Robust hybrid control based on inverse fuzzy process models

Takagi-Sugeno type fuzzy models are universal approximators for nonlinear dynamic processes. If they are trained to represent the inverse plant characteristics they can be used as feedforward controllers. The achievable control performance strongly depends on the model quality, and the simple inverse model controller does not cope with disturbances and process uncertainty. As a consequence, a hybrid control scheme is proposed which considerably improves the robustness properties. This paper reviews the identification of both forward and inverse fuzzy models. The difficulties accompanying the latter task are discussed. The control scheme based on the idea of disturbance observation is introduced and thoroughly analyzed. Finally, the controller is applied to a cooling blast with nonlinear behavior and variant dynamics