Variance Reduction of Quadcopter Trajectory Tracking in Turbulent Wind

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