Abstract:
In this paper, a nonlinear adaptive neural
network control is proposed for trajectory tracking of a
model-scaled helicopter. The purpose of this research
is to reduce the ultimate bounds of tracking errors
resulted from small coupling forces (or small parasitic
body forces) and aerodynamic uncertainties. The proposed
control is designed under backstepping framework,
with neural network compensators being added.
Updating laws of neural networks are designed through
projection algorithm, so that adaptive parameters are
bounded. Derivatives of virtual controls are obtained
through command filters. It is proved that, by using
neural network compensators, tracking errors of the
closed-loop system can be restricted within very small
ultimate bounds. Superiority of the proposed nonlinear
adaptive neural network control over a backstepping
control is demonstrated by simulation results.
