Robust adaptive nonlinear flight control applications using neural networks

Online neural networks are used to enhance feedback linearizing controllers of uncertain nonlinear systems. These systems may be non-affine with respect to the control. The controller architecture is based on inversion of a linearized plant model. A multi-layer perceptron neural network is added to compensate for the inversion error. Online weight update laws are derived from Lyapunov analysis, which guarantees boundedness of all signals. Robustness with respect to unmodeled input dynamics is provided by the addition of a nonlinear damper. The controller is demonstrated using the full, nonlinear models of the XV-15 tilt-rotor and the Caltech ducted fan. Numerical results demonstrate the effectiveness of the control in response to model inversion error, and degraded actuator performance.