| Resumen |
In the control theory area one of the most successful schemes is the indirect adaptive control strategy; however, one of the most important problems of this approach is the identification phase, because only if an excellent approximation of the nonlinear system is achieved the controller will show a good performance. In this paper we present a novel approach of an indirect adaptive control using hierarchical fuzzy CMAC neuronal networks. We show the full design and the stability analysis of this new structure. Experiment results are obtained via our prototype of the ball and plate system. |
