Variance Reduction of Quadcopter Trajectory Tracking in Turbulent Wind

TL;DR

This paper develops a control strategy to reduce the impact of turbulent wind on quadcopter trajectory tracking by minimizing a combined mean and variance cost, demonstrating improved performance over traditional methods.

Contribution

It introduces a minimum cost variance controller based on coupled algebraic Riccati equations for quadcopters in turbulent environments, a novel approach in this context.

Findings

Variance and tracking error are reduced compared to LQR.

Linearized model used for controller design.

Preliminary simulations show promising results.

Abstract

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).