Due to their ability to hover and to fly "low and slow" VTOL UAVs are of extreme strategic importance. The task of control of VTOL UAVs is however complicated as they exhibit highly coupled nonlinear dynamics and inherent instability. These intricacies limit the performance of linear control techniques since a single linearized state space model represents only a limited part of the flight envelope. Moreover, in case of miniature rotorcraft UAVs, the problem is often accentuated due to inaccurate linear modeling, noisy sensor measurements and external disturbances. In this paper, a control architecture is presented which extends the validity of a linear optimal full state feedback law via online parameter identification and parameter dependent adaptive control. An Extended Kalman Filter is used for the combined problem of state and parameter estimation. Based on the estimated parameters the state feedback gain is calculated by solving the Riccatti equation for quadratic optimized control online. Online estimates of trim values are added to the inputs to account for varying trim conditions. The parameter identification algorithm is tested with flight data and validated against an identified linear model. The control architecture is tested in Software In The Loop simulation and in realtime with Hardware in the Loop simulation. The robust performance of the control architecture in presence of noisy data, parameter uncertainties, external disturbances and unknown trim conditions indicate the feasibility of such an approach for the control of miniature VTOL UAVs.