The main purpose of this paper is to develop an onboard adaptive and robust flight control system that improves control, stability, and survivability of a small unmanned aerial system in off-nominal or out-of-envelope conditions. The aerodynamics of aircraft associated with hazardous and adverse onboard conditions is inherently nonlinear and unsteady. The presented flight control system improves functionalities required to adapt the flight control in the presence of aircraft model uncertainties. The fault tolerant inner loop is enhanced by an adaptive real-time artificial neural network parameter identification to monitor important changes in the aircraft’s dynamics due to nonlinear and unsteady aerodynamics. The real-time artificial neural network parameter identification is done using the sliding mode learning concept and a modified version of the self-adaptive Levenberg algorithm. Numerically estimated stability and control derivatives are obtained by delta-based methods. New nonlinear guidance logic, stable in Lyapunov sense, is developed to guide the aircraft. The designed flight control system has better performance compared to a commercial off-the-shelf autopilot system in guiding and controlling an unmanned air system during a trajectory following. 1. Introduction The effectiveness of aircraft flight control systems in off-nominal conditions is heavily dependent upon the quality and accuracy of physics-based models and numerical simulations. The accuracy of these models depends on the mathematical formulation that describes the true physics, how uncertainty affects the outputs, and whether the model mismatch can be identified and estimated. In classical flight dynamics, the measured aerodynamic forces and moments are represented by differentiable functions and expanded in Taylor series. Although it is effective in benign portions of the flight envelope, this mathematical structure is incapable of modeling nonlinear and unsteady aerodynamics associated with hazardous and adverse onboard conditions. Adverse onboard conditions include vehicle impairment; system faults, failures, and errors and vehicle damage. External hazards and disturbances include wind shear and poor visibility; wake vortices; thunderstorms; snow and icing conditions. Aircraft robust control systems are designed to deal with uncertainty in dynamic models due to modeling errors, noise, and disturbances. The motivation of this work is to develop a robust flight control system to improve control, stability, and survivability of a small unmanned aerial system (UAS) in hazardous and
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