Nonlinear adaptive aggressive control using recurrent neural networks for a small scale helicopter

Abstract

This paper presents a nonlinear adaptive aggressive controller to provide the small scale helicopter with full authority of a variety of flight conditions. Adaptive backstepping technique is employed to systematically synthesize the proposed controller with the online parameter adaptation rule to the vehicle mass variations and with the recurrent neural network (RNN) approximation to the coupling effect between the force and moment controls. This single and systematic design methodology is shown to achieve the semi-global ultimate boundedness of the closed-loop helicopter dynamics and accommodate the aggressive control of flight maneuvers from hovering to trajectory tracking. The high-fidelity and well-validated nonlinear model of a small scale helicopter incorporating with unmodeled dynamics and measurement uncertainties is adopted in the numerical simulations. The performance and merits of the proposed controller are exemplified by conducting three simulation scenarios including the slalom maneuver described in the ADS33.