systems

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3 Author(s)
Ortiz, F. ; Dept. de Control Automatico, CINVESTAV-IPN, Mexico City ; Wen Yu ; Moreno-Armendariz, M.

In this paper, adaptive hierarchical fuzzy CMAC neural network controller (HFCMAC), for a certain class of nonlinear dynamical system is presented. The main advantages of adaptive HFCMAC control are: Better performance of the controller because adaptive HFCMAC can adjust itself to the changing enviroment and can be implemented in real time applications. The proposed method provides a simple control architecture that merges hierarchical structure, CMAC neural network and fuzzy logic. The input space dimension in CMAC is a time-consuming task especially when the number of inputs is huge this would be overload the memory and make the neuro-fuzzy system very hard to implement. This is can be simplified using a number of low-dimensional fuzzy CMAC in a hierarchical form. A new adaptation law is obtained for the method proposed, the overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Simulation results for its applications to one example is presented to demonstrate the performance of the proposed methodology.

Published in:

Artificial Intelligence - Special Session, 2007. MICAI 2007. Sixth Mexican International Conference on

Date of Conference:

4-10 Nov. 2007

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