Constrained Adaptive Backstepping Flight Control: Application to a Nonlinear F-16/MATV Model

Abstract

A constrained adaptive backstepping approach is used to design a flight control law for the nonlinear model of an F-16/MATV fighter aircraft. The objectives of the control law are to track command trajectories with the total velocity VT, the angle of attackand the stability-axes roll rate ps. Furthermore, regulation of the sideslip angleis provided. On-line parameter update laws that make use of B-spline neural networks are used to approximate the aerodynamic force and moment coefficients. Using the neural networks inside the adaptive backstepping framework ensures a stable weight updating process. The parameter update laws are able to compensate for any uncertainties or changes in the aerodynamics. The control law makes use of command filters to implement any physical or operating constraints on the control variables and states. The effect of these constraints on the input and states is estimated and used by the update laws to ensure a stable parameter estimation process even when these limitations are in effect. Simulation examples are presented to evaluate the control law on the nonlinear model of an F-16 fighter aircraft with Multi-Axis Thrust Vectoring (MATV) model. Initial simulations verify that the adaptive control law performs well on the undamaged aircraft model, the B-spline networks are able to estimate the dependency of the aerodynamic data on the aircraft variables. Longitudinal maneuvers with some symmetric structural damage and actuator damage scenarios are also simulated, where the adaptive control law has to deal with large sudden changes in the dynamics of the F-16/MATV model. The results of these simulations show that the constrained adaptive backstepping control law is able to provide accurate tracking, even after these sudden failures have occurred.