A neural network based MRAC scheme with application to an autonomous nonlinear rotorcraft in the presence of input saturation

This paper develops a neural-network-based model reference adaptive control (MRAC) scheme for a rotorcraft in the presence of input saturation. Such a control scheme provides acceptable tracking performance for the rotorcraft in a wide range of flight conditions. Combined with hyperbolic tangent functions, the MRAC scheme is capable of tracking the reference signals without violating input constraints. A modified projection operator is utilized to prevent system from parameter drift due to the strong nonlinearity and uncertainty of the rotorcraft mathematical model. Stability of the proposed MRAC scheme is proved based on Lyapunov stability theory. The performance of the resulting controller is tested by conducting numerical simulations for an autonomous rotorcraft in various flight missions.