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This paper presents the implementation of a modified state observer-based adaptive dynamic inverse controller for the Black Kite micro aerial vehicle. The pitch and velocity adaptations are computed by the modified state observer in the presence of turbulence to simulate atmospheric conditions. This state observer uses the estimation error to generate the adaptations and, hence, is more robust than model reference adaptive controllers which use modeling or tracking error. In prior work, a traditional proportional-integral-derivative control law was tested in simulation for its adaptive capability in the longitudinal dynamics of the Black Kite micro aerial vehicle. This controller tracks the altitude and velocity commands during normal conditions, but fails in the presence of both parameter uncertainties and system failures. The modified state observer-based adaptations, along with the proportional-integral-derivative controller enables tracking despite these conditions. To simulate flight of the micro aerial vehicle with turbulence, a Dryden turbulence model is included. The turbulence levels used are based on the absolute load factor experienced by the aircraft. The length scale was set to 2.0 meters with a turbulence intensity of 5.0 m/s that generates a moderate turbulence. Simulation results for various flight conditions show that the modified state observer-based adaptations were able to adapt to the uncertainties and the controller tracks the commanded altitude and velocity. The summary of results for all of the simulated test cases and the response plots of various states for typical flight cases are presented.

This paper presents some issues encountered while flight testing a Model Reference Adaptive dynamic inverse controller (MRAC), on a General Aviation (GA) fly-by-wire test bed. Adaptation was replaced with a Modified State Observer (MSO) method which shows promising results in simulation. The flight test results of the MRAC summarized in this paper show PLA surging issues due to adaptation during the commanded flight path angle and airspeed. To overcome this problem, MSO based adaptation methodology is adopted to control the longitudinal dynamics of a typical general aviation aircraft. The advantage of MSO is that it adapts to estimation error, not modeling or tracking error. The controller performance is evaluated for elevator and engine dynamics along with the thrust saturation limits. The elevator model considered in this study deals with a very large delay (200 msec) in the elevator servo. The thrust saturation depends on engine shaft horse power limits that simulate the flight test condition. A controlled flight simulation is carried out including the turbulence effects observed during flight. Controller design using the saturated thrust is used in flight test conditions, where the thrust generated by the engine is not an observable state. The simulation results showing the controller adaptability to flight path angle and airspeed commands are promising. The random PLA surge seen during the simulation and flight test of the baseline MRAC controller is not observed in the simulation results of MSO adaptation controller. Hardware-In-Loop (HIL) Ground Tests and Flight test of MSO based controller is scheduled in the near future. Nomenclature = Coefficient of drag, lift, and pitching moment = Aerodynamic forces in forward and vertical direction Iyy = Aircraft moment of inertia about y axis M = Aircraft pitching moment Q = Aircraft pitch rate T = Thrust U, W = Forward and vertical aircraft velocities ̅ = Mean geometric chord M = Aircraft Mass ̂ = Non-dimensional pitch rate ̅ = Dynamic pressure S = Reference Area θ, γ, α = Aircraft pitch angle, flight path angle, and angle of attack δe = Elevator deflection = Thrust-line angle, positive up

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