# AERO-LQG: Aerial-Enabled Robust Optimization for LQG-Based Quadrotor Flight Controller

## TL;DR

AERO-LQG introduces an evolutionary strategy-based framework for tuning LQG parameters in quadrotor control, significantly improving energy efficiency and robustness in mission-specific flight modes.

## Contribution

It presents a novel aerial-enabled robust optimization method for LQG tuning using evolutionary strategies, addressing the challenge of weight selection in optimal control.

## Key findings

- Performance improvements of several tens of percent in simulated hover mode.
- Demonstrates the effectiveness of evolutionary strategies for control parameter optimization.
- Potential for enhanced energy efficiency and robustness in quadrotor flight control.

## Abstract

Quadrotors are indispensable in civilian, industrial, and military domains, undertaking complex, high-precision tasks once reserved for specialized systems. Across all contexts, energy efficiency remains a critical constraint: quadrotors must reconcile the high power demands of agility with the minimal consumption required for extended endurance. Meeting this trade-off calls for mode-specific optimization frameworks that adapt to diverse mission profiles. At their core lie optimal control policies defining error functions whose minimization yields robust, mission-tailored performance. While solutions are straightforward for fixed weight matrices, selecting those weights is a far greater challenge-lacking analytical guidance and thus relying on exhaustive or stochastic search. This interdependence can be framed as a bi-level optimization problem, with the outer loop determining weights a priori. This work introduces an aerial-enabled robust optimization for LQG tuning (AERO-LQG), a framework employing evolutionary strategy to fine-tune LQG weighting parameters. Applied to the linearized hovering mode of quadrotor flight, AERO-LQG achieves performance gains of several tens of percent, underscoring its potential for enabling high-performance, energy-efficient quadrotor control. The project is available at GitHub.