Nonlinear Flight Control Design Using Constrained Adaptive Backstepping

Abstract

The design of an adaptive backstepping flight control law for the F-16/MATV (multi-axis thrust vectoring) aircraft is discussed. The control law tracks reference trajectories with the angle of attack a, the stability-axes roll rate p s , and the total velocity V T . Furthermore, the sideslip angle β has to be kept at zero. B-spline neural networks are used inside the parameter update laws of the backstepping control law to approximate the uncertain aerodynamic forces and moments. Command filters are used to implement the constraints on the control surfaces and the virtual control states. The stability of the parameter estimation process during periods of saturation is guaranteed by using a modified tracking error definition, in which the effect of the saturation has been filtered out. The controller and its performance are evaluated on a nonlinear, six-degrees-of-freedom dynamic model of an F-16/MATV aircraft in a number of simulation scenarios.