  We consider a quadcopter operating in a turbulent windy environment. The
turbulent environment may be imposed on a quadcopter by structures, landscapes,
terrains and most importantly by the unique physical phenomena in the lower
atmosphere. Turbulence can negatively impact quadcopter's performance and
operations. Modeling turbulence as a stochastic random input, we investigate
control designs that can reduce the turbulence effects on the quadcopter's
motion. In particular, we design a minimum cost variance (MCV) controller
aiming to minimize the cost in terms of its weighted sum of mean and variance.
We linearize the quadcopter dynamics and examine the MCV controller derived
from a set of coupled algebraic Riccati equations (CARE) with full-state
feedback. Our preliminary simulation results show reduction in variance and in
mean trajectory tracking error compared to a traditional linear quadratic
regulator (LQR).
