A nonlinear self-organizing fuzzy control system

For nonlinear system control problems traditional control methodology finds it difficult in obtaining accurate mathematic models and adapting them to real world states, while neural network models with self-organization ability on the other hand have become promising to cope with complex systems with little priori knowledge. In this paper, we propose a control design strategy, in which a fuzzy decision implementation is combined with self-organization procedures, and the fuzzy input-output mapping is learned to achieve successful control of a nonlinear plant