Abstract
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 neuro-fuzzy 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.
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