Abstract
A nonlinear adaptive autopilot is designed for the inertial trajectory control of a sixdegrees-of-freedom, high-fidelity F-16 aircraft model. The control system is decomposed in four feedback loops constructed using a single control Lyapunov function. The aerodynamic force and moment functions of the aircraft model are assumed to be unknown during the control design phase and will be approximated online. B-spline neural networks are used to partition the flight envelope into multiple connecting regions. In each partition a locally valid linear-in-the-parameters nonlinear aircraft model is defined, of which the unknown parameters are adapted online by Lyapunov based update laws. These update laws take aircraft state and input constraints into account so that they do not corrupt the parameter estimation process. The performance of the proposed control system has been assessed in numerical simulations of several types of trajectories at different flight conditions. Simulations with a locked control surface and uncertainties in the aerodynamic forces and moments are also included. The results demonstrate that the proposed control laws achieve closed-loop stability even in the presence of these uncertain parameters and actuator failures.
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