Neuro-Fuzzy Control of Nonlinear Systems - Application in a Ball and Beam System

This study shows both the development and characteristics of some of the main techniques used to control nonlinear systems. Starting from a fuzzy controller, it was possible to apply similar learning techniques to those used in Artificial Neural Networks (ANNs), and evolve to ANFIS and NEFCON neuro-fuzzy models. These neuro-fuzzy models were applied to a real ball and beam plant and both their adaptations and their results were discussed. For each controller developed the input variables, the parameters used to adapt the variables and the algorithms applied in each one are specified. The tests were performed in a ball and beam plant and the results are directed toward obtaining a comparison between the initial and final evolution phase of the neuro-fuzzy controllers, as well as the applicability of each one according to their intrinsic characteristics.