This paper presents an adaptive control approach for controlling the longitudinal dynamics of a generic lifting surface using imbedded flow control actuators. Such actuators offer a unique opportunity for rapid maneuvering and gust rejection with low power consumption. The model's state is controlled over a broad range of angles of attack and model dynamic characteristics when the baseline flow is fully attached using bi-directional pitching moment that is effected by flow-controlled trapped vorticity concentrations on the pressure and suction surfaces near the trailing edge of the lifting surface. The proposed adaptive non-model based control approach is motivated by the difficulty in constructing a reasonably accurate physical model of active flow control actuators. In addition, during dynamic maneuvers, possible interactions between unsteady fluid dynamics and vehicle dynamics introduce additional uncertainty. System modeling uses a neural network based architecture to capture the non-linearities of the flow actuation process. This model is employed for the simulation studies performed in this paper. The control architecture employs a neural network based adaptive element that permits adaptation to both parametric uncertainty and unmodeled dynamics. This paper represents an extension of the authors' previous efforts to control a wing in pitch using synthetic jets. In the current design, the variable dynamic properties of the model are achieved in a wind tunnel using a novel traverse based on force control. The basic force control concept is addressed and simulations for longitudinal control are provided.