  Nowadays, the application of fully autonomous system like rotary wing
unmanned air vehicles (UAVs) is increasing sharply. Due to the complex
nonlinear dynamics, a huge research interest is witnessed in developing
learning machine based intelligent, self-organizing evolving controller for
these vehicles notably to address the system's dynamic characteristics. In this
work, such an evolving controller namely Generic-controller (G-controller) is
proposed to control the altitude of a rotary wing UAV namely hexacopter. This
controller can work with very minor expert domain knowledge. The evolving
architecture of this controller is based on an advanced incremental learning
algorithm namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS). The
controller does not require any offline training, since it starts operating
from scratch with an empty set of fuzzy rules, and then add or delete rules on
demand. The adaptation laws for the consequent parameters are derived from the
sliding mode control (SMC) theory. The Lyapunov theory is used to guarantee the
stability of the proposed controller. In addition, an auxiliary robustifying
control term is implemented to obtain a uniform asymptotic convergence of
tracking error to zero. Finally, the G-controller's performance evaluation is
observed through the altitude tracking of a UAV namely hexacopter for various
trajectories.
