Generación y optimización de controladores difusos utilizando el modelo NEFCON

the design of algorithms that operate on un-modeled dynamics plants still represents a challenge in automatic control area. a solution could be the use of algorithms able to learn in real time by direct interaction with the plant. nefcon, allows to build a mamdani fuzzy controller able to learn rules and adapt the fuzzy sets. the main advantage of nefcon compared with other learning approaches, is that its design express the current error state of the plant to be controlled. however, a disadvantage of nefcon is its poor exploration of the states of the plant during the learning; disable its application on nonlinear dynamic systems. in this work the addition of gaussian noise to the states of the plant is proposed with the objective to assure a wide exploration of the states, simplifying the convergence, when it is applied to nonlinear systems. in particular, the effectiveness of our proposal is shown in the control of the "ball and beam" dynamic system.