Maximum entropy adaptive control of chaotic systems

We present an adaptive control strategy for controlling chaos in nonlinear dynamical systems. The proposed method is a neuro-fuzzy model as a globally coupled map based on entropy optimization, which combines an identified system fuzzy model and a control input update rule. The stability analysis of the resulting control scheme is shown by a property of contraction mappings. Numerical examples are given to illustrate the transition between chaotic states and stable equilibrium states.