Flatness-based adaptive fuzzy control for nonlinear dynamical systems

The paper proposes flatness-based adaptive fuzzy control for single-input nonlinear dynamical systems. Such systems can be written in the Brunovsky form via a transformation of their state variables and control input. The resulting control signal is shown to contain nonlinear elements, which in case of unknown system parameters can be approximated using neuro-fuzzy networks. Using Lyapunov stability analysis it is shown that one can compute an adaptation law for the neurofuzzy approximators which assures stability of the closed loop. The performance of the proposed flatness-based adaptive fuzzy control scheme is tested through simulation experiments on benchmark nonlinear dynamical systems.