In the present age of increased demand for unmanned aerial vehicles, flapping wing unmanned aerial vehicle applications have become of interest, primarily because of their ability to fly silently and at lower speeds. This work explores new territory through the development of an adaptive flight controller for a bird-like flapping wing aircraft, using modified strip theory to model the aircraft's aerodynamics and Newtonian equations of motion for the flight dynamics. The aircraft model is validated using existing data from the Slow Hawk Ornithopter. The goal of this thesis is to explore various adaptive flight control architectures, such as Model Reference Adaptive Control and Adaptive Neural Network Inverse Control, leading to an advanced controller to govern the longitudinal flight characteristics of the flapping wing aircraft. An acceptable model of slow hawk ornithopter was developed and modelled in the MATLAB/SIMULINK. The Model Reference Adaptive Controller with Adaptive Bias Corrector was successfully able to adapt to deficiencies in the system with a PI controller and improved the tracking performance compared to no adaptation. It was observed that in case of a B-Matrix failure the Adaptive controller was not able to reduce the error in oscillating magnitude to zero. The same observation was also made for system with a PD controller.