Modified state observer based adaptive control law for general aviation and micro aerial vehicles

Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Aerospace EngineeringThe aim of this research is to assess the capability of Modified State Observer (MSO) based adaptations for use in controlling the longitudinal flight dynamics of a General Aviation (GA) type airplane and a Micro Aerial Vehicle (MAV). For the GA type aircraft, the report details the issues encountered while flight testing a neural network based Model Reference Adaptive Controller (MRAC), on a General Aviation fly-by-wire test bed. The flight test results of the MRAC presented in this report 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. A controlled flight simulation is carried out including the turbulence effects observed during flight. The simulation results show the controller is able to track flight path angle and airspeed commands in the presence of turbulence. 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. For the MAV, the simulation is carried out using the aerodynamic derivatives of the Black Kite 300 mm wing span MAV developed by CSIR-NAL Bangalore. The controller is tested in simulation for its ability to adapt to modeling errors for the highly responsive MAV. Simulation results are presented that show the MSO based adaptive controller's ability to respond to altitude and airspeed commands with elevator and engine failures in the presence of parameter uncertainties. The MSO adaptation, along with the nonlinear dynamic inverse controller developed using the mathematical model of the MAV, enables the pilot to control the vehicle with a lower workload